methods of task analysis in special education

The Radiant Spectrum

Task Analysis in Special Education: How to Deconstruct a Task

• September 15, 2022 April 11, 2024

As educators, we often go through the process of deconstructing a task by breaking down a complex skill into smaller steps so that students are able to learn the skill gradually, and easily. This process is known as Task Analysis and is especially crucial when teaching students with special needs.

We typically learn in two ways, explicitly and implicitly. Explicit learning is the intentional experience of acquiring a skill or knowledge, while implicit learning is the process of learning without conscious and deliberate awareness, such as learning how to talk and eat. Our students with special needs benefit more from explicit teaching and learning because they often face challenges acquiring skills implicitly due to the need for contextual understanding, communication skills, and so on. 

For explicit teaching and learning to be effective, it is important to have a thorough understanding of the skill through task analysis.

Task Analysis involves a series of thought processes:

1. Goal Selection: Know exactly what it is that you want to teach

Be clear and specific about the goal or the skill that you want to teach. Avoid having too many sub-goals. 

• Negative example: Play a complete song.
• Positive example: Press keys on the piano by following the alphabets shown on a flashcard or music score.

2. Identify any prerequisite skills, if any 

In our earlier example of teaching the sequence of piano keys, some of the prerequisite skills will include:

• Literacy skills of alphabets and/or colours
• Matching skills of alphabets and/or colours
• Visual referencing skills in top-down and/or left-right motion
• Motor skill of only using one finger to press the key, or to imitate an action

Prerequisite skills are important because these skills help to make the learning more feasible and increase the possibility of successfully performing the new skill. 

3. Write a list of all the steps needed to complete the skill you want to teach

A skill can be completed in a single step, or in a series of sequential steps. It is thus helpful that we list down all the steps needed to complete the skill we want to teach. With this, the Task Analysis becomes more detailed and effective. Let’s take the above goal and list down the steps needed. 

Goal: Press keys on the piano by following the alphabets shown on a flashcard or music score.

The keys steps needed to complete this task are:

• Look up at the flashed alphabet.
• Process and retain the information in the learner’s working memory.
• Look down at the piano keys.
• Find the corresponding key by scanning past non-target keys.
• Identify and stop at the target key.
• Aim and press with one finger. 

4. Identify which steps your child can do and which he/she cannot yet do

The next step will be to know the current skill level of your learner by identifying which steps the learner can do, and which the learner cannot. Assume the learner has the following challenges:

• Not consistent in visual referencing skill of looking up and down repeatedly.
• Unable to focus and scan more than 4 keys at one time.
• Often mistakes Letter G for C and vice versa. 

This means that this learner will have challenges in completing Steps 3, 4, and 5 in the above Task Analysis. 

5. Isolate any gap skills, if needed, and teach them first

The steps in which the learner cannot do or has challenges in are known as gap skills . After identifying the gap skills, take time to isolate the skills, teach them, and bridge them. This process takes time. For example, looking at the gap skills in the above example: 

• Visual Referencing Skill: 

This is an abstract skill that takes time to build. It is unlikely that the learner can learn and master this in a couple of weeks. Therefore, to bridge this, the teacher should intentionally provide opportunities for top-down visual referencing across activities and settings, such as taking a toy from a shelf above and keeping them back on top, or sorting activities whereby one item is on top, and one is at the bottom. 

• Working Memory Stamina

This is also another skill that takes time to build. Teaching it across settings and activities will be more effective and efficient. 

This is a skill that can be taught together with the target skill. Since the learner mistakes G for C and vice versa, and is unable to scan more than 4 keys at any one time, reduce the sequencing to CDEF or FGAB such that there is only either C or G in the target sequence. Once the learner is more confident, isolate C and G so that the learner learns to differentiate the two before the full sequence is introduced again. 

Once the gap skills are bridged, the likelihood of the learner performing the target skill will increase vastly.

6. Determine the strategy to be used when completing the target skill, with or without gap skills

At this stage, the learner might still have some gap skills to work on, but the teacher decides to move on to teaching the actual target skill. There are generally three strategies to use:

• Backward Chaining

As the name suggests, Backward Chaining involves the teacher helping the student complete all the steps in the front, leaving only the last step for the learner to do. This also means that the teacher focuses on the last step in the teaching process. The teacher then slowly moves to teach the step before the last until the learner is able to complete all the steps.

• Forward Chaining

This is the opposite of Backward Chaining. The teacher starts teaching from the first step and then moves on chronologically. 

• Total Chaining

This strategy involves the learner in all the steps and the teacher teaches all the steps to the learner with prompts. The learner is learning all the steps. 

It is common to have tried all three strategies before the teacher is able to decide which one works best, so do not be afraid to evaluate and change your mind halfway!

7. Develop a systematic teaching plan, implement, assess and evaluate the progress

After you decide on your teaching strategy, you can then plan and start the actual teaching. Do remember to assess and evaluate the learner’s progress regularly so as to make the learning effective!

Task Analysis may be a long and daunting process at the beginning. However, the more you do it, the better you get at it. In fact, we are practising the steps of Task Analysis as we write this article for you! Practice more and you will soon see how useful it is. 

Interested in more tips on teaching to children with special needs? You can read about the importance and features of a good classroom set-up here !

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Free resources, what you need to know about task analysis and why you should use it.

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Returning to the Effective Interventions in Applied Behavior Analysis series , I wanted to talk a little about the use of task analysis and why it’s important.  For more information on how we use task analyses, check out this post on using shaping and this one on using chaining .

Everyone in special education has probably heard about task analysis.  It’s a decidedly unexciting topic in some ways, but it so critical to systematic instruction that we have to address it.  In addition, there is a lot of misinformation flying around out there so hopefully this will address just what you need to know about them.  

We are told to use them frequently in the classroom to break skills down into smaller components.  So, we set up steps, like we do for some of our mini-schedules like the one below for washing hands .  Sometimes task analyses have visuals to support them and sometimes they are just written out for the staff.

What is a task analysis?

So if you aren’t familiar with the jargon, what does task analysis mean? The National Professional Development Center found task analysis to be an evidence-based practice, which interests me because I don’t think that behavioral task analysis is actually an intervention.  I see the task analysis process as part of other interventions.  A task analysis is simply a set of steps that need to be completed to reach a specific goals.  There are basically two different ways to break down a skill.  You can break it down by the steps in a sequence to complete the task.  The hand washing task analysis does that.  In this type of task analysis you have to complete one step to be ready for the next.  For instance, you have to turn on the water to get your hands wet.  This type of task analysis is typically used with chaining which will be the topic of our next post.

You can also have a task analysis that breaks skills down into smaller chunks, like increasing time.  A task analysis for remaining in a group might do that like the examples at the bottom of the picture above.   In this type of task analysis, each step replaces the one that comes before it.  So, when you sit for 5 minutes, that includes all the steps that come before.  This type of task analysis is usually used with shaping, which I will talk about in 2 weeks.

Why Do I Need a Task Analysis?

So if a task analysis is just breaking skills down into smaller skills, you probably do it all the time. Conducting a task analysis isn’t a very time-consuming process.

But, why is using a specific task analysis that is established for a student important?  Below are 3 reasons to answer that question.

1.  Consistency

In order to assure that everyone is teaching a skill in the same way, breaking it down into the same steps is critical.  I am willing to bet that if you ask parents, paraprofessionals or even your significant other how they brush their teeth, you will find some variation in the order of the steps.  Your significant other doesn’t put the cap back on the toothpaste.  You keep the water running while you brush your teeth while your paraprofessional turns it off to conserve water until she is ready to rinse.  My point is that we all have individual differences in the steps of completing simple, everyday tasks.  Now, imagine that you are a student who is having difficulty learning to brush his teeth.  If you show me one way and prompt me through the steps, and then the parapro shows me another way and prompts me through her steps, and my mom shows me a third way, I’m going to be pretty confused.  It’s the beginning of the year and we all need a laugh, so here’s a great video example of why it’s important.  Archie and Michael can’t even agree on how to put on shoes and socks.  Imagine if they both tried to teach one of our students to put theirs on (no, I would not recommend doing this)–how confused would that student be?

Take Away Point: Writing down a task analysis assures that everyone follows the same steps and teaches the student the same way.  Then your instruction is much less confusing and more efficient. 2. Tailor It To The Student

Students need task analyses that are tailored to their needs.  I love starting with standard ones, so I don’t have to reinvent the wheel, but then adjusting them to meet the needs of this student.  Here’s why the individualization is so important.  If your steps are too large for the student, he or she may not make it to the next step and will stall out.  For instance, if you are teaching Molly to stay in a group activity for 20 minutes and your task analysis jumps from 5 minutes (that she can currently stay) to 10 minutes, she might never be successful at jumping to 10 minutes and won’t progress.  She might do better if we went to 7 minutes next and then 9 minutes.  On the other hand, for Max, if we were teaching the same skill and we had him stay in the group for 5 minutes, then 7 minutes and then 9 minutes it might take a very long time when he could have made the jump straight to 10 minutes.  Each student is different and we have to figure out how to individualize their steps based on their past data.  Is it taking too long to master a step? Step it down to a smaller step.  Is he mastering steps really quickly?  Make the steps bigger.

3. It is the Basis for Systematic Instruction

Breaking skills down is a critical component of discrete trial programming as well as teaching life skills and other chaining and shaping applications.  Discrete trial programs are made up of smaller steps that lead to a larger goal.  Learn 1 letter, then 2, then 3?  That’s a shaping task analysis.  Our research shows us that it’s important to break skills down for students with autism in order to eliminate extraneous variables that might mess up their learning.  Teaching systematically is the key to success with any student and especially any student in special education.  It’s also key for some of our students to be able to show progress.  While it’s not exciting to say that a student has mastered 4 of the 8 steps of tying his shoes, it better than being able to say AGAIN that he can’t tie his shoes–assuming he had no steps mastered earlier in the year. It’s slow progress, but it’s progress.

So, how do you use task analyses in your classroom and why do you think they are important?  Are there questions you have about task analyses or teaching strategies that use them? Please share them in the comments and I will try to address them.  In the meantime, I’ll be back next Thursday to talk about ways to make your instruction using task analyses more efficient.

• Read more about: Curriculum & Instructional Activities , Effective Interventions in ABA

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Task Analysis in Special Education: Break Down Complex Tasks into Managable Chunks to Aid Students with Disabilities

• Categories : Teaching students with learning disabilities
• Tags : Special ed information for teachers & parents

What is Task Analysis?

Task analysis is a process by which a task is broken down into its component parts. Everyone uses task analysis at some point, even if it is unconsciously. How else would anyone learn to complete processes? As the adage goes, you have to walk before you can run. It is easy to forget that some tasks need to be broken down into chunks, because after a time, they become like second nature to us. We often expect students to be able to figure out the steps involved in completing a task. But with a special needs population, where you might have children with processing disorders or difficulty with organization, it’s necessary to take the time to express the different parts of a task until the student has mastered each one.

Consider telling a student to put his coat on to go home at the end of the day. It seems self-explanatory. Yet when you think about it, there are several steps involved. Where is the coat? If the student isn’t already holding it, he has to go to a location to retrieve it. Once that is accomplished, how does he put on the coat? He could just stick his arms in, but then it would be backwards. He could lay it on the floor, stick both arms in upside down and then flip it overhead, but that in itself is three steps. He could put one arm in and then send the coat around his back until he finds the other sleeve to put his arm into – three more steps. Finally, should he just leave the coat hanging open? Is there a zipper, snaps or buttons? Working any of those fasteners requires several operations. So, the simple instruction of putting on a coat to go home is not as simple as it may have initially seemed.

How Does Task Analysis Work?

Like any other undertaking, Task Analysis can also be deconstructed into steps:

• Determine what task you want the student to perform
• Figure out what steps will be required to complete the task.
• Teach the student one step until the student displays mastery of it.
• Decide what order to teach the steps in. You might have the student master the last step,then second to last and so on until the entire task can be done independently. Or vice versa, you can work from the first step to the last. This is known as chaining.
• As each part of the process is learned, add it to the chain until the task can be completed independently.

Task Analysis can be an invaluable tool for a special educator trying to help students gain independence. Whether the students have cognitive, physical or communication impairments, they can benefit from this process.

Role Of Task Analysis In Special Education

Task analysis is a powerful teaching strategy that has been proven to be highly effective in special education. By breaking down complex skills and tasks into smaller, more manageable steps, task analysis helps students with special needs to learn and master new skills at their own pace. 

This method is not only highly effective but also highly individualized, allowing teachers to tailor their instruction to meet the unique needs of each student. From teaching life skills to improving academic performance, task analysis can be a valuable tool for supporting the development and success of students of all abilities.

In this post, we will explore what is task analysis in special education, its benefits, how to implement it, and some real-world examples to easily comprehend how it has helped students with special needs succeed in the classroom and beyond. 

What is task analysis in special education? 

Task analysis is often used in special education as a tool for teaching functional skills such as cooking, personal hygiene, and money management. The process involves identifying the steps required to complete a task, teaching each step systematically, and providing ongoing support and feedback until the student can perform the task independently. By breaking down tasks into smaller parts, task analysis makes it easier for students with special needs to learn new skills and develop their independence.

Teaching a new task to a student can be a challenging but rewarding experience for both the educator and the student. To ensure success, it is important to follow a systematic approach that involves clearly identifying and breaking down the task, teaching it using appropriate strategies, providing practice and feedback, and gradually integrating the steps into a complete task.

• Identifying the task: This involves clearly defining the task to be taught, including the specific skills required to complete the task and the goals to be achieved. It is important to identify the task in detail, including the materials and equipment needed, the steps involved, and any potential obstacles that may arise.
• Breaking down the task: The complex task is divided into smaller, more manageable parts or steps. Each step is then described in detail, including the actions required, the sequence of steps, and any prompts or cues that may be needed.
• Teaching the task: Each step of the task is taught to the student using direct instruction, modeling, or other teaching strategies. It is important to provide clear and concise instructions and to use a variety of teaching methods that are appropriate for the student’s learning style.
•   Practice and feedback: The student is given opportunities to practice each step until they can perform it correctly and independently. Feedback and support are provided as needed, and the student is encouraged to ask questions and seek clarification if needed.
•   Integrating steps: Once the student has mastered each step, they are gradually integrated into a complete task. The teacher or caregiver may provide additional support and guidance as needed, and the student is encouraged to practice the task until they are able to perform it independently.

Examples of task analysis in special education

Summarized below are some examples that will help gain a deeper understanding of the power of task analysis and how it can be applied in their own educational setting. So buckle up, and let’s dive into the exciting world of task analysis in education!

1. Writing

Task analysis can be a powerful tool for identifying the component skills involved in writing, and breaking them down into smaller, more manageable parts. These skills may include brainstorming , outlining, drafting, revising, and editing. By teaching each skill separately and explicitly, teachers can help students develop a more robust writing skill set. 

For example, students can learn how to generate ideas and organize them into a logical structure using outlining techniques as well as graphic organizers which can help arrange the data for meaningful writing afterward. They can then focus on writing a coherent and well-structured draft, before revising it for clarity, coherence, and cohesion. Finally, they can edit their writing for grammar, punctuation, and spelling errors.

This approach can also help students identify their strengths and weaknesses in writing, and target areas for improvement. Students who struggle with generating ideas, for example, can receive targeted instruction on how to generate ideas and organize them effectively. Similarly, students who struggle with editing can receive targeted instruction on how to identify and correct common errors. By breaking down the writing process into discrete sub-tasks, and providing targeted instruction and feedback on each one, teachers can help students become more confident, competent writers.

2. Math problem-solving

Task analysis can be useful for deconstructing the complex process of solving math problems into a set of smaller, more manageable steps. These steps may include reading and understanding the problem statement, identifying relevant information, selecting an appropriate strategy, carrying out calculations accurately, and checking the solution for errors. By teaching each step separately, teachers can help students develop a more robust problem-solving skillset.

Students who struggle with selecting an appropriate strategy, for example, can receive targeted instruction on how to use problem-solving heuristics such as working backward or making a diagram. Similarly, students who struggle with carrying out calculations accurately can receive targeted instruction on how to use mathematical operations and formulas effectively. By breaking down the problem-solving process into discrete sub-tasks, and providing targeted instruction and feedback on each one, teachers can help students become more confident and competent math problem solvers.

3. Reading comprehension

Task analysis can be an effective way to help students develop their reading comprehension skills. Reading comprehension involves a complex set of cognitive processes, such as activating background knowledge, identifying the main idea, making inferences, predicting outcomes, and synthesizing information. By breaking down these skills into smaller, more manageable parts, teachers can help students become more proficient readers. 

This method can assist children in becoming more engaged readers and developing critical thinking abilities. Teachers may help children become more confident and proficient readers by breaking down the reading comprehension process into discrete subtasks and offering targeted teaching and feedback on each one.

4. Laboratory experiments

Task analysis may be a useful method for teaching students how to plan and carry out scientific studies. Identifying the research topic, planning the study, choosing and measuring variables, controlling for confounding factors, and evaluating the results are all processes in a laboratory experiment. Teachers may assist children to build their scientific inquiry abilities by dividing these stages down into smaller, more manageable components. 

For example, by examining scientific literature and discussing ideas, students might learn how to identify a research question. The research can then be designed by selecting relevant variables and controls. They can also learn how to effectively measure variables and account for confounding factors. Finally, kids can learn how to assess experiment data and form conclusions. This technique can help students become more involved and proficient scientists, as well as improve their critical thinking and problem-solving abilities. 

5. Language learning

Task analysis can be a useful tool for language teachers to break down language learning into smaller, more manageable parts. Language learning involves a range of skills, including listening, speaking, reading, and writing, as well as grammatical and vocabulary knowledge. By breaking down these skills into discrete sub-tasks, teachers can provide targeted instruction and feedback to help students develop their language proficiency. For example, students can learn how to listen for specific information, understand the main points of a conversation or lecture, and respond appropriately.

 They can also learn how to speak clearly, express their ideas, and ask questions in different situations. In addition, students can learn how to read and comprehend different types of texts, such as news articles, academic papers, and literary works. This can include skills such as understanding the structure of a text, identifying key information, and inferring meaning from context. Teachers can also introduce new vocabulary words and provide opportunities for students to use them in context, such as in a conversation or writing exercise.

What is the purpose of task analysis?

Task analysis is used in a variety of settings, but it is particularly important in special education. The goal of task analysis in special education is to support individuals with disabilities in acquiring new skills, improving existing ones, and becoming more independent.

Task analysis has several benefits. First, it allows educators to assess a student’s strengths and weaknesses, identify areas that need improvement, and develop a plan of action to support their growth and development. By breaking down tasks into smaller, more manageable steps, special education teachers can provide targeted instruction and support to help students acquire new skills, build confidence, and improve their overall functioning.

In addition, task analysis can help students understand and perform complex tasks more effectively. By breaking down tasks into smaller steps, students can see the connections between different parts of the task and understand how they fit together. This can lead to improved memory and problem-solving skills and greater overall independence.

Task analysis also provides a way to track progress over time. By regularly assessing and re-analyzing tasks, educators can see how students are developing and adjust their instruction and support accordingly. This can help ensure that students are making steady progress toward their goals.

What are the advantages of task analysis in special education?

Task analysis in special education offers several advantages that make it an important tool for supporting the development of individuals with disabilities. Some of the key advantages include:

1.   Improved Learning Outcomes: By breaking down complex tasks into smaller, more manageable steps, task analysis can help individuals with disabilities acquire new skills and improve existing ones more effectively. This can lead to improved learning outcomes and greater overall independence. This can help teachers create an effective learning environment. 

2. Targeted Instruction and Support: Task analysis allows educators to assess a student’s strengths and weaknesses and provide targeted instruction and support to help them overcome challenges and reach their full potential.

3. Increased Confidence: By breaking down tasks into smaller, more manageable parts, students can build confidence as they successfully complete each step. This can help build their self-esteem and increase their overall motivation to continue learning.

4. Better Understanding of Tasks: Task analysis helps students understand complex tasks more effectively by breaking them down into smaller, more manageable steps. This can improve their problem-solving skills and overall independence.

5.  Improved Memory: Task analysis can improve memory by breaking down tasks into smaller steps that are easier to remember. This can lead to improved recall and performance over time.

6. Regular Progress Monitoring: Task analysis provides a way to track progress over time, which can help ensure that students are making steady progress toward their goals.

Task analysis in special education benefits not only students but also teachers and the special education community as a whole. This can result in a more positive and productive learning environment, with improved outcomes for all involved. Additionally, task analysis can also foster collaboration between special education teachers and other professionals, such as occupational and physical therapists. By working together to analyze tasks and determine the best ways to support students, these professionals can develop a more comprehensive and effective approach to meeting the needs of individuals with disabilities.

I am Shweta Sharma. I am a final year Masters student of Clinical Psychology and have been working closely in the field of psycho-education and child development. I have served in various organisations and NGOs with the purpose of helping children with disabilities learn and adapt better to both, academic and social challenges. I am keen on writing about learning difficulties, the science behind them and potential strategies to deal with them. My areas of expertise include putting forward the cognitive and behavioural aspects of disabilities for better awareness, as well as efficient intervention. Follow me on LinkedIn

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Data Collection Using Task Boxes: Progress Monitoring

Hey there, special educators! Let’s dive into the dynamic world of task boxes and how they can revolutionize data collection in your special education classroom. If you’ve been searching for a great way to track student progress, particularly for IEP goals, you’re in the right place. Task boxes are not just about providing different basic skills training; they're a cornerstone in creating an effective independent work station for your special education students.

Table of Contents

Understanding the Task Box System

First thing's first: What are task boxes? They're physical items, often thematic or skill-specific, stored in drawer bins or file folders, forming a part of a structured work task system. These can range from themed printable work task boxes to themed task cards, focusing on early math, language arts, or even life skills. The best thing about task boxes is their versatility, catering to students of multiple ability levels, from preschool to high school.

Two Ways to Track Progress

When it comes to data collection, there are two main approaches:

1. Skill-Specific Data Tracking – This involves collecting data on the particular task card or activity your student is working on. Whether it’s addition problems, completing a sentence, or a specific part of speech therapy, the data sheets are focused on the skill at hand.

2. Process-Oriented Data Tracking – This approach is all about looking at the entire process of using task boxes independently. It's about student independence, work endurance, and how well they manage the whole system of task boxes – a crucial part of occupational therapy and vocational education.

Which task box data approach will you be incorporating into your classroom?

Setting Up an Independent Work System Using Task Boxes

Creating an independent work station with task boxes is a fantastic way to foster student progress. Start with a set of tasks tailored to different levels of ability, incorporating flash cards, file folder activities, or even mini schedules for visual support. These stations are not just for morning work or direct instruction periods; they can be a central part of your lesson plans.

Tailoring to Individual Needs

Every special education student is unique. Utilize task analysis to create task card task boxes that meet the beginning of a student's journey in your classroom. Whether it's for a student in middle school or a whole entire grade level, the task boxes can be the perfect tool to address specific needs.

Data Collection Using Performance Tasks

Ready to start progress monitoring? Try out these free data collection sheets to keep track of student work. These data sheets can be simple yet comprehensive, capturing everything from the total time spent on tasks to the specific achievements in different tasks.

Get started with task box tracking with these free data collection sheets! They can be found inside the Facebook Community built to support you:

Benefits of Using Task Boxes in the Special Education Setting

• Enhances Independence – Students learn to complete tasks on their own, boosting their confidence.
• Tracks Progress Efficiently – With dedicated data sheets, tracking progress towards IEP goals becomes more structured.
• Supports a Range of Skills – From vocational education to early math and language arts, task boxes cover it all.
• Flexible for Different Levels – Whether it's a staff member working with a small group in direct instruction or a student working independently, task boxes make it easy to cater to different levels.
• Integrates with Other Therapies – These bite-size performance tasks are a great addition to occupational therapy and speech therapy programs.

Task boxes are not just great activities; they're an integral part of your special education classroom. They support independent work habits, facilitate student work in new skills, and provide different ways to engage with learning materials. Whether you're a seasoned special education teacher or a new staff member, integrating a task box system into your classroom is a step towards fostering a more inclusive and effective learning environment.

And there you have it – a complete guide to using task boxes for data collection in your special education classroom! Embrace these new resources, and watch as your students thrive in an environment tailor-made for their success. Keep an eye out for task box bundles and other helpful information to continuously evolve your approach. Happy teaching!

I'm Jennifer!

I’m Jennifer and I was a special educator in the elementary school setting over the past decade. I entered the classroom every day dedicated to making learning inclusive AND engaging.

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Teaching Students About Lassi: A Refreshing Cultural Experience

Teaching students about the origin of the word “meme”, teaching students about land mines: an important lesson in global awareness, teaching students about how the atmosphere acquires most of its energy from the sun, teaching students about tim minchin: a multidisciplinary approach, teaching students about the antoinette perry award for excellence, teaching students about the jean seberg’s legacy, teaching students about the volkswagen thing: an unconventional approach, teaching students about the american renaissance, what are the 4 components of task analysis.

Task analysis is a process in which broad goals are broken down into small objectives or parts and sequenced for instruction. Task analysis is the process of developing a training sequence by breaking down a task into small steps that a child can master more easily. Tasks, skills, assignments, or jobs in the classroom become manageable for all children, which allows them to participate fully in the teaching and learning process.

In early childhood settings, teachers focus task analysis on activities necessary for successful participation in the environment. Four ways to develop the steps needed for a task analysis include watching a master, self-monitoring, brainstorming, and goal analysis. Early childhood teachers can use each of these approaches to identify and record the 4 incremental steps:

–Watching a master: To know how to help children walk the balance beam, watch someone who is doing this task well.

–Self-monitoring: To know how to help children make a paper-mache turkey, review the steps that you follow in accomplishing the task.

–Brainstorming: To know how to help children plan a garden in a school plot, ask all the children to give you ideas

–Goal analysis: To know how to help children develop conflict resolution strategies, review the observable and nonobservable aspects of this task, and identify ways to see how it is accomplished.

It is important to remember that the number of steps in a task analysis depends upon the functioning level of the child as well as the nature of the task. I hope you enjoyed this brief explanation of task analysis and its 4 components. If you have anything that you would add to the article, please leave it in the comments below.

To help teachers further understand the components of task analysis and how it can be used in the classroom, below we have an included an informational video that was compiled by professors at Virginia Commonwealth University.

Concepts and Strategies for Serving the Whole ...

12 activities that teachers can use to ....

Matthew Lynch

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Task analysis.

Task Analysis is a method of observing participants in action performing their tasks. Task Analysis helps figuring out how users perform tasks and how a system, product or service should be designed for users so that they can achieve their intended goals. Task Analysis also helps determine what user goals are i.e. what designers must design for; how do users determine or measure the completion of tasks, what sort of personal, social as well as cultural attributes influence the user’s performance, etc.

Quick details: Task Analysis

Structure: Structured

Preparation: Respondent recruitment, Tasks outline, Recording tools

Deliverables: Recordings, Transcripts, Data

More about Task Analysis

Task analysis can be used in a number of situations such as when we are designing a website, when we want to test a prototype and task analysis can also be part of user testing/validation. It is important that task analysis is performed during or before the design phase so that the insights obtained can be easily incorporated into the product, service or system being designed.

Task Analysis can be performed one on one or online depending on the project under consideration. In order to analyze the way a user performs tasks, complicated and time consuming tasks can be broken down into subtasks, which can be analyzed as well as observed individually.

Types of Task Analysis

Task Analysis is of two types depending on the end-goal and composition. If the task analysis involves analyzing qualitative end-goals such as decision-making, emotions, problem-solving skills, recall, then it is termed as Cognitive Task Analysis. Whereas, if the Task Analysis involves breaking down a complex task into subtasks, analyzing the subparts and deducing the nature of the whole based on its composite parts, then such analysis is termed as Hierarchical Task Analysis.

Advantages of Task analysis

1. great understanding of users and their end-goals.

Task analysis allows the researcher to not only understand the participants end goals but also their competence in performing the task, the triggers that lead to the task, the triggers that disrupt the user’s flow during the task as well as the tools the user employs to perform the task.

2. High level understanding of user environments

Task Analysis also gives an indication of the user’s environment and whether or not the environment is conducive to perform the task .

3. Relevant at every stage of the project

Task Analysis can be conducted at any stage of the product or service development but the earlier it is conducted, the better.

4. Practical

Task analysis helps to highlight the practical aspects that come into play when a user is performing a task .

5. Determine gaps between set processes and actual steps in performing a task

This method also helps figure whether there is a difference between the way the user actually performs the task and the way the user says they perform the task .

Disadvantages of Task analysis

1. time consuming.

If the task analysis were performed with a large sample of participants, the activity would be time consuming. Online tools may help is recording data but actual observation will happen when the researcher is present at the time the task is being performed.

2. Complex findings

Depending on whether the task analysis method is cognitive or hierarchical, the findings may be complex and not that easily analyzable.

3. Discrepancy in the pace of performing the task

If the users do not give sufficient time for performing a task during the exercise, then the system or product can be off from what the user requirement is. This may be due to the users rushing through a task during the exercise which they otherwise perform at a relaxed pace. This would be true even when the user performs a task hastily otherwise but during the exercise, performs it in a relaxed manner.

4. Diverse Viewpoints

If the scope of the project is large, then user testing may result in large amount of diverse data may be difficult to collate and analyze .

Think Design's recommendation

Task Analysis goes a long way in enhancing the usability of your product/ organization if done correctly. Use this as a method if your objective is to assist users in performing their tasks or if you are intending to improve organizational efficiency by understanding the tasks and then optimizing them. 

Consider the following recommendations to improve the impact of your task analysis exercise:

• Understanding linkages and hierarchy of tasks is important to get a bird’s eye perspective. Map your understanding in a hierarchical flow-chart and then use that to analyze.
• More often than not, there are handoffs involved during tasks. Understand those handoffs and mapping people to tasks will improve your understanding of the kind of organization you are working for. More importantly, this will give you the linkages among people, tasks and systems/tools which are much needed while redefining a product/ organization.
• The purpose of this exercise is to understand the current state and how this could become a baseline for the future state. Hence, Task Analysis should be done as an analysis than a documentation of what is existing. Always question why the tasks are being done the way they are being done and find out how the situation can be improved.
• As a UX practitioner, you are dealing with emotions of the user and you want to make amends in the areas where users are frustrated. When you map out tasks, it might greatly help if you also capture emotions while performing those tasks.

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Data-driven exploratory method investigation on the effect of dyslexia education at brain connectivity in Turkish children: a preliminary study

• Original Article
• Open access
• Published: 13 July 2024

Cite this article

You have full access to this open access article

• Şerife Gengeç Benli 1 ,
• Semra İçer 1 ,
• Esra Demirci 2 ,
• Zehra Filiz Karaman 3 ,
• Zeynep Ak 4 ,
• İrem Acer 4 ,
• Gizem Rüveyda Sağır 4 ,
• Ebru Aker 4 &
• Büşra Sertkaya 2  

Dyslexia is a specific learning disability that is neurobiological in origin and is characterized by reading and/or spelling problems affecting the development of language-related skills. The aim of this study is to reveal functional markers based on dyslexia by examining the functions of brain regions in resting state and reading tasks and to analyze the effects of special education given during the treatment process of dyslexia. A total of 43 children, aged between 7 and 12, whose native language was Turkish, participated in the study in three groups including those diagnosed with dyslexia for the first time, those receiving special education for dyslexia, and healthy children. Independent component analysis method was employed to analyze functional connectivity variations among three groups both at rest and during the continuous reading task. A whole-brain scanning during task fulfillment and resting states revealed that there were significant differences in the regions including lateral visual, default mode, left frontoparietal, ventral attention, orbitofrontal and lateral motor network. Our results revealed the necessity of adding motor coordination exercises to the training of dyslexic participants and showed that training led to functional connectivity in some brain regions similar to the healthy group. Additionally, our findings confirmed that impulsivity is associated with motor coordination and visuality, and that the dyslexic group has weaknesses in brain connectivity related to these conditions. According to our preliminary results, the differences obtained between children with dyslexia, group of dyslexia with special education and healthy children has revealed the effect of education on brain functions as well as enabling a comprehensive examination of dyslexia.

Avoid common mistakes on your manuscript.

Introduction

Dyslexia is a specific learning disability that is neurobiological in origin and is characterized by persistent reading and/or spelling problems affecting the development of language-related skills (Miciak and Fletcher 2020 ; Lyon et al. 2003 ). This disorder is known to affect approximately 5–17% of children and continue into adulthood (Rüsseler et al. 2018 ; Shaywitz 1998 ). In our country, it is stated that approximately 10% of school-age children have dyslexia (Çeliktürk Sezgin and Akyol 2015 ). While the diagnosis of dyslexia is usually made as a result of the child not being able to learn to read at the desired level in the years when a child typically starts learning to read (8–9 years of age), the age of diagnosis may be earlier or later due to some environmental (Theodoridou et al. 2021 ) and familial factors (Centanni et al. 2019 ). Dyslexia is mostly diagnosed at school ages (Yang et al. 2022 ), and this disorder negatively affects the child’s academic success and self-confidence (Kana et al. 2023 ; Wajuihian 2011 ; Wajuihian and Naidoo 2011 ). Early diagnosis of dyslexia is quite crucial as it will directly affect children’s personal development, academic success, and social life skills (Soğanci and Kulesza 2023 ; Huang et al. 2020 ; Nevill and Forsey 2023 ). Therefore the current study focuses on primary school children aged between 7 and 12 years old.

In recent years, various neuroimaging methods have gained remarkable importance in the diagnosis of neuropsychiatric diseases such as dyslexia (Cainelli et al. 2023 ; Gallego-Molina et al. 2023 ). Among neuroimaging methods, functional magnetic resonance imaging (fMRI) has become an extremely important tool thanks to its power in revealing the functional structure of the brain (Prasad et al. 2020 ). Functional MR imaging can be used to investigate the functional structure of the brain at rest without giving any stimulus, or it can be planned to show the functional structure of the brain during the designed process by giving people visual, auditory or a different stimulus/task. fMRI image analysis methods are focused on exploring functional changes in the brain, which exhibit different superior aspects, primarily emphasizing hypothesis-driven or exploratory approaches. Seed-based analysis, which examines the functional connectivity of any selected region in the brain with the whole brain or another region (Icer et al. 2018 ; S. İçer et al. 2023 ), and independent component analysis (ICA), which probes into the independent neurodynamic network structure of the whole brain with an exploratory approach as performed in this study, are among the main methods employed in this field (Icer et al. 2019 ).

Functional MRI studies performed specifically for dyslexia are mostly aimed at understanding the functional changes the brain shows during a stimulus or task given to individuals with dyslexia. In studies conducting reading analyzes on individuals diagnosed with dyslexia, different task configurations have been designed in the literature, resulting in notable decreases and increases in functional activation in certain brain regions (Li and H.-Y. 2022 ; Martin et al. 2016 ; Devoto et al. 2022 ). Studies aimed at analyzing and understanding the functional connectivity of the dyslexic brain during rest are conducted less frequently than task-based studies. (Seitzman et al. 2019 ; Schurz et al. 2015 ; Farris et al. 2011 ; Koyama et al. 2013 ; Ye et al. 2014 ). On the other hand, studies on resting state and task-based research examining both the resting structure of the dyslexic brain and the functional changes during dyslexia-specific neurocognitive tasks are very limited (Turker et al. 2023 ; Gosse et al. 2022 ). Ye et al. ( 2014 ) applied ICA analysis to examine dynamically modulated functional networks in the processing of incongruent and congruent words in 20 native German-speaking participants. ICA analysis has revealed that in several brain regions, such as the supplementary motor area, were modulated by the incongruous endings of sentences (Ye et al. 2014 ). Rüsseler et al. ( 2018 ) used independent component analysis to identify brain networks involved in the perception of audiovisual speech in a group of adult readers with dyslexia (n = 13) and a group of fluent readers (n = 13). The findings identified several components, including the fusiform gyrus and occipital gyrus, are modulated differently in fluent readers and readers with dyslexia (Rüsseler et al. 2018 ). Greeley et al. ( 2021 ) made an intergroup comparison with a group of readers with reading difficulties (n = 42) and a group of adolescents and children with typical reading abilities (n = 19) using rapid ICA and seed analysis. The results of ICA indicated that the group with reading difficulties exhibited alterations in sensorimotor, salience, and cerebellar networks (Greeley et al. 2021 ). Mohammadi et al. 2020 performed the ICA approach in adult groups consisting of 20 illiterate and 20 normal readers using resting state fMRI. In addition, literacy training was given to the illiterate group for 7 months and then the groups were evaluated with seed-based correlation analysis. Inter-group analysis revealed changes in connectivity in the left fronto-parietal network, basal ganglia network, and visual network, both before and after training (Mohammadi et al. 2020 ). In addition to these adult studies, the fact that dyslexia is first noticed in childhood makes it very important to investigate the functional structure and neurobiological basis of dyslexia in childhood.

Although it is not a routine method applied in the psychiatric treatment processes of individuals with dyslexia, special reading education programs are generally applied to children with dyslexia along with their school education. As a result of a comprehensive literature review conducted in this direction, the participant groups, the age range of the participants, the language used in reading tasks, the status of receiving training for dyslexia, and the rest/task studies conducted on the brain networks of interest have given in detail in Table  1 .

As compared to recent studies in the literature detailed in the Table 1 , superior aspects and unique contributions of this study can be summarized as follows.

In this study, both rs-fMRI data and reading task-fMRI data, specifically designed for our study in which the participants performed continuous reading, were obtained from participants belonging to three groups. With ICA analysis, which is an exploratory method, all brain networks and all functional connections in these networks were examined. In the literature, there are a limited number of ICA studies focusing on dyslexic children within a specific age range. In our study, the participants include children with dyslexia diagnosed for the first time (Dys), dyslexic children who received special education (EDys), and healthy control group children (HC), between the ages of 7 and 12, and ICA analysis was conducted to their fMRI data. This research is the first to employ a continuous reading task focusing on dyslexia with participants who are native Turkish speakers, by making them read a Turkish text selected according to their grade levels. Participants were asked to continuously read a text selected according to grade levels from a specific learning difficulties battery, which would be at eye level and comfortable for them to read, and MRI scans were performed. The implementation of a continuous reading task related to dyslexia, the participants' native language being Turkish, and the reading text being a Turkish text chosen according to grade level are among some of the unique aspects of the study. Participants were asked to continuously read a text from a specific learning difficulties battery, which would be at eye level and comfortable for them to read, and MRI scans were performed. Unlike few studies in the literature focusing on special education conducted at different periods of time, the inclusion of individuals who have been trained for a period of 12–24 months is expected to create a difference in functional connectivity in the dyslexia group who received special education, which forms another unique aspect of the study.

In the light of relavant literature, the aim of this study is to reveal functional markers based on dyslexia by examining the functions of brain regions during resting state and continuous reading tasks and to elaborate on the effects of special education given during the treatment process of dyslexia. In the second part of the study, information about our original data set is given and the methods used in rs-fMRI/task-fMRI analysis are detailed. In the third section, the results obtained from these methods are evaluated and compared with relevant literature findings.

Materials and methods

The current section of the study analyzing the effects of dyslexia and special education involves the following steps: selection of participants, structural and functional data acquisition, experimental design, data preprocess and postprocess.

Participants

A total of 43 children, whose native language is Turkish, were included in this study, comprising 13 children diagnosed with dyslexia for the first time, 15 children who received special education for dyslexia, and 15 healthy control children.

All dyslexic children were diagnosed by an experienced child and adolescent psychiatrist based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) (American Psychiatric Association. 2013 ) and also the specific learning disability (SLD) battery, which comprises subtests that assess literacy and basic arithmetic skills, and tests that assess disorders or problems in visual perception, ranking and sequencing skills, the hand-eye–ear test of the head, lateralization, and fine motor skills (Turgut Turan et al. 2016 ), was also performed to children with dyslexia. Children with dyslexia who have any central nervous system diseases such as epilepsy, cerebral palsy, developmental delay, and who have any other psychiatric disorders were not included in the study. Children with hearing and vision problems were also excluded.

Children diagnosed with dyslexia for the first time (Dys) : 13 children, who are newly diagnosed with dyslexia according to DSM-V (Nevill and Forsey 2023 ) criteria, who have no additional psychiatric diagnosis, and who had no special education on dyslexia, aged between “7–12”, right-hand dominant, and who had an IQ level of 80 and above, were included in the study. In addition, apart from the 13 children included in the study, two children who underwent MRI were not included in the study due to the high head movement observed as a result of the pre-processing, one child could not be included because the functional MRI scan performed during the reading could not be completed successfully, and one child was not included in the study because a pathological mass was detected in the brain.

Children with dyslexia who received special education (EDys) : 15 children, who were diagnosed according to DSM-V criteria, with no additional psychiatric diagnosis, aged between 7 and 12, right-hand dominant, with an IQ level of 80 and above, and who received dyslexia education between 12 and 24 months, were included in the study. An individual education program is implemented for each child ( https://orgm.meb.gov.tr/meb_iys_dosyalar/2021_05/21130110_Ogrenme_Guclugu.pdf , 2024 ).

Healthy Control Group (HC) : 16 children, who applied to the child psychiatry outpatient clinics of Erciyes University (ERÜ), Faculty of Medicine, between the ages of “7–12”, right-hand dominant, with an IQ level of 80 and above, and without any psychiatric disorders, were determined for the control group. Afterwards, MRI scans were performed on these children and while 15 children were included in the study, one child was excluded from the study due to high head movement observed during the preprocessing. Demographic information and varios performance values of the participants are given in Table  2 .

This study has been approved by the Erciyes University Clinical Research Ethics Committee (Decision No: 2022/504). Written informed consent was obtained from both children and their parents.

Data acquisition

The functional and structural MR images of each participant were acquired using a Siemens Magnetom Aera 1.5 Tesla MRI scanner with a 20-channel head coil at Erciyes University Mustafa Eraslan and Fevzi Mercan Children’s Hospital, Department of Pediatric Radiology (Icer et al. 2018 ).

Structural MRI data acquisition

Structural MR data were collected with T1-weighted structural MPRAGE (magnetization-prepared rapid gradient-echo). The implemented scan parameters were sagittal orientation, echo time (TE) = 2.670 ms, repetition time (TR) = 1900 ms, 256 × 256 matrix, isotropic resolution = 1.3 mm, flip angle = 15°, and total scan time = 4 min 18 s for 192 slices, respectively. These structural MRI images were used to coregistrate the functional images to the participants’ brain anatomy during the pre-processing stage (Icer et al. 2019 ).

Functional MRI data acquisition

Functional MR data were collected in an oblique plane (parallel to the anterior commissure–posterior commissure) using a T2*-weighted echo-planar imaging sequence with the following imaging parameters: TR = 2800 ms, TE = 53 ms, flip angle = 90°, field of view = 192 mm, 25 slices covering the whole brain, slice thickness = 5 mm, in-plane resolution = 2 × 2 mm. Two separate screenings were carried out for each participant as resting state and reading tasks, and each scan lasted 6 min and 14 s, and a total of 130 volumes of images were obtained at each scanning.

During a resting-state fMRI scan, paticipants were instructed to keep their eyes open and rest during the scan, keeping them relaxed, still, and thinking to a minimum. Under these conditions, brain activities were scanned. After the completion of the resting-state fMRI scan, a specific text determined according to the child’s grade level based on the specific learning difficulty battery (Karakaş et al. 2017 ) was placed in the upper inner region of the MRI device at the child’s eye level. Texts in different font styles and font sizes were arranged for children to read. These texts were written in children’s native language (Turkish). Participants were primary school 2, 3 and 4th grade students. Therefore, three different texts were separately prepared for students at different grades. All texts were selected from textbooks issued by the Ministry of National Education, Republic of Turkey. Relevant texts were selected in a way that the child could comfortably read based on their academic level, and the task given to the child was to silently read through the text and then start reading it again from the beginning after completing it. This reading action was continued throughout the scanning period, and brain activity was examined. Unlike the stop-and-go tasks commonly seen in the literature, a continuous task condition was implemented. Figure  1 illustrates the methodological framework of the study.

Flowchart of the whole data acquisition process

Data preprocessing

Pre-processing steps for functional MR images are practically carried out with auxiliary software. In this study, the data preprocessing process was carried out with the FMRIB Software Library (FSL 6.0.4) ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki ). The same preprocessing steps were performed for the resting state and the continuous reading task. Relavant preprocessing steps used in the study are as follows; brain extraction, slice timing correction, motion correction, spatial smoothing, ICA AROMA, temporal filtering and linear domain registration.

Initially, non-brain structures were extracted from anatomical and functional images using FSL FMRIB's brain extraction tool (BET) (Smith 2002 ). Next, slice timing correction was performed by shifting all slices to be aligned as if they were acquired at the same point and time using voxel time series interpolation relative to a reference slice. Motion correction was carried out using the middle volume as the initial template and applying 6 degrees of freedom (DOF)—3 rotations and 3 translations—to each volume with FLIRT optimization method to remove motion artifacts (Jenkinson et al. 2002 ). Children with estimated maximum absolute head motion > 2 (mm or °) or mean motion > 0.5 (mm or °) were excluded from the study to minimize the effects of unwanted motion (Winkler et al. 2014 ; Sörös et al. 2019 ). Spatial smoothing involves obtaining the intensity value of each voxel by taking the mean of the values of neighboring voxels, thereby reducing high-frequency fluctuations between adjacent voxels (Chen and Calhoun 2018 ).

In the study, a low-pass Gaussian filter was applied to the images, eliminating high-frequency signals and preserving low-frequency information. The smoothing process was performed using a Gaussian kernel with FWHM = 5 mm in each fMR image volume separately. After spatial smoothing, FSL’s ICA-AROMA tool (version 0.3-beta), an automatic motion artifact removal tool based on independent component analysis, was used to detect artifacts and then remove these components from the data (Pruim et al. 2015 ). The ICA AROMA process was carried out after smoothing and before filtering. By means of AROMA, temporal filtering was applied to functional images to eliminate motion artifacts. Through temporal filtering, low-frequency artifacts were removed with high-pass temporal filtering using the local form of a straight line with cut-off = 100 s = 0.01 Hz, which is the recommended value for fMRI data (Smith et al. 2004 ). The functional data were registered to the high-resolution anatomical images using a 6-degree-of-freedom linear transformation (DOF) applied in the FLIRT linear registration tool available in FSL (Jenkinson et al. 2002 ).

To overcome some of the shortcomings of FLIRT, registration of structural images to the 2-mm MNI standard space template was performed using a 12-degree-of-freedom linear transformation through the nonlinear registration tool FNIRT (Andersson et al. 2008 ). This ensures better alignment of the structures. Finally, the low-resolution fMRI image was registered to the standard space, and the two transformations were merged. The preprocessing steps applied to anatomical and functional images are illustrated in Fig.  2 .

Flowchart of fMRI data preprocessing

Functional connectivity analysis

In this study, independent component analysis (ICA) was applied to fMRI data obtained during the resting and reading states. We performed ICA analysis on group data using FSL’s MELODIC software ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC/ ), which implements probabilistic ICA. The methodology used to make group inferences when performing group ICA analysis in the MELODIC tool is multi-session temporal concatenation. This method is most suitable for resting-state data as there is no common time course for subjects, unlike the task-oriented design. Temporal ICA combines single-subject data sets over time and obtains independent components on the combined data matrix (Vos et al. 2018 ). Since the reading task in our study was continuous, an analysis method equivalent to resting state ICA analysis was applied. In this study, the number of independent components determined for both the resting state and the reading task was 20. As a result of group ICA analysis, 20-component ICA maps were obtained (Smith et al. 2009 ; Biswal et al. 2010 ; Wang and Li 2015 ).

In this study, after multi-subject group ICA analysis were completed, dual regression was performed on FSL to evaluate the functional connectivity differences between Dys, EDys and HC groups (Andersson et al. 2008 ). Dual regression allows the definition of a set of network maps and corresponding time series to be compared between groups associated with group ICA components within each individual’s spatial space (Vos et al. 2018 ). ICA maps obtained as a result of group ICA were utilized as input in dual regression, in which multivariate temporal regression was performed to evaluate individual spatial maps using time courses. Group analysis was then performed by entering participants’ individual independent component (IC) spatial maps into a generalized linear model (GLM) framework using an appropriate design matrix and corresponding contrasts. Independent component analysis and dual regression analysis are summarized in Fig.  3 .

Flowchart of independent component analysis and dual regression analysis

In the general linear model, three explanatory variables are defined as follows: Dys, EDys and HC groups. Then, 2 sample t-test contrasts corresponding to all possible combinations were defined to evaluate the differences between groups (6 contrasts) ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM ).

Binary regression with variance normalization was performed to demonstrate the activity and spatial extent of resting-state networks (RSN). Regarding statistical analysis, different component maps were collected in 4D files across participants and tested voxel-wise for statistically significant differences between groups using FSL’s randomized tool performing non-parametric permutation testing. In order to check through multiple comparisons, 5000 permutations were applied for each indicated contrast using the Threshold free cluster enhancement (TFCE) technique. Finally, family wise error (FWE) correction was performed for multiple comparisons applying TFCE using the significance threshold of p < 0.05. Regions with differences between groups were used to extract mean z values from each spatial map (FWE-corrected p < 0.05) (Winkler et al. 2014 ; Rytty et al. 2013 ).

Results and discussion

In this study, 20-component ICA analysis was applied to fMRI data obtained from children during both resting-state and reading task fMRI scans, and the results obtained for networks and regions specifically relevant to our study are presented. Figure  4 provides group means of brain networks corresponding to 6 independent components for reading task and resting-state conditions. Table 3 indicates voxel-wise activations in regions of interest within the relevant brain networks for both sessions for each group.

Mean networks of each group after dual regression

As a result of the ICA analysis, dual regression analysis was applied to the networks formed in both the resting state and the reading task in order to see the differences between the groups. With the dual regression analysis (comparison between groups) performed for resting and reading task, the regions showing significant differences are presented numerically and visually in Table  4 and Fig.  5 .

Significant connectivity differences between groups in resting state and reading task

The findings obtained for the networks of interest within the scope of this study were evaluated by taking into account Table  3 , which was obtained based on group means.

Visual network

During the resting state, activation was observed only in the Dys group in the sub-gyral region of the right occipital lobe in the lateral visual network, while during the reading task, activation was again observed only in the Dys group in the same region. However, it is noteworthy that there is increased activation during the reading task compared to resting state. Visual processing difficulties could be attributed to the right occipital lobe sub-gyral.

According to the results of the second-level analysis, during the reading task in the lateral visual network, it was found that there was greater activation in the left middle occipital gyrus region in HC compared to EDys (HC > EDys). Again, the difference in activation in the left middle occipital gyrus observed in healthy controls during the reading task (Table  4 ; Fig.  5 ) can be considered as a finding that details such as distance and depth in these children with dyslexia, they have not yet developed as much as healthy children, despite receiving training. According to the second level analysis results, it was found that there was more activation in the right PCC region in Edys than in Dys (EDys > Dys) during rest in the medial visual network.

Considering the relationship between PCC and anxiety (Gorka et al. 2023 ), the increased connectivity at the right PCC observed in dyslexics who received education during rest (Table  4 ) can be considered a finding of the effect of special education on both anxiety and episodic memory-related (Alsulami 2019 ; Stoitsis et al. 2008 ) differences in these children. This situation may be a sign of improvement in anxiety. As seen in Gorka’s study, PCC has been reported to be associated with increased task performance of anxiety, supporting our results (Stoitsis et al. 2008 ). In a study featuring an audiovisual task, increased activation in the right cingulate gyrus region was observed in the HC group compared to the Dys group (Kronschnabel et al. 2014 ). Barquero et al. ( 2014 ) conducted a systematic review of the literature on reading intervention in children and adults with studies using fMRI and MEG imaging methods. According to the findings obtained from the meta-analysis, it was stated that participants with reading difficulties manifested increased functional activation following the reading intervention in the right posterior cingulate and left middle occipital gyrus. As a result of their studies, it was found that these regions are probably active in processes that will improve reading ability (Barquero et al. 2014 ).

Default mode network

In the default mode network, the right middle temporal gyrus region was activated only in the Dys group at rest, in which activation increased in both the Dys and HC groups during reading. Considering the role of MTG in language (Briggs et al. 2021 ; Zhang et al. 2017 ), semantic memory (Xu et al. 2015 ), visual perception (Stein 2014 ), and multimodal sensory integration (Mesulam 1998 ), the activation of the right middle temporal gyrus (MTG) in the EDys and HC groups during the reading task can be considered a benefit of the training. When the default mode network was evaluated at rest, functional activation was observed only in Dys group in the right middle temporal gyrus region and only in HC group in the left middle temporal gyrus. However, unlike the right MTG which does not activate during rest in educated individuals, it is observed that the left MTG activates during rest in HC group. This situation can also be considered as one of the connectivity differences between healthy individuals and dyslexics.

In a study by Gosse et al., involving dyslexic children (n = 16) and healthy control (n = 16) group with a mean age of 9.3, it was emphasized that there was a decrease in functional connectivity in the left middle temporal gyrus regions during resting-state in dyslexic children. The function of the middle temporal gyrus is critical for reading as it is responsible for the retrieval of visually presented items (Gosse et al. 2022 ). In a study by Schurz et al., examining brain areas related to reading in dyslexic readers (n = 15) and typical readers (n = 14) aged 16–20 years, both task-based and resting-state functional connectivity analyses were conducted. They revealed a decreased functional connectivity between the left middle temporal gyrus and inferior frontal gyrus in dyslexic readers (Schurz et al. 2015 ). This finding was consistently obtained for two different reading tasks and resting-state conditions. However, a meta-analysis study involving Chinese dyslexic children reported hyperactivation in the right middle temporal gyrus region (Li and H.-Y. 2022 ).

Frontoparietal network

In the left frontoparietal network, during reading, activation was observed only in Dys in the left supramarginal gyrus, while activation was observed in EDys and HC in the right posterior lobe pyramid. This situation may indicate that EDys approaches HC with training. However, activation in the right posterior lobe pyramidis is observed during reading in both HC and Edys, but not in Dys. During resting state, activation is observed in the left inferior parietal, right cerebellum, and right precuneus in HC, with no differences found in EDys and Dys. However, activation is seen in both Edys and HC in the left middle temporal gyrus, while only Dys lacks activation. In the left superior temporal gyrus, it is seen that there is activation only in Dys. In this context, it is seen that EDys group begins to resemble HC group in some regions where the connectivity of Dys group is very different from HC.

In another study involving 16 dyslexic children and 15 children with typical readers, aged between 8 and 16, all participants performed three tasks (phonological, picture naming and semantic (3 types) tasks) during fMRI acquisition. During the complex sentence reading task, activation in the bilateral superior temporal gyrus was observed in the dyslexia group. When the dyslexic group was compared to typical readers during the same task, functional activation was observed in the right superior temporal gyrus (Prasad et al. 2020 ). Additionally, in a study involving dyslexic readers (n = 13, mean age = 24) and typical reader (n = 13, mean age = 25.3) where auditory-visual stimuli were presented during a reading task, it was shown that typical readers exhibited greater superior temporal activations in scenarios with combined auditory-visual stimuli compared to auditory/visual stimuli alone. On the other hand, such an increase effect is not found in dyslexic readers. In one study, the superior temporal gyrus plays a critical role in the integration of acoustic and visual speech, thus highlighting the potential of superior temporal dysfunction to underlie weak auditory-visual integration in dyslexia (Ye et al. 2017 ). In another study involving auditory and visual stimuli, Rüsseler et al. designed a task-based study to examine audio-visual speech perception in dyslexic individuals, comparing them with 13 dyslexic and 13 typical readers. The study findings revealed that, when comparing consistent stimuli with inconsistent stimuli, the typical reader group showed increased activation in the bilateral superior temporal gyrus, while in the dyslexic group, this pattern was reversed (Rüsseler et al. 2018 ).

Unlike the foregoing studies in which audio-visual stimulation was employed, this study only incorporates the continuously text reading task. Given relavant findings in the literature, this indicates that the results in our study may vary depending on the experimental paradigm. In addition, the fact that there are differences in the bilateral superior temporal gyrus between the dyslexic group and the healthy group underpins the functional significance of this region in connection with the dyslexia disorder and the specific stimulus given.

Ventral attention network

It is known that there are morphological differences in the insula in the dyslexia group (Black 2004 ). However, the lack of activation in the left insula in the dyslexia group in functional studies draws attention as a finding similar to our study. It is known that the insula region is associated with emotional regulation (Jang et al. 2018 ). Since the left insula is affected in dyslexia, the activation of the right insula, which is thought to be due to compensation, has been supported by other functional imaging studies (Paulesu et al. 1996 ).

In addition, it is suggested that the left insula, which is not active in dyslexics unlike healthy controls in PET scans (Paulesu et al. 1996 ) of both different tasks, has an important role in connecting different phonological codes. If these results are reproducible, the value of studying pathological groups to determine the functional anatomy of the normal brain system will be confirmed (Paulesu et al. 1996 ). In a task-based study conducted by Łuniewska et al., involving typical readers (n = 90) or individuals with dyslexia (n = 20), with children at familial risk for dyslexia (n = 55) and those without any familial risks (n = 35), fMRI scans were performed at the beginning of primary school and repeated 2 years later. In dyslexic children, low activation was observed in the right insula during the initial scan, while over the 2 year period, brain activation during phonological processing increased in the right insula. In typical readers, a decrease in brain activation was observed in the left hemisphere’s language areas, including the insula, after 2 years (Łuniewska et al. 2019 ).

In our study, in the ventral attention network, the right insula region was activated in Dys group in the resting state, while the left insula region was activated in HC group. However, it was observed that only the right insula was activated in the healthy group during reading. In addition, no activation was observed in the right cingulate gyrus and right superior frontal gyrus regions in EDys and HC groups during the reading task, while activation was observed only in Dys group.

Orbitofrontal network

It was maintained in some studies that there is a potential relationship between the right anterior cingulate gyrus and vision within the orbitofrontal network (Shinoura et al. 2013 ). Also, the dorsal ACC modulates the tracking of visible targets, visual attention, and the interface between cognition and emotion, thereby affecting emotional self-control and problem-solving capacity. In a study conducted on Brazilian children, a mixed experiment related to the event was carried out using a meaningful word and pseudoword reading test. Participants were asked to choose “Yes” or “No” options when asked about the meaningfulness of the word. As a finding of the reading task in the study, it was revealed that in typical readers, there was more activation in the left anterior cingulate cortex (ACC) dorsal part compared to dyslexic readers (Buchweitz et al. 2019 ).

In our study, in the orbitofrontal network, activation was observed in the right anterior cingulate gyrus during both rest and reading state in all three groups. However, during reading, there was an increase in connectivity compared to resting state in the healthy group, while a decrease was observed in Dys and EDys groups. The increase in activation seen during reading in HC was not found in the Dys and EDys groups; on the contrary, there was a decrease in activation, which was thought to be related to visual processing. Considering the visual function of this region, the anterior cingulate cortex should be evaluated in addition to focusing only on the temporooccipital region regarding visual processing (Huda et al. 2020 ). Also, regarding the relationship of this area with impulsivity (Baker and Ireland 2007 ), it could be said that healthy individuals may able to control their impulsivity better than dyslexia.

Lateral motor network

In the literature, it was reported that the connection between the inferior parietal lobule (IPL) and certain cortical areas is decreased in dyslexic readers both during tasks and at rest (Schurz et al. 2015 ). Additionally, dysfunction of the IPL in dyslexic children has been reported in numerous studies (Maisog et al. 2008 ; Richlan et al. 2009 , 2011 ). In our study, in the lateral motor network, activation is observed in the left inferior parietal lobule of the lateral motor cortex during both rest and reading in HC group, while activation is not observed in either EDys or Dys groups. The significant differences observed between healthy and dyslexic groups in both reading and resting conditions may highlight the presence of impairments in motor coordination and phonological awareness (Martin et al. 2016 ; Devoto et al. 2022 ; Pellegrino et al. 2023 ) in the clinical presentation of neurodevelopmental disorders. Considering this aspect, the necessity arises to integrate existing interventions aimed at improving motor coordination into special education programs. It also draws attention to the need to review the impact of existing phonological awareness studies.

A potential limitation of functional connectivity/activation studies based on reading tasks is that different reading tasks may produce different functional connectivity patterns. As noted by Koyama et al., there is still no consensus on the “optimal” task for characterizing the neural networks underlying reading and dyslexia through reading-based functional connectivity research. It is emphasized that one possible optimal solution is to examine functional connectivity during both reading tasks and resting state conditions (Koyama et al. 2010 ).

Conclusions

We can summarize main findings in our study, together with the results and comments we highlighted above, as follows.

- It has been revealed that there is a need to integrate phonological awareness and motor coordination activities into dyslexia education in accordance with the findings of lateral motor networks.

- In dyslexic groups, it has been observed that dyslexia oriented educational activities contribute to the improvement in functional connectivity of certain brain regions making them closer to that of healthy groups.

- Most brain regions that yield significant results in our study appear to be in common with the literature. Considering the current results, the functional connectivity differences in our study support the need to analyze many factors such as motor skills, phonological awareness, impulsivity and anxiety in dyslexia.

The unique aspects of this study are that all participants were asked to perform both resting and continuous reading tasks, and that it consisted of three compatible data groups, such as MRI protocols and pre-processing processes as well as demographic characteristics of participants. The study’s other distinctive aspects are that the participants read the Turkish text selected according to their grade levels and that all participants were Turkish and their mother tongue was Turkish, ensuring nationality and language compatibility. In addition, it was ensured that participants with dyslexia did not have any other psychiatric diseases that could be confused with dyslexia, and children with pure dyslexia were included in the study.

One limitation of our study is the small sample size to reach a generalizable interpretation over results. The preliminary results presented in this study can be studied in a more comprehensive way by increasing the number of data and applying more diverse functional analysis methods.

Data availability

This study has been carried out within the scope of the TÜBİTAK project and data sharing cannot be performed yet.

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Acknowledgements

We would like to thank Erciyes University Pediatric Radiology Department, MR Technician Tuğba Özalp, and Ünal Şahinci for data acquisition.

Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK). This study has been supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK) under project number 122E131.

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Şerife Gengeç Benli & Semra İçer

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Zehra Filiz Karaman

Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Erciyes University, Kayseri, Turkey

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Conception and design of the study: Ş.G.B., S.İ., and E.D., Data acquisition: E.D., Ş.G.B., B.S, and Z.F.K. Data analysis: S.İ., Ş.G.B., Z.A., İ.A., G.R.S. and E.A, Manuscript writing: Ş.G.B., S.İ., E.D., Z.A., İ.A., G.R.S., and E.A, Final approval of manuscript: All authors.

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This study has been approved by the Erciyes University Clinical Research Ethics Committee (Decision No: 2022/504).

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Gengeç Benli, Ş., İçer, S., Demirci, E. et al. Data-driven exploratory method investigation on the effect of dyslexia education at brain connectivity in Turkish children: a preliminary study. Brain Struct Funct (2024). https://doi.org/10.1007/s00429-024-02820-5

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DOI : https://doi.org/10.1007/s00429-024-02820-5

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