Hybrid education—integrating online and in-person instruction—has become a prominent educational model in the post-COVID-19 educational landscape. While prior research has examined the benefits and challenges of hybrid learning, as well as general acceptance patterns through frameworks such as the technology acceptance model and the theory of planned behavior, critical gaps remain in identifying the factors shaping students’ behavioral intentions toward this educational model. This study addresses these gaps by analyzing the interplay of external variables (such as prior hybrid learning experience, employment status, course type, commute time, and work-family balance) and socio-demographic attributes (such as age, gender, academic program and family obligations). Data were collected from a diverse sample of undergraduate and graduate students at a Midwestern university to assess predictors of hybrid learning adoption intentions. The results offer practical recommendations for educational policymakers, institutions, and instructional designers to optimize hybrid course development, refine digital learning environments, and establish evidence-based guidelines for post-pandemic education. By clarifying the determinants of student engagement, this research contributes to enhanced educational experiences and the sustainable implementation of hybrid learning models in contemporary higher education.
","abstract_html":"<p>Hybrid education—integrating online and in-person instruction—has become a prominent educational model in the post-COVID-19 educational landscape. While prior research has examined the benefits and challenges of hybrid learning, as well as general acceptance patterns through frameworks such as the technology acceptance model and the theory of planned behavior, critical gaps remain in identifying the factors shaping students’ behavioral intentions toward this educational model. This study addresses these gaps by analyzing the interplay of external variables (such as prior hybrid learning experience, employment status, course type, commute time, and work-family balance) and socio-demographic attributes (such as age, gender, academic program and family obligations). Data were collected from a diverse sample of undergraduate and graduate students at a Midwestern university to assess predictors of hybrid learning adoption intentions. The results offer practical recommendations for educational policymakers, institutions, and instructional designers to optimize hybrid course development, refine digital learning environments, and establish evidence-based guidelines for post-pandemic education. By clarifying the determinants of student engagement, this research contributes to enhanced educational experiences and the sustainable implementation of hybrid learning models in contemporary higher education.</p>","abstract_has_math":false,"creators":["Zhang, Heng"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Open Access Dissertation","degree_discipline":"College of Technology","degree_department":null,"school":null,"contributors":["Giri Jogaratnam, PhD","Bilquis Ferdousi, PhD","Dorothy McAllen, PhD"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-01-01T08:00:00Z","date_published":"2025-01-01T08:00:00Z","updated_at":"2026-07-24T02:17:53Z","subjects":["Educational Psychology","Educational Technology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.emich.edu/theses/1288","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Giri Jogaratnam, PhD","Bilquis Ferdousi, PhD","Dorothy McAllen, PhD"]},{"key":"dc:creator","label":"Author","values":["Zhang, Heng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-06-19T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["College of Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Educational Psychology","Educational Technology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.emich.edu/theses/1288"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Hybrid education—integrating online and in-person instruction—has become a prominent educational model in the post-COVID-19 educational landscape. While prior research has examined the benefits and challenges of hybrid learning, as well as general acceptance patterns through frameworks such as the technology acceptance model and the theory of planned behavior, critical gaps remain in identifying the factors shaping students’ behavioral intentions toward this educational model. This study addresses these gaps by analyzing the interplay of external variables (such as prior hybrid learning experience, employment status, course type, commute time, and work-family balance) and socio-demographic attributes (such as age, gender, academic program and family obligations). Data were collected from a diverse sample of undergraduate and graduate students at a Midwestern university to assess predictors of hybrid learning adoption intentions. The results offer practical recommendations for educational policymakers, institutions, and instructional designers to optimize hybrid course development, refine digital learning environments, and establish evidence-based guidelines for post-pandemic education. By clarifying the determinants of student engagement, this research contributes to enhanced educational experiences and the sustainable implementation of hybrid learning models in contemporary higher education.
"]},{"key":"dc:title","label":"Title","values":["Understanding students' decision to proceed with hybrid education: Examining the post-COVID-19 scenario"]}]}],"canonical_facts":{"dc:contributor":["Giri Jogaratnam, PhD","Bilquis Ferdousi, PhD","Dorothy McAllen, PhD"],"dc:creator":["Zhang, Heng"],"dc:date.available":["2025-06-19T07:00:00Z"],"dc:description.abstract":["Hybrid education—integrating online and in-person instruction—has become a prominent educational model in the post-COVID-19 educational landscape. While prior research has examined the benefits and challenges of hybrid learning, as well as general acceptance patterns through frameworks such as the technology acceptance model and the theory of planned behavior, critical gaps remain in identifying the factors shaping students’ behavioral intentions toward this educational model. This study addresses these gaps by analyzing the interplay of external variables (such as prior hybrid learning experience, employment status, course type, commute time, and work-family balance) and socio-demographic attributes (such as age, gender, academic program and family obligations). Data were collected from a diverse sample of undergraduate and graduate students at a Midwestern university to assess predictors of hybrid learning adoption intentions. The results offer practical recommendations for educational policymakers, institutions, and instructional designers to optimize hybrid course development, refine digital learning environments, and establish evidence-based guidelines for post-pandemic education. By clarifying the determinants of student engagement, this research contributes to enhanced educational experiences and the sustainable implementation of hybrid learning models in contemporary higher education.
"],"dc:identifier":["https://commons.emich.edu/theses/1288"],"dc:subject":["Educational Psychology","Educational Technology"],"dc:title":["Understanding students' decision to proceed with hybrid education: Examining the post-COVID-19 scenario"],"thesis:degree_discipline":["College of Technology"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T02:17:53Z"}