Effect of Optional Assessments on Student Engagement, Learning Approach, Stress, and Perceptions of Online Learning during COVID-19
Bibliographic record
Abstract
Due to the COVID-19 pandemic, courses were forced into an online format as universities paused in-person learning and consequently, students were required to adjust to online learning. The objective of the current study was to determine the effect of optional assessments designed to promote engagement in a fourth-year asynchronous online nutritional science course. Seven optional engagement assessments were assigned and students’ stress levels, learning approach, and perceptions of online learning were assessed via surveys at the start and end of the semester. A total of 79.8% (n=210) students completed all seven optional engagement assessments. Further, 56.6% (n=149) reported that the assessments helped them feel more engaged with course content. Perceived stress levels did not change during the semester. Surface learning approach scores increased from the beginning to end of the semester, whereas deep learning approach scores remained unchanged. Surface learning scores were positively correlated with experiencing more stress from organizing and maintaining a schedule, more stress associated with time management, and finding time management more challenging. Deep learning approaches were positively correlated with students feeling engaged in the course, comprehending the course content, and stimulating new ideas/perspectives. Collectively, this data demonstrates that optional engagement assessments can improve student perceptions of online learning, however, these outcomes are related to students’ use of surface versus deep learning approaches.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".