%0 Conference Paper %1 Wang_2021 %A Wang, Chunpai %A Sahebi, Shaghayegh %A Zhao, Siqian %A Brusilovsky, Peter %A Moraes, Laura O. %B Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization %D 2021 %I ACM %K knowledge-tracing myown sequence-mining student-modeling umap2021 %P 179-188 %R 10.1145/3450613.3456831 %T Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization %U https://doi.org/10.1145%2F3450613.3456831 %X Knowledge Tracing (KT), which aims to model student knowledge level and predict their performance, is one of the most important applications of user modeling. Modern KT approaches model and maintain an up-to-date state of student knowledge over a set of course concepts according to students’ historical performance in attempting the problems. However, KT approaches were designed to model knowledge by observing relatively small problem-solving steps in Intelligent Tutoring Systems. While these approaches were applied successfully to model student knowledge by observing student solutions for simple problems, such as multiple-choice questions, they do not perform well for modeling complex problem solving in students. Most importantly, current models assume that all problem attempts are equally valuable in quantifying current student knowledge. However, for complex problems that involve many concepts at the same time, this assumption is deficient. It results in inaccurate knowledge states and unnecessary fluctuations in estimated student knowledge, especially if students guess the correct answer to a problem that they have not mastered all of its concepts or slip in answering the problem that they have already mastered all of its concepts. In this paper, we argue that not all attempts are equivalently important in discovering students’ knowledge state, and some attempts can be summarized together to better represent student performance. We propose a novel student knowledge tracing approach, Granular RAnk based TEnsor factorization (GRATE), that dynamically selects student attempts that can be aggregated while predicting students’ performance in problems and discovering the concepts presented in them. Our experiments on three real-world datasets demonstrate the improved performance of GRATE, compared to the state-of-the-art baselines, in the task of student performance prediction. Our further analysis shows that attempt aggregation eliminates the unnecessary fluctuations from students’ discovered knowledge states and helps in discovering complex latent concepts in the problems.

@inproceedings{Wang_2021, abstract = {Knowledge Tracing (KT), which aims to model student knowledge level and predict their performance, is one of the most important applications of user modeling. Modern KT approaches model and maintain an up-to-date state of student knowledge over a set of course concepts according to students’ historical performance in attempting the problems. However, KT approaches were designed to model knowledge by observing relatively small problem-solving steps in Intelligent Tutoring Systems. While these approaches were applied successfully to model student knowledge by observing student solutions for simple problems, such as multiple-choice questions, they do not perform well for modeling complex problem solving in students. Most importantly, current models assume that all problem attempts are equally valuable in quantifying current student knowledge. However, for complex problems that involve many concepts at the same time, this assumption is deficient. It results in inaccurate knowledge states and unnecessary fluctuations in estimated student knowledge, especially if students guess the correct answer to a problem that they have not mastered all of its concepts or slip in answering the problem that they have already mastered all of its concepts. In this paper, we argue that not all attempts are equivalently important in discovering students’ knowledge state, and some attempts can be summarized together to better represent student performance. We propose a novel student knowledge tracing approach, Granular RAnk based TEnsor factorization (GRATE), that dynamically selects student attempts that can be aggregated while predicting students’ performance in problems and discovering the concepts presented in them. Our experiments on three real-world datasets demonstrate the improved performance of GRATE, compared to the state-of-the-art baselines, in the task of student performance prediction. Our further analysis shows that attempt aggregation eliminates the unnecessary fluctuations from students’ discovered knowledge states and helps in discovering complex latent concepts in the problems. }, added-at = {2021-07-12T23:22:37.000+0200}, author = {Wang, Chunpai and Sahebi, Shaghayegh and Zhao, Siqian and Brusilovsky, Peter and Moraes, Laura O.}, biburl = {https://www.bibsonomy.org/bibtex/252d796b193b50732fc8bf7a030178bcf/brusilovsky}, booktitle = {Proceedings of the 29th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization | Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3450613.3456831}, interhash = {88b677af0b23445e7f00f755ffa3e31f}, intrahash = {52d796b193b50732fc8bf7a030178bcf}, keywords = {knowledge-tracing myown sequence-mining student-modeling umap2021}, month = jun, pages = {179-188}, publisher = {{ACM}}, timestamp = {2021-07-12T23:22:37.000+0200}, title = {Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization}, url = {https://doi.org/10.1145%2F3450613.3456831}, year = 2021 }