Revolutionizing Learning Disability Identification Through Process Data Analysis

By: Sofia Westmoreland

Early identification of learning disabilities is crucial for providing timely interventions and improving educational outcomes. However, many students with learning disabilities go undiagnosed for many years, leading to academic struggles, low self-esteem, and behavioral problems. Process data that can be collected via state-standardized testing offers a promising solution to this challenge. Research shows that analyzing process data compelled from the math portion for standardized tests can indicate if a student is more at risk for having a learning disability. While data can be collected from these standardized tests that could allow for further indication of having an increased risk of having a learning disability, further research needs to be done before this can be practically implemented. Because of this opportunity, the creation of a federal grant program that would allow states to further this research could revolutionize early intervention for learning disabilities by allowing for students who are at higher risk of having a learning disability to be identified.

Issues Identified
Research shows that up to 40% of people with learning difficulties are not diagnosed in childhood, which can have a significant impact on their long-term academic and employment success. Additionally, it is significantly more difficult to obtain a diagnosis as an adult, as many of the traditional diagnostic tools are designed for children.

Second, traditional screening and diagnosis methods can contribute to the problem. Traditional screening methods, such as teacher referrals and standardized tests, can be time-consuming and subjective and often fail to identify students with mild or specific learning disabilities. These methods may not adequately capture the diverse range of learning difficulties that students may experience. Another factor of these traditional screening methods is that they often fail to identify students who represent ethnic and racial minorities (non-caucasian identifying students), leading to a disproportionate number of white and middle to upper-class students receiving a diagnosis. Additionally, many school districts face a personal shortage, which can limit the time and resources available for thorough assessments and interventions. For example, the National Association of School Psychologists recommends schools hire one psychologist per 500 students. Despite these standards being set, the current national average for the 2022-2023 school year was one psychologist for every 1119 students. Because of this drastic shortage of school psychologists, many students with learning disabilities may go undiagnosed, delaying the implementation of appropriate supports and services.

I have personally experienced the challenges of late diagnosis firsthand. Growing up in a rural school district with limited resources, I struggled academically, often feeling lost and frustrated, and my school simply did not have enough resources to support me academically. Despite my best efforts, I couldn’t understand why I couldn’t read and write as easily as my peers. It wasn’t until I was in a smaller, more specialized English classroom at the age of 17 that my teacher indicated that I showed signs of having a learning disability. This realization came as a shock, but it also provided a sense of relief and understanding. With the appropriate accommodations and support, I was finally able to overcome the challenges posed by my learning disability and achieve my academic goals.

Areas of Opportunities
The area of using process data to indicate whether a student is at an increased risk of having a learning disability has been explored, yet further research is needed in order to fully understand the full potential of the process data. Process data can be defined as the data collected when the student interacts with the assessment. Examples of process data taken from online assessments can include keystroke speed, time-stamped records, response changes, mouse movements, and navigation patterns. This process data can show key information in a student’s process on how they arrive at their answer. In a study conducted by Xin Win, researchers looked into the process data of autistic students on the National Assessment of Educational Progress (NAEP) Mathematics Assessment. The results from this study show that a variety of variables involving the math assessment, such as the linguistic complexity of the word problems, the differences between accommodated and unaccommodated students, and the accuracy rate when using calculators, can all affect a student’s performance on the test. As a result of these findings, the researchers suggested ways to alter the test to better suit students with learning disabilities, such as simplifying the language in word problems and offering explicit instructions in equation problems. While this data shows promising findings, an area of opportunity would be to expand this research from students with autism to students with learning disabilities to get data that reflects a larger number of students.

Taking a closer look into the NAEP Math Assessment, this assessment shows a promising focus for this research because the NAEP Assessments are taken in such a way that it offer a data set that represents students nationally. The math assessment is especially critical in this research because it shows the greatest disparities in the scores of students with and without a disability. Because of this disparity, it is critical to look into this assessment because it can offer the most significant research opportunities.

One of the most critical aspects of this report is the fact that more research is needed in order to understand the full complexities of how the process data can be used to its full potential. If more correlations can be found between the process data and people who are diagnosed with learning disabilities, this data has the potential to shape the way in which we indicate whether or not a student is potentially at risk of having a learning disability. This is critical because the process data is easily accessible through online state standardized testing. If this process data can be used, it can alleviate the disparities within the learning disability diagnosis process. 

Recommendations
To realize the full potential of process data analysis, the U.S. Department of Education should establish a competitive grant program to fund research projects focused on the development and validation of process data analysis tools for early identification of learning disabilities. Similar to the already existing Competitive Grants for State Assessments, this grant program would allow states to apply for funding to support research and development in the field of process data analysis that can be gathered through the online state standardized testing process and the NAEP Mathematics Assessment. By providing financial support, the research can encourage innovation and collaboration among researchers, educators, and technology providers to advance the use of process data for early risk identification and intervention. Another outcome of this research, as shown by Win’s study, is the potential to make the NAEP test more accessible to those who have learning disabilities. As shown by the previous research, there are a myriad of ways in which the test can be improved in order to make the instructions and questions more accessible to those with learning disability without compromising the academic rigor of the question.

Because the NAEP Mathematics Assessment is not given to every student in the state, the assessment could simply serve as a starting point for further research, which could then shift its focus to the online state standardized tests that reach a broader student population. Shifting this research to individual state assessments would allow states to conduct their own studies and refine identification methods based on their unique student populations and testing structures. With the creation of a federal competitive grant program, states could secure funding to apply this research to their own assessments, ensuring a more tailored and effective approach. Additionally, shifting to state-standardized assessments rather than the NAEP Mathematics Assessment would be more effective in gathering data because state assessments can reach the maximum number of students. Expanding this research to state assessments would not only increase the sample size but also enhance the accuracy and applicability of the findings. Once the research is completed and implemented, integrating these insights into state assessments would provide a systematic way to identify students at higher risk for learning disabilities early on. This approach would fulfill the primary goal of this initiative—reducing the number of students who go undiagnosed throughout their early education and ensuring that they receive the necessary support to succeed academically.

To ensure ethical and responsible use of process data, it is important to establish clear guidelines for data collection, analysis, and interpretation. The process data collected from this research should not be used as a sole diagnostic tool but as an additional data point to inform further assessment and intervention. If a student exhibits signs of a learning disability based on process data, they should be referred to appropriate professionals for comprehensive evaluation and diagnosis. Simply put, process data alone should not be used to make diagnostic decisions but can support the identification process.

Conclusion 
Early identification of learning disabilities is crucial for providing timely interventions and improving educational outcomes. However, numerous challenges, including late diagnosis and ineffective screening methods, hinder the timely identification of students with learning disabilities. By leveraging the potential of process data analysis, we can revolutionize the identification process by allowing for the NAEP Mathematics assessment and state-standardized testing.

To address these challenges and capitalize on this opportunity, the U.S. Department of Education should establish a competitive grant program to fund research and development in process data analysis. This program should prioritize data privacy, ethical considerations, and the development of user-friendly tools for educators. By investing in research, training, and technology, we can ensure that process data is used effectively to identify students with learning disabilities, provide targeted support, and improve educational outcomes for all.

* The National Center for Learning Disabilities provides a platform for its Young Adult Leadership Council members and alumni to share their experiences and perspectives. The views expressed in these pieces are their own and do not necessarily reflect the thoughts or opinions of NCLD. These writings are intended to share personal insights and should not be cited as official positions or credible sources for policy or research purposes.