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Record W2613583126

This Way or That Way: A Multiple Intelligences Approach to Differentiated Instruction

2017· article· en· W2613583126 on OpenAlexaboutno aff
Kaylin Martin

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsDifferentiated instructionTheory of multiple intelligencesMathematics educationComputer scienceIndividualized instructionPedagogyTeaching methodSociologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0450.000

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.

Opus teacher head0.094
GPT teacher head0.320
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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