Undergraduate students' motivational profiles before and during the <scp>COVID</scp> ‐19 pandemic: The role of educational climate and trait self‐control
Bibliographic record
Abstract
BACKGROUND: Universities faced important and sudden changes following the lockdown measures imposed during the COVID-19 pandemic. Traditional educational practices were disrupted as campuses were closed while distance learning was hastily adopted. AIMS: This study documents the evolution of university students' autonomous and controlled motivation for their studies following campus closures by relying on a person-centred perspective. More specifically, it examines motivation profiles and their temporal stability across two time points taken before and during the pandemic, while also considering the role of educational climate, trait self-control and control variables (sex and age) as predictors of profile membership. SAMPLE: A total of 1940 university students participated in this study by responding to online questionnaires at two time points, before (Time 1) and after (Time 2) the pandemic. METHODS: We relied on latent profile and latent transition analyses to estimate motivation profiles, their temporal stability and their predictors. RESULTS: A four-profile solution (Self-Determined, Moderately Motivated, Extrinsically Motivated, Amotivated) was selected and replicated at both time points. We observed a low degree of variability in profile membership over time, especially for the Amotivated profile. A need-supportive educational climate and trait self-control consistently predicted a greater likelihood of membership into more adaptative profiles (Self-Determined, Moderately Motivated). CONCLUSIONS: The COVID-19 pandemic did not drastically change the motivational profiles of university students. Nevertheless, educational climate and self-control appeared to 'protect' students against the endorsement of more problematic motivation profiles both before and during the pandemic, making them important targets for intervention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".