Learners’ goal profiles and their learning patterns over an academic year
Bibliographic record
Abstract
The present study aimed to examine distance learners’ goal profiles and their contrasting patterns of learning and achievements at three different points during an academic year, i.e. in the beginning of the course in relation to learners’ general orientations to learning, at the middle of the course in relation to learners’ completion of an assignment, and towards the end of the course in relation to learners’ preparation for course examination. Two hundred seventy-six adult distance learners completed three survey questionnaires that assessed their motivation and learning at these three learning points. Using person-centred analytical procedures, this study located four groups of learners based on different combinations of mastery and performance-approach goals. MANOVA results have shown that multiple-goal learners (High mastery/High performance, HH) who held strong mastery and performance-approach goals used more deep and regulatory strategies and showed a higher level of learning interest across three waves of surveys than did those focusing solely on mastery (HL) or performance-approach goals (LH). However, the multiple-goal learners did not have better achievement levels compared to those focusing solely on mastery goals (HL). Given that multiple goal learners learnt with a more engaged pattern, it is less likely that these motivated learners will drop out of distance learning courses and programs. Future studies should explore how these goals can be promoted simultaneously in distance learning.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".