Heo, Samnyeong2022-07-192022-07-192022-07-18vt_gsexam:35289http://hdl.handle.net/10919/111286As more students interact with online learning platforms and eTextbooks, they generate massive amounts of data. For example, the OpenDSA eTextbook system collects clickstream data as users interact with prose, visualizations, and interactive auto-graded exercises. Ideally, instructors and system developers can harness this information to create better instructional experiences. But in its raw event-level form, it is difficult for developers or instructors to understand student behaviors, or to make testable hypotheses about relationships between behavior and performance. In this study, we describe our efforts to break raw event-level data first into sessions (a continuous series of work by a student) and then to meaningfully abstract the events into higher-level descriptions of that session. The goal of this abstraction is to help instructors and researchers gain insights into the students' learning behaviors. For example, we can distinguish when students read material and then attempt the associated exercise, versus going straight to the exercise and then hunting for the answers in the associated material. We first bundle events into related activities, such as the events associated with stepping through a given visualization, or with working a given exercise. Each such group of events defines a state. A state is a basic unit that characterizes the interaction log data, and there are multiple state types including reading prose, interacting with visual contents, and solving exercises. We harnessed the abstracted data to analyze studying behavior and compared it with course performance based on GPA. We analyzed data from the Fall 2020 and Spring 2021 sections of a senior-level Formal Languages course, and also from the Fall 2020 and Spring 2021 sections of a data structures course.ETDenIn CopyrightClickstream dataOpenDSALMSElectronic TextbookFormal LanguagesStudent BehaviorThesis