Exploring Online Learning: Student Feedback on Factors for Effective Online Learning Post COVID-19
Bibliographic record
Abstract
This paper reports on a study of students’ views of online learning, the obstacles and challenges they experience, as well as their suggestions for improving the online learning model. The COVID-19 pandemic resulted in an unprecedented move for traditional post-secondary institutions from mostly in-person learning pre-pandemic to a quick transition to online learning to comply with pandemic-related restrictions. Despite years of progressive growth in the use of the online learning model before COVID, for many post-secondary students, this emergency remote teaching was their first exposure to an entirely online learning experience. Students who might otherwise have not selected to study online were suddenly obligated to do so. Yet, there is an associated behavioural and technological learning curve for students to overcome to be comfortable and academically succeed with online learning. The goal of this study is to discern students’ experiences, motivations, benefits, and barriers with online education and how this shapes their intention of continuing or not with online learning. To investigate this, we conducted a survey of 177 Canadian post-secondary using open-ended questions. Qualitative data analysis was used to arrive at the primary findings related to themes of 1) technology learning curve, 2) accessibility, 3) engagement, 4) agency, 5) distraction and procrastination, 6) support, and 7) isolation. These findings can aid universities and colleges to understand student views on online learning and, hence, to design and support this form of education more effectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".