Classroom Research in Large Cohorts: An Innovative Approach Based on Questionnaires and Scholarship of Teaching and Learning on Multiple-Intelligences
Bibliographic record
Abstract
In this work we focussed on assessment and quantification of students’ prior knowledge at the start of their classes and the learning and teaching feedback given by them after their classes. Using questionnaires, we collected data on prior-knowledge/Student Learning Abilities – SLAs and, students’ performance/Learning Outcomes – LOs. Our analysis shows that typically, in any classroom the SLAs follow a non-linear trend. This pattern, identified in group learning, requires proportionally distributed intervention by staff, with more support to those in need of help with learning. We show that the above approach, underpinned by an application of Multiple Intelligences and e-learning facilities, supports weaker students and helps them to achieve higher pass percentages and better LOs. This innovation in terms of evidence-based identification of need for support and selective intervention helps in optimal use of staff time and effort as compared to a one-method-fits all approach to learner development and academic achievement.
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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 | 0.101 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".