The Role of Prior Online Learning Experience in Student Learning under ERT – A Comparative Study
Bibliographic record
Abstract
The pandemic provided great research opportunities in learning/teaching. How was students’ learning experience under emergency remote teaching (ERT)? The purposes of this study are to explore students’ learning experience, and to observe any differences under two lenses – internationalization and prior online learning. An online study was conducted in the Management/Business programs of a major Canadian research university and four Russian universities in 2021. A total of 683 students participated in this study, two-thirds of which were Canadian and one-third were Russian. The degree of internationalization (e.g. international students) is higher in Canadian universities, while a higher percentage of Russian students had prior online learning experience before the pandemic in this study. The research findings showed that the degree of internationalization did not play a significant role in the difference of student learning; while the prior learning experience played a crucial role under ERT. Russian students adjusted better to the new learning mode, reported a more positive view of online learning, had a better learning experience and performance, and had a preference to continue with online learning post-pandemic. This research sheds light on students’ preference for online learning that was influenced by prior exposure to online learning.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".