Cuff-KT generates personalized output-layer parameters for knowledge tracing models without fine-tuning, reporting AUC improvements of about 10% and 4% under intra- and inter-learner shifts.
DBE-KT22: A Knowledge Tracing Dataset Based on Online Student Evaluation
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abstract
Online education has gained an increasing importance over the last decade for providing affordable high-quality education to students worldwide. This has been further magnified during the global pandemic as more students switched to study online. The majority of online education tasks, e.g., course recommendation, exercise recommendation, or automated evaluation, depends on tracking students' knowledge progress. This is known as the \emph{Knowledge Tracing} problem in the literature. Addressing this problem requires collecting student evaluation data that can reflect their knowledge evolution over time. In this paper, we propose a new knowledge tracing dataset named Database Exercises for Knowledge Tracing (DBE-KT22) that is collected from an online student exercise system in a course taught at the Australian National University in Australia. We discuss the characteristics of the DBE-KT22 dataset and contrast it with the existing datasets in the knowledge tracing literature. Our dataset is available for public access through the Australian Data Archive platform.
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Cuff-KT: Tackling Learners' Real-time Learning Pattern Adjustment via Tuning-Free Knowledge State Guided Model Updating
Cuff-KT generates personalized output-layer parameters for knowledge tracing models without fine-tuning, reporting AUC improvements of about 10% and 4% under intra- and inter-learner shifts.