Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized learning platform to students. In recent years, methods based on Recurrent Neural Networks (RNN) such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) outperformed all the traditional methods because of their ability to capture a complex representation of human learning. However, these methods face the issue of not generalizing well while dealing with sparse data which is the case with real-world data as students interact with few KCs. In order to address this issue, we develop an approach that identifies the KCs from the student's past activities that are relevant to the given KC and predicts his/her mastery based on the relatively few KCs that it picked. Since predictions are made based on relatively few past activities, it handles the data sparsity problem better than the methods based on RNN. For identifying the relevance between the KCs, we propose a self-attention based approach, Self Attentive Knowledge Tracing (SAKT). Extensive experimentation on a variety of real-world dataset shows that our model outperforms the state-of-the-art models for knowledge tracing, improving AUC by 4.43% on average.
|Original language||English (US)|
|Title of host publication||EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining|
|Editors||Collin F. Lynch, Agathe Merceron, Michel Desmarais, Roger Nkambou|
|Publisher||International Educational Data Mining Society|
|Number of pages||6|
|State||Published - 2019|
|Event||12th International Conference on Educational Data Mining, EDM 2019 - Montreal, Canada|
Duration: Jul 2 2019 → Jul 5 2019
|Name||EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining|
|Conference||12th International Conference on Educational Data Mining, EDM 2019|
|Period||7/2/19 → 7/5/19|
Bibliographical noteFunding Information:
This work was supported in part by NSF (1447788, 1704074, 1757916, 1834251), Army Research Office (W911NF1810344), Intel Corp, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute.
© EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining. All rights reserved.
- Knowledge tracing
- Massive open online courses
- Sequential recommendation