Incremental Knowledge Tracing from Multiple Schools
This addresses privacy issues in educational data analysis for schools, but it is incremental as it applies an existing method to a new constraint.
The paper tackled the problem of building knowledge tracing models without pooling data from multiple schools due to privacy concerns, using a continual learning framework with the SAKT algorithm on the ASSISTment 2009 dataset, achieving performance similar to pooled data.
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.