Running Out of Time: Leveraging Process Data to Identify Students Who May Benefit from Extended Time

Burhan Ogut; Ruhan Circi; Huade Huo; Juanita Hicks; Michelle Yin

Vol. 17 No. 2 (2025) IEJEE Special Issue: Large Scale Assessment: Challenges and Innovations

Running Out of Time: Leveraging Process Data to Identify Students Who May Benefit from Extended Time

Burhan Ogut
Ruhan Circi
Huade Huo
Juanita Hicks
Michelle Yin

Published March 23, 2025 | Pages: 253-265 | Views: 14

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Abstract

This study explored the effectiveness of extended time (ET) accommodations in the 2017 NAEP Grade 8 Mathematics assessment to enhance educational equity. Analyzing NAEP process data through an XGBoost model, we examined if early interactions with assessment items could predict students’ likelihood of requiring ET by identifying those who received a timeout message. The findings revealed that 72% of students with disabilities (SWDs) granted ET did not use it fully, while about 24% of students lacking ET were still actively engaged when timed out, indicating a considerable unmet need for ET. The model demonstrated high accuracy and recall in predicting the necessity for ET based on early test behaviors, with minimal influence from background variables such as eligibility for free lunch, English Language Learner (ELL) status, and disability status. These results underscore the potential of utilizing early assessment behaviors as reliable predictors for ET needs, advocating for the integration of predictive models into digital testing systems. Such an approach could enable real-time analysis and adjustments, thereby promoting a fairer assessment process where all students have the opportunity to fully demonstrate their knowledge.

This study explored the effectiveness of extended time (ET) accommodations in the 2017 NAEP Grade 8 Mathematics assessment to enhance educational equity. Analyzing NAEP process data through an XGBoost model, we examined if early interactions with assessment items could predict students’ likelihood of requiring ET by identifying those who received a timeout message. The findings revealed that 72% of students with disabilities (SWDs) granted ET did not use it fully, while about 24% of students lacking ET were still actively engaged when timed out, indicating a considerable unmet need for ET. The model demonstrated high accuracy and recall in predicting the necessity for ET based on early test behaviors, with minimal influence from background variables such as eligibility for free lunch, English Language Learner (ELL) status, and disability status. These results underscore the potential of utilizing early assessment behaviors as reliable predictors for ET needs, advocating for the integration of predictive models into digital testing systems. Such an approach could enable real-time analysis and adjustments, thereby promoting a fairer assessment process where all students have the opportunity to fully demonstrate their knowledge.&nbsp;

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Keywords

Extended Time Accommodation, NAEP Assessment, Process Data, Machine Learning, Test-Taking Behavior,, Machine Learning,Equitable Accommodations.

Affiliations

Burhan Ogut

American Institutes for Research

Ruhan Circi

American Institutes for Research

Huade Huo

American Institutes for Research

Juanita Hicks

American Institutes for Research

Michelle Yin

Northwestern University

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How to Cite

Ogut, B., Circi, R., Huo, H., Hicks, J., & Yin, M. (2025). Running Out of Time: Leveraging Process Data to Identify Students Who May Benefit from Extended Time. International Electronic Journal of Elementary Education, 17(2), 253–265. Retrieved from https://iejee.com/index.php/IEJEE/article/view/2324