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Analysis of Knowledge Tracing performance on synthesised student data

  • 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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Metadaten
Author:Panagiotis Pagonis, Kai Hartung, Di Wu, Munir Georges, Sören Gröttrup
Language:English
Document Type:Preprint
Year of first Publication:2024
Publisher:arXiv
Place of publication:Ithaca
Pages:7
Review:nein
Open Access:ja
URN:urn:nbn:de:bvb:573-44771
Related Identifier:https://doi.org/10.48550/arXiv.2401.16832
Faculties / Institutes / Organizations:Fakultät Informatik
AImotion Bavaria
Licence (German):License Logo Creative Commons BY 4.0
Note:
Die veröffentlichte Version dieses Preprints ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4535
Release Date:2024/02/14