Categorizing and Comparing Students' Interactions in eTextbooks

The rise of interactive eTextbooks opens new opportunities for enhancing student engagement and learning outcomes. However, analyzing student interactions within these digital platforms remains challenging. This study examines student engagement profiles in OpenDSA, an interactive eTextbook for data structures and algorithms courses. Using session-level interaction data, we categorize engagement into four distinct engagement states, defined as types of student activities: Reading, Visualization, Proficiency Exercises, and Multiple-Choice Exercises. Although OpenDSA also integrates third-party programming exercises through CodeWorkout, these activities were excluded from our analysis because the fine-grained interaction logs required for behavioral modeling were not accessible. We then apply clustering techniques to identify distinct engagement profiles, characterized by the frequency of transitions and total engagement time spent in each engagement state. Our research addresses two key questions: (1) What engagement profiles can be identified from students' interactions across these four engagement states? (2) How do these engagement profiles correlate with students' academic performance? Our findings reveal four distinct engagement profiles: Highly Engaged Learners, exhibiting frequent transitions and high engagement across all engagement states; Moderately Engaged Learners, characterized by sporadic interactions and below-average overall engagement; Balanced Learners, maintaining consistent and moderate engagement across engagement states, and Minimally Engaged Learners, demonstrating limited engagement and infrequent state transitions. Statistical analysis confirms that students in profiles with frequent and diverse engagement significantly outperform minimally engaged learners academically. These results underline the critical role of active, varied engagement in student success and underline the potential of session-level data for monitoring and optimizing student engagement. We believe our findings will be valuable to eTextbook developers, providing actionable insights to guide the design of digital content and targeted interventions that improve student engagement and performance.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and#38; Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Sapkota, Jharana

Chair dc:contributor.committeechair

• Farghally, Mohammed Fawzi Seddik

Committee members dc:contributor.committeemember

• Shaffer, Clifford A.
• Mohammed, Mostafa Kamel Osman

• Undergraduate Education
• Learning Environment
• Educational Data Mining
• Student Engagement
• eTextbooks

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:44927

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/140024