Looking back to move forward: comparison of instructors’ and undergraduates’ retrospection on the effectiveness of online learning using the nine-outcome influencing factors

The COVID-19 pandemic precipitated a rapid global shift to online learning, impacting 99% of students at its peak. While extensive research examined immediate responses and discrete aspects of online education (e.g., technology, pedagogy, socio-emotional dimensions), fewer studies have explored retrospective perspectives using a comprehensive framework that includes both student and instructor voices. This study addresses that gap by examining post-pandemic retrospections of undergraduates and teachers in China through the lens of nine outcome-influencing factors: behavioral intention, instruction, engagement, interaction, motivation, self-efficacy, performance, satisfaction, and self-regulation. It aims to: (Q1) understand how both groups retrospectively perceive online learning effectiveness post-pandemic; (Q2) identify which of the nine factors most significantly impacted experiences and why; and (Q3) propose recommendations for enhancing future online learning. The study is situated in a comprehensive university in China and seeks to inform policy and practice as digital learning remains prominent.

Online learning expanded rapidly during COVID-19, offering flexibility and potential parity with face-to-face instruction under certain conditions, yet presenting challenges such as digital divides, motivation, self-regulation, engagement, and technical issues. Prior research shows mixed outcomes: some report improved engagement and skills; others highlight no significant differences with traditional learning when supports are equivalent, and disadvantages for certain groups. Determinants of online outcomes include engagement, instructional design, technology infrastructure, student–teacher interaction, and self-regulation. Students often value flexibility and access but report issues with motivation, isolation, technical problems, and desire for more interactive, engaging content and community. Teachers report both benefits (flexibility, potential for equity) and challenges (technology, course design, workload, interaction deficits). The theoretical framework guiding this study (Yu’s model) interrelates nine factors: behavioral intention influences engagement; instruction shapes engagement and self-efficacy; engagement and self-regulation drive performance; interaction bolsters motivation and self-efficacy; motivation sustains engagement/self-regulation; performance and satisfaction reflect the cumulative effects and feed back into future motivation and engagement. The literature indicates a need for holistic, integrated approaches addressing these interrelated elements, with attention to social presence, transactional distance, and contextual constraints (e.g., infrastructure, socioeconomic factors).

Design: Qualitative study using thematic analysis guided by a nine-factor framework (behavioral intention, instruction, engagement, interaction, motivation, self-efficacy, performance, satisfaction, self-regulation). Context: First semester of the 2022–2023 academic year (post-pandemic period) at a comprehensive university in China. Participants: 46 first-year undergraduates (37 male, 9 female; 11 English majors, 35 non-English) and 18 experienced teachers (≥5 years teaching; 13 female, 5 male; English and non-English disciplines). Sampling: Convenience sampling following an email inquiry about prior online learning experience; ethics approval obtained (Wenzhou University, WGY202302); informed consent secured; anonymity ensured via pseudonyms. Data collection: Students maintained reflective diaries throughout the semester responding to four prompts: (1) state and attitude toward online learning; (2) problems/shortcomings; (3) reasons for problems; (4) measures to improve online learning. Teachers responded to the same four questions in 20–30 minute interviews conducted on campus. Data analysis: Thematic analysis with initial coding structured by the nine-factor framework while remaining open to emergent themes. Steps included familiarization, initial coding, theme development, review, and definition/naming. Rigor: Peer debriefing, member checking, and an audit trail were used to enhance trustworthiness. Outputs included mapping student and teacher subthemes to the nine categories and quantifying attitudes toward disadvantages per factor (Tables 3–5).

- Instruction emerged as the top-ranked factor influencing online learning effectiveness for both students and teachers, followed by engagement, self-regulation, interaction, and motivation; performance and satisfaction were less emphasized. Thematic subthemes included feedback quality, supervisory presence, emotional connection, and technical stability. - Attitudes toward online learning: Among teachers (n=18), ~38.9% supported online learning, ~50% did not support it, and 2 were neutral. Among students (n=46), 34.8% were positive and 34.8% neutral toward online learning, indicating more mixed views among students. - Divergent perspectives on instruction: Teachers frequently attributed challenges to technological/infrastructural issues and platform limitations (e.g., monitoring engagement), whereas students focused on instructional quality, engagement of content, and the need for closer supervision/interaction. - Engagement and interaction deficits: Lack of face-to-face cues (eye contact, body language) and delayed or text-based communication reduced social presence, attention, and participation, contributing to distractions and passive learning. - Self-regulation vs. self-efficacy: Students emphasized difficulties with self-regulation (time management, distractions) and reliance on teacher supervision; teachers highlighted self-efficacy and technological/app design constraints affecting monitoring and feedback. - Performance and satisfaction: Many students reported low efficiency and difficulty focusing outside the classroom; approximately 46% expressed dissatisfaction with teachers’ online teaching approaches (e.g., monotonous delivery), citing boredom and fatigue. - Technology and course currency: Teachers and students noted outdated materials, unstable networks, and app/platform shortcomings that hindered real-time supervision, interaction, and responsiveness. - Practical improvement suggestions: Increase interactive elements, contextualize content, emulate engaging online formats, assign preview tasks, and use in-class quizzes to stimulate interest and attention.

Findings address Q1 by revealing mixed but polarized teacher attitudes and more neutral student stances, reflecting varied experiences and readiness for online modalities. For Q2, instruction’s primacy underscores its central role in shaping engagement, self-efficacy, and ultimately performance and satisfaction within the nine-factor framework. Differences in emphasis—teachers on technological/instructional delivery constraints and students on content engagement and supervision—reflect role-specific priorities and align with research on transactional distance and social presence. The prominence of self-regulation difficulties among students and self-efficacy concerns among teachers illustrates how the framework’s interdependencies manifest in practice: diminished interaction and social presence reduce engagement, undermining self-regulation and performance, which then lowers satisfaction and future behavioral intention. The results support a holistic strategy integrating pedagogical design (active, engaging content), technological reliability (stable platforms, analytics for monitoring), and socio-emotional supports (enhanced interaction and presence). These insights are relevant for optimizing online learning ecosystems, informing policy on infrastructure investments, faculty development, and student skills training.

This study contributes a retrospective, dual-perspective analysis of online learning effectiveness post-COVID-19 using a nine-factor framework. Both teachers and students prioritized instruction, followed by engagement, interaction, motivation, and self-regulation; performance and satisfaction were less salient. Divergences emerged: teachers emphasized technological and delivery challenges and self-efficacy in instruction, while students underscored instructional quality, engagement, and the need for supervision, reflecting limited learner autonomy. Practical recommendations include enhancing interactive pedagogies, strengthening social presence, aligning course content with student interests and current trends, improving platform functionality and stability, and incorporating structured supports (e.g., previews, in-class quizzes). Future research should employ broader, more diverse samples across contexts and cultures, examine interrelationships among the nine factors longitudinally, and focus on interventions to develop students’ self-regulated learning and motivation for sustained online learning success.

- Single-institution sample (one comprehensive university in China) limits generalizability across contexts and cultures. - Modest sample sizes (46 students, 18 teachers) and convenience sampling may introduce selection bias. - Reliance on qualitative methods (reflective diaries and interviews) without complementary quantitative or mixed-methods measures may constrain triangulation and depth of inference. - Potential influence of post-pandemic transitional conditions (resumption of face-to-face learning) on retrospective perceptions.