This Way or That Way: A Multiple Intelligences Approach to Differentiated Instruction
Bibliographic record
Abstract
The theory of Multiple Intelligences (MI) was proposed by Howard Gardner in 1983 and has since been recognized as a valuable tool in the world of teaching. Research that has been conducted since its inception has explored how MIs are developed and identified, the social and academic benefits to MI theory, strategies for applying the theory in classrooms, as well as teacher efficacy in using the theory. However, there is currently a lack of data exploring how MI theory and approaches are actually being applied in Ontario classrooms. This qualitative study uses an extensive literature review to discuss existing research, as well as three semi-structured interviews with Ontario teachers from different school boards, grades, and years of teaching experience. The study serves to investigate what teachers in Ontario know about MI theory and how they incorporate it into their differentiated instruction (DI) strategies. Analysis of the data reveals a lack of knowledge of MI theory, a notable focus on MI assessment over instruction, an alarming amount of expressed challenges to applying the theory, as well as a lack of support and resources for further professional development. Most of these findings support the existing literature. This study has significant implications for the academic community regarding student achievement and delivers recommendations for improvements and future research, the key recommendation being more and better professional development opportunities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".