More Like this

1. Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual Differences

   https://doi.org/10.3389/frai.2022.807320  

   Akhuseyinoglu, Kamil; Brusilovsky, Peter (February 2022, Frontiers in Artificial Intelligence)

   Educational data mining 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 group students into cohorts with similar behavior. However, few attempts have been done to connect and compare behavioral patterns with known dimensions of individual differences. To what extent learner behavior is defined by known individual differences? Which of them could be a better predictor of learner engagement and performance? Could we use behavior patterns to build a data-driven model of individual differences that could be more useful for predicting critical outcomes of the learning process than traditional models? Our paper attempts to answer these questions using a large volume of learner data collected in an online practice system. We apply a sequential pattern mining approach to build individual models of learner practice behavior and reveal latent student subgroups that exhibit considerably different practice behavior. Using these models we explored the connections between learner behavior and both, the incoming and outgoing parameters of the learning process. Among incoming parameters we examined traditionally collected individual differences such as self-esteem, gender, and knowledge monitoring skills. We also attempted to bridge the gap between cluster-based behavior pattern models and traditional scale-based models of individual differences by quantifying learner behavior on a latent data-driven scale. Our research shows that this data-driven model of individual differences performs significantly better than traditional models of individual differences in predicting important parameters of the learning process, such as performance and engagement. 

   more » « less
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. 

   more » « less
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. 

   more » « less
4. Prediction of Student Academic Performance Using a Hybrid 2D CNN Model

   https://doi.org/10.3390/electronics11071005  

   Poudyal, Sujan; Mohammadi-Aragh, Mahnas J.; Ball, John E. (April 2022, Electronics)

   Opportunities to apply data mining techniques to analyze educational data and improve learning are increasing. A multitude of data are being produced by institutional technology, e-learning resources, and online and virtual courses. These data could be used by educators to analyze and understand the learning behaviors of students. The obtained data are raw data that must be analyzed, requiring educational data mining to predict useful information about students, such as academic performance, among other things. Many researchers have used traditional machine learning to predict the academic performance of students, and very little research has been conducted on the architecture of convolutional neural networks (CNNs) in the context of the pedagogical domain. We built a hybrid 2D CNN model by combining two different 2D CNN models to predict academic performance. Our sample comprised 1D data, so we transformed it to 2D image data to test the performance of our hybrid model. We compared the performance of our model with that of different traditional baseline models. Our model outperformed baseline models, such as k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, in terms of accuracy. 

   more » « less
5. Investigating Student Interest in a Minecraft Game-Based Learning Environment: A Changepoint Detection Analysis

   https://doi.org/10.5281/zenodo.12729844  

   Zhou, Yiqiu; Paquette, Luc (January 2024, International Educational Data Mining Society)

   Benjamin, Paaßen; Carrie, Demmans Epp (Ed.)

   Extensive research underscores the importance of stimulating students' interest in learning, as it can improve key educational outcomes such as self-regulation, collaboration, problem-solving, and overall enjoyment. Yet, the mechanisms through which interest manifests and impacts learning remain less explored, particularly in open-ended game-based learning environments like Minecraft. The unstructured nature of gameplay data in such settings poses analytical challenges. This study employed advanced data mining techniques, including changepoint detection and clustering, to extract meaningful patterns from students' movement data. Changepoint detection allows us to pinpoint significant shifts in behavior and segment unstructured gameplay data into distinct phases characterized by unique movement patterns. This research goes beyond traditional session-level analysis, offering a dynamic view of the learning process as it captures changes in student behaviors while they navigate challenges and interact with the environment. Three distinct exploration patterns emerged: surface-level exploration, in-depth exploration, and dynamic exploration. Notably, we found a negative correlation between surface-level exploration and interest development, whereas dynamic exploration positively correlated with interest development, regardless of initial interest levels. In addition to providing insights into how interest can manifest in Minecraft gameplay behavior, this paper makes significant methodological contributions by showcasing innovative approaches for extracting meaningful patterns from unstructured behavioral data within game-based learning environments. The implications of our research extend beyond Minecraft, offering valuable insights into the applications of changepoint detection in educational research to investigate student behavior in open-ended and complex learning settings. 

   more » « less