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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".