Analysis of Knowledge Tracing performance on synthesised student data

• URL: http://arxiv.org/abs/2401.16832v1
• Date: Tue, 30 Jan 2024 09:19:50 GMT
• Title: Analysis of Knowledge Tracing performance on synthesised student data
• Authors: Panagiotis Pagonis and Kai Hartung and Di Wu and Munir Georges and S\"oren Gr\"ottrup
• Abstract summary: Knowledge Tracing aims to predict the future performance of students by tracking the development of their knowledge states. Despite all the recent progress made in this field, the application of KT models in education systems is still restricted from the data perspectives. Our work shows that using only synthetic data for training can lead to similar performance as real data.
• Score: 3.9227982854973438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge Tracing (KT) aims to predict the future performance of students by tracking the development of their knowledge states. Despite all the recent progress made in this field, the application of KT models in education systems is still restricted from the data perspectives: 1) limited access to real life data due to data protection concerns, 2) lack of diversity in public datasets, 3) noises in benchmark datasets such as duplicate records. To resolve these problems, we simulated student data with three statistical strategies based on public datasets and tested their performance on two KT baselines. While we observe only minor performance improvement with additional synthetic data, our work shows that using only synthetic data for training can lead to similar performance as real data.

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