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A Study of Graduate Students’ Achievement Motivation, Active Learning, and Active Confidence Based on Relevant Research

Jen-chia chang.

1 Graduate Institute of Technological and Vocational Education, National Taipei University of Technology, Taipei City, Taiwan

2 Office of Physical Education, Soochow University, Taipei City, Taiwan

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Graduate students’ failure to graduate is of great concern, with the failure to graduate due to the dissertation being the most influential factor. However, there are many factors that influence the writing of a dissertation, and research on these factors that influence graduate students’ learning through emotion and cognition is still quite rare. A review of past research revealed that the main factor causing graduate students to drop out midway is not completing their thesis, followed by factors including insufficient achievement motivation, lack of learning strategy, and low confidence. The graduation rate of graduate students has been emphasized by the academic community; therefore, this study investigated the correlation between graduate students’ achievement motivation, active learning, and academic confidence in writing research. The study invited graduated students from two universities of science and technology situated in the northern region of Taiwan to complete the questionnaire. In this study, valid data for validation analysis were collected from 173 respondents, and the results showed that achievement motivation positively influenced active learning (higher-order learning, integrative learning, reflective learning) and that active learning (higher-order learning, integrative learning, reflective learning) positively influenced academic confidence. From the above findings, it can be seen that to help graduate students from University of Science and Technology to effectively complete their graduate studies, students should develop good motivation to adopt active learning strategies to enhance their academic self-confidence.


In Taiwan, the delayed graduation of graduate students has become an important educational issue of social concern ( Ho et al., 2020 ). Gardner (2009) found that the reasons for the low graduation rate of doctoral students include being unable to complete their degree theses, among others. The completion of the degree thesis is an important milestone and the biggest obstacle for graduate students ( Blum, 2010 ). Muszynski (1990) found that graduate students who fail to graduate in time may be uninterested in the research topic, have low academic confidence, or have too many research papers to complete. Spaulding and Rockinson-Szapkiw (2012) interviewed 76 doctoral graduates and found that motivation, persistence factors, and completion strategies were necessary to complete their dissertations.

However, Pulford et al. (2018) found that people must have motivation before they are willing to put in the effort and persevere. Usta (2017) noted that there are significant mutual influences among an individual’s learning achievement, motivation, and confidence. Belshaw et al. (2020) found that learners who use passive learning not only have lower learning efficiency and less comprehension, but their comprehension level is also low. It has also been found that learners with higher levels of active learning are more willing to engage in learning and gain more knowledge from it, thereby reducing delayed graduation ( Schmidt et al., 2009 ). Wehrens (2008) suggested that learners with low confidence or feelings of inferiority may have limited learning progress, drop out of school, or have other problems. Academic confidence refers to the student’s belief in the learning task and in achieving the learning goal ( Sander and Sander, 2005 ), which reflects the student’s beliefs and expectations for success in the academic field. Students also prefer to perform activities or tasks in which they feel competent ( Bandura, 1977 ; Eccles et al., 1989 ).

In addition, when students are able to succeed academically, it can also be attributed to their motivation and willingness to put in the effort, which in turn brings motivation for learning and academic confidence, and encourages them to take action and persevere ( Pulford et al., 2018 ). Chemers et al. (2001) found that students’ lack of academic confidence is inextricably linked to their expectation of success and has a significant effect on academic performance, as academic confidence controls students’ desire to learn. If students’ academic confidence is low, it will affect their desire to learn. If they do not have sufficient academic confidence, they may not be eager to learn and may not continue their studies ( Ireson and Hallam, 2009 ). Sander and de la Fuente (2020) found that academic confidence helps students to effectively acquire learning strategies and skills. In addition, de la Fuente et al. (2013) found an interdependent effect between academic confidence, learning methods, and achievement in a study of 2,429 psychology students. Based on this, this study validated the control-value theory of achievement emotion (CVTAE) according to Pekrun (2006) .

Regarding CVTAE, Pekrun (2006) mentioned that learners’ assessment of control and value directly predicts their emotional responses, with control referring to the learners’ actions and beliefs about learning tasks (e.g., self-efficacy and attribution) and value referring to the importance learners place on learning tasks and outcomes ( Pekrun et al., 2011 ). When learners attach more importance to learning tasks, good outcomes are achieved ( Pekrun, 2006 ). Garn et al. (2017) found that learners’ perceptions of academic achievement can enhance or reduce the positive or negative outcomes faced in the learning environment. This study used the CVTAE perspective to analyze the correlation between graduate students’ academic achievement motivation, active learning, and academic confidence. It was hoped that the results of this study could be used to help improve the academic confidence of graduate students.

Achievement Motivation

McClelland (1961) rationalize the motivation of achievement and divide it into three demands (1) achievement, (2) connection, and (3) power. Pintrich and Schunk (1996) defined motivation as the process by which an individual is motivated and sustained in order to achieve a goal, thereby laying an important foundation for accomplishing the goal (e.g., planning, learning, and decision-making). Bigge and Hunt (1980) defined achievement motivation as the motivation to perform and actively work toward a goal without interruption, and gain a sense of accomplishment in the process. Individuals who set a goal they want to achieve will take action to achieve it ( Linnenbrink, 2005 ). Therefore, achievement motivation is also considered as a source of motivation to influence or maintain behavior ( Reeve, 2009 ). The motivation for achievement in this study refers to the graduate student’s motivation for wanting to complete a degree thesis.

Achievement motivation is a subjective value, as well as a psychological drive that helps individuals achieve their goals ( Singh, 2011 ). Urdan and Kaplan (2020) found that each scholar’s definition of achievement motivation is somewhat different; however, achievement motivation is inextricably linked to learning outcomes, emotions, and strategies. Research has found that achievement motivation has the ability to predict academic ability and task success ( Liao et al., 2012 ). In addition, Saiti et al. (2017) suggested that graduate student learning goals and motivation are inextricably linked to individual engagement in learning tasks to meet personal needs and expectations.

Active Learning

Bonwell and Eison (1991) noted that active learning occurs when an individual takes the initiative to perform a task and thinks about why they are doing it and that active learning is one of the processes of learning that requires learners to learn independently or in groups ( Singer et al., 2012 ). According to previous scholars, active learning is based on three components (1) higher-order learning: learners focus on the exchange of information and knowledge, (2) integrative learning: learners learn from experience, and (3) reflective learning: learners reflect on whether they have learned after the course ( Fink, 2003 ). Matsushita (2018) suggested that active learning is about driving learners to act and to reflect on learning through action. Graduate students are expected to develop independent research knowledge, skills, and experiences before writing their theses ( Davis et al., 2017 ). Active learning emphasizes learners’ deep learning, understanding, and engagement in the learning process ( Matsushita, 2018 ), and active learning expects learners to think, analyze, discuss, and make decisions with their peers as part of an active learning process ( Freeman et al., 2014 ). Active learning in this study refers to the ability of graduate students to understand the learning content completely, to apply their knowledge, and to learn from their mistakes.

In addition, Budsankom et al. (2015) showed that learners’ psychology is a direct and effective influence on higher-order learning. However, Robertson and Howells (2008) argue that integrative learning allows learners to “learn by doing” during the experiential process, which can deepen learners’ memory and practical skills. In addition, Hwang et al. (2014) showed that integrative learning not only allows learners to think deeply about how to solve problems during the learning process, but also to make a reflective search for what they did not do well when their performance was not as expected. Lucas (2001) pointed out that the learner’s motivation affects the learning strategy to be used in learning. Conversely, if learners have low achievement motivation, they will choose to complete their assignments at a lower standard ( Davidson, 2002 ). Research has found that achievement motivation has a positive effect on active learning ( Everaert et al., 2017 ). Therefore, the hypotheses related to the interaction between achievement motivation and active learning were as follows:

H 1 : Achievement motivation significantly and positively affects higher-order learning. H 2 : Achievement motivation significantly and positively affects integrative learning. H 3 : Achievement motivation significantly and positively affects reflective learning.

Academic Confidence

Rosenberg (1965) noted that confidence is an individual’s evaluation of self and that academic confidence is an important competency in the academic world ( Adnan et al., 2011 ). Academic confidence refers to learners’ beliefs about performing tasks and achieving academic goals ( Sander and Sander, 2005 ), which reflects the learners’ beliefs or expectations about success in the field of study. Wehrens (2008) indicated that when learners’ self-confidence is low, it leads to poor performance in learning outcomes, and Bénabou and Tirole (2002) showed that self-confidence has a strong influence on learning performance. Learners usually choose to perform tasks they believe they can accomplish ( Bandura, 1977 ; Eccles et al., 1989 ), Basturkmen et al. (2014) found that a growing number of graduate students are joining research programs, and found that graduate students need support and encouragement in their writing. For graduate students, academic research, knowledge, skills, competencies, and practices are a serious challenge ( Lee and Aitchison, 2009 ). Aithal and Kumar (2018) suggested that the application of knowledge is skill-dependent and that the application of skills requires confidence. Academic self-confidence in this study refers to the ability of graduate students to find their own research topics and to complete them.

In addition, Retnowati et al. (2018) showed that one of the reasons learners choose to avoid problems with Higher-order Thinking skills is because of low self-confidence and the belief that they cannot achieve the task. Di Francesca (2020) pointed out that engaged learners in active learning environments may build confidence, and some scholars believe that active learning can enhance learners’ responsiveness, confidence, and motivation. ( Robinson, 2017 ; Sibona and Pourrezajourshari, 2018 ). Research suggests that active learning has an important impact on the development of academic confidence ( O'Flaherty and Costabile, 2020 ). Pekrun (2006) proposed that CVTAE defines learners’ emotions as a regulatory process and that emotions can regulate individuals’ cognition, motivation, etc. Whereas good emotions stimulate individual behavior, bad emotions reduce individual behavior ( Pekrun, 2006 ; Pekrun et al., 2009 ). Self-confidence is a good source of emotion for the learner, which means that self-confidence and behavior are inextricably linked. Therefore, the hypotheses related to the interaction between active learning and academic confidence were as follows:

H 4 : Higher-order learning significantly and positively affects academic confidence. H 5 : Integrative learning significantly and positively affects academic confidence. H 6 : Reflective learning significantly and positively affects academic confidence.

Materials and Methods

Research framework.

Pekrun (2006) and Pekrun (2009) proposed that CVTAE theory can be used to observe whether learners’ beliefs affect their mobility and that when learners judge that a learning task is accomplishable, the next step will be to take action to achieve the goal. Therefore, based on the above literature, we proposed an initial model to investigate the relationship between achievement motivation, active learning, and academic confidence, as shown in Figure 1 .

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Object name is fpsyg-13-915770-g001.jpg

Research model.

This study was conducted using online questionnaires with purposive sampling, and the questionnaires were collected from November 10, 2019 to December 5, 2019 from graduate students enrolled in universities of science and technology in Taipei City and New Taipei City.


There were 205 participants in this study. After 32 invalid samples were deleted, 173 valid samples were collected, indicating a questionnaire recovery rate of 84.39%, as shown in Table 1 .

Basic information.

Gendermale: 60 (34.7%)
Female: 113 (65.3%)
The Institute studied atPublic Schools: 136 (78.6%)
Private Schools: 37 (21.4%)
The academic degreeMaster’s: 156 (90.2%)
Doctorate: 17 (9.8%)
Research AreasEducation: 57 (32.9%)
Arts and Humanities: 23 (13.3%)
Technology and Engineering: 24 (13.9%)
Business: 38 (22.0%)
Medical: 3 (1.7%)
Biological and Scientific: 8 (4.6%)
Other: 20 (11.6%)


The questionnaire in this study was adapted from a scale developed by a related researcher and was divided into the three components of achievement motivation, active learning, and academic confidence. The results were assessed using a Likert 5-point scale (with answers ranging from 1: strongly disagree to 5: strongly agree ).

McClelland theorized achievement motivation in 1961 and identified three different needs for it: (1) the need for achievement; (2) the need for connection; and (3) the need for power ( McClelland, 1961 ). Linnenbrink (2005) noted that achievement goals motivate individuals to engage in task behavior. Some scholars believe that motivation can influence individual behavior, generate interest, or serve as a sustaining force ( Reeve, 2009 ). Pintrich (2003) suggested that motivation is inextricably linked to academic behavior. Hong et al.’s (2017) “Measuring intrinsic motivation of Chinese learning” was adopted to measure participants’ perceptions of their achievement motivation.

In Bonwell and Eison (1991) defined active learning strategies as learners actively doing something and thinking about why they are doing it ( Bonwell and Eison, 1991 ). Fink (2003) expanded on the previous foundation to include three additional items: (1) a focus on the learner in the exchange of information and knowledge; (2) allowing learners to actually observe situations; and (3) reflective learning, which involves learners thinking on their own or discussing with others. In addition, Singer et al. (2012) pointed out that active learning is defined as a learning process which requires learners to organize and integrate learning content, either independently or in groups. Therefore, this study revised Entwistle and McCune’s (2004) “Comparison of Scales from Inventories Measuring Study Strategies” to measure the participants’ perceptions of their active learning.

Bénabou and Tirole (2002) suggested that confidence refers to an individual’s belief in his or her own ability, while Stankov et al. (2012) suggested that confidence is a state in which an individual is certain of the success of a task or behavior. However, insufficient confidence or feelings of inferiority can cause learners to perform poorly in their learning ( Wehrens, 2008 ). Some studies have pointed out that confidence has a motivational effect on learning ( Bénabou and Tirole, 2002 ). Therefore, this study revised Sander (2009) “Academic Behavioural Confidence Scale” to measure the participants’ perceptions of their academic confidence.

Data Analysis

Structural Equation Modeling (SEM) is commonly used in the fields of psychology, sociology, and education ( Teo et al., 2013 ), and is often used to analyze the correlations between potential variables ( Hair et al., 2014 ). This study used SPSS for the descriptive statistics, Cronbach’s alpha reliability, and external validity, and used AMOS for the model and fitness validation.

Results and Discussion

Item suitability analysis.

The item analysis in this study was conducted using first-order confirmatory factor analysis. According to scholarly recommendations: the χ 2 / df value should not be greater than 5; the RMSEA should not be greater than 0.1; neither the GFI nor the AGFI should be less than 0.8; and the factor loading (FL) should not be less than 0.5 ( Hair et al., 2010 ; Kenny et al., 2015 ). As a result, the number of items regarding achievement motivation was reduced from nine to five; higher-order learning was reduced from seven to four; integrative learning was reduced from seven to five; reflective learning was reduced from seven to five; and academic confidence was reduced from seven to four items ( Table 2 ).

Item analysis by first-order confirmatory factor analysis.

IndexThresholdAchievement motivationHigher-order learningIntegrative learningReflective learningAcademic confidence
/ < 52.6120.6281.9171.0561.682

Construct Reliability and Validity Analysis

The reliability of this study was first verified by Cronbach’s α to verify the internal consistency, and then the composite reliability (CR) was used to check the reliability ( Hair et al., 2010 ). In this study, the Cronbach’s α values ranged from 0.789 to 0.903 and the CR values ranged from 0.783 to 0.903, all of which met the criteria suggested by scholars, as shown in Table 3 .

Reliability and validity analysis.

Items FL value

 = 3.617,  = 0.725, Cronbach’s α = 0.882, CR = 0.875, AVE = 0.585
1. I am willing to put in extra effort to avoid the research from being abandoned halfway.3.711.0100.84815.073
2. No matter how big the obstacles are, I will try to overcome them when conducting research.3.700.8430.81111.880
3. I want to be admired through my research performance.3.570.9170.70611.217
4. I will try to carry out the research without worrying that the process will be too difficult.3.500.8190.64812.845
5. When I encounter a difficult research problem, I will change my approach according to the time and place.3.610.7960.79211.625

 = 3.646,  = 0.734, Cronbach’s α = 0.843, CR = 0.846, AVE = 0.584
1. I learn from understanding.3.780.9510.90010.808
2. I learn from understanding, not from remembering.3.710.9520.82413.587
3. I will look for different ways to solve the problem and make assumptions to recheck them.3.530.8040.57810.658
4. When I read, I judge the meaning of the message and apply it to the appropriate context, for example, a conference event.3.570.8440.71712.617

 = 3.697,  = 0.725, Cronbach’s α = 0.903, CR = 0.903, AVE = 0.651
1. In class discussions, I bring together knowledge, ideas, or concepts from different courses.3.650.7980.7259.660
2. I often combine what I have been taught in school with my daily life experience.3.750.8980.80911.395
3. When learning new knowledge, I try to link it to my past learning experiences.3.800.9250.89511.454
4. I find myself thinking about the commonalities of the different course content.3.690.8310.80212.399
5. I try to integrate ideas, information, or experiences into new and more complex explanations and relationships.3.600.8130.79414.111

 = 3.642,  = 0.626, Cronbach’s α = 0.790, CR = 0.789, AVE = 0.485
1. I think about the problem from a third person’s (his/her) perspective so that I can better understand someone else’s point of view.3.800.8260.7659.162
2. I can learn new things from my mistakes and can change the way I understand the problem or concept.3.510.7820.58110.842
3. I tried to come up with ideas to build on in the study topic.3.620.7800.73211.421
4. I will plan my overall study time to get the most out of my studies.3.620.8090.69411.365

 = 3.474,  = 0.674, Cronbach’s α = 0.789, CR = 0.783, AVE = 0.476
1. I can easily find innovative research topics and have the confidence to develop a career in academic research.3.350.8670.62912.370
2. I am a good self-studier and have the confidence to do well in my research.3.570.8570.66211.863
3. I know how to consult my seniors when I encounter difficulties, and I have the confidence to develop my career in academic research.3.490.8930.69815.206
4. I like to do all kinds of reflective reasoning, so I have confidence in my academic research career development.3.490.8260.76314.286

The average validity of this study was measured by the factor loading (FL) and average variance extracted (AVE). Firstly, it has been recommended by scholars that the FL value should not be lower than 0.5 and that items which are lower than the recommended standard should be deleted ( Hair et al., 2010 ). The FL values for achievement motivation ranged from 0.648 to 0.848; the FL values for higher-order learning ranged from 0.578 to 0.9; the FL values for integrative learning ranged from 0.725 to 0.895; the FL values for reflective learning content ranged from 0.581 to 0.765; and the FL values for academic confidence ranged from 0.629 to 0.763, as shown in Table 3 . In addition, scholars believe that the AVE value should not be less than 0.4 ( Fraering and Minor, 2006 ), as shown in Table 3 .

Scholars have pointed out that the AVE square root value of each construct should not be lower than the Pearson correlation coefficient value of the remaining constructs, in order for the constructs to have construct discriminant validity ( Zainudin, 2015 ). The correlation value of each construct should not exceed 0.85; if the correlation is higher than 0.85, it will give rise to the concern of multivariate co-linearity ( Awang, 2012 ). The results of this study showed that the constructs had sufficient construct discriminant validity, as shown in Table 4 .

Construct discrimination analysis.

S. No.Constructs12345
1.Achievement motivation
2.Higher-order learning0.549
3.Integrative learning0.5950.655
4.Reflective learning0.5330.5940.725
5.Academic confidence0.5240.5920.6340.616

The value on the diagonal is the square root of AVE and the other values are the related coefficients.

Index Analysis

According to recommendations, χ 2 / df should not be higher than 5, RMSEA should not be higher than 0.1, PNFI and PGFI should not be lower than 0.5 ( Hair et al., 2010 ), and GFI, AGFI, NFI, NNFI, CFI, IFI, and RFI should not be lower than 0.8 ( Abedi et al., 2015 ). In the present study, the results were as follows: χ 2  = 347.041; df . = 203; χ 2 / df . = 1.710; RMSEA = 0.064; GFI = 0.842; AGFI = 0.803; NFI = 0.853; NNFI = 0.923; CFI = 0.932; IFI = 0.933; RFI = 0.833; PNFI = 0.676; and PGFI = 0.750. The results all met the criteria suggested by scholars, and therefore this study had a good model index.

Path Analysis

The model validation results showed that achievement motivation had a positive effect on higher-order learning ( β  = 0.721, p  < 0.001, t  = 9.141), achievement motivation had a positive effect on integrative learning ( β  = 0.749, p  < 0.001, t  = 8.173), achievement motivation had a positive effect on reflective learning ( β  = 0.746, p  < 0.001, t  = 8.016), higher-order learning had a positive effect on academic confidence ( β  = 0.259, p  < 0.01, t  = 2.424), integrative learning had a positive effect on academic confidence ( β  = 0.295, p  < 0.01, t  = 2.424), and reflective learning had a positive effect on academic confidence ( β  = 0.391, p  < 0.01, t  = 2.797), as shown in Figure 2 .

An external file that holds a picture, illustration, etc.
Object name is fpsyg-13-915770-g002.jpg

Verification model. ** p  < 0.01 and *** p  < 0.001.

The explanatory power of achievement motivation for higher-order learning was 51.9%; the explanatory power of achievement motivation for integrative learning was 55.7%; the explanatory power of achievement motivation for reflective learning was 56.1%; and the explanatory power of active learning (higher-order learning, integrative learning, and reflective learning) for learning confidence was 62.9%, as shown in Figure 2 .

Achievement Motivation Positively Influences Academic Active Learning

Entwistle (1998) and Vermunt and Vermetten (2004) stated that teachers encourage learners to adopt active learning because it leads to better learning outcomes, while Skaalvik and Skaalvik (2005) stated that learners’ understanding of learning objectives depends on the level of self-understanding. Ferla et al. (2010) confirmed that learners’ cognitive abilities predict their overall learning strategies, and Everaert et al. (2017) proved that achievement motivation has a positive relationship with active learning. The results of this study validated H 1 , H 2 , and H 3 , and showed that learners’ achievement motivation significantly and positively influences active learning (higher-order learning, integrative learning, and reflective learning), echoing the above study. It could be seen that the higher the learner’s motivation to achieve, the more enthusiastic the learner will be about the learning task and so take the initiative to learn.

Active Learning Positively Influences Academic Confidence

Adnan et al. (2011) identified academic confidence as an important competency for scholars in academia, while Sander and Sander (2005) stated that academic confidence gives learners the motivation to perform learning tasks and achieve goals. Libby (1991) confirmed that active learning can increase learning motivation, learning interest, confidence, problem-solving abilities, communication skills, and judgment, while Sibona and Pourrezajourshari (2018) confirmed that active learning can increase confidence. O'Flaherty and Costabile (2020) confirmed that active learning has a positive relationship with academic confidence. This study verified the results of H 4 , H 5 , and H 6 and confirmed that the active learning (higher-order learning, integrative learning, and reflective learning) of learners has a positive effect on their academic confidence, echoing the above study. This result indicated that the more active learners are in learning, the more their academic confidence will increase.

Conclusion and Recommendations

Conclusion and limitations.

Ehrenberg et al. (2009) found that the most important factor for learners to drop out of graduate school is the need to write a dissertation. Graduate students’ motivation, persistence, and strategies used to complete their degree dissertations are essential factors ( Spaulding and Rockinson-Szapkiw, 2012 ). Of course, the learner’s confidence that he or she can complete the research is also an essential factor ( Sander and Sander, 2005 ). In this study, six research hypotheses were formulated using CVTAE theory and were used to determine learners’ mobility, beliefs, and importance of learning ( Pekrun, 2006 ). Through validating the CVTAE framework, a theoretical model of the relationship among achievement motivation, active learning (higher-order learning, integrative learning, and reflective learning) and academic confidence was developed. The results showed that achievement motivation positively influences active learning (higher-order learning, integrative learning, and reflective learning) and that active learning (higher-order learning, integrative learning, and reflective learning) positively influences academic confidence. Therefore, thesis is the biggest factor influencing graduation, and in order to increase the graduation rate, we should improve the motivation of graduate students’ research, and encourage them to submit more journals to train their ability to write and further enhance their self-confidence.

Some studies have found that the establishment of students’ academic confidence is influenced by the importance of mentoring ( Gearity and Mertz, 2012 ). The achievement motivation and active learning used in this study were based on using the students’ self-assessments as the main influence. This study was not concerned with whether external factors would affect students’ confidence building, which was a limitation of this study. In addition, this study only investigated the relationship among achievement motivation, active learning, and academic confidence but did not address the students’ background variables, such as intelligence, gender, and family economics. It is suggested that future researchers expand the study population to include different background variables so as to create different models or use background variables as control variables.


The dissertation/ thesis is the last hurdle before a graduate student graduates, and it is also the hurdle that causes the most attrition of graduate students. A dissertation is a paper that a graduate student must produce within a few months, and it includes a number of processes, such as problem identification and validation, literature collection, data collection and analysis, and writing. If a graduate student fails to complete the dissertation on time, he or she will be required to delay graduation for one or more semesters ( Dupont et al., 2013 ). Komarraju et al. (2009) suggested that motivation affects learners’ interest, emotion, and confidence in learning tasks, while CVTAE theory holds that controlling the learner’s beliefs is an essential factor, and that whether the learner will take action on a learning task depends on whether he or she thinks the task can be completed ( Pekrun, 2006 , 2009 ). Similar to the concept of self-efficacy proposed by Bandura (1997) , in this study, the feasibility of a task was found to be judged by the learner before action, after which the learner’s judgment of the feasibility becomes a critical factor. Therefore, this study confirmed that the higher the motivation of graduate students to achieve, the higher the willingness to take the initiative to learn and the higher the academic confidence will be. Graduate students can gain a sense of accomplishment by setting goals, such as completing scale development or searching classical literature, and gradually increase their proportion of active learning through the guidance of their supervising professors, and eventually conduct independent research and gain academic confidence in research.

Sharma (2018) analyzed research related to stress and found that most of the studies believe that stress has negative effects on learners; however, the studies confirmed that an appropriate level of stress is beneficial for enhancing the achievement motivation of learners. It is suggested that subsequent studies include stress and investigate whether stress interacts differently with achievement motivation, active learning, and academic confidence.

In addition, Arsenis and Flores (2021) suggested that learner confidence has a critical influence on learning and achievement performance. As learners’ confidence in their learning ability affects their perceived performance on learning tasks, it is suggested that future research include learning achievement to investigate whether academic confidence predicts learning performance.

Gearity and Mertz (2012) noted that if graduate students want to find a direction for their research, they must first find supervising professors who can help them. Under the professor’s guidance, they must find the direction, questions, and structure of their research, and the professors must supervise the students to complete their research. Welton et al. (2015) confirmed that the supervising professor’s supervisory style or interaction with the graduate student also affects the completion of the graduate student’s thesis. The failure of graduate students to graduate is not only influenced by personal factors, but also by external factors. The results of this study showed that academic motivation, active learning, and academic self-confidence are positively influenced, but it is worthwhile to investigate whether the influence of external factors (e.g., Advisors) will change their influence.

Data Availability Statement

Ethics statement.

Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

J-CC and J-NY: concept and design and drafting of the manuscript. Y-TW and J-NY: acquisition of data and statistical analysis. J-CC and Y-TW: critical revision of the manuscript. All authors contributed to the article and approved the submitted version.

This study was partially funded by the Ministry of Science and Technology of Taiwan, with grant number MOST 110-2511-H-027-001.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Psychology: Research and Review

  • Open access
  • Published: 22 December 2017

Achievement goals and life satisfaction: the mediating role of perception of successful agency and the moderating role of emotion reappraisal

  • Wangshuai Wang 1 ,
  • Gong Sun 3 ,
  • Zhiming Cheng 4 &
  • Xin-an Zhang 1  

Psicologia: Reflexão e Crítica volume  30 , Article number:  25 ( 2017 ) Cite this article

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Achievement goals are cognitive representations that guide behavior to a competence-related future end state. Existing theories and empirical findings suggest that achievement goals are potentially related to life satisfaction. However, the relationship between achievement goals and life satisfaction remains relatively unexplored in the psychology literature. In this study, we examined how, why, and when achievement goals affect life satisfaction using original survey data from China. The results suggest that achievement goals were positively related to life satisfaction ( R 2  = .20, 90% CI [.11, .26]), that the perception of successful agency fully mediated the relationship between achievement goals and life satisfaction ( R 2  = .22, 90% CI [.12, .27]), and that emotion reappraisal moderated the relationship between achievement goals and life satisfaction ( R 2  = .34, 90% CI [.23, .39]). Our study indicates that achievement goals have a positive influence on life satisfaction and help to elucidate the mechanism and boundary condition of this influence.

An achievement goal refers to “a future-focused cognitive representation that guides behavior to a competence-related end state that the individual is committed to either approach or avoid” (Hulleman, Schrager, Bodmann, & Harackiewicz, 2010 , p. 423). In the past three decades, there has been a large body of literature published on achievement goals (see Hulleman et al., 2010 , for a meta-analytic review). Existing research shows that individuals differ in their behaviors and preferences in pursuit of achievement goals (Harackiewicz & Sansone, 1991 ). For example, one may easily recall that in school years, certain students worked hard and performed well on exams, demonstrating high achievement goals. In contrast, other students were not strongly concerned regarding academic performance, did not study, and had poor performance in exams, which denoted low motivation for achievement goals.

One stream of research has identified the antecedents of achievement goals. For example, age is negatively related to achievement goals; females have a stronger mastery of goal orientation than males in an academic setting, whereas self-efficacy and perceived social environment, including peer relationships and sense of belonging, are positive predictors of achievement goals (Ablard & Lipschultz, 1998 ; Anderman & Anderman, 1999 ; Bong, 2009 ; Phillips & Gully, 1997 ).

More recently, attention has been directed to the consequences of pursuing achievement goals. For instance, achievement goals positively predict long-term academic performance (Harackiewicz, Barron, Tauer, Carter, & Elliot, 2000 ). Moreover, achievement goals can activate intrinsic motivation (Cury, Elliot, Sarrazin, Da Fonseca, & Rufo, 2002 ). Based on this finding, Lee, Sheldon, and Turban ( 2003 ) argue that achievement goals promote academic enjoyment. In contrast, researchers also find that negative emotions can be exacerbated by achievement goals due to high expectations. For example, students aspiring for high achievement goals may experience more anxiety during tests (Flanagan, Putwain, & Caltabiano, 2015 ).

The existing literature on life satisfaction shows that demographic variables, including gender, age, income, and education level, are associated with life satisfaction (Gannon & Ranzijn, 2005 ; Johnson & Krueger, 2006 ) and that a person who is more satisfied with life is more diligent, performs better at his/her job, and has a higher commitment to the organization (Efraty, Sirgy, & Claiborne, 1991 ; Greenhaus, Bedeian, & Mossholder, 1987 ). More recent research finds that expectation and aspiration are important to job and life satisfaction (Cheng, Wang, & Smyth, 2014 ; Gao & Smyth, 2010 ). Similarly, academic goal progress is found to influence both academic and life satisfaction (Ojeda, Flores, & Navarro, 2011 ; Singley, Lent, & Sheu, 2010 ). Furthermore, Keller and Siegrist ( 2010 ) suggest that both goal pursuit and life satisfaction are psychological resources.

Although these aforementioned studies suggest potential connections between achievement goals and life satisfaction, few studies have directly tested this relationship. In particular, it is unclear in the literature whether achievement goals influence life satisfaction in a positive or a negative way. On the one hand, individuals with high achievement goals can be substantially motivated by mental energy in the face of challenge (Grant & Dweck, 2003 ). On the other hand, these people also need to make a concerted effort in the stressful and laborious process of pursuing their goals (Senko & Harackiewicz, 2005 ).

People are paying increasing attention to the improvement of the quality of life. Life satisfaction’s fundamental role and indispensability have been acknowledged by worldwide respondents (Diener, Oishi, & Lucas, 2003 ). Therefore, to help fill the gaps in the literature and to respond to the practical necessity, this research examines the association between achievement goals and life satisfaction. We also investigate why and when achievement goals influence life satisfaction by examining the underlying mechanism through perception of successful agency and the boundary condition of emotion reappraisal. It is also surprising that little research on achievement goals, successful agency, and emotional reappraisal have been conducted in non-Western cultures (e.g., Chinese culture), which leaves a potentially rewarding empirical research area to be explored. Existing studies suggest that there are significant cultural differences in positive psychology (e.g., Diener, Diener, & Diener, 1995 ; Spencer-Rodgers, Peng, Wang, & Hou, 2004 ). It is, therefore, very important to examine these constructs using data drawn from non-Western cultures.

Taken together, in this research, we first answer an important but unresolved question: what is the relationship between achievement goals and life satisfaction? We further advance our study by testing the potential mediation and moderation of this relationship. The current research also has significant practical implications for the general public—including but not limited to workers and students—on the means to successfully pursue greater happiness.

Life satisfaction is a global cognitive judgment across a broad set of activities concerning one’s quality of life (Diener et al., 2003 ; Matud, Bethencourt, & Ibáñez, 2014 ). Various factors are related to life satisfaction, such as finances (Johnson & Krueger, 2006 ), family and marital relationships (Adams, King, & King, 1996 ; Cheng & Smyth, in press ), health conditions (Canha, Simões, Matos, & Owens, 2016 ), coping strategies (Nunes, Melo, Júnior, & Eulálio, 2016 ), and sexual behaviors (Cheng & Smyth, 2015 ).

Although the direct evidence for the link between achievement goals and life satisfaction is limited, previous research has provided some indirect support. For instance, the self-determination theory theorizes two forms of motivation, which are controlled motivation and autonomous motivation (Ryan & Deci, 2000 ). Controlled motivation originates either from self-imposed pressures or from external pressures, such as pleasing others or complying with demands, both of which have an externally perceived locus of causality. In contrast, autonomous motivation stems from one’s self, thereby having an internally perceived locus of causality (Weinstein & Ryan, 2010 ). Setting high achievement goals, in many cases, reflects one’s own values; thus, it is internally driven and inspires autonomous motivation (Cury et al., 2002 ). Importantly, literature based on self-determination theory indicates that autonomous motivation positively contributes to well-being (Ryan & Deci, 2000 ).

Moreover, individuals often want to maintain a sense of control, expecting everything to be in line with their plans (Park & Baumeister, 2017 ). However, there are always discrepancies between expectations and reality. Under certain circumstances, the experiences of hardships often demotivate people and make them feel dissatisfied with life. Achievement goals can provide a person with motivation (Pintrich, 2000 ), which serves as mental energy helpful in overcoming the difficulties and obstacles in life (Capa, Audiffren, & Ragot, 2008 ). As a result, people who set achievement goals for themselves are less affected by experiences that can have negative effects on life satisfaction.

Furthermore, researchers find that setting achievement goals is helpful to one’s educational and occupational performance, since it results in better grades at school and upward career mobility (Gould, 1980 ; Harackiewicz et al., 2000 ; Harackiewicz, Barron, Tauer, & Elliot, 2002 ). The successes in academic and job domains boost self-efficacy and self-esteem (Bachman & O’Malley, 1977 ; Leary, Tambor, Terdal, & Downs, 1995 ; Tay, Ang, & Van Dyne, 2006 ), both of which can enhance satisfaction with life (Du, Bernardo, & Yeung, 2015 ; Joseph, Royse, Benitez, & Pekmezi, 2014 ). Therefore, we propose the following hypothesis:

Hypothesis 1: Achievement goals are positively correlated with life satisfaction.

Perception of successful agency is a sense of determination to be successful in pursuing goals, by which hope is fueled (Snyder et al., 1991 ). Perception of successful agency is conceptually similar to self-efficacy, and they are shown to be positively and moderately correlated (Magaletta & Oliver, 1999 ). However, successful agency is more future-oriented than is self-efficacy (Snyder et al., 1991 ). Thus, perception of successful agency is more closely related to achievement goals compared to self-efficacy.

We hypothesize that achievement goals are positively related to perception of successful agency. This is because achievement goals usually lead people to maintain high standards and strive to accomplish difficult tasks (Phillips & Gully, 1997 ). After making every effort to ensure success, people are likely to hold positive expectations towards the outcomes. This notion is supported by the effort justification theory (Aronson & Mills, 1959 ), which states that people’s expectations are in direct proportion to his/her effort. As expectations continue rising, they tend to attribute an even greater value to an outcome that they put effort into achieving.

In addition, we propose that perception of successful agency is positively associated with life satisfaction for two reasons. First, perception of successful agency makes one’s life meaningful. Feldman and Snyder ( 2005 ) suggest that perception of successful agency per se is actually a component of meaning, because factor analysis shows a single factor underlying the two constructs. People who feel that their life is more meaningful also report higher satisfaction with life (Park, Park, & Peterson, 2010 ; Steger, Frazier, Oishi, & Kaler, 2006 ). Second, according to the notion that hope copes with obstacles and enhances meaning in life, several empirical research has revealed a positive relationship between hope and life satisfaction (Bailey, Eng, Frisch, & Snyder, 2007 ; Bronk, Hill, Lapsley, Talib, & Finch, 2009 ; O’Sullivan, 2011 ; Przepiorka, 2017 ). Because perception of successful agency is one dimension of hope, we expect its relationship with life satisfaction to be similar. Based on the above discussion, we hypothesize that:

Hypothesis 2: Perception of successful agency mediates the relationship between achievement goals and life satisfaction.

Individuals exert considerable control over their emotions but differ in their use of specific emotion regulation strategies. Of these, the two most widely used strategies are reappraisal and suppression (Gross & John, 2003 ). Emotion reappraisal is a cognitive change of emotional impact by construing a potentially emotion-eliciting situation. For example, people can feel upset or frustrated in a traffic jam. However, if drivers reevaluate the current situation and consider a traffic jam as an unexpected opportunity to enjoy the beautiful scenery along the road, they can probably feel better off. This act of recognizing and changing the pattern of thoughts falls into emotion reappraisal. Compared with suppression, reappraisal is a much more effective regulation strategy (Gross, 1998 ; Gross & John, 2003 ). People who habitually use emotion reappraisal are less likely to be depressed (Feinberg, Willer, Antonenko, & John, 2012 ), experience more positive emotions and fewer negative emotions, and have better social functioning (Gross & John, 2003 ).

Achievement goals promote one’s expectation of the end state, which cannot always remain perfect. Failing to meet a goal means that most of the early efforts become sunk costs, which leads to decreased self-confidence and increased self-blame. These negative self-cognitions, in turn, trigger severe emotional reactions (Brown & Dutton, 1995 ), such as depression and anxiety (Ellenhorn, 2005 ; Hewitt & Flett, 1991 ). Consequently, when emotion reappraisal is low, the negative consequences caused by failure are unable to be adjusted in time, which lowers a person’s perceived quality of life. In this condition, the positive relationship between achievement goals and life satisfaction is attenuated. In contrast, when emotion reappraisal is high, individuals take an optimistic attitude to negotiate stressful situations and thus become more immune to the pressure of goal failure (Gross & John, 2003 ). As a result, their satisfaction with life remains positively correlated with achievement goals. Therefore, we propose the following hypothesis:

Hypothesis 3: Emotion reappraisal moderates the positive relationship between achievement goals and life satisfaction, such that the relationship is stronger when emotion reappraisal is high rather than low.

Participants and procedures

Data were collected via a survey from a sample of 225 participants in mainland China in late 2016 using Sojump ( http://www.sojump.com ), which is a professional online survey platform similar to Amazon’s Mechanical Turk. Sojump has a large, diverse workforce consisting of over 2.6 million users with different demographic backgrounds. It provides reliable crowdsourcing services and has been used in previous psychological research (e.g., Chen, Austin, Miller, & Piercy, 2015 ; Li, Chen, & Huang, 2015 ). Respondents in the current study were randomly recruited from Sojump. Before starting the survey, they were told that their responses would remain confidential. After completing the survey, they received a monetary reward. Previous research has documented that giving a monetary reward to participants can improve their motivation in responding, thus being beneficial to the quality of survey data (Esterman, Reagan, Liu, Turner, & DeGutis, 2014 ). Online studies even amplify this advantage. A monetary incentive can inspire participants to respond carefully when researchers are unable to monitor how the participants fill in the survey, which is why plenty of psychological studies using online platforms pay for participation (e.g., Saleem, Anderson, & Barlett, 2015 ; Stroessner, Scholer, Marx, & Weisz, 2015 ).

All of the respondents were adults. Among the respondents, 106 were males, and 119 were females; 73, 23, and 4% of them were 18–35, 36–53, and above 54 years old, respectively. Forty-one and 42% of the respondents’ monthly salary ranged from 2000 to 4000 yuan and from 4001 to 6000 yuan, respectively. The majority of the sample was well-educated: 53, 21, and 6% of them held bachelor’s degrees, master’s degrees, and PhDs as their highest degrees, respectively. With regard to job tenure, 63% of the participants had worked in their companies for more than 4 years, whereas 29 and 8% of them had worked in their companies for 2 to 3 years and less than 2 years, respectively.

We created a Chinese version of a set of measures for achievement goals, emotion reappraisal, perception of successful agency, life satisfaction, and social desirability. To ensure the accuracy of the translation, we followed Brislin’s ( 1986 ) translation and back-translation procedures. Specifically, the items of the scales were first translated into Chinese by a native Chinese speaker with excellent knowledge of English. Next, this process was reversed by a native English speaker with excellent command of Chinese. For a very small number of items, the back-translation procedure resulted in inconsistencies. However, these inconsistencies were resolved by discussion between the two translators and the researchers. To further validate the translation, we conducted a pretest involving 20 randomly recruited participants from Sojump before implementing the formal survey. After the completion of the pretest survey, participants declared that the survey questions were easily understood and that there were no barriers to responding. The participants in the pilot study were not included in the final sample because combining two sources of samples may rule in the confounding due to different times of data collection. Moreover, we performed another set of statistical analyses with the participants in both the pilot and formal study. No significant difference was found compared with the current results. Therefore, we only reported the analyses in the formal study.

  • Achievement goals

Achievement goals were measured by the Achievement Goal Striving Scale, which is a ten-item scale adapted from Goldberg’s ( 1999 ) International Personality Item Pool (IPIP). It has been widely used in previous studies and has proven to have good reliability and validity (Hirschfeld, Lawson, & Mossholder, 2004 ). On a seven-point scale (1 = not at all characteristic ; 7 = very characteristic ), participants rated how characteristic each statement best described themselves. An example item is “I go straight for the goal.” We used Omega to estimate reliability, because compared to Cronbach’s alpha, Omega provides a better estimate with more appropriate assumptions (Crutzen & Peters, 2017 ; McNeish, in press ). All of the items were averaged to create the score for achievement goals (Omega = .91).

  • Emotion reappraisal

Emotion reappraisal was assessed using the reappraisal subscale of the Emotion Regulation Questionnaire. This instrument is a six-item measure developed by Gross and John ( 2003 ). Participants indicated their agreement with each item on a seven-point scale (1 = strongly disagree ; 7 = strongly agree ). An example item is “I control my emotions by changing the way I think about the situation I’m in.” All of the items were averaged to create the score for emotion reappraisal (Omega = .96).

  • Perception of successful agency

We measured perception of successful agency using Snyder et al.’s ( 1991 ) Agency subscale of the Hope Scale (e.g., Chang, 2003 ; Gallagher & Lopez, 2009 ), which consists of four items. Participants were asked to evaluate the extent to which each item applied to them on a seven-point scale (1 = definitely false ; 7 = definitely true ). An example item is “I energetically pursue my goals.” All of the items were averaged to create the score for perception of successful agency (Omega = .95).

  • Life satisfaction

We assessed life satisfaction using the five-item Satisfaction with Life Scale developed by Diener, Emmons, Larsen, and Griffin ( 1985 ). On a seven-point scale (1 = strongly disagree ; 7 = strongly agree ), participants reported the overall satisfaction with their life under different indicators. An example item is “In most ways my life is close to my ideal.” All of the items were averaged to create the score for life satisfaction (Omega = .94).

Control variables

In the survey, we also collected information on some important variables that are potentially correlated with life satisfaction, such as gender, age, income, education level (Gannon & Ranzijn, 2005 ; Johnson & Krueger, 2006 ), job tenure (Adams et al., 1996 ), and social desirability bias. We used the Marlowe–Crowne Social Desirability Scale (Form C) with 13 true-false format items (Reynolds, 1982 ) to assess social desirability (Omega = .77). An example item is “It is sometimes hard for me to go on with my work.”

Data analysis

We began the analyses by conducting a series of confirmatory factor analyses using LISREL8.8, to verify the distinctness of the variables included in our models. Because our sample size was relatively small, we constructed item parcels in these confirmatory factor analyses. Specifically, four indicators were formed for constructs that contained more than four items by sequentially grouping the highest loading items with the lowest loading ones (Little, Cunningham, Shahar, & Widaman, 2002 ). After parceling, the total number of indicators decreased to 16, since the number of parcels for each construct was four. We assessed the models by comparing four indicators of fit, including the chi-square/degrees of freedom ratio ( χ 2 / df ), comparative fit index (CFI), non-normed fit index (NNFI), and root mean square error of approximation (RMSEA). Good fits are obtained when χ 2 / df is less than 5 and RMSEA is less than .10, whereas NNFI and CFI are greater than or equal to .90 (Bentler, 1990 ; Steiger, 1990 ).

Prior to hypothesis testing, we conducted exploratory factor analyses to ensure that the scales used retained their intended structure (Crutzen & Peters, 2017 ). Next, correlations among study variables were calculated using Pearson’s correlation coefficients, providing initial support for the hypotheses. Next, we performed hierarchical regressions using SPSS for the purpose of hypothesis testing, in which independent variables were mean-centered to reduce multicollinearity (Cohen, Cohen, West, & Aiken, 2003 ). Afterwards, as a robustness check for small samples (Preacher & Hayes, 2008 ), we used a bias-corrected and accelerated bootstrapping procedure (5000 samples were taken) to further examine the achievement goal–perception of successful agency–life satisfaction link. Next, simple slope analysis was applied to probe the nature of the interaction effect (Aiken & West, 1991 ). Finally, we employed another statistical analysis, which included both successful agency and emotion reappraisal in a single model. Again, we adopted the bootstrapping method as in Model 5 in Hayes ( 2013 ). As suggested by Cohen ( 1990 ), we reported all effect sizes and confidence intervals in the statistical analyses. Fisher’s z and its 95% confidence intervals were calculated in the correlational analysis (Rosenthal, 1991 ). We chose R 2 as the index of effect sizes for regression analyses and computed the 90% confidence interval for each R 2 (Smithson, 2001 ).

To support disclosure and replication in scientific research (Peters, Abraham, & Crutzen, 2015 ) and facilitate future meta-analyses, the data, syntax and statistical outputs used in the present study are available at https://pan.baidu.com/s/1qXLFvq8 .

Exploratory factor analyses

The measurement instruments were in line with their intended structure, as a single latent variable was observed for each construct, and all scales used in this research were unidimensional (Crutzen & Peters, 2017 ).

Measurement model results

The baseline model contained four factors: achievement goals, emotion reappraisal, perception of successful agency, and life satisfaction. We also examined six alternative models against the baseline model. As shown in Table  1 , the results suggested that the baseline model fits the data reasonably well ( χ 2 (98) = 348.79, CFI = .96, NNFI = .95, RMSEA = .09). The alternative models all exhibited significantly poorer fit than the baseline model. Therefore, we treated the four variables as distinct constructs in later analyses.

Descriptive statistics and correlations

All scales met the distributional assumptions with skewness and kurtosis values lower than ± 1. More specifically, the absolute values for skewness (kurtosis) ranged from .02 to .59 (.19 to .69). The descriptive statistics and correlations among variables are presented in Table  2 . Age was positively related to gender ( r  = .16, p  < .05; Fisher’s z  = .16, 95% CI [.03, .29]) and income ( r  = .30, p  < .01; Fisher’s z  = .31, 95% CI [.18, .44]); education was positively correlated with income ( r  = .24, p  < .01; Fisher’s z  = .24, 95% CI [.11, .37]) and negatively correlated with job tenure ( r  = − .26, p  < .01; Fisher’s z  = − .27, 95% CI [− .14, − .40]). Consistent with our hypotheses, achievement goals had a significant positive correlation with life satisfaction ( r  = .42, p  < .01; Fisher’s z  = .45, 95% CI [.32, .58]) and perception of successful agency ( r  = .83, p  < .01; Fisher’s z  = 1.12, 95% CI [.99, 1.25]). Perception of successful agency was also significantly related to life satisfaction ( r  = .44, p  < .01; Fisher’s z  = .47, 95% CI [.34, .60]).

Hypotheses test results

Table  3 displays the results of the regression analyses for testing Hypothesis 1 (achievement goals are positively related to life satisfaction) and Hypothesis 2 (perception of successful agency mediates the relationship between achievement goals and life satisfaction). The results supported these hypotheses. First, achievement goals were positively and significantly related to life satisfaction ( β  = .42, p  < .01; R 2  = .20, 90% CI [.11, .26]), supporting Hypothesis 1. Second, to test mediation, we followed Baron and Kenny’s procedure ( 1986 ).

In Model 1, we regressed successful agency on the control variables and achievement goals. In Model 2, we regressed life satisfaction on the same variables as in Model 1. In Model 3, we regressed life satisfaction on the controls, achievement goals, and successful agency. The results supported Hypothesis 2. First, achievement goals were significantly related to successful agency ( β  = .85, p  < .01; R 2  = .71, 90% CI [.65, .74]). Second, achievement goals were significantly related to life satisfaction ( β  = .42, p  < .01; R 2  = .20, 90% CI [.11, .26]). Third, successful agency was significantly related to life satisfaction ( β  = .25, p  < .01; R 2  = .22, 90% CI [.12, .27]), even after achievement goals were controlled for. In addition, the insignificant coefficient for achievement goals ( β  = .20, p  > .05) indicated that successful agency completely mediated the relationship between achievement goals and life satisfaction. Furthermore, a 5000 resample bootstrap suggested a significant indirect effect via successful agency ( b  = .24, SE = .09, 95% CI [.06, .42]). This finding again supported Hypothesis 2. Additionally, following MacKinnon, Lockwood, Hoffman, West, and Sheets ( 2002 ), we calculated the z coefficient, which results from the division of the mediated effect by its standard error. Consistent with prior findings, the calculation yielded a significant result ( z value = 2.24, p  < .05).

Table  4 presents the results for the tests of Hypothesis 3. Model 1 contained the control variables only. In Model 2, achievement goals and emotion reappraisal were added. In Model 3, the interaction term between achievement goals and emotion reappraisal was added. In support of Hypothesis 3, the interaction effect of achievement goals and emotion reappraisal was statistically significant ( β  = .31, p  < .01; R 2  = .34, 90% CI [.23, .39]), and there was a significant change in the multiple squared correlation coefficient (Δ R 2 ).

Figure  1 shows that the effect of the two-way interaction between achievement goals and emotion reappraisal was in the expected direction. Following the simple slope analyses, we found that achievement goals at a high level of emotion reappraisal were positively related to life satisfaction ( β  = 1.29, p  < .01), whereas achievement goals at a low level of emotion reappraisal were not significantly related to life satisfaction ( β  = .10, p  > .30).

Simple slope analyses. Moderating effect of emotion reappraisal on the relationship between achievement goals and life satisfaction. Error bars represent standard errors

Finally, a model that included both successful agency and emotion reappraisal was tested. Figure  2 illustrates the coefficients ( R 2  = .34, 90% CI [.24, .40]). The interaction term remained significant and the indirect effect of achievement goals on life satisfaction through successful agency was also significant ( b  = .27, SE = .09, 95% CI [.10, .44]). These results provided convergent support for our hypotheses.

Conceptual and statistical diagram. Research model with important coefficients. Effect size R 2  = .34, 90% CI [.24, .40]

This study used original survey data to examine the influence of achievement goals on life satisfaction, the mediating role of perception of successful agency, and the moderating role of emotion reappraisal. Consistent with our predictions, achievement goals are positively associated with life satisfaction. Furthermore, we show that this relationship is mediated by perception of successful agency. The simple slope analyses reveal that the positive relationship between achievement goals and life satisfaction holds when emotion reappraisal is high but not when it is low.

The present research contributes to the literature in three ways. First, we contribute to the scant literature on the relationship between achievement goals and life satisfaction. Our findings help fill this gap by showing that achievement goals are positively correlated with life satisfaction. The previous literature provides indirect and mixed evidence for this relationship (Lee et al., 2003 ; Senko & Harackiewicz, 2005 ). We reconcile these differences by empirically testing this relationship, thereby adding to the literature investigating the consequences of achievement goals (e.g., Harackiewicz et al., 2000 ; Cury et al., 2002 ; Lee et al., 2003 ; Flanagan et al., 2015 ). This finding is also in alignment with previous research documenting the overlap between aspiration and well-being (e.g., Cheng et al., 2014 ; Gao & Smyth, 2010 ). However, aspiration involves positive expectations regardless of how much effort has been exerted, which obviously should positively contribute to happiness. In contrast, the present research shows that even if considerable effort must be devoted, setting achievement goals is still beneficial to well-being. Therefore, this study complements previous findings by going beyond the aspiration effect.

Second, we identify the psychological process through which achievement goals are related to life satisfaction. Therefore, we shed some light on the role of perception of successful agency in the relationship between achievement goals and life satisfaction. This finding is consistent with the extant literature showing hope as a positive predictor of life satisfaction (Bailey et al., 2007 ; Bronk et al., 2009 ; O’Sullivan, 2011 ). Our research further demonstrates that perception of successful agency, as a dimension of hope, also contributes to life satisfaction. Given that hope is a multidimensional construct and that little research has probed into its sub-dimensions’ downstream effects, the present research serves as a pioneer study.

Third, we examine the moderating role of emotion reappraisal to provide a richer understanding of the relationship between achievement goals and life satisfaction. We show that by cognitively reappraising emotion, people who set achievement goals live a happier life. This result is in line with a body of research that elucidates the positive function of emotion reappraisal in buffering anxiety and enhancing well-being (Feinberg et al., 2012 ; Gross & John, 2003 ). Moreover, self-determination theory suggests a positive link between autonomous motivation and well-being (Ryan & Deci, 2000 ). Through examining the moderating role of emotion reappraisal, we specify the boundary condition under which the positive relationship between achievement goals, a form of autonomous motivation, and well-being ceases to exist. Thus, our study represents an important advancement in self-determination theory.

The findings also offer valuable insights into practice. For example, enhancing employee’s job satisfaction is of vital importance for many organizations. This research implies that organizations can boost employee’s job satisfaction by inspiring their motivation for achievement goals. In addition, we find that if individuals suffer from failure in the process of goal pursuit, they need to reappraise their emotions to restore well-being. Emotion reappraisal can function as a catalyst for well-being when the situation goes against one’s wishes (Gross & John, 2003 ). The reason why it helps individuals to be less affected by negative events is that emotion reappraisal ccurs early in the emotion-generative process and alters the trajectory of the emotion before the emotional response is generated (Gross, 2002 ). This has direct practical implications for career development. Consider a scenario in which an ambitious young man aims high in career development and spares no effort at work to get a promotion. However, it turns out that one of his competing colleagues receives the promotion instead, so he fails to achieve his promotion goal at present. Under this circumstance, if he leverages the emotion reappraisal strategy, he may see the competitive situation as an external force that drives him to become better at work, which is actually beneficial to career development in the long run. Consequently, he may feel more positive instead of frustrated or hopeless. This reappraisal would re-motivate him to continue working hard and improving himself until he succeeds.

This research has several limitations that could be solved in future research. First, caution should be exercised before generalizing our results based on Chinese data to Western societies. The meanings of some constructs may be different in China than in Western societies. Although there is no evidence showing the constructs used in the present study contain inconsistent meanings across different cultures, the previous literature indicates that some well-established concepts in Western culture are perceived in another way in China (e.g., Cheung et al., 2001 ; Wang, 2007 ). Future studies can directly test whether this difference applies to the study variables in this research. Second, the study is based on cross-sectional data. Therefore, our findings may not imply causality. In future studies, causal inference may be drawn based on experimental data. Meanwhile, caution should be taken for using cross-sectional data to test mediation (Kline, 2015 ; Maxwell, Cole, & Mitchell, 2011 ) because cross-sectional analysis can imply the existence of an indirect effect even when the true longitudinal indirect effect is zero. Adopting a longitudinal design in future research would help provide stronger evidence for the process account. Third, the sample size is limited in the present study. Future research can avoid this problem by adopting the sample size estimation approach suggested by Moinester and Gottfried ( 2014 ), which should be done before or at an early stage of a study. Fourth, we only identify one moderator in our model. Scholars may investigate other ways through which the relationship between achievement goals and life satisfaction is moderated. For example, similar to emotion reappraisal, psychological resilience refers to the capacity of positive adaptation in adversity (Ong, Bergeman, Bisconti, & Wallace, 2006 ). According to this definition, it is also a potential moderator between achievement goals and life satisfaction. Fifth, we do not explore the relationships between different types of achievement goals and life satisfaction. Previous research has shown that different types of achievement goals have competing effects on performance (Grant & Dweck, 2003 ), self-regulation (Lee et al., 2003 ), and reactions to imperfection (Stoeber, Stoll, Pescheck, & Otto, 2008 ); therefore, it is essential to further test whether each type of achievement goal has similar or distinct effects on life satisfaction in future studies. Finally, we did not collect participants’ information regarding whether they work in urban or rural environments, which has been shown to be related to well-being (Liang & Wang, 2014 ). Future research should control for this variable.


Through a survey study of 225 participants in China, we find that achievement goals are positively related to life satisfaction. Furthermore, the relationship between achievement goals and life satisfaction is mediated by perception of successful agency and moderated by emotion reappraisal. This research provides a comprehensive understanding of how, why, and when achievement goals boost life satisfaction, which is theoretically contributive and practically important.


Comparative fit index

Confidence interval

International Personality Item Pool

Non-normed fit index

Root mean square error of approximation

Standard error

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Wang, W., Li, J., Sun, G. et al. Achievement goals and life satisfaction: the mediating role of perception of successful agency and the moderating role of emotion reappraisal. Psicol. Refl. Crít. 30 , 25 (2017). https://doi.org/10.1186/s41155-017-0078-4

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McClelland’s Three Needs Theory: Power, Achievement, and Affiliation

The Three Needs Theory, also known as need theory, is the best-known theory of David McClelland, a Harvard professor who spent thirty years conducting research on motivation. He sought to understand human nature and develop tools to measure how people make choices.

David McClelland

McClelland’s Theory of Three Needs outlines the three desires that an individual could possibly have. Each person is motivated by power , affiliation , or achievement . One trait is usually more dominant, but the others are present in an individual as well.

There are many theories that examine motivation , some of which have similar elements to McClelland’s theory. For example, in his Achievement model , McClelland studies those who try and be better and achieve more. This is similar to both Herzberg’s ideas on high and low achievers as well as Maslow’s theory of Self-Actualization .

While his ideas are used primarily to assess work performance, McClelland conducted other studies that centered on motivation. He researched how motivation affected one’s health; an individual’s drive to succeed can cause stress, high blood pressure, or abnormal hormone levels. This demonstrated that internal factors, i.e. a motive, can cause a physical response. Some were not convinced, but these ideas provided a foundation for future studies.

Motivation – Three Needs Theory:

  • High: Must win at any cost, must be on top, and receive credit.
  • Low: Fears failure, avoids responsibility.
  • High: Demands blind loyalty and harmony, does not tolerate disagreement.
  • Low: Remains aloof, maintains social distance.
  • High: Desires control of everyone and everything, exaggerates own position and resources.
  • Low: Dependent/subordinate, minimizes own position and resources.

Source: David McClelland, 1961, The Achieving Society.

The Power Motive

If an individual’s predominant motive is power, they are motivated to influence others and take control. While the extreme example of Hitler in Nazi Germany may come to mind, this motive actually takes on a more mild form of coach or leader. They do not seek to implement a dictatorship but hope to motivate others, delegate responsibility and influence those around them.

A coach is a good example because it perfectly illustrates the idea of letting the players (or in this case, employees), do their job while they assess the situation and make decisions. The players and employees are aware of their responsibilities and are highly skilled to perform them.

McClelland found that power-motivated individuals were best suited for leadership positions within a company. If they were able to effectively delegate tasks in the workforce, they were often able to be successful leaders. This does not mean that all power-motivated individuals will make good leaders. Each personality is unique and those who are aggressive and authoritative may actually decrease the work performance of their employees.

The Power Motive is not without its own disadvantages. Individuals who are motivated by power are often flighty and frustrate easily. They have no problem moving locations or changing situations if the opportunity presents itself. They have a reputation for being “ladder climbers,” or working their way up the organization as soon as a better position presents itself. Some believe that they are not invested in their role, but just simply biding their time until the next one is available. As they will simply abandon their position in their search for fame, recognition and wealth, it is difficult to dispel those ideas.

The Achievement Motive

If an individual’s predominant motive is achievement, they are motivated to do better for the sake of doing so. They hope to exceed expectations and are pleased when they surpass their peers. These individuals like challenges and want to be in charge of their success.

Achievement-oriented individuals will change the situation or the location if they feel like it is not meeting their needs. They do not like working in groups because they do not like having limited control over the outcome. Instead, they prefer to do work where the results are clear and visible.

Many entrepreneurs are motivated by achievement. They have the drive to be successful and this is, in turn, vital to the economy. However, this may not mean that they are the best bosses to their employees. Achievement-motivated individuals often prefer to do things themselves, leading them to micromanage things in a business. They prefer not to work in a team and often fail to share the workload and responsibility. It is a double-edged sword: they experience success and rise to management positions, but this same personality is what keeps them from being successful in those roles.

The Affiliation Motive

If an individual’s predominant motive is affiliation, they are essentially motivated by social connections. They are primarily motivated to fit in and please others, and value their relationships with their peers. These individuals appreciate familiar situations and are unlikely to leave their work location. They also do not like working alone and try to avoid disappointing their coworkers and managers at all costs.

Even though affiliation-motivated individuals work well in a team, they are often not the best employees. They are not motivated to do better as they are content to stay in their position. There is no drive to improve their employee status or their personal position, which makes them, in effect, the least effective workers.

How it is used

The Three Needs Theory is most often used in business or corporate settings. It has enabled the use of personality tests in employees. Originally dismissed as irrelevant, personality tests became more popular when managers were attempting to discover what motivates their employees. Personality tests also enable the manager to learn more about each individual.

People require different things from their workplace. Individuals motivated by power may need clear expectations and steps needed to advance in their careers. Individuals motivated by achievement may need regular opportunities to solve a problem. Individuals motivated by affiliation may need consistent feedback on the job that they are doing.

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Wealth at Birth and its Effect on Child Academic Achievement and Behavioral Problems

In this article, we examine the association between family wealth and academic achievement and socioemotional behaviors of children ages 5 to 12. We examine whether wealth prior to birth and at ages 4 or 5 affects academic test scores and behavioral problems during two periods of childhood, ages 5 to 8 and ages 9 to 12, for a large and relatively recent cohort of children. We also examine associations between different forms of wealth (e.g., home equity) and child achievement and behaviors. Finally, we assess whether wealth prior to birth mediates racial/ethnic disparities in child achievement and disparities in achievement by maternal education/ability (AFQT). Results of our analysis indicate that wealth, particularly financial wealth that is the most liquid, has a modest positive association with achievement test scores. We also find that wealth is associated with fewer behavioral problems, but these results are less robust.

The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.


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What it means to be a successful male academic

  • Dritjon Gruda 0

Dritjon Gruda is an organizational behaviour researcher at the Católica Porto Business School and the Research Centre in Management and Economics at the Universidade Católica Portuguesa.

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Close up of kids hands playing with colorful blocks near father working on the laptop. Work from home during quarantine concept. Top view, flat lay.

Success as an academic doesn’t have to come at the expense of family. Credit: Getty

How do you become successful in academia? At numerous international conferences, I’ve heard eminent scholars emphasize the necessity of prioritizing work above everything else, including family and children. One memorable instance occurred in 2018, at a large international conference in my field. At a session for postdocs and junior faculty members about obtaining tenure and building successful careers, someone on the panel advocated for meticulously scheduling personal life, including sex with romantic partners, to boost work productivity. Other advice included minimizing time with your children to allow you to revise and resubmit manuscripts. These tips were alarmingly well-received by many of the 300 young academics, both male and female, in attendance. I left the session questioning whether I was the only one who found the advice unsettling.

At various conferences and events, I have attended numerous workshops on achieving better work–life balance. I have noticed a stark gender disparity among the panellists — more than three-quarters are female. This is presumably because most of these panels address the greater challenges that women in academia face in balancing work and family life — and justifiably so. But what advice is there for emerging male academics? The typical advice that I received from senior scientists was straightforward: avoid taking parental leave, minimize your childcare responsibilities and stay steadily focused on research.

I understand the value of hard work in academia and beyond. But I am deeply concerned by the intensity with which this message — namely to disregard everything else — is delivered to younger scientists, along with how this advice seems especially geared towards men. Is having a singular focus on career, to the exclusion of family life, the only path to success? And even if it were, is it right?

I began my journey as an academic in 2017, when I earned a PhD in management and psychology. By 2020, I had achieved tenure at Maynooth University in Kildare, Ireland, a milestone that felt surprisingly anticlimactic, especially amid the COVID-19 pandemic. What had a much greater impact on me, my career and my perspective was becoming a father in 2021.

Fatherhood fundamentally altered my definition of success, challenging the advice I’d received to sacrifice family life for work. My wife, a manager at an international pharmaceutical company, and I committed early on to sharing parenting responsibilities as equitably as possible. In the year after my daughter’s arrival, I took night shifts for feedings and managing her colic. I fully embraced parental leave and rearranged my work schedule to avoid attending any meetings before 10 a.m.. Today, I start my workday after dropping off my daughter at day care and finish it in time to pick her up — a routine that has redefined my professional life. No more working on the couch while watching a movie with my wife. No more working on holidays or at the weekend. I work from 9 a.m. to 5 p.m. at the latest. The laptop stays closed after I come home.

Surprising consequences

So, did my career tank? Did I become less successful? Quite the opposite. I was offered a position as an invited associate professor at Católica Porto Business School in Portugal, where I conduct research on anxiety, leadership and personality. The number of papers I’ve had accepted at conferences, a metric I use to judge progress on ongoing research projects, has tripled over the last year. My journal-submission rate has doubled. Overall, the pace has picked up, not slowed down. This is down to, I think, my better work–life balance: I’m more productive in the limited time I devote to work.

Most importantly, however, my definition of success has evolved from focusing on publications and citations to prioritizing meaningful work that doesn’t compromise my family life. I’ve adopted a policy of transparent communication with my colleagues by openly discussing the need to adjust work commitments to accommodate family time. In doing so, I’ve noticed that others also feel more comfortable opening up and being more honest about their own family–work dynamics.

I now choose projects judiciously, declining those that require extensive travel or time away from my family. In the past, I might have joined projects that would have required me to sacrifice more of my personal life. Now, I won’t.

Jon and daughter sitting at their favorite beach place with the rest of the family.

Dritjon Gruda and his daughter relax at the beach. Credit: Dritjon Gruda

This honest communication seems to have made me more relatable, particularly to senior colleagues who share these values and often express regret over not making similar choices. Some have said to me: “I wish I did the same when I first became a father.” And many female colleagues were surprised to hear about the changes I made after becoming a father. Some even expressed a degree of disappointment that their partners did not make similar changes when they first became parents.

I am in a privileged position to choose to step away from work: the ability to take a more balanced approach without jeopardizing my career is a luxury that is not available to everyone. Many academics with children face structural barriers or a lack of support from the other parent, or are at career stages with limited institutional support and flexibility. Nonetheless, I feel there is immense value in openly discussing the adjustments we make when parenthood reshapes our priorities — and this is particularly relevant for new fathers who are even less likely to voice their experiences. Only by sharing our perspectives can we encourage others to reconsider their own priorities and, over time, potentially influence institutional policies to foster more-supportive and equitable work environments.

An overemphasis on work to the detriment of personal life — an approach that is often called a ‘masculine work ethic’ — isn’t a hallmark of masculinity, but rather a path to personal and familial conflict. Male researchers who prioritize their roles as fathers and husbands while excelling in their academic careers are evidence that there is nothing masculine about working yourself to burnout or worse.

I love being an academic. I love the pursuit of knowledge and being paid to work on exciting research. But every day, my family shows what I tell doctoral students: prioritizing family life does not detract from professional success, it enhances it.

doi: https://doi.org/10.1038/d41586-024-02105-1

This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice. Guest posts are encouraged .

Competing Interests

The author declares no competing interests.

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Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda

  • Review Article
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  • Published: 05 May 2021
  • Volume 34 , pages 39–71, ( 2022 )

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research paper on need for achievement

  • TuongVan Vu   ORCID: orcid.org/0000-0001-6700-2439 1 , 2 ,
  • Lucía Magis-Weinberg 3 ,
  • Brenda R. J. Jansen 4 ,
  • Nienke van Atteveldt 1 , 2 ,
  • Tieme W. P. Janssen 1 , 2 ,
  • Nikki C. Lee 1 , 2 ,
  • Han L. J. van der Maas 4 ,
  • Maartje E. J. Raijmakers 1 , 2 ,
  • Maien S. M. Sachisthal 4 &
  • Martijn Meeter 1 , 2  

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The question of how learners’ motivation influences their academic achievement and vice versa has been the subject of intensive research due to its theoretical relevance and important implications for the field of education. Here, we present our understanding of how influential theories of academic motivation have conceptualized reciprocal interactions between motivation and achievement and the kinds of evidence that support this reciprocity. While the reciprocal nature of the relationship between motivation and academic achievement has been established in the literature, further insights into several features of this relationship are still lacking. We therefore present a research agenda where we identify theoretical and methodological challenges that could inspire further understanding of the reciprocal relationship between motivation and achievement as well as inform future interventions. Specifically, the research agenda includes the recommendation that future research considers (1) multiple motivation constructs, (2) behavioral mediators, (3) a network approach, (4) alignment of intervals of measurement and the short vs. long time scales of motivation constructs, (5) designs that meet the criteria for making causal, reciprocal inferences, (6) appropriate statistical models, (7) alternatives to self-reports, (8) different ways of measuring achievement, and (9) generalizability of the reciprocal relations to various developmental, ethnic, and sociocultural groups.

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In most countries, motivation for school clearly declines throughout school time (Martin, 2009 ; OECD, 2016 ; Scherrer & Preckel, 2019 ) with individual heterogeneity in changes depending on specific motivation constructs across academic domains (Gaspard et al., 2020 ; Scherrer & Preckel, 2019 ). Given this undesirable decline and the fact that motivation can be targeted by interventions, motivation has long been a central focus of educational psychology. The influence of motivation on achievement is well-documented (Burnette et al., 2013 ; Gottfried et al., 2013 ; Greene & Azevedo, 2007 ; Valentine et al., 2004 ). Yet the reverse relation is also often found, as achievement can affect motivation through experiences of success or failure (Garon-Carrier et al., 2016 ; Guay et al., 2003 ; Jansen et al., 2013 ). A common view is that both the “motivation → achievement” and “achievement → motivation” links exist and that motivation and achievement influence each other in a reciprocal manner over time (Huang, 2011 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Möller et al., 2009 ).

Researchers have been studying this reciprocal relationship between motivation and achievement for at least 20 years (Marsh et al., 1999 ). However, further insights into the nature of the relationship are currently lacking; features such as the direction of causality, behavioral mediating pathways, possible effect of the time scale, and generalizations to different motivation constructs and population groups are currently not well understood. These issues are important not just from a scientific viewpoint, but also from a practical viewpoint. To be able to design the most effective interventions aimed at improving achievement and motivation, we need to improve our understanding of the reciprocity to identify the best timing, duration, content, and appropriate target variables of such interventions, as well as other contextual factors contributing to their success.

Our objective is to summarize the current understanding of motivation-achievement interactions (drawing mainly from the academic motivation literature) and to identify the theoretical and methodological challenges that could inspire further advances to understand such specific features of this reciprocal relationship. While an exhaustive review of the literature is beyond the scope of the current paper (see the Special Issue on Prominent Motivation Theories: The Past, Present, and Future on Contemporary Educational Psychology, edited by Wigfield and Koenka, 2020 ), we start with a summary of how influential theories of academic motivation have conceptualized reciprocity between motivation and achievement, and the types of empirical evidence that have been found in support of the reciprocal relationships. In our current understanding, we have found areas of consensus, but have also identified sizable gaps. This leads to a recommended research agenda for future empirical studies on the reciprocal relations between motivation and academic achievement and suggestions on how these insights could inform future interventions.

Reciprocal Relations in Theories of Academic Motivation

Commonalities between theories.

Individual differences in academic achievement are partly the result of differences in motivation for learning (Arens et al., 2017 ; Burnette et al., 2013 ; Eccles & Wigfield, 2002 ; Guay et al., 2003 ; Huang, 2011 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Robbins et al., 2004 ; Seaton et al., 2014 ). This robust finding has spawned a wealth of theories on academic motivation and how to stimulate it. These theories differ in both substance and focus, but also have many common elements. Figure 1 represents an attempt to synthesize, for the purposes of this paper, some of the commonalities of well-established theories that have had an impact in the field of academic motivation (leaning strongly on the seminal review of Eccles & Wigfield, 2002 and adding theories that have gained traction since). Our goal is not to comprehensively review and synthesize the existing theories (although this is an urgent task, Koenka, 2020 ), but rather to illustrate how the commonalities between the theories suggest a framework in which the reciprocal relationships between motivation and achievement can be studied and understood.

figure 1

The motivation-achievement cycle, a summary model of motivation-achievement interactions, capturing some of the commonalities within prominent theories of academic motivation. Blue boxes denote motivation constructs, green (dotted) arrows behavioral intermediaries (quality of learning and quantity of learning), and yellow boxes and arrows denote achievement-related constructs (flow and perceived performance). Gray arrows denote outside influences that are themselves not part of motivation-achievement interactions (e.g., cultural and social influences that affect both expectancies and values)

Motivation has up to 102 definitions (Kleinginna & Kleinginna, 1981 ), but is often seen as a condition that energizes (or de-energizes) behaviors. In many theories, motivation results from what can be called an appraisal of the behavior that one is motivated to perform (the word appraisal is rarely used with regard to motivation, but the processes described are akin to those captured in the emotion literature). In that appraisal, two elements are combined (Eccles & Wigfield, 2002 ): the value attached to the behavior and its outcomes, and the expectancy of the likelihood of certain outcomes of the behavior. These two sides, expectancy and value, are explicit in expectancy-value theory (Eccles & Wigfield, 2002 , 2020 ), attribution theory (Graham, 2020 ; Weiner, 2010 ), control-value theory (Pekrun, 2006 ; Pekrun et al., 2017 ), and Dweck’s integrative theory (Dweck, 2017 ).

Many other theories focus either on the value attached to behavior or on expectancies. Theories on the values side of the ledger (goal theories, flow theory, self-determination theory, individual differences theories, and interest theories) focus on interest, goals, needs for relatedness, competence, and autonomy. Theories on the expectancy side, notably self-efficacy theory, control theories, social-cognitive self-regulation theories, and the process-oriented metacognitive model, focus on how students’ beliefs (or perception ) about their competence and efficacy (i.e., academic self-concept, see below), expectancies for success or failure, and sense of control over achievement affect motivation. Different constructs have been studied that tap into these beliefs underlying one’s expectancies, such as academic self-concept, self-efficacy, locus of control, and perceived control.

A motivation construct frequently used to study the reciprocal motivation-achievement relationship is academic self-concept (hitherto, ASC, discussed in further details in section “Different motivation constructs” below) which is how individuals evaluate their ability specifically in an academic domain (Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Shavelson et al., 1976 ). ASC is a component distinct from physical, social, and emotional self-concepts within the multidimensional, hierarchical model of self-concept (Marsh & Craven, 2006 ; Marsh & Martin, 2011 ). ASC is itself also multidimensional and usually measured by the Self Description Questionnaire (Marsh et al., 1999 ; Marsh & O’Neill, 1984 ); its academic subscales tap into general academic self-concept, math self-concept, and verbal self-concept. Much empirical research on motivation-achievement interactions operationalizes motivation as ASC in a certain academic domain, most often in mathematics and verbal subjects such as language and reading (Guay et al., 2003 ; Seaton et al., 2014 ); for meta-analyses and reviews, see Burnette et al. ( 2013 ), Eccles and Wigfield ( 2002 ), Marsh and Craven ( 2006 ), Marsh and Martin ( 2011 ), and Robbins et al. ( 2004 ).

It is worth noting that many theories posit that beliefs about the self (including self-concept and self-esteem and mindset/implicit theory of self attributes) are important causes of human behavior and learning (Bandura, 1997 ; Carver & White, 1994 ; Deci & Ryan, 2000 ; Molden & Dweck, 2006 ). Although the idea that ASC or other beliefs about the self affect achievement has been challenged (see the discussion in Marsh & Craven, 2006 ), there has also been much empirical research in support of it (Burnette et al., 2013 ; Gottfried et al., 2013 ; Greene & Azevedo, 2007 ; and the meta-analyses of Huang, 2011 ; Valentine et al., 2004 ). One suggested pathway is that positive self-beliefs can lead to self-affirmative, self-regulatory, academic behaviors (or achievement behaviors , see below) such as exerting effort, demonstrating persistence, and selecting goals that are conducive to the achievement of academic goals.

Another pathway for beliefs about the self to act as a causal agent on academic achievement, according to self-worth theory (Covington, 2000 ), is that students with positive beliefs about themselves assign high and positive values to academic activities. Academic activities are then viewed as important, intrinsically interesting, of high expected utility and of low cost, which leads to high achievement (Valentine et al., 2004 ). Also, in self-determination theory, feelings of competence are a precursor of intrinsic motivation, again leading to a higher value being assigned to academic activities if one feels competent. This would then lead to behaviors that support later achievement. A recent study of more than 30,000 college students found that need for competence (relative to need for autonomy and relatedness) is the strongest predictor of perceived learning gains (Yu & Levesque-Bristol, 2020 ).

An appraisal of values and expectancies leads to the decision to engage (Cleary & Zimmerman, 2012 ; Kuhl, 1984 ; Schunk & DiBenedetto, 2020 ). According to the self-regulatory account of motivation (Cleary & Zimmerman, 2012 ; Schunk & DiBenedetto, 2020 ), students first identify values and expectancy of learning activities, then engage in self-regulatory processes (self-instruction, attention focusing, task strategies, etc.). Following their performance, students conduct self-evaluations, infer causal attributions, and make adaptive or maladaptive attributions of their successes and failures. This account stresses the importance of metacognition, where students who can monitor their learning processes can then maintain their engagement in the learning cycle.

The appraisal of values and expectancies can also trigger academic emotions, such as pride in achievement, hope, boredom, and enjoyment. Control-value theory (Pekrun, 2006 ; Pekrun et al., 2017 ) describes how such emotions codetermine what are termed achievement behaviors —behaviors that are conducive to the achievement of academic goals. In line with dominant theories of emotion (e.g. Frijda, 1988 ; Lazarus, 1999 ), Pekrun ( 2006 ) assumed that an appraisal of control of the learner and the value of learning activities lie at the basis of academic emotions. For example, if a learner values an academic outcome and believes it is somewhat under his or her control, he or she may feel the emotion of hope. While it is not certain that the same kinds of appraisal lie at the basis of both motivation and academic emotions, it would seem plausible and parsimonious. Indeed, Pekrun ( 2006 ) suggested that this is the case, though he cautioned that more research is needed.

Figure 1 may raise the question of what actually distinguishes motivation from emotions, since both seem to result from an appraisal of the situation, and both energize or de-energize certain behaviors. This is a valid question, and Kleinginna and Kleinginna ( 1981 ) already noted that a sharp line between motivation and emotion is difficult to draw (also see Berridge, 2018 ). Emotions will typically be more temporary than motivation, but this is a fuzzy distinction. Emotions and motivation may also interact. Emotions may for example make a learner assign more or less value to academic activities, or may change the learner’s expectations around their chances of success or failure, which then changes the appraisal that underlies motivation. Literature showing that emotions and academic achievement also form reciprocal relationships over time has recently emerged (Putwain et al., 2018 ).

Pathways from Motivation to Achievement and Vice Versa

While it is generally accepted that motivation affects achievement, it is not completely clear how . Theoretically, two routes can be discerned (see Fig. 1 ). The first is the quantity (frequency and intensity) of academic behaviors aimed at achievement (such as effort, persistence, etc.) (Cury et al., 2008 ; Dettmers et al., 2009 ; Doumen et al., 2014 ; Marsh et al., 2016 ; Pinxten et al., 2014 ; Plant et al., 2005 ; Trautwein et al., 2009 ). As a second route, higher levels of motivation could also be associated with higher quality of academic behaviors; for example, by adopting effective learning strategies, adaptive meta-cognitive strategies, spaced practice, elaboration, retrieval practice, interleaving, dual coding, and so on. Several theories of academic motivation support the idea that higher motivation leads to higher quality behaviors. Both intrinsic motivation (self-determination theory, Deci & Ryan, 2000 ) and interest (interest theories, Alexander et al., 1994 ) have been linked to deeper learning (Alexander et al., 1994 ; Schiefele, 1999 ; Scott Rigby et al., 1992 ). Positive academic motivations have also been suggested to facilitate creative learning strategies (control-value theory, Pekrun, 2006 ), and incremental implicit beliefs (growth mindset) to facilitate mastery-oriented strategies (Burnette et al., 2013 ).

Effects of achievement on motivation may also take two routes. The first is through perceived achievement. Many theories, such as self-efficacy theory (Bandura, 1997 ), expectancy-value theory (Eccles & Wigfield, 2002 ), control theories (Skinner, 1995 ), and attribution theory (Weiner, 2010 ) explicitly suggest that past achievement leads a learner to experience feelings of self-efficacy and perception of control. What matters most in this regard is the learner’s own evaluation of this outcome, for which we use the term perceived performance in Fig. 1 . High perceived performance will thus change the expectancies of learners (i.e., make them trust that good outcomes are attainable), but it may also alter the value attached to learning activities. For example, in self-determination theory, the feeling of competence (strengthened by positive perceived achievement) is a basic need that increases the intrinsic value of learning.

The second route from achievement to motivation is central to flow theory (Csikzentmihalyi, 1990 ). An activity in which the learner is holistically immersed can generate a feeling of flow, which is rewarding in its own right and alters the value attached to the academic behaviors.

External Factors Affecting Motivation, Effort, and Achievement

Figure 1 suggests a positive feedback loop, with motivation feeding achievement, and achievement feeding motivation—an idea that is alluded to in some theories (Cleary & Zimmerman, 2012 ; Eccles & Wigfield, 2002 ; Schunk & DiBenedetto, 2020 ). Most explicit in this regard is the self-regulatory account of motivation (Cleary & Zimmerman, 2012 ) where the pathway between self-regulation and achievement is a cyclical feedback loop. Schunk and DiBenedetto ( 2020 ) suggest an iterative process between perceived progress, self-efficacy, and goal pursuit. Bandura’s social cognitive theory also stresses the reciprocity of the interactions between behavioral, environmental, and personal factors (Bandura, 1997 ). Crucially, this raises the question of how such a positive feedback loop could get started, and how, once started, it could lead to any other outcome than either perfect motivation and achievement, or negative motivation and failure. The answer to those questions may rest in the external influences on motivation and achievement. These are indicated in Fig. 1 by the gray arrows:

Extrinsic rewards and requirements tied to achievement, e.g., schools or parents, may change the value attributed to academic behavior, and so change motivation. Although this has been described in self-determination theory as potentially detracting from intrinsic motivation (Deci & Ryan, 2000 ), it may also jolt a motivation-achievement cycle that would otherwise not start (Hidi & Harackiewicz, 2001 ). Supporting autonomy and creating relatedness are other ways in which external actors can increase the value attached to learning, increasing motivation and achievement (Deci & Ryan, 2000 ).

Cultural norms (described in control theories and control-value theory, Pekrun, 2006 ; Skinner, 1995 ), social learning, and verbal persuasion by others (social cognitive theory, Bandura, 1997 ) can alter the expectations, values, and attributional processes of learners (expectancy-value theory, attribution theories, Eccles & Wigfield, 2020 ; Graham, 2020 ), and therefore keep a motivation-achievement cycle going that would otherwise falter or not start up.

Effort is not only a result of the learner’s motivation but also of outside requirements (e.g., deadlines and exams set by the educational institution, Kerdijk et al., 2015 ). Such outside requirements can lead to achievement in the absence of strong motivation.

Quality of learning is not only affected by motivation but also by the abilities of the learner and the quality of teaching, instructions, and study materials. Thus, achievement can increase in the absence of stronger motivation, because of better support for learning.

Perceived achievement is not only determined by true achievement but also by elements of educational design, such as the form in which feedback is given (e.g., a grade that either accentuates the ranking of the student or the degree to which the study material was mastered, or feedback on effort instead of performance, De Kraker-Pauw et al., 2017 ). Perceived achievement is also subject to interpretative, comparison, and attributional processes (described in attribution theories, Graham, 2020 ; Weiner, 2010 ). This means that true high achievement can still fail to support motivation (e.g., when a sibling performs even better), or low achievement can be viewed in such a way so as to not be detrimental for motivation.

Such external factors are not only important for a complete causal understanding of motivation-achievement interactions (i.e., highly relevant for educational researchers) but also because they offer entry points for interventions that enhance motivation, achievement, or both (i.e., highly relevant for educators).

What Avenues for Empirical Research Have Been Explored?

Figure 1 shows that theories of academic achievement imply a reciprocal relationship between motivation and achievement. A comprehensive review of studies is beyond the scope of this manuscript (see narrative reviews and meta-analyses) (Huang, 2011 ; Marsh & Craven, 2006 ; Scharmer, 2020 ; Valentine et al., 2004 ; Valentine & Dubois, 2005 ), but we will review the kinds of evidence that have been brought to bear in support of such reciprocal relationships. Analyzing this evidence allows future directions on the field to be charted.

The earliest support for the relationship between motivation (focusing specifically on self-concepts and other self beliefs) and academic achievement comes from cross-sectional and correlational studies, reviewed by Hansford and Hattie ( 1982 ). These studies established a relationship between self-concepts and academic achievement, but no causal paths. Subsequent work set out to investigate the causal and temporal ordering of the effects using structural equation models (SEMs) and longitudinal data (e.g., Marsh et al., 1999 ). To date, the majority of evidence for the reciprocal relationship between self-concept and achievement has come from such time-series or cross-sectional data collected at schools, to which various SEMs have been fitted (see Marsh & Craven, 2006 for a narrative review and Huang, 2011 for a meta-analysis of such studies).

More recent studies showcase impressive efforts of researchers to use large sample sizes and longitudinal data of up to six waves, allowing changes in motivation and achievement of students to be tracked across their school career (e.g., Marsh et al., 2018 ; Murayama et al., 2013 ). A recent meta-analysis (Scharmer, 2020 ), which includes such studies that were published between 2011 and August 2020, showed that overall, the pooled effect of achievement on motivation was twice (β = .12) the pooled effect of motivation on achievement (β = .06), though both are what is conventionally considered a small effect. These findings are in line with Valentine and DuBois ( 2005 ) who found that academic achievement had a stronger effect on self-belief than vice versa. In contrast, Huang ( 2011 )’s meta-analysis found a slightly larger effect of self-concept on achievement than the other way around. Valentine and DuBois ( 2005 )’s findings were also more similar to Scharmer’s ( 2020 ) in terms of the size of the effects (achievement on self-belief: β = 0.08; self-belief on achievement: β = 0.15). Huang ( 2011 ), however, found considerably larger ranges of effects overall (achievement on self-concept: β = 0.19–0.25; self-concept on achievement: β = 0.20–0.27).

There have also been interventions and randomized controlled field studies in which either self-concept or other motivation constructs were manipulated (e.g., Savi et al., 2018 ; Vansteenkiste et al., 2004 ), thereby allowing for causal inferences. The meta-analysis of these studies by Lazowski and Hulleman ( 2016 ) showed that, while interventions targeting motivation usually led to positive outcomes on achievement (medium effect size; average Cohen’s d of 0.49), it did not matter which theory was at the basis of the intervention— all theories of motivation performed about equally well. However, experimental studies that look at the reverse causal path, manipulating achievement (or the perception of achievement) to affect motivation, are scarce. One example is an intervention study by Betz and Schifano ( 2000 ) where students were ensured of successful completion of a task followed by affirmation of their accomplishments with applause and verbal praise. This resulted in an increase in self-efficacy (a motivation construct highly related to ASC, Bong & Skaalvik, 2003 ). Nevertheless, to the best of our knowledge, few studies have done both: combining experimental manipulation and longitudinal design to investigate reciprocal motivation-achievement relations (an exception that we are aware of is Bejjani et al., 2019 which will be discussed later).

Research Agenda

The overview given above suggests that empirical evidence for reciprocal relations between motivation and achievement exists. However, several features of such relationships are still poorly understood. Also, some doubts about the robustness of the effects have recently surfaced (which we discuss in detail in section “Choice of appropriate statistical models” below). In other words, there are still unanswered theoretical and empirical questions about the reciprocal relationship between motivation and academic achievement. Below, we outline these issues and a research agenda for future research that can answer these remaining questions. These are organized into questions pertaining to theoretical lacunae, methodological challenges, and questions about the scope of theories and the generalizability of empirical results.

Theoretical Lacunae

Multiple motivation constructs.

First, as we presented above, many motivation theories have implicitly or explicitly conceptualized the relationship between a plethora of motivation constructs and achievement as reciprocal. However, to date, a large amount of empirical research on reciprocal motivation-achievement interactions has mainly studied ASC (Arens et al., 2019 ; Brunner et al., 2010 ; Chen et al., 2013 ; Dicke et al., 2018 ; Gottfried et al., 2013 ; Grygiel et al., 2017 ; Guay et al., 2003 ; Guo et al., 2015 ; Möller et al., 2011 ; Niepel et al., 2014a , 2014b ; Retelsdorf et al., 2014 ; Viljaranta et al., 2014 ; Walgermo et al., 2018 ; for meta-analyses and reviews, see Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Valentine et al., 2004 ; Valentine & Dubois, 2005 ) . This raises the question of whether findings generalize to other motivation constructs that are related yet could also have a distinctive reciprocal relationship with academic achievement.

Moreover, although the studies involving ASC were groundbreaking attempts to show reciprocal relations, there are several reasons why future studies should contemplate using different motivation constructs other than ASC. First and foremost, ASC and achievement are highly intertwined, as items in ASC questionnaires usually ask students to report on their achievement (e.g., “I get good marks in most academic subjects,” “I learn quickly in most academic subjects” (Marsh & O’Neill, 1984 ). Fulmer and Frijters ( 2009 , p. 228) in their critique of how motivation is measured in educational psychology also made the point that “self-report measures confound the measurement of motivation with other variables, such as ability and attention.”

Second, a meta-analysis investigating mean-level changes of a number of important motivation constructs concluded that the decline in motivation shows non-trivial differences across these constructs (Scherrer & Preckel, 2019 ). An important implication of this finding is that more attention should be paid to differentiation among multiple motivation constructs in future empirical studies.

Third, ASC might also be less malleable than other motivation constructs since general self-concept is relatively stable—especially for those at lower levels (Scherbaum et al., 2006 ). Research into the Big-Fish-Little-Pond phenomenon (i.e., students in high-achieving classes having lower ASC than those with comparable aptitude in regular classes) suggests that domain-specific ASC (more so than general ASC) is influenced by social comparison (Fang et al., 2018 ; Marsh et al., 2018 ). Nevertheless, it may be hard to manipulate ASC in a randomized controlled trial (although it has been indirectly done by affirming general self-esteem and personal values, Cohen et al., 2009 ). Other motivation constructs that can be modified through external influences (e.g., situational interest, perceived control, etc.) might yield useful guidance for designing interventions.

Furthermore, the heavy focus on ASC may reflect an emphasis on a cognitive, intrapsychological theoretical view of motivation while losing sight of social, contextual, historical, and environmental factors that arguably also play important roles (see the Special Issue on Prominent Motivation Theories: The Past, Present, and Future on Contemporary Educational Psychology, edited by Wigfield and Koenka, 2020 ). Last but not least, ASC is mainly self-reported and, despite the availability of well-constructed measures, it suffers from all the caveats inherent to self-report measures (see section “Alternatives to self-reports” below).

Given that there are other well-studied motivation constructs such as achievement goals, self-efficacy, interest, and intrinsic motivation (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ), further research with multiple non-ASC motivation constructs included as concomitant predictors of academic achievement is therefore much needed. In recent investigations of the reciprocal relationship between motivation and achievement, motivation constructs other than ASC have started to be included (e.g., self-efficacy in Grigg et al., 2018 ; Schöber et al., 2018 ; achievement goals in Scherrer et al., 2020 ; intrinsic motivation in Hebbecker et al., 2019 ; and interest in Höft & Bernholt, 2019 ). Yet, these studies are still small in number. Twenty-four out of 41 studies included in the meta-analysis of Scharmer ( 2020 ) still used ASC as the main motivation construct of interest.

Behaviors as Mediating Factors in the Motivation → Achievement Link

As mentioned above, theories of academic motivation imply several pathways through which motivation influences achievement and vice versa (see Fig. 1 ). For the motivation → achievement link, the rationale is that motivation leads to active and effortful commitment to learning (e.g., E. Skinner et al., 1990 ), implying that motivation constructs that are beliefs about competence and efficacy influence achievement by inducing self-regulatory, academic behaviors. In a similar vein, the volition theory of motivation (Eccles & Wigfield, 2002 ; Kuhl, 1984 ) posits that motivational beliefs only lead to the decision to act. Once the individual engages in action, volitional processes are required and determine whether the intention is fulfilled. Thus, self-regulatory processes theoretically mediate the link between beliefs and accomplishment of the task.

However, there is a relative paucity of empirical research and especially longitudinal studies that include measures of such regulatory processes. Usually, when studies found reciprocal relations between ASC and other motivation constructs and achievement, they left unanswered which pathways mediate the link between such beliefs and achievement (Marsh & Martin, 2011 ). To our knowledge, initial attempts to study mediating processes in longitudinal designs (Marsh et al., 2016 ; Pinxten et al., 2014 ; Trautwein et al., 2009 ) yielded mixed findings with regards to the role of effort in the relationship between ASC and academic achievement. This may be due to the fact that there are multiple operationalizations and evaluations of the construct effort (Massin, 2017 ), which may have varying relations with academic achievement. Specifically, Marsh et al. ( 2016 ) and Pinxten et al. ( 2014 ) measured subjective effort—i.e., students were asked to rate their own effort expenditure. Students might perceive that having to try hard (i.e., expending a great deal of effort) is indicative of a lack of academic ability (Baars et al., 2020 ). Subjective effort, as opposed to objective effort, might therefore have a very different relation to motivation and achievement.

In non-longitudinal studies looking at the relations between academic motivation and achievement, the evidence on behavioral mediators also shows differentiation related to how effort is measured. When effort is measured as quality of learning (e.g., selecting adaptive goals, adopting higher-quality learning strategies, etc.), there is some evidence for a positive link between academic achievement and effort (Trigwell et al., 2013 ). However, when effort is measured as a quantity of learning (such as study time, practice time, time-on-task, persistence, etc.), this relationship seems either weak or only significant after controlling for quality of learning (Cury et al., 2008 ; Dettmers et al., 2009 ; Doumen et al., 2014 ; Plant et al., 2005 ) or even negative (the labour-in-vain effect, Koriat et al., 2006 ; Nelson & Leonesio, 1988 ; Undorf & Ackerman, 2017 ). This provides suggestions for future attempts to parse the mediating factors in the motivation → achievement link in reciprocal relations between these two constructs. It is most fruitful to measure subjective and objective measures of quantity and quality of learning (and use triangulation of methods, as strongly suggested by Scheiter et al., 2020 ) and compare their effects on academic achievement.

Irrespective of what operationalization is chosen, it is important to note that it is not trivial to evaluate and conceptualize effort (see extensive discussions in Baars et al., 2020 ; Scheiter et al., 2020 ). Is effort the allocation of cognitive control, i.e., mental effort (Kool & Botvinick, 2018 ), or the intention to think deeply, regardless of the amount of time spent (Haynes et al., 2016 ), or a preference for thinking hard (Beck, 1990 ), a decision process rather than a capacity or resource that is physically limited (Gendolla & Richter, 2010 )? Yet, only by measuring regulatory processes that mediate the motivation → achievement pathway, we can make progress in understanding the underlying mechanism of mutual influences between motivation and achievement.

Mutualistic Perspective and the Network Approach

Next, studies have typically investigated relations between one or a small number of motivation constructs (e.g., ASC and interest, Walgermo et al., 2018 ). The discussion above and Fig. 1 show that multiple motivation constructs are linked to academic achievement, which may also all be mutually related. Like many topics in psychology, there is a huge overlap in terms and variables in the literature on motivation and achievement; the same construct may have different names, or different constructs go under the same name (this is known as Jingle-Jangle fallacies; e.g., Marsh et al., 2003 ). One possible solution to the Jingle-Jangle fallacies with regard to motivation was proposed by Marsh et al. ( 2003 ), who presented a factor model with two higher-order factors (dubbed learning and performance ) that explained relations between motivation constructs. In this approach, assumptions on the number of factors and factor structure are necessary.

The network approach is different; it does not assume an a priori structure of motivation factors. Instead, it uses the (bidirectional) partial correlations between variables in empirical data and in doing so clusters of variables which can be interpreted as constructs may emerge. The idea of a network of mutual relations to model psychological constructs was introduced by van der Maas and colleagues (van der Maas et al., 2006 , 2017 ) as an explanation for the positive correlations (the positive manifold) between intelligence sub-test scores. This led to a productive area of research with applications in many areas of psychology (Dalege et al., 2016 ; Robinaugh et al., 2020 ; Sachisthal et al., 2019 , 2020 ; Zwicker et al., 2020 ). The general hypothesis in psychological network models is that correlations between observed behaviors, such as cognitive functions, psychopathological symptoms, and attitudes (or, motivation constructs ), are not due to unobserved common causes, but to a network of interacting psychological, social, and/or biological factors. These observed behaviors are the nodes in the network and the partial correlations are the edges.

An example of how such a network approach can be applied to the area of motivation can be found in a study of interest in science (Sachisthal et al., 2019 ). This study included measures of students’ value of science, their science engagement, and achievement. The correlations between these measures were modeled as a network, within which clusters of variables emerged. These can be seen as empirically derived constructs, replacing the at times arbitrary theoretical separation between (motivation) constructs. Given that in motivation research many constructs with considerable overlap exist (Anderman, 2020 ; Hattie et al., 2020 ), such empirically derived concepts may prove especially relevant.

Within this network, variables with the strongest direct relationships can be identified. A positive change in a central variable should lead to a positive change throughout the network and these central variables may differ between contexts. For example, enjoyment emerged as the central node in the network of Dutch students, whereas engagement behaviors emerged as central in the network of Colombian students and therefore different approaches for increasing science interest are advised for the two countries (Sachisthal et al., 2019 ). Central variables may be efficient intervention targets as interventions informed by network analyses have been shown to be highly effective as these central variables were later shown to be predictive of subsequent behaviors (e.g., Sachisthal et al., 2020 ). Moreover, further support for this assumption comes from a recent study by Zwicker et al. ( 2020 ) who identified guilt as the central node in the network of attitude and environmental behaviors. They then successfully manipulated guilt which increased willingness to engage in such behaviors.

In sum, these works exemplify how network approaches can be used (1) to model distinctive but highly related motivation and achievement constructs simultaneously and map their relations and (2) to derive hypotheses about which included constructs may be efficient targets for interventions (see Borsboom, 2017 , for an overview). Moreover, the fact that network analyses found different central variables in different populations also showcases how such an approach can flexibly capture interactions between motivation factors in real life. Last but not least, at a more abstract level, a mutualistic network approach can potentially solve the question of the mechanisms of the impact of motivation on achievement (also raised in Hattie et al., 2020 as an important avenue for future research). Specifically, how clusters of motivation constructs, behavior, and achievement interact with one another can be modeled, and how reciprocal relations between them arise over time. This can only be achieved when multiple motivation constructs are measured in one single study (as argued above in section “Multiple motivation constructs”).

Time Scale of the Interactions (Short vs. Long Cycle)

Another gap in the literature that we identified is that much research on the reciprocity between motivation and achievement has been done with data collected at large time intervals, which reflect changes that happen over months or years (e.g., Harackiewicz et al., 2008 ; Marsh et al., 2005 , 2016 ; Nuutila et al., 2018 ). For example, it is common for studies to include data collected per academic semester or year (e.g., Gottfried et al., 2013 ); sometimes, other time intervals have been used, such as weeks (e.g., Yeager et al., 2014 ). However, theories of motivation such as self-determination theory or expectancy-value theory are not formulated with an explicit time scale, and the interactions they describe seem framed in terms that suggest that the effects of motivation constructs happen without delays (i.e., on a time scale of seconds). Recent accounts of motivation are situated ones, which also call attention to fine-grained, moment-to-moment fluctuations that occur during learning engagement (Schunk & DiBenedetto, 2020 ). This raises the question how such fast dynamics can be captured if constructs are measured with large time lags in between.

It is possible that there are interactions between motivation and achievement at both short and long timescales, and that these are qualitatively different. We will refer to these hypothetical interactions at different time scales as short (or fast) and long (or slow) cycles between motivation and achievement. Some constructs may change in slower cycles (e.g., achievement goal orientation, mindset, academic self-concept) than others (e.g., autonomy, or even faster: emotions). In research focusing on interest and achievement emotions, for instance, a stable, so-called trait level (e.g., individual interest) is often distinguished from a shorter, task-dependent state level (e.g., situational interest) (see Hidi & Renninger, 2006 ; Renninger & Hidi, 2011 for interest; Pekrun, 2006 for achievement emotions). Nesselroade’s ( 1991 ) model of within-person psychological change also distinguishes between state and trait. The former is rapid and potentially more easily reversed than the latter. Developmental processes are thought to underlie trait constructs, for instance suggesting that the repeated experience of a positive state (i.e., enjoyment) will lead to a positive trait value. While it has been suggested that reciprocal relations play a more central role on the trait level—e.g., explaining the stronger relations between emotion antecedents and emotions (Bieg et al., 2013 ), studies investigating reciprocal relations are still missing at a state (or task) level . Furthermore, the difference between slow and fast change is also more salient for certain constructs than for others. For example, in one rare study where the within-task changes in multiple motivation constructs was studied, researchers found that while students’ self-efficacy generally grew throughout the progress of a task, interest did not (Niemivirta & Tapola, 2007 ). This suggests that when studies do not consider fast vs. long cycles of constructs, the effects of a faster changing variable on a slowly changing variable can be missed.

The remedy to these problems is to consider using data collected at either diverse time intervals or with theoretically informed time intervals to capture the ebbs and flows of the relations between constructs over time and their corresponding short and long cycles (Duff et al., 2015 ; McNeish & Hamaker, 2019 ). In addition, special attention should be paid to “short cycles”—especially since fast-changing constructs may be more effective targets for interventions. Intensive longitudinal designs can help uncover potential “real-time” causal variance attributable to a construct that would be missed when it is measured at relatively lengthy intervals such as one academic semester or year (McNeish & Hamaker, 2019 ). This may also help when developmental trajectories are characterized by non-linear trends that cannot be captured by low-frequent measurements (McNeish & Hamaker, 2019 ). A deliberate choice of time intervals and the use of non-questionnaire measures will also be helpful in this respect (see section “Alternatives to self-reports” below).

A related but distinguishable issue is the stability of the reciprocal relation between motivation and achievement. Whether or not reciprocal effects of motivation and achievement are stable across school careers is a question with significant theoretical and practical consequences (Marsh et al., 2018 ). Two recent studies found motivation declines to be associated with particular academic stages, for example some constructs such as achievement goal orientation specifically dropped in the transition to secondary school (Scherrer et al., 2020 ). The Scherrer et al. ( 2020 ) data are however among the first longitudinal attempts that can reveal how such declines could potentially impact the reciprocity between motivation and achievement. Theoretically, one could assume that the impact of motivation on achievement is low early in a new environment (e.g., a school transition) where learners experience considerable uncertainty regarding their competence and academic standing (Eccles et al., 1993 ; Valentine et al., 2004 ). When the learning environment is stable, the impact of achievement on subsequent motivation might be more substantial. Some support for such a pattern is provided in Scherrer et al. ( 2020 ) who found the reciprocal effects only in later time points and not in earlier time points after transition into secondary school. However, these studies were not designed specifically to test the transition vs. non-transition contrast, prompting the need for subsequent longitudinal studies that focus on the effect of school transition (to our knowledge, Rudolph et al., 2001 is among the first but only has two waves of data).

Methodological Challenges

When extant research finds the relationships between motivation and achievement, the interpretation with regards to causal relations remains difficult due to the lack of experimental manipulation (Granger, 1980 ; Holland, 1986 ; Marsh et al., 2018 ; Mega et al., 2014 ). In almost every study investigating reciprocal motivation and achievement relations, the need for experimental designs in which either motivation or achievement is manipulated is raised as a suggestion for future research (Marsh et al., 2016 , 2018 ; Mega et al., 2014 ; Pinxten et al., 2014 ). The term “effect” in many existing studies is used only in “conventional statistical sense and standard path analytic terminology, as representing a relation that is not necessarily causal” (Marsh et al., 2018 , p. 268).

Research that aims to establish causality in the reciprocal relationship between motivation and achievement would need to meet three preconditions. The first precondition of causality is order , that is “x must precede y temporally” (Antonakis et al., 2010 , p. 1087). Causality of reciprocal effects requires both orders (x precedes y, y precedes x), as well as alternations of x and y (x precedes y, which is again followed by x). The pale blue (with solid outline) squares in Fig. 2 show this alteration of measurements of motivation and achievement. The top pale blue rectangle starts with motivation, whereas the bottom starts with achievement. The second precondition is correlation : “x must be reliably correlated with y (beyond chance)” (Antonakis et al., 2010 , p. 1087).

figure 2

Representation of three types of study designs that can investigate the relationships between motivation and academic achievement. (1) The gray box shows that to establish that motivation causes academic achievement (top) or vice versa (bottom), experimental manipulation is needed, intervening on the predictor at time point 1, which influences the outcome at time point 2 and so on. The straight thin arrows are the cross-lagged relations and the curved arrows the autoregressive relations. (2) The light blue boxes (top and bottom) illustrate the types of design where reciprocity but not necessary causal effects between motivation and achievement can be established. (3) The green boxes (top and bottom) show the type of design that can investigate both reciprocity and causality between motivation and achievement (i.e., a study where experimental manipulation is included and reciprocal relationships are measured). t time-point, M motivation, A achievement

Several studies with high quality and quantity of longitudinal data meet these two pre-conditions (e.g., Arens et al., 2017 ; Bossaert et al., 2011 ; Chamorro-Premuzic et al., 2010 ; Chen et al., 2013 ; Collie et al., 2015 ; Dicke et al., 2018 ; Grygiel et al., 2017 ; Hebbecker et al., 2019 ; Höft & Bernholt, 2019 ; Marsh et al., 2016 , 2018 ; Miyamoto et al., 2018 ). In these studies, autoregressive paths (the curved arrows in Fig. 2 , which go from measurement of a variable at one time point to the measurement of the same variable at the next time point) and cross-lagged paths (the straight arrows in Fig. 2 , which go from measurement of a variable at one time point to the measurement of a different variable at a later time point) are found. In other words, autoregressive paths represent the direct effects of variables on themselves over time and cross-lagged paths the direct effects of two variables on each other over time. Such cross-lagged paths show the reciprocity between the variables but not necessarily causality in these relations (Usami et al., 2019 ). Correlation between different variables, measured at different time points, is a necessary but not sufficient requirement of causality in mutual relations. Establishing causality of reciprocal effects requires the experimental manipulation of at least one of the two variables.

Importantly, to our knowledge, no studies of the mutual relations between motivation and achievement also satisfy the third precondition of causality, that is the manipulation of x has an effect on y at a later time point, followed by (a) repeated measure(s) of x (and y) (Antonakis et al., 2010 ). In Fig. 2 , manipulation is indicated by the thick arrow. In the upper panel of Fig. 2 , the manipulation of motivation affects achievement in the gray (with dash outline) part of the figure. If the manipulation is followed by an alteration of the variables with cross-relations, the findings would support causality of motivation in reciprocal relations between motivation and achievement. We searched for such studies in meta-analyses of interventions (Harackiewicz et al., 2014 ; Lazowski & Hulleman, 2016 ; Sisk et al., 2018 ), in the latest meta-analysis of longitudinal studies (Huang, 2011 ) and Scharmer ( 2020 ). We encountered two studies that contained both an experimental manipulation of a motivation construct and subsequent multiple, alternate measurements of motivation and performance. Cohen et al. ( 2009 ) found that structured writing assignments to prompt African American students to reflect on their personal values (i.e., self-affirmation interventions) resulted in improved academic achievement (GPA), as well as self-perception and an increased rate of remediation, in the following school year for low-achieving African Americans. Yeager et al. ( 2019 ), in a large-scale mindset intervention, also had more than one wave of manipulated motivation and measurement of achievement. Although the authors discuss the role of a recursive process Yeager & Walton, 2011 ) , neither of these interventions modeled reciprocal effects between motivation and performance (Cohen et al., 2009 ; Yeager et al., 2019 ).

In the lower panel of Fig. 2 , the arrow indicates manipulation of achievement. A manipulation of achievement that affects motivation, which is again cross-related to achievement, would support a causal effect of achievement in reciprocal relations between achievement and motivation. However, it is hard to manipulate achievement independently from motivation. For example, manipulations of instruction, modeling, practice, and self-correction may all affect achievement, but they may do so partly by making the material more appealing, raising motivation at the same time or before achievement is raised. New manipulations are needed that raise, for example, perceived performance without raising performance per se, as a way to circumvent such issues. For causal inferences, experiments would ideally include (double-blinded) random assignment, which is possible in the lab but poses important practical problems in the classroom (cf. Savi et al., 2018 ). In sum, future research with the types of studies that can investigate both reciprocity and causality between motivation and achievement would be highly valuable.

Choice of Appropriate Statistical Models

Although the existence of the reciprocal relationship between motivation and performance is generally agreed upon, there are also empirical works that fail to establish such a relationship (Fraine et al., 2007 ) or cast doubts on the robustness of the reciprocal effects (Burns et al., 2020 ; Ehm et al., 2019 ). Such studies most importantly also point out that the choice of sophisticated statistical models to investigate such relationships can have implications for the conclusion drawn (e.g., Burns et al., 2020 ; Ehm et al., 2019 ). Ehm et al. ( 2019 ) specifically found that although a cross-lagged panel model (CLPM) supported reciprocal motivation-achievement relations, other models did not—such as the random-intercept CLPM, which Hamaker et al. ( 2015 ) showed to be more effective than CLPM in explicitly modeling within- and between-individual changes across time. In addition, as Usami et al. ( 2019 )—in their comprehensive unified framework of longitudinal models—demonstrated, it is important to identify the existence of third time-varying or time invariant variables (such as stable traits) that can have a causal effect on the longitudinal relationship but are yet accounted for in a model. Substantial knowledge about such confounders will help researchers select the correct statistical model. Again, this issue is closely related to the short vs. long cycle of the constructs discussed above.

Alternatives to Self-Reports

Most studies investigating reciprocal relationship between motivation and achievement have measured motivation through questionnaires probing ASC (e.g., the Academic Self-Description Questionnaire by Marsh & O’Neill, 1984 ). Despite their evident psychometric benefits, self-reports (including questionnaires) of motivation suffer from many inherent caveats. Fulmer and Frijters ( 2009 ) list several that are relevant. First of all, questionnaires are subjective and rely on the assumption that motives are consciously accessible, declarative, and communicable to other people, while as discussed above, motivation arises from partially inaccessible and non-declarative cognition and emotions. Students may also differ in their capacity to reliably answer the questions (e.g., consider alexithymia—a psychological trait that is characterized by difficulties with verbalization of one’s own emotions and psychological introspection, Lumley et al., 2005 ). Second, the lack of rigor in the conceptualization of motivation constructs often becomes apparent when using questionnaires (we discuss concrete issues related to ASC in the Different Motivation Constructs section). This is closely related to the Jingle-Jangle Fallacies discussed in Marsh et al. ( 2003 , p. 192). Third, questionnaires might not measure reliably motivation constructs that are not trait-like and subject to temporal and situational fluctuations (e.g., situational interest) (also see our discussion of this point in Time scale of the relations section above). In practice, self-reports cannot be sampled with high frequency during learning (see process-oriented measures below). Fourth, questionnaires are problematic from a developmental perspective because, across age groups, there might be varying factor structures in empirical data. Furthermore, some children may be too young to process some motivation constructs. Finally, self-reports are sensitive to demand characteristics and a tendency to give socially desirable answers (e.g. students who are familiar with the implicit theory of intelligence might tend to report that they endorse a growth mindset, Lüftenegger & Chen, 2017 ).

Most recent discussions of motivation-achievement interactions emphasize the need for alternative methods to self-report questionnaires. These alternatives include experience sampling, daily diaries, think-aloud protocols, observations, and structured interviews (Eccles & Wigfield, 2020 ). These alternatives have their strengths, but some limitations remain, such as the subjective nature of these measures and a possible high demand on research participants’ cognitive resources when a large number of measures are administered during a session. In addition, some demand frequent small breaks during a task to report internal states, which may interfere with the flow of the task.

Several alternative methods are available to observe and measure motivation or engagement “online” during learning, for example by using frequent choices of learners or video observations (Järvenoja et al., 2018 ). With the development of new technologies, it is now also possible to collect such data longitudinally on a large scale. For example, MathGarden, an online math learning tool, provides access to math learning data of thousands of students. Motivation is indexed by the frequency and length of voluntary, self-initiated practice, and can be linked to learning and performance (Hofman et al., 2018 ). Other promising process-oriented measures are eye-tracking and facial emotional expressions (D’Mello et al., 2008 ; Grafsgaard et al., 2014 , 2011 ; Nye et al., 2018 ; van Amelsvoort & Krahmer, 2009 ).

Another process-oriented approach uses physiology for high-frequency and non-interfering measures of motivational states. We will briefly discuss the use of autonomic nervous system (ANS) and central nervous system (CNS) measures. ANS techniques can be used to measure arousal , which is defined as higher activation of the sympathetic relative to the parasympathetic system. Motivated and effortful behavior is accompanied by increased arousal, and thus ANS techniques can provide an index of motivation. Popular techniques are electrodermal activity (EDA), electrocardiograms (ECG), and impedance cardiography (ICG). Sympathetic arousal measured with EDA has been associated with emotion, cognition, and attention (Critchley, 2002 ). Sympathetic arousal can also be measured with pre-ejection period (Tavakolian, 2016 )—which is the time in between “the electrical depolarization of the left ventricle and the beginning of the ventricular ejection” (Lanfranchi et al., 2017 , p. 145). One shared challenge with EDA and ECG is that arousal is a “fuzzy” construct, meaning many things, yet nothing specific (Mendes, 2016 ). A common factor that elicits EDA is subjective salience or motivational importance . Pre-ejection period is often used as an index for effort mobilization in studies investigating motivational intensity theory (Brehm & Self, 1989 ). Suppression of parasympathetic activity, which can be measured as reduction in high frequency heart rate variability, has been associated with effortful control (Spangler & Friedman, 2015 ) and emotion regulation (Beauchaine, 2015 ), but a recent meta-analysis supports a more general role in top-down self-regulation (Holzman & Bridgett, 2017 ).

A CNS measure of motivational states can be provided by electroencephalography (EEG). Higher mental effort/workload has been associated with attenuated parietal alpha activity (Brouwer et al., 2012 , 2014 ; Fink et al., 2005 ), higher frontal theta activity (Cavanagh & Frank, 2014 ; Klimesch, 2012 ), and a higher theta/alpha ratio. Another useful EEG index of motivation is asymmetrical frontal activity, which has been proposed to index motivational direction . Approach and avoidance motivation are respectively related to greater left and right frontal activity (Kelley et al., 2017 ).

It should be noted that none of these process-oriented measures has currently been established as reliable enough to replace verbal reports. A standard conclusion is that “more research is needed” (Holzman & Bridgett, 2017 ). A constructive way forward, which Fulmer and Frijters ( 2009 ) and Scheiter et al. ( 2020 ) strongly advocate, is to triangulate multiple methods, including self-reported and process-oriented measures. Given that physiological measures are relatively new, triangulation can help establish their reliability and validity. For example, EEG could be measured along with behavioral process-oriented task measures of effort. This allows testing whether fluctuations in theta and alpha activities are due to subjective effort mobilization and not due to other processes such as emotional arousal. Such triangulation studies can point the way to reliable online measures of motivation that do not rely exclusively on self-reports.

Measuring Achievement

While achievement is a less-fraught construct than motivation, it still presents its own challenges. First, achievement is nearly always bound to a specific domain, for example mathematics (Arens et al., 2017 ) or reading skill (Ehm et al., 2019 ; Sewasew & Koester, 2019 ). It is unclear whether findings generalize from one domain to others. It is possible that there are quantitative or even qualitative differences between domains in how motivation and achievement interact, for example as a function of the feeling of flow that is or is not associated with performance within the domain.

A second aspect of achievement that may affect results is the type of measurement used. Achievement can be measured using standardized tests and grades in schools (Arens et al., 2017 ; Marsh et al., 2016 ), but for example also through teacher or self-assessment (Chamorro-Premuzic et al., 2010 ). These tend to vary substantially in reliability and validity and yield different results (e.g., stronger reciprocity for school grades than for test scores; Marsh et al., 2016 ). Moreover, in longitudinal studies, it is often difficult to assess whether performance at different moments in time truly reflects the same skill. For example, studies of reading skill may assess basic letter decoding skills in a first wave, and complex reading comprehension in the last (Sewasew & Schroeders, 2019 ). Such changes in tested skills are likely to lead to a lower stability of scores, and skew estimates of change over time. This consideration would speak for designs (discussed above) with shorter periods between measurement waves, where the same measures can be used in different waves.

A third aspect of achievement which may be important is that achievement can be construed as mastery of skills, which usually grows over time, or as performance relative to peers, which by definition cannot grow for all students. Studies typically use raw test scores as a dependent measure to assess this (Huang, 2011 ; Scharmer, 2020 ), which reflect mastery of skills. What is communicated to students, on the other hand, tends to be performance relative to peers (e.g., rankings or grades, which tend to be age-normed either explicitly or implicitly). This implies that perceived performance (see Fig. 1 ) will be based on relative performance, and not on the absolute achievement that researchers tend to study.

Scope of the Theories and Generalizability of Findings

Studies investigating motivation-achievement interactions have often studied the development of these processes separately during childhood, adolescence, and early adulthood. It is therefore unclear whether results can be generalized across developmental stages. Furthermore, as in many subfields of psychology, the majority of research in this area has been conducted in Western, educated, industrialized, rich, and democratic (WEIRD) societies (Henrich et al., 2010 ), where, for example, rates of schooling are much higher than other places (e.g. the Global South). Here, we outline considerations of generalizability across developmental stages and ethnic and sociocultural settings.

Generalization Across Developmental Stages

Childhood and adolescent development is characterized by rather different trajectories for academic achievement (with a general pattern of improvement with age) than for academic motivation (with a general pattern of decrease during adolescence, as well as diversification in sources of motivation) (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ). As a result, we can speculate that the reciprocal relationships between motivation and achievement will change with age. Below, we first highlight findings on changes in motivation across development, and next describe the consequences of developmental differences on reciprocal relations between motivation and achievement, as a function of age, developmental, and academic stages (such as puberty or school grade).

The way in which value guides goal pursuit transforms profoundly from childhood to adolescence to adulthood (Davidow et al., 2018 ), and is reflected in changes in reward sensitivity and cognitive control. At the individual level, motivational beliefs related to competence, control and agency, intrinsic and extrinsic motivation, and subjective task value undergo significant changes throughout the lifespan (Wigfield et al., 1998 , 2019 ). Social cognitive accounts often postulate that the development of more sophisticated cognitive capacities with age allows adolescents to improve performance but also to be more aware of their own abilities and those of their peers (Dweck, 2000 , Scherrer and Preckel, 2019 ). As children go through school, previously held optimistic beliefs on competency become more realistic or even pessimistic (Fredricks & Eccles, 2002 ; Jacobs et al., 2002 ; Scherrer & Preckel, 2019 ; Watt, 2004 ). A meta-analysis by Scherrer and Preckel ( 2019 ) found a small but significant overall decrease in several motivation constructs including academic self-concept, intrinsic motivation, mastery, and performance-approach achievement goals over the course of elementary and secondary school. However, for several other constructs, including self-esteem, academic self-efficacy, and performance-avoidance achievement goals, there was no consistent developmental trend across empirical studies. Overall, this heterogeneity in developmental patterns of various motivation constructs suggests that the reciprocal interactions with achievement may also follow different trajectories across development and still need to be investigated.

Beyond the individual level, social influences on learning and motivation within the family, peer, and school contexts (see Fig. 1 ) also play a significant role in the changes in motivation and achievement (Nolen & Ward, 2008 ; Wigfield et al., 1998 ). Sensitivity to social context continues to develop through childhood and adolescence, transforming through the different school stages (Ladd et al., 2009 ). Broadly speaking, motivation for academic activities decreases between childhood and adolescence, and motivation reorients toward social and novel situations (Crone & Dahl, 2012 ). According to the stage-environment fit account, the decline in academic motivation in adolescents is driven by a mismatch between their newly developed needs and their social settings (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ). Specifically, the transition to middle and high schools is usually accompanied by changes in peer relationships, friendship, and teacher-student relationships, an increase in normative and performance-focused evaluation and a decrease in perceived autonomy. Adolescence is especially characterized by heightened social influences on motivation (Casey, 2015 ): social interactions become increasingly important and peer affiliation motivation peaks (Brown & Larson, 2009 ).

Indeed, peer relationships show a stronger influence on academic self-concept for seventh graders, compared to fifth graders (Molloy et al., 2011 ). As children transition into middle school, there is increased competition for grades and typically a larger pool of peers that serve as a reference group (Molloy et al., 2011 ). During adolescence, same-aged peers in school can motivate academic achievement to a larger extent, and a stronger focus on performance rather than mastery goals is sometimes empirically observed (Maehr & Zusho, 2009 , but see Scherrer et al., 2020 ; Scherrer & Preckel, 2019 where meta-analytic findings point to declines in both mastery and performance goals).

In sum, individual developmental changes in self-concept, self-regulation, social influence, and the values attributed to certain academic goals suggest that reciprocal motivation-achievement relations from one age group cannot be readily generalized to other ages (Marsh & Martin, 2011 ). Qualitative and quantitative differences in the reciprocal relationship between motivation and achievement thus seem plausible, but the lack of developmentally appropriate measures complicates comparisons across different stages (Fulmer & Frijters, 2009 ). Populations of different ages have distinct motivation factor structures (Rao & Sachs, 1999 ) and young children do not yet have the cognitive and memory capacity to process some motivation constructs and contextual references (Fulmer & Frijters, 2009 ).

Taken together, it is critical to understand how changes in motivation interact with changes in abilities, and affect behavior across different age groups and school career. The literature would greatly benefit from an integration of research across a broader age range, and identifying continuities and discontinuities in the reciprocal relationship between motivation and performance across development. One way to do this is to leverage accelerated longitudinal designs, with multiple measurements of cohorts with different starting ages and differentiation between multiple motivation constructs (Guay et al., 2003 ; Marsh & Martin, 2011 ; Scherrer & Preckel, 2019 ).

Generalization Across Sociocultural Settings

The reciprocal relationship between motivation and achievement may also take different shapes across contexts, as students belong to different ethnic, gender, socioeconomic (SES), and cultural groups. However, the majority of current research on the reciprocal relations between motivation and academic achievement has suffered from what can be considered a sampling bias problem (Pollet & Saxton, 2019 ), i.e., conducted using homogenous samples in terms of ethnicity (Marsh & Martin, 2011 ) and cultural background (Henrich et al., 2010 ). In the meta-analysis by Valentine et al. ( 2004 ), which showed that samples from non-Western countries tended to have larger effect sizes than those from Western countries, there were only four non-Western samples out of a total of 60 samples. In her meta-analysis of Scharmer ( 2020 ), 90% of samples were collected in WEIRD countries (Australia, USA, and Western Europe, with fully half using German samples). This is problematic, given that even within WEIRD samples, motivation of students from different groups (e.g., African Americans vs. European Americans) is influenced by different factors, and may contribute differently to their academic achievement (Cohen et al., 2009 ). Ten years later, the remark of Marsh and Martin ( 2011 ) thus still stands that it is premature to conclude that the reciprocal relationship between motivation and achievement is universal.

Demonstrating this across diverse populations is important for three reasons. Firstly, even the same motivation construct might contribute differently to achievement across groups. For example, Chiu and Klassen ( 2010 ), using PISA data and a very large and diverse sample ( N participant = 88,590, N country = 34), found a positive link between mathematics self-concept and mathematics achievement, but this relationship was moderated by cross-country differences in cultural orientations (specifically, degree of egalitarianism, rigidity in gender roles, aversion to uncertainty). As mentioned above, Sachisthal et al. ( 2019 ) also showed that across populations different motivation constructs are central in the network of constructs.

Second, it is not unlikely that different groups have diverging motivation constructs. For instance, general self-concept is conceptualized differently across cultures (Becker et al., 2012 ; Taras et al., 2010 ; Vignoles et al., 2016 ). Thus, the extent to which academic self-concept contributes to a general sense of self likely differs across groups (Hansford & Hattie, 1982 ). Chen and Wong ( 2015 ) also found that Chinese students assigned different meanings to performance-approach and performance-avoidance goals than what is usually found in Western populations. As a result, interventions may need to target different factors in different sociocultural settings.

Finally, there might be culture-dependent or population-specific pathways connecting the relationship between motivation and achievement. For example, culture is likely to have a strong influence on attributional processes (see extensive theoretical discussion in Graham, 2020 ; empirical data in Chiu & Klassen, 2010 ) and implicit theory of intelligence (W. W. Chen & Wong, 2015 ). Chiu and Klassen ( 2010 ) found that calibration of mathematics self-concept (i.e., the degree to which judgments of one’s competence in a domain accurately reflect actual performance) was positively related to mathematics achievement. However, this link was significantly stronger in places where the prevailing culture was more egalitarian or more tolerant of uncertainty.

Such findings suggest differences between sociocultural contexts are not just gradual but also likely to be qualitative. This would threaten the generalizability of findings (Henrich et al., 2010 ). Note that many of the empirical studies cited in this section are non-longitudinal. Reciprocal relationships between motivation and achievement may look different from what we currently know when representative samples are included. It is thus highly relevant for future motivation research to increase ethnic, and other group diversity in their studies. This can be done by better sampling within geographical boundaries (Pollet & Saxton, 2019 ) and by reaching out to under-researched territories such as in Africa, Middle East, Southeast Asia, Central Asia, and South America.

Diversifying study populations can be tough, but is essential for new understanding of human universals and specifics in motivation. For example, collecting experimental data across countries offers alternative perspectives to experimental set-ups and findings, which subsequently prompt researchers to rethink the constructs of interest and their operationalizations (Vu et al., 2017 ). Nevertheless, there are innovative solutions to overcome practical difficulties, including collaborating with researchers who reside in places where certain specificity and universality in motivation constructs can be expected (as outlined in some of the examples above) and making use of networks of researchers such as Psychological Science Accelerator to get access to multiple laboratories and populations across the world ( https://psysciacc.wordpress.com/ ).

Discussion and Conclusions

We have summarized theories of motivation and analyzed these specifically with regards to how they conceptualize reciprocal interactions between motivation and achievement. This led to a summary of pathways between motivation and achievement, depicted in Fig. 1 . The common denominator between theories suggested reciprocal positive influences of motivation on achievement and vice versa, which has been supported by much previous research. We reviewed the strengths of the underlying data, but also limitations and gaps in the evidence. This led to a research agenda consisting of the following recommendations for future studies on the relationship between motivation and performance: (1) include multiple motivation constructs (on top of ASC), (2) investigate behavioral mediators, (3) consider a network approach, (4) align frequency of measurement to expected change rate in intended constructs and include multiple time scales to better understand influences across time-scales, (5) check whether designs meet the criteria for measuring causal, reciprocal inferences, (6) choose an appropriate statistical model, (7) apply alternatives to self-reports, (8) consider various ways of measuring achievement, and (9) strive for generalization of the findings to various age, ethnic, and sociocultural groups.

One of the hardest problems to solve is the lack of studies that allow for firm causal inferences. Most studies contain sophisticated statistical analyses of longitudinal data. While impressive, the underlying data remains correlational in nature and susceptible to explanations in terms of the presence of a (time-varying or time-invariant) third variable (or variables) that is not accounted for in a model, yet does have a causal effect on the outcomes. Usami et al. ( 2019 ) outline three assumptions that need to be checked when making causality inferences in the context of longitudinal designs. These are the assumptions of consistency, of positivity after controlling for confounders, and of no unobserved confounders (see full the discussion in Usami et al., 2019 ). In our view, the trickiest is the third assumption: “the relation between x and y must not be explained by other causes”(Antonakis et al., 2010 , p. 1087; Usami et al., 2019 ). There seems to be no way to conclusively rule out the presence of such confounders. Substantial knowledge about potential confounders and their characteristics, and using that knowledge to select the most appropriate cross-lagged model, is necessary.

We argued that the strongest support for causal claims on motivation-achievement relations would be studies manipulating either motivation or achievement at one time point and studying the effects on motivation-achievement interactions across subsequent time points. Such studies do not yet exist to our knowledge. Many studies do show effects of manipulations affecting motivation thereby having an effect on achievement, but these studies have not looked at longitudinal interactions. The other pathway (i.e., achievement → motivation) has not been studied extensively, because of difficulties identifying manipulations that would directly affect achievement but not motivation.

A way to work around this problem is to manipulate perceived achievement, instead of true achievement (our lab study, manuscript in preparation). In this experiment, participants perform a learning task that lasts an hour. Their motivation and achievement are measured at multiple consecutive time points. Halfway through the experiment, a manipulation of perceived feedback is introduced: participants receive rigged feedback that their achievement has dropped to below peer average. The causal relations between motivation and achievement can be examined because manipulated perceived achievement leads to corresponding changes in motivational beliefs, to changes in motivational behaviors and eventually, to changes in actual achievement across multiple consecutive time points. Another example of manipulation of achievement can be found in Bejjani et al. ( 2019 ) where they used a feedback manipulation (a competence-threatening IQ score) to study its effect on subsequent motivation and learning.

Furthermore, we have argued that motivation can best be seen as a constellation of highly related, multidimensional constructs, and manipulations of motivation may directly or indirectly influence achievement and vice versa. An innovative method to study the motivation-achievement relationship can be a network approach, where observational and interventional data are used to estimate a causal graph. The idea is that to estimate causal relations, one variable can be manipulated at a time, and its effects on other variables can be observed. The network approach is also beneficial in the classroom context where there are many variables to take into account which cannot be independently manipulated (Yeager & Walton, 2011 ).

Our discussion of various theories of motivation in education showed how densely motivation and performance are interlinked. They can best be seen as a cycle of mutually reinforcing relations. While a cycle suggests a closed loop, we list several options for outside intervention, which are represented by the gray arrows in Fig. 1 . Some of these are well-researched practical interventions, such as autonomy support and training in helpful attributions (Hulleman et al., 2010 ). Others are excellent avenues for future research. For example, designing how feedback reaches the learner offers opportunities for motivation support. Research has shown how to provide negative feedback in a way that does not lower a learner’s motivation (Fong et al., 2019 ), how peer comparison can be harnessed for motivation (Mumm & Mutlu, 2011 ), or how feedback can be given without giving away that errors have been made (Narciss & Huth, 2006 ). It is our impression that this research has so far not reached all classrooms.

In conclusion, this view of a cycle between motivation and achievement, as shown in Fig. 1 , has intuitive appeal and fits well with theories of academic motivation. However, empirical evidence for a cycle is far from complete. The research agenda we have presented contains important challenges for future research aimed at elucidating how motivation and achievement exactly interact, and whether a cycle and a network of constructs are good ways of conceptualizing these interactions. As academic motivation typically drops considerably in adolescence and may be lower for some groups (e.g., through the effects of SES, stereotype threat, and the likes), such evidence is necessary for gaining knowledge on how to best intervene in the cycle, and bring out the best in each student.

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We would like to thank Sibel Altikulaç, Abe Hofman, and Simone Plak who participated in our Expert Workshop in Motivation-Performance Cycle in Math Learning in Amsterdam in June 2019 where we discussed ideas for this paper. We would like to thank Milene Bonte, Wouter van den Bos, Camila Bosano, and Bruce McCandliss who are members of the advisory board for the Jacobs Foundation project of which the subproject to write this manuscript is a part. We also want to thank Asmar Isilak who helped with the first database search for empirical studies on the motivation-achievement cycles in learning .

This work was supported by the Jacobs Foundation Science of Learning pilot grant to Nienke van Atteveldt and Brenda R. J. Jansen [project number 2019 1329 00]. Nienke van Atteveldt was also supported by a Starting Grant from the European Research Council (ERC, grant #716736). The funders had no role in study design, decision to publish, or preparation of the manuscript.

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Vu, T., Magis-Weinberg, L., Jansen, B.R.J. et al. Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda. Educ Psychol Rev 34 , 39–71 (2022). https://doi.org/10.1007/s10648-021-09616-7

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The Education and Skills Directorate is one of twelve substantive departments of the OECD and provides policy analysis and advice on education to help individuals and nations to identify and develop the knowledge and skills that drive better jobs and better lives, generate prosperity and promote social inclusion.

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The OECD Directorate for Education and Skills seeks to help individuals and nations to identify and develop the knowledge, skills and values that drive better jobs and better lives, generate prosperity and promote social inclusion. It assists OECD countries and partner economies in designing and managing their education and skills systems, and in implementing reforms, so that citizens can develop the knowledge, skills, attitudes, and values they need throughout their lives.

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Experts Introduce the First Nutrition Guidelines for People Taking Anti-Obesity Drugs

Imagesbybarbara / Getty Images

Key Takeaways

  • The first comprehensive evidence-based review on nutrition recommendations for people taking anti-obesity medications has been published. It offers guidance for calorie and nutrient intake. 
  • Nearly 40% of American adults are living with obesity. Some of them will need to take these new medications to achieve and maintain a weight that better supports their health.
  • Not eating enough contributes to nutrient deficiencies and loss of muscle mass for people who are trying to lose weight.

Roughly 42% of all adults in the United States live with obesity . Eating a nutritious diet and exercising are fundamental for weight management, but some people need more help getting to and staying at a weight that supports their health.

As new anti-obesity medications enter the market, there hasn't been much guidance about nutrition for patients who are on these drugs.

A recent research paper offers a comprehensive review of nutrition recommendations for patients on Wegovy or Zepbound, which can reduce appetite and increase satiety. This new guidance can help clinicians identify and manage patients who are at risk of nutritional deficiencies because of reduce food intake, the researchers wrote.

The authors of the paper acknowledged that target nutrient intakes vary from person to person, and there’s no dietary pattern that’s considered the best or most effective for weight loss.

They recommend a balance of nutrient-dense foods and beverages that provide plenty of vitamins and minerals. Additionally, they recommend choosing foods that are low in added sugars , saturated fats, and sodium.

Here’s a general breakdown of nutrient intake guidelines for people on Wegovy or Zepbound.

Calories provide your body with the energy it needs to perform basic functions like breathing, moving, and thinking. The general energy intake during weight loss is between 1,200 and 1,500 calories per day for women and between 1,500 and 1,800 for men.

That said, energy requirements vary depending on your age, sex, body weight, physical activity levels, and other factors. Your energy intake should be personalized according to your needs and goals and by a nutritionist.

You might find it useful to track calories, but Isabella Ferrari, MCN, RD, CSO, LD , senior clinical manager at Doherty Nutrition, told Verywell that calorie counting can be detrimental for some people.

“It’s super important to have a dietitian on your side when you’re trying to lose weight because we don’t want the calorie counting or calorie tracking to become an obsessive behavior where people can’t live their life without knowing how many calories they’re going to track,” said Ferrari.

People living with obesity need a protein intake of at least 60 to 75 grams per day, and up to 1.5 grams per kilogram of body weight per day is recommended, especially if you’re having bariatric surgery or other weight reduction treatments.

The recommended protein allowance for most adults with no health concerns is 0.8 grams per kilogram of body weight per day.


A common weight loss misconception is that you need to cut carbohydrates to lose weight. However, research has shown that severe carb restriction does not produce long-term weight reduction and may even restrict the nutrition that you’d normally get from eating plenty of fruits, vegetables, and whole grains.

If you’re taking newer anti-obesity medications, Almandoz recommends focusing on balanced nutrition. The recommended amount of carbohydrates for healthy adults can work for people trying to lose weight: 135 to 245 grams per day for a 1200- to 1500-calorie diet, or 170 to 290 grams per day for a 1500- to 1800-calorie diet.

For patients who are recommended or prefer a low-carbohydrate diet, Almandoz suggests making sure that you’re drinking 2 to 3 liters of fluid per day.

Dietary fats help your body absorb fat-soluble vitamins, like vitamins A, D, E, and K. While there’s less evidence for recommended fat intake ranges, the Acceptable Macronutrient Distribution Range (AMDR) for fat for most adults is 20% to 35% of energy intake for a 1,200- to 1,500- calorie diet. 

Around 90% of Americans don’t get enough fiber , but this nutrient is key for preventing constipation and helping you feel fuller longer. The adequate intake level of fiber is 21 to 25 grams per day for women and 30 to 38 grams for men. To meet your fiber requirements, focus on fiber-dense foods like:

  • Whole grains 

“Unfortunately, many people in the U.S. consume a lower-quality diet that is high in ultra-processed foods,” said Almandoz. “Without appropriate nutrition assessment and guidance, we run the risk that people who take these new anti-obesity medications will just eat less of a low-quality diet.”

If you don’t eat a lot of fiber, you’ll want to ramp up slowly to avoid issues like constipation.

Since you don’t want to run the risk of nutritional deficiencies and loss of muscle mass, talk with your healthcare provider and nutritionist about your diet if you’re considering anti-obesity medications.

What This Means For You

If you’re considering an anti-obesity drug, be sure to talk to your healthcare provider and a nutritionist about how to make sure you’re getting adequate nutrition while you’re taking the medications.

Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity and severe obesity among adults: United States, 2017–2018 . NCHS Data Brief , no 360. Hyattsville, MD: National Center for Health Statistics. 2020.

Almandoz JP, Wadden TA, Tewksbury C, et al. Nutritional considerations with antiobesity medications . Obesity (Silver Spring) . Published online June 10, 2024. doi:10.1002/oby.24067

Koliaki C, Spinos T, Spinou Μ, Brinia ΜE, Mitsopoulou D, Katsilambros N. Defining the optimal dietary approach for safe, effective and sustainable weight loss in overweight and obese adults . Healthcare (Basel) . 2018;6(3):73. doi:10.3390/healthcare6030073

Salleh SN, Fairus AAH, Zahary MN, Bhaskar Raj N, Mhd Jalil AM. Unravelling the effects of soluble dietary fibre supplementation on energy intake and perceived satiety in healthy adults: evidence from systematic review and meta-analysis of randomised-controlled trials . Foods . 2019;8(1):15. doi:10.3390/foods8010015

By Kayla Hui, MPH Hui is a health writer with a master's degree in public health. In 2020, she won a Pulitzer Center Fellowship to report on the mental health of Chinese immigrant truck drivers.


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