WHAT WE NEED FOR ENCODING OF MEMORY AND EMOTIONAL RECONSOLIDATION
Bibliographic record
Abstract
BACKGROUND: It is known that an interactive design and good participants' involvement strengthens the motivation to engage in learning processes. Previous research suggests attitude-behaviour consistency with relevance of subjective meaning and interest in learning. This observational study aims to measure the attitude of medical students. METHODS: The connotative meaning and perception of e-learning were explored. A semantic differential scale was given to all students (N=328) of a case-based blended-learning (CBBL) course, 296 medical students were included in this study. RESULTS: The online-survey completion rate was 100%. An exploratory principal components analysis with varimax rotation was performed. Five components could be extracted that explained 47.21% of the total variance. The five components are best described by the following adjectives taken from the item pool: "soft, emotional, playful", "clear and organised", "vigorous and serious", "vivid and outgoing", "economical and introverted". An additional qualitative analysis revealed relevant positive connotations ascribed to e-learning by the students: freedom in time and space for learning, interdisciplinary approach and communication, playfulness and clear, structured procedure. CONCLUSION: Our study demonstrated that a specific set of aspects is essential for students to feel comfortable and affect-cognitively engaged to learn and gain the best exam grades.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".