Journal of Data Science and Modeling

BRAF: A Behavioral Response Analysis Framework for Student Patterns in the Era of Digital Assessment

Articles in Press

Document Type : Research Manuscript

Authors

Seyede Fatemeh Noorani 1
Fatemeh orooji 2
Amir Hushang TajFar 3

1 Department of Information and Computer Engineering, Faculty of Technology and Engineering, Payame Noor University (PNU), P.O. Box 19395-4697, Tehran, Iran

2 Department of Educational Sciences and Learning Technologies,Mehralborz University, Tehran, Iran

3 Department of Information and Computer Engineering, Faculty of Technology and Engineering, Payame Noor University (PNU) Tehran, Iran

https://doi.org/10.22054/jdsm.2026.93565.1096
Abstract
In the era of digital assessment, learners' behavioral data offer new opportunities for analyzing response patterns. This study presents the Behavioral Response Analysis Framework (BRAF), integrating behavioral data and machine learning to analyze responses in a randomized sequential online test. Data from 282 students completing a six-item test were analyzed using $t$-tests, Pearson correlation, Isolation Forest, one-way ANOVA, and Random Forest.
Isolation Forest identified 29 students (10.3\%) with anomalous response-position patterns, whose mean score was significantly lower than that of other students (3.76 vs. 4.70; $p < 0.001$). No significant difference was found between median-based PositionBias groups. Overall, 45.7\% of students changed their answers at least once, with answer-changing associated with a 0.61-point lower mean score ($p < 0.001$). Random Forest assigned greater importance to TotalChanges than PositionBias (53.3\% vs. 46.7\%), although predictive performance was limited.
The findings suggest that multivariate analysis may improve screening for unusual response-position patterns. Answer-changing was associated with lower performance, but causality cannot be established. BRAF is therefore best viewed as a screening framework for targeted human review rather than a diagnostic tool.

Keywords

Behavioral Response Analysis
Positional Bias
Response Change
Isolation Forest
Randomized Adaptive Testing
Learning Analytics
Subjects
Journal of Data Science and Modeling

Articles in Press, Accepted Manuscript
Available Online from 14 September 2026

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