The Impact of Achievement Motivation on Project-Based Autonomous Learning —— An Empirical Study on the 2017 NBEPC
Bibliographic record
Abstract
In an era where information and knowledge are updated ever faster, learners’ autonomous learning ability becomes more and more important and is even regarded as one of the key factors to pedagogical success and lifelong learning. While project-based learning is widely adopted in higher education worldwide, learners’ motivation, especially achievement motivation, in adopting autonomous learning strategies to proceed with such kind of projects seems a field relatively less touched. To test the role of achievement motivation in the adoption of autonomous learning strategies in contests, the authors conducted an experiment to 70 participants in 10 contest teams who were involved in the preliminary contest of 2017 NBEPC. Questionnaire survey method was adopted and the result indicates that: 1) teams with high achievement motivation have better application of autonomous learning strategies in the contest; 2) students using more autonomous learning strategies score higher in the contest results; 3) all three phases of autonomous learning have significant relevance with the contest result; 4) all seven types of autonomous learning strategies show significant relevance with the contest result. Despite the limitation of the study, the result is quite significant in learning practice.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".