Seeing beyond variables: applying a person-centered approach to identifying regulation strategy profiles among Finnish preclinical medical and dental students
Bibliographic record
Abstract
BACKGROUND: High-quality learning during medical school and beyond requires appropriate study strategies and taking responsibility for one's studies, thus self-regulation of one's learning. In contrast to traditional studies focusing on a variable-centered approach, a person-centered approach to regulation strategies was utilized. METHODS: The participants were 162 Finnish medical and dental students who answered the regulation scale of the Inventory of Learning Styles at three measurement points. First, the functionality of the scale was analyzed in Finnish medical education context. Latent profile analyses were used to examine regulation strategy profiles. Last, the connections of these profiles with the study success were investigated. RESULTS: The analyses yielded a three-factor solution, which was reliable across time. Four profiles of regulation strategies were identified and they were found to be connected to study success: Students with the lowest self-regulation and increasing lack of regulation performed worse than the other groups. CONCLUSION: The use of a person-centered approach along with variable-centered approach increases understanding of the complex nature of learning in higher education. Person-centered approach could be used as a tool for supporting student learning and to help early diagnosing of learning difficulties, since it enables individualization of students with different regulation strategy profiles.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".