Joyful learning: using active and collaborative strategies in the inclusive classroom
Bibliographic record
Abstract
In today's classroom, teachers are required to present curriculum in such a way as to ensure that all learners in the class meet the prescribed learning outcomes. As the range of diversity of the needs of students increase due to larger class sizes, class composition and lack of resource staff, the classroom teacher is faced with the challenge of meeting the needs of all learners. The purpose of this research was to plan, implement and evaluate active and collaborative learning strategies to engage all students within the classroom, with an emphasis on identified challenged students. The question is: Through implementation of a variety of active and collaborative strategies, will the participation and on-task behaviour of students increase? This research was completed in two phases. During Phase I in which active learning strategies were integrated into lesson planning for a four week period, indications of increased student engagement was observed. In Phase 2, both active learning strategies and cooperative learning strategies were integrated during planning for Social Studies and Science classes. Observations and test results during the three month term suggest that integration of these strategies did indeed increase on-task behaviour of learners during the activities. However, the main benefit of the strategies implemented was that the activities provided the teacher the opportunity to observe, assist and assess the learning of all students within the classroom. --P. ii.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".