Online learning has witnessed a rapid growth following its widespread adoption across global higher education throughout the COVID-19 global pandemic. Although online learning was intended to maintain continuity of education during the public health emergency, the issue of student engagement in emergency online learning (EOL) has risen to prominence. Given the close association between student engagement and academic outcomes, investigating factors influencing student engagement in EOL is crucial for informing effective educational policies and interventions that support student success. In the absence of a comprehensive theoretical model for understanding student engagement in the unique context of EOL, this study proposed an extended community of inquiry model for predicting student engagement in EOL, incorporating learner, learning community, technological, and family factors. The model’s predictive power was validated by collecting 1,988 cases from a midsized Chinese private university using a translated and adapted questionnaire. Specifically, this study examined students’ overall and four-dimensional engagement, compared group differences in engagement based on demographics, and identified the factors influencing student engagement in EOL. Descriptive analyses revealed that students consistently reported medium to high levels of engagement, but they also reported higher levels of behavioral and cognitive engagement than vii emotional and social engagement. Independent-samples t tests and one-way between-groups analyses of variance unveiled significant differences in engagement across age, gender, grade level, and grade point average (GPA) groups, with younger, 1st-year male students with lower GPAs being less engaged. Hierarchical multiple regression analyses demonstrated the model’s significant predictive power, explaining 66.1% to 81.8% of the variance in overall and four- dimensional engagement. Notably, adaptability was most predictive of overall and cognitive engagement, and facilitating conditions, perceived usefulness, and social presence were most predictive of behavioral, emotional, and social engagement, respectively. These results offer valuable insights for universities, instructors, students, and families seeking to enhance students’ EOL engagement. Recommendations include fostering students’ adaptability and online learning self-efficacy, building online learning communities, optimizing technological infrastructure and online learning platforms, and incorporating family support into education.
","abstract_html":"<p>Online learning has witnessed a rapid growth following its widespread adoption across global higher education throughout the COVID-19 global pandemic. Although online learning was intended to maintain continuity of education during the public health emergency, the issue of student engagement in emergency online learning (EOL) has risen to prominence. Given the close association between student engagement and academic outcomes, investigating factors influencing student engagement in EOL is crucial for informing effective educational policies and interventions that support student success. In the absence of a comprehensive theoretical model for understanding student engagement in the unique context of EOL, this study proposed an extended community of inquiry model for predicting student engagement in EOL, incorporating learner, learning community, technological, and family factors. The model’s predictive power was validated by collecting 1,988 cases from a midsized Chinese private university using a translated and adapted questionnaire. Specifically, this study examined students’ overall and four-dimensional engagement, compared group differences in engagement based on demographics, and identified the factors influencing student engagement in EOL. Descriptive analyses revealed that students consistently reported medium to high levels of engagement, but they also reported higher levels of behavioral and cognitive engagement than vii emotional and social engagement. Independent-samples t tests and one-way between-groups analyses of variance unveiled significant differences in engagement across age, gender, grade level, and grade point average (GPA) groups, with younger, 1st-year male students with lower GPAs being less engaged. Hierarchical multiple regression analyses demonstrated the model’s significant predictive power, explaining 66.1% to 81.8% of the variance in overall and four- dimensional engagement. Notably, adaptability was most predictive of overall and cognitive engagement, and facilitating conditions, perceived usefulness, and social presence were most predictive of behavioral, emotional, and social engagement, respectively. These results offer valuable insights for universities, instructors, students, and families seeking to enhance students’ EOL engagement. Recommendations include fostering students’ adaptability and online learning self-efficacy, building online learning communities, optimizing technological infrastructure and online learning platforms, and incorporating family support into education.</p>","abstract_has_math":false,"creators":["Cao, Limei"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Education","degree_department":null,"school":null,"contributors":["Michelle Hall","Douglas D. Havard","Mark Maier"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-01T07:00:00Z","date_published":"2024-05-01T07:00:00Z","updated_at":"2026-07-24T01:38:37Z","subjects":["student engagement","emergency online learning","community of inquiry","Chinese private university","Educational Assessment, Evaluation, and Research","Educational Leadership","Educational Methods","Educational Technology","Higher Education","Online and Distance Education"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/education_dissertations/79","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Michelle Hall","Douglas D. 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Although online learning was intended to maintain continuity of education during the public health emergency, the issue of student engagement in emergency online learning (EOL) has risen to prominence. Given the close association between student engagement and academic outcomes, investigating factors influencing student engagement in EOL is crucial for informing effective educational policies and interventions that support student success. In the absence of a comprehensive theoretical model for understanding student engagement in the unique context of EOL, this study proposed an extended community of inquiry model for predicting student engagement in EOL, incorporating learner, learning community, technological, and family factors. The model’s predictive power was validated by collecting 1,988 cases from a midsized Chinese private university using a translated and adapted questionnaire. Specifically, this study examined students’ overall and four-dimensional engagement, compared group differences in engagement based on demographics, and identified the factors influencing student engagement in EOL. Descriptive analyses revealed that students consistently reported medium to high levels of engagement, but they also reported higher levels of behavioral and cognitive engagement than vii emotional and social engagement. Independent-samples t tests and one-way between-groups analyses of variance unveiled significant differences in engagement across age, gender, grade level, and grade point average (GPA) groups, with younger, 1st-year male students with lower GPAs being less engaged. Hierarchical multiple regression analyses demonstrated the model’s significant predictive power, explaining 66.1% to 81.8% of the variance in overall and four- dimensional engagement. Notably, adaptability was most predictive of overall and cognitive engagement, and facilitating conditions, perceived usefulness, and social presence were most predictive of behavioral, emotional, and social engagement, respectively. These results offer valuable insights for universities, instructors, students, and families seeking to enhance students’ EOL engagement. Recommendations include fostering students’ adaptability and online learning self-efficacy, building online learning communities, optimizing technological infrastructure and online learning platforms, and incorporating family support into education.
"]},{"key":"dc:source","label":"Dc Source","values":["Cao, L. (2024). Investigating factors influencing Chinese private college students’ engagement in emergency online learning [Doctoral dissertation, Chapman University]. Chapman University Digital Commons. https://doi.org/10.36837/chapman.000565"]},{"key":"dc:title","label":"Title","values":["Investigating Factors Influencing Chinese Private College Students’ Engagement in Emergency Online Learning"]}]}],"canonical_facts":{"dc:contributor":["Michelle Hall","Douglas D. Havard","Mark Maier"],"dc:creator":["Cao, Limei"],"dc:date.available":["2026-05-07T07:00:00Z"],"dc:description.abstract":["Online learning has witnessed a rapid growth following its widespread adoption across global higher education throughout the COVID-19 global pandemic. Although online learning was intended to maintain continuity of education during the public health emergency, the issue of student engagement in emergency online learning (EOL) has risen to prominence. Given the close association between student engagement and academic outcomes, investigating factors influencing student engagement in EOL is crucial for informing effective educational policies and interventions that support student success. In the absence of a comprehensive theoretical model for understanding student engagement in the unique context of EOL, this study proposed an extended community of inquiry model for predicting student engagement in EOL, incorporating learner, learning community, technological, and family factors. The model’s predictive power was validated by collecting 1,988 cases from a midsized Chinese private university using a translated and adapted questionnaire. Specifically, this study examined students’ overall and four-dimensional engagement, compared group differences in engagement based on demographics, and identified the factors influencing student engagement in EOL. Descriptive analyses revealed that students consistently reported medium to high levels of engagement, but they also reported higher levels of behavioral and cognitive engagement than vii emotional and social engagement. Independent-samples t tests and one-way between-groups analyses of variance unveiled significant differences in engagement across age, gender, grade level, and grade point average (GPA) groups, with younger, 1st-year male students with lower GPAs being less engaged. Hierarchical multiple regression analyses demonstrated the model’s significant predictive power, explaining 66.1% to 81.8% of the variance in overall and four- dimensional engagement. Notably, adaptability was most predictive of overall and cognitive engagement, and facilitating conditions, perceived usefulness, and social presence were most predictive of behavioral, emotional, and social engagement, respectively. These results offer valuable insights for universities, instructors, students, and families seeking to enhance students’ EOL engagement. Recommendations include fostering students’ adaptability and online learning self-efficacy, building online learning communities, optimizing technological infrastructure and online learning platforms, and incorporating family support into education.
"],"dc:identifier":["https://digitalcommons.chapman.edu/education_dissertations/79"],"dc:source":["Cao, L. (2024). Investigating factors influencing Chinese private college students’ engagement in emergency online learning [Doctoral dissertation, Chapman University]. Chapman University Digital Commons. https://doi.org/10.36837/chapman.000565"],"dc:subject":["student engagement","emergency online learning","community of inquiry","Chinese private university","Educational Assessment, Evaluation, and Research","Educational Leadership","Educational Methods","Educational Technology","Higher Education","Online and Distance Education"],"dc:title":["Investigating Factors Influencing Chinese Private College Students’ Engagement in Emergency Online Learning"],"thesis:degree_discipline":["Education"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:38:37Z"}