Exploring the Predictive Relationship between Course Clarity and Utility Value on Engagement for Online Post-Secondary Students in Canada
Bibliographic record
Abstract
The purpose of this quantitative, predictive, correlational study was to examine how well course clarity and utility value predict student engagement among online learners at a Canadian post-secondary institution. This study is important because it can provide educators and curriculum developers with insights into the factors that positively impact student engagement. Supporting student engagement ensures learners receive a beneficial education and that the institution delivers relevant, engaging programming to retain its students. The sample comprised 68 online post-secondary learners studying at a college in Canada. Data were collected using portions of the Online Learning Climate Scale, the Expectancies and Values in Higher Education Instrument, and the Online Student Engagement Scale, sent via their online learning management system and hosted on the online survey platform Qualtrics. The results of the multiple linear regression analysis indicated a significant predictive relationship between student engagement and course clarity and utility value. These results indicate that course clarity and utility value do impact student engagement scores. It is recommended that further research examine diversity within the demographic sample, compare asynchronous and synchronous online learning environments, and employ a mixed-methods methodology to gain a more comprehensive understanding of online student engagement.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".