%0 Conference Paper %1 9457801 %A Mirzaei, Mehrdad %A Sahebi, Shaghayegh %A Brusilovsky, Peter %B 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) %D 2020 %K behavior-analysis behavior-patterns matrix-factorization myown %P 283-290 %R 10.1109/WIIAT50758.2020.00041 %T Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors %U https://ieeexplore.ieee.org/abstract/document/9457801 %X 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.

@inproceedings{9457801, abstract = {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.}, added-at = {2021-07-13T19:03:52.000+0200}, author = {Mirzaei, Mehrdad and Sahebi, Shaghayegh and Brusilovsky, Peter}, biburl = {https://www.bibsonomy.org/bibtex/228b82903d2078a55442a1d9b5cbf9b76/brusilovsky}, booktitle = {2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)}, description = {Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors | IEEE Conference Publication | IEEE Xplore}, doi = {10.1109/WIIAT50758.2020.00041}, interhash = {828ee957509598f44efe3df01384eac1}, intrahash = {28b82903d2078a55442a1d9b5cbf9b76}, keywords = {behavior-analysis behavior-patterns matrix-factorization myown}, month = dec, pages = {283-290}, timestamp = {2021-07-14T03:57:51.000+0200}, title = {Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors}, url = {https://ieeexplore.ieee.org/abstract/document/9457801}, year = 2020 }