Implementation of Student Course Evaluation: Pandemic Impact on the Non-Constraint Engagement (NCE) Model
Bibliographic record
Abstract
Traditionally, students’ feedback in the form of Student Course Evaluation (SCE) has been paper-based and made mandatory on students. Even when SCE is made online and voluntary, one major obstacle is low response rate. The Non-Constraint Engagement (NCE) Model is a newly introduced method in enhancing SCE in our institution, that attempts to overcome the common limitations of SCE. This study aims to examine the stability and sustainability of the NCE model implementation before, during and after the peak of the first wave of the COVID-19 pandemic in the Canadian University Dubai (CUD). The NCE Model was initially piloted between 2014 and 2015 and was found to be effective. To test its feasibility and sustainability over a longer period of time, an SCE exercise was implemented among undergraduate students across four faculties. For analysis, we used SCE data from 2015 to 2021. Evaluations were performed via Moodle in the online Learning Management System (LMS) before mid-term of each semester. There were two domains of SCE: course rating and instructor rating. Results showed acceptable and stable response rates, despite SCE being voluntary. The COVID-19 pandemic did not cause a fall in student participation. Instead, following the outbreak arrival, there was a sharp increase in SCE response rates. Similarly, students’ rating on their courses and instructors remains high despite the massive, sudden change from physical to online instruction. This study introduces a new approach, the NCE model, which can be tested in other educational settings to enhance SCE.
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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 | 0.035 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".