Sequence Analysis-Enhanced AI: Transforming Interactive E-Book Data into Educational Insights for Teachers

Abstract

This study explores the potential of large language models as interfaces for conducting sequence analysis on log data from interactive E-Books. As studies show, qualitative methods are not sufficient to comprehensively study the process of interaction with interactive E-Books. The quantitative method of educational data mining (EDM) has been considered as one of the most promising approaches for studying learner interactions with E-Books. Recently, sequence analysis showed potential in identifying typical patterns of interaction from log data collected from the Estonian Interactive E-Book Platform Opiq, allowing one to see the types of sessions from students in different grades, clusters of students based on the amount of the content they studied, and the interaction type they preferred. The main goal of the present study is to understand how teachers can utilize insights from CustomGPT to enhance their understanding of students’ interaction strategies with digital learning environments (DLEs) such as Opiq, and what the potential areas for further development of such tools are. We specified the process for developing a chatbot for transferring teachers’ queries into sequence analysis results and gathered feedback from teachers, allowing us both to estimate current design solutions to make sequence analysis results available and to find potential vectors of its development. Participants provided explicit feedback on CustomGPT, appreciating its potential for group and individual analysis, while suggesting improvements in visualization clarity, legend design, descriptive explanations, and personalized tips to better meet their needs. Potential areas of development, such as integrating personalized learning statistics, enhancing visualizations and reports for individual progress and mitigating AI hallucinations by expanding training data, are described.