ES-KT-24: A Multimodal Knowledge Tracing Benchmark Dataset with Educational Game Playing Video and Synthetic Text Generation

TL;DR

ES-KT-24 is a comprehensive multimodal dataset combining educational game videos, synthetic questions, and detailed logs to enhance knowledge tracing research across diverse languages and subjects.

Contribution

The paper introduces ES-KT-24, a novel multimodal dataset with synthetic text and game videos, filling gaps in existing KT datasets and enabling advanced research.

Findings

LKT models outperform traditional DKT models in this dataset.

The dataset covers multiple languages and subjects, promoting diversity.

Benchmark results demonstrate the dataset's utility for KT research.

Abstract

This paper introduces ES-KT-24, a novel multimodal Knowledge Tracing (KT) dataset for intelligent tutoring systems in educational game contexts. Although KT is crucial in adaptive learning, existing datasets often lack game-based and multimodal elements. ES-KT-24 addresses these limitations by incorporating educational game-playing videos, synthetically generated question text, and detailed game logs. The dataset covers Mathematics, English, Indonesian, and Malaysian subjects, emphasizing diversity and including non-English content. The synthetic text component, generated using a large language model, encompasses 28 distinct knowledge concepts and 182 questions, featuring 15,032 users and 7,782,928 interactions. Our benchmark experiments demonstrate the dataset's utility for KT research by comparing Deep learning-based KT models with Language Model-based Knowledge Tracing (LKT) approaches. Notably, LKT models showed slightly higher performance than traditional DKT models, highlighting the potential of language model-based approaches in this field. Furthermore, ES-KT-24 has the potential to significantly advance research in multimodal KT models and learning analytics. By integrating game-playing videos and detailed game logs, this dataset offers a unique approach to dissecting student learning patterns through advanced data analysis and machine-learning techniques. It has the potential to unearth new insights into the learning process and inspire further exploration in the field.