Incremental Knowledge Tracing from Multiple Schools
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Abstract (arXiv)
Knowledge tracing is the task of predicting a learner's future performance based on the history of the learner's performance. Current knowledge tracing models are built based on an extensive set of data that are collected from multiple schools. However, it is impossible to pool learner's data from all schools, due to data privacy and PDPA policies. Hence, this paper explores the feasibility of building knowledge tracing models while preserving the privacy of learners' data within their respective schools. This study is conducted using part of the ASSISTment 2009 dataset, with data from multiple schools being treated as separate tasks in a continual learning framework. The results show that learning sequentially with the Self Attentive Knowledge Tracing (SAKT) algorithm is able to achieve considerably similar performance to that of pooling all the data together.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 10 references that are in the map. First posted on arXiv 7 Jan 2022.
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References · 8
- 2017Attention Is All You Needcited by 59,932
- 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 415
- 2015Deep Knowledge Tracingcited by 328
- 2016Dynamic Key-Value Memory Networks for Knowledge Tracingcited by 170
- 2019A Self-Attentive model for Knowledge Tracingcited by 169
- 2020SAINT+: Integrating Temporal Features for EdNet Correctness Predictioncited by 57
- 2020BKT-LSTM: Efficient Student Modeling for knowledge tracing and student performance predictioncited by 5
- 2021Riiid! Answer Correctness Prediction Kaggle Challenge: 4th Place Solution Summarycited by 4
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