Navigating Online Teaching Challenges: Best Practices and Institutional Recommendations for Faculty Support
Bibliographic record
Abstract
The expansion of online education has transformed higher education, requiring faculty to adapt to new teaching methodologies, technological tools, and institutional policies. This study examines faculty experiences with online teaching, focusing on training, workload, compensation, intellectual property concerns, student engagement, and institutional support. A mixed-methods approach was employed, including survey responses from 40 faculty members and in-depth interviews with 15 participants. Findings reveal that while 72% of faculty found institutional training moderately effective, many preferred peer mentorship and hands-on workshops over traditional professional development sessions. Faculty reported that online course development required significantly more effort than in-person instruction, with 80% spending over 100 hours on initial course design. Compensation disparities were evident, as 40% of faculty received no additional pay or course releases for online course development. Intellectual property concerns emerged, with 45% of faculty uncertain about course ownership policies. Engagement challenges were widely reported, with 70% of faculty struggling to maintain student participation, particularly in asynchronous courses. Faculty identified the digital divide as a persistent barrier to student success. This study underscores the need for institutions to improve faculty training models, establish equitable compensation structures, clarify intellectual property policies, and implement targeted student engagement strategies. Strengthening collaboration between faculty and instructional designers and offering more flexible synchronous and asynchronous learning policies may enhance faculty and student experiences. Future research should explore long-term faculty experiences and institutional approaches to sustainable online education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".