More Like this

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. 

   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. Personalized Learning in a Virtual Hands-on Lab Platform for Computer Science Education

   https://doi.org/10.1109/FIE.2018.8659291  

   Deng, Yuli ; Lu, Duo ; Chung, Chun-Jen ; Huang, Dijiang ; Zeng, Zhen ( October 2018 , IEEE Frontiers in Education Conference (FIE))

   This Innovate Practice full paper presents a cloud-based personalized learning lab platform. Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learner's behavior and assessing learner's performance for personalization. However, it is rarely addressed in existing research. In this paper, we propose a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. With that in mind, ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. For example, instructors may provide more detailed instructions to help slow starters, while assigning more challenging labs to those quick learners in the same class. To evaluate ThoTh Lab, we conducted an experiment and collected data from an upper-division cybersecurity class for undergraduate students at Arizona State University in the US. The results show that ThoTh Lab can identify learning style with reasonable accuracy. By leveraging the personalized lab platform for a senior level cybersecurity course, our lab-use study also shows that the presented solution improves students engagement with better understanding of lab assignments, spending more effort on hands-on projects, and thus greatly enhancing learning outcomes. 

   more » « less
4. 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)

   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
5. Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep Learning

   https://doi.org/10.1109/TLT.2019.2912162  

   Botelho, Anthony ; Varatharaj, Ashvini ; Patikorn, Thanaporn ; Doherty, Diana ; Adjei, Seth ; Beck, Joseph ( January 2019 , IEEE Transactions on Learning Technologies)

   The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are only able to provide aid to students who interact with the system. With this in mind, student persistence emerges as a prominent learning construct contributing to students success when learning new material. Conversely, high persistence is not always productive for students, where additional practice does not help the student move toward a state of mastery of the material. In this paper, we apply a transfer learning methodology using deep learning and traditional modeling techniques to study high and low representations of unproductive persistence. We focus on two prominent problems in the fields of educational data mining and learner analytics representing low persistence, characterized as student "stopout," and unproductive high persistence, operationalized through student "wheel spinning," in an effort to better understand the relationship between these measures of unproductive persistence (i.e. stopout and wheel spinning) and develop early detectors of these behaviors. We find that models developed to detect each within and across-assignment stopout and wheel spinning are able to learn sets of features that generalize to predict the other. We further observe how these models perform at each learning opportunity within student assignments to identify when interventions may be deployed to best aid students who are likely to exhibit unproductive persistence. 

   more » « less