RESEARCH SECTION: Curriculum integration and at‐risk students: a Canadian case study examining student learning and motivation
Bibliographic record
Abstract
The combining of subject areas or disciplines, referred to in this article as curriculum integration, has been recognised as being linked to high levels of student motivation and learning. Sheryl MacMath of the University of Toronto, Jillian Roberts of the University of Victoria, and John Wallace and Xiaohong Chi of the University of Toronto discuss the findings of their case study (n = 23 students) based in a Canadian secondary school where an integrated unit on energy was taught to pupils identified as being ‘at risk’ of not completing high school. Teacher and student interviews, classroom observations and surveys were used in the case study to investigate student motivation and learning. Results from the study illustrate that students experienced higher levels of motivation and academic success compared to work on previous units. The authors explore how higher levels of student self‐efficacy were also recorded due to the repetition of content in different classrooms and across different contexts. The authors argue that further research in this area should examine more than student learning and motivation and highlights the need to focus specifically on opportunities for successful academic experiences where student efficacy is increased.
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 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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".