Motivation and Performance of First-year Students
Bibliographic record
Abstract
Adjusting to academic life and managing to perform well at university is challenging for any first-year student. One of the keys to study success is motivation. In line with the social cognitive approach, two motivational constructs are considered: self-efficacy and attribution. Previous studies predominantly took a ‘snapshot’ of first year students' motivation, thereby ignoring the fact that students re-evaluate their self-efficacy as they experience success and failure over time. It is believed that a better understanding of such changes might inform targeted interventions. This case study investigated the development of self-efficacy beliefs and attribution among first-year students in an Economics undergraduate program. One hundred and four students completed three questionnaires at the start of their first academic year, two months later and after they received the results of their first semester exams. Repeated multivariate tests were conducted in order to analyse significant differences in self-efficacy and attribution scores over time. The results suggest that unsuccessful students hold unrealistic self-efficacy beliefs about courses that are new to them. Furthermore, attributions were dependent on the course involved and on students’ exam results. As a consequence, it is suggested to organize early detection and to provide feedback in order to render these beliefs more truthful.
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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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".