Exploring student approaches to learning through sequence analysis of reading logs

Abstract

In this paper, we aim to explore students' study approaches (e.g., deep, strategic, surface) from the logs collected by an electronic textbook (eBook) system. Data was collected from 89 students related to their reading activities both in and out of the class in a Freshman English course. Students are given a task to study reading materials through the eBook system, highlight the text that is related to the main or supporting ideas, and answer the questions prepared for measuring their level of comprehension. Students in and out of class reading times and their usage of the marker feature were used as a proxy to understand their study approaches. We used theory-driven and data-driven approaches together to model the study approaches of students. Our results showed that three groups of students who have different study approaches could be identified. Relationships between students' reading behaviors and their academic performance is also investigated by using association rule mining analysis. Obtained results are discussed in terms of monitoring, feedback, predicting learning outcomes, and identifying problems with the content design.