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Grasping or forgetting? MAKT: A dynamic model via multi-head self-attention for knowledge tracing
33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021 ; 2021-July:399-404, 2021.
Article in English | Scopus | ID: covidwho-1404147
ABSTRACT
The outbreak of the COVID-19 pandemic arises enormous attention to online education then knowledge tracking is an increasingly crucial task with its vigorous development. However, the surge of student historical interactions and the lack of prior knowledge is engendering a sequence of issues, such as the decrease in prediction accuracy while the increase in training time. Simultaneously, most existing approaches fail to provide in-depth insights into why a student is likely to answer the question incorrectly and what affects the knowledge state of the student. To address those issues, we propose a multi-head self-attention model named MAKT for dynamic knowledge tracing, which makes the prediction results interpretable at the model and instance level. The customized multi-head self-attention layer has high training efficiency owing to its parallelization capability and spends about 6 seconds in each epoch on a single GPU. We further visualize the attention weights of MAKT and student knowledge acquisition tracking, finding that not all historical interactions are equally important but the recent interactions profoundly establish the knowledge state of students. In the end, extensive experiments on three datasets demonstrate the robustness and superiorities of MAKT, improving ACC by 1.14 % and AUC by 1.20 % on average. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021 Year: 2021 Document Type: Article