Students Motivations For Voluntary Remedial Learning In High School
Bibliographic record
Abstract
Most high schools offer remedial learning sessions to their students; however, very little is known about the perception of these activities, especially with regards to the students motivations. In order to gain insights into both topics, an exploratory study was conducted within a sample of 1388 high school students in the Province of Quebec (Canada). Participants were selected on the basis of their voluntary participation or non-participation in remedial learning sessions held at school. School motivation was measured with a questionnaire based on Deci and Ryans SDT (Self Determination Theory); perceptions were gathered and measured with open-ended and multiple choice items. Quantitative results indicated that participants of remedial learning sessions were more strongly motivated towards school. No significant relationships were found between attendance and grades, with similar proportions of high, average and below average participating and non-participating students; however, among participants, girls outnumbered boys by 2:1. Two main categories emerged from qualitative analyses: remedial learning can be seen as: a) an instrumental support linked to the subject matter; or b) a relational support which fosters better learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".