{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140024"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140024","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Categorizing and Comparing Students' Interactions in eTextbooks","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Sapkota, Jharana"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science & Applications","degree_department":"Computer Science and#38; Applications","school":null,"contributors":[],"advisors":[],"committee_chairs":["Farghally, Mohammed Fawzi Seddik"],"committee_members":["Shaffer, Clifford A.","Mohammed, Mostafa Kamel Osman"],"year":2025,"date_issued":"2025-12-17","date_published":"2025-12-17","updated_at":"2026-07-22T22:20:31Z","subjects":["Undergraduate Education","Learning Environment","Educational Data Mining","Student Engagement","eTextbooks"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44927"],"render_values":[{"text":"vt_gsexam:44927","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140024","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Farghally, Mohammed Fawzi Seddik"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Shaffer, Clifford A.","Mohammed, Mostafa Kamel Osman"]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science and#38; Applications"]},{"key":"dc:creator","label":"Author","values":["Sapkota, Jharana"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-18T09:01:20Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-12-18T09:01:20Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-17"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science & Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Undergraduate Education","Learning Environment","Educational Data Mining","Student Engagement","eTextbooks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44927"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140024"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["As college classrooms increasingly adopt digital tools, understanding how students use these resources is more important than ever. This research explores how students interact with an online textbook used in computer science courses. The textbook includes different types of content, such as readings, interactive visualizations, and practice exercises. By analyzing data from student sessions, similar to tracking how long and how often someone uses each feature, we discovered that students fall into four main categories based on how engaged they are. These range from highly engaged students, who actively use all parts of the textbook, to minimally engaged students, who interact very little. Our findings show a strong connection between the level of engagement and academic performance. Students who used the textbook actively and in diverse ways tended to do better in their courses. These insights can help instructors and textbook developers improve digital learning tools and better support student success."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Categorizing and Comparing Students' Interactions in eTextbooks"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Farghally, Mohammed Fawzi Seddik"],"dc:contributor.committeemember":["Shaffer, Clifford A.","Mohammed, Mostafa Kamel Osman"],"dc:contributor.department":["Computer Science and#38; Applications"],"dc:creator":["Sapkota, Jharana"],"dc:date.accessioned":["2025-12-18T09:01:20Z"],"dc:date.available":["2025-12-18T09:01:20Z"],"dc:date.issued":["2025-12-17"],"dc:description.abstract":["The rise of interactive eTextbooks opens new opportunities for enhancing student engagement and learning outcomes. 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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."],"dc:description.abstractgeneral":["As college classrooms increasingly adopt digital tools, understanding how students use these resources is more important than ever. This research explores how students interact with an online textbook used in computer science courses. The textbook includes different types of content, such as readings, interactive visualizations, and practice exercises. By analyzing data from student sessions, similar to tracking how long and how often someone uses each feature, we discovered that students fall into four main categories based on how engaged they are. These range from highly engaged students, who actively use all parts of the textbook, to minimally engaged students, who interact very little. Our findings show a strong connection between the level of engagement and academic performance. Students who used the textbook actively and in diverse ways tended to do better in their courses. These insights can help instructors and textbook developers improve digital learning tools and better support student success."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:44927"],"dc:identifier.uri":["https://hdl.handle.net/10919/140024"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Undergraduate Education","Learning Environment","Educational Data Mining","Student Engagement","eTextbooks"],"dc:title":["Categorizing and Comparing Students' Interactions in eTextbooks"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science & Applications"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:31Z"}