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1. Data-Driven Modeling of Learners’ Individual Differences for Predicting Engagement and Success in Online Learning

   https://doi.org/10.1145/3450613.3456834  

   Akhuseyinoglu, Kamil; Brusilovsky, Peter (June 2021, the 29th ACM Conference on User Modeling, Adaptation and Personalization)

   null (Ed.)

   Individual differences have been recognized as an important factor in the learning process. However, there are few successes in using known dimensions of individual differences in solving an important problem of predicting student performance and engagement in online learning. At the same time, learning analytics research has demonstrated that the large volume of learning data collected by modern e-learning systems could be used to recognize student behavior patterns and could be used to connect these patterns with measures of student performance. Our paper attempts to bridge these two research directions. By applying a sequence mining approach to a large volume of learner data collected by an online learning system, we build models of student learning behavior. However, instead of following modern work on behavior mining (i.e., using this behavior directly for performance prediction tasks), we attempt to follow traditional work on modeling individual differences in quantifying this behavior on a latent data-driven personality scale. Our research shows that this data-driven model of individual differences performs significantly better than several traditional models of individual differences in predicting important parameters of the learning process, such as success and engagement. 

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2. Who are My Peers? Learner-Controlled Social Comparison in a Programming Course

   Kamil Akhuseyinoglu; Aleksandra Klasnja Milicevic; Brusilovsky, Peter (September 2022, European Conference on Technology Enhanced Learning (EC-TEL 2022))

   Hilliger, Isabel; Muñoz-Merino, Pedro J.; De Laet, Tinne; Ortega-Arranz, Alejandro; Farrell, Tracie (Ed.)

   Studies of technology-enhanced learning (TEL) environments indicated that learner behavior could be affected (positively or negatively) by presenting information about their peer groups, such as peer in-system performance or course grades. Researchers explained these findings by the social comparison theory, competition, or by categorizing them as an impact of gamification features. Although the choice of individual peers is explored considerably in recent TEL research, the effect of learner control on peer-group selection received little attention. This paper attempts to extend prior work on learner-controlled social comparison by studying a novel fine-grained peer group selection interface in a TEL environment for learning Python programming. To achieve this goal, we analyzed system usage logs and questionnaire responses collected from multiple rounds of classroom studies. By observing student actions in selecting and refining their peer comparison cohort, we understand better whom the student perceives as their peers and how this perception changes during the course. We also explored the connection between their peer group choices and their engagement with learning content. Finally, we attempted to associate student choices in peer selection with several dimensions of individual differences. 

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3. Decoding individual differences in STEM learning from functional MRI data

   https://doi.org/10.1038/s41467-019-10053-y  

   Cetron, Joshua S.; Connolly, Andrew C.; Diamond, Solomon G.; May, Vicki V.; Haxby, James V.; Kraemer, David J. M. (May 2019, Nature Communications)

   Abstract Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural ‘score’ to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science. 

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4. Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors

   https://doi.org/10.1109/WIIAT50758.2020.00041  

   Mirzaei, Mehrdad; Sahebi, Shaghayegh; Brusilovsky, Peter (December 2020, 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT))

   null (Ed.)

   Modern online learning platforms offer a wealth of learning content while leaving the choice of content for study and practice to the learner. Recent work has demonstrated that many students use inefficient learning strategies that lead to lower performance in this context. The ability to detect inefficient learning behavior by monitoring learning data opens a way to timely intervention that could lead to better learning and performance. In this work, we propose SB-DNMF, a structure-based discriminative non-negative matrix factorization model aimed to distinguish between common and distinct learning behavior patterns of low- and high-learning gain students. Our model can discover latent groups of students' behavioral micro-patterns while accounting for the structural similarities between these micro-patterns based upon a weighted edit-distance measure. Our experiments demonstrate that SB-DNMF can find meaningful latent factors that are associated with students' learning gain and can cluster the behavioral patterns into common (trait), and performance-related groups. 

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5. Example Explorers and Persistent Finishers: Exploring Student Practice Behaviors in a Python Practice System

   Poh, Alison; Hridi, Anurata; Barria-Pineda, Jordan; Brusilovsky, Peter; Akram, Bita (July 2025, Proceedings of 9th Educational Data Mining in Computer Science Education (CSEDM) Workshop at EDM 2025)

   Understanding student practice behavior and its connection to their learning is essential for effective recommender systems that provide personalized learning support. In this study, we apply a sequential pattern mining approach to analyze student practice behavior in a practice system for introductory Python programming. Our goal is to identify different types of practice behavior and connect them to student performance. We examine two types of practice sequences: (1) by login session and (2) by learning topic. For each sequence type, we use SPAM (Sequential PAttern Mining) to identify the most frequent micro-patterns and build behavior profiles of individual learners as vectors of micro-pattern frequencies observed in their behavior. We confirm that these vectors are stable for both sequence types (p < 0.03 for session sequences and p < 0.003 for topic sequences). Using the vectors, we perform K-means clustering where we identify two practice behaviors: example explorers and persistent finishers. We repeat this experiment using different coding approaches for student sequences and obtain similar clusters. Our results suggest that example explorers and persistent finishers might represent two typical types of divergent student behaviors in a programming practice system. Finally, to better understand the relationship between students' background knowledge, learning outcomes, and practice behavior, we perform statistical analyses to assess the significance of the associations among pre-test scores, cluster assignments, and final course grades. 

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