  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.
