MétaCan
Menu
Back to cohort
Record W2652242640 · doi:10.3389/feduc.2017.00032

On the Possibility of Preferred Performance Styles and Their Link to Learning Styles

2017· article· en· W2652242640 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Education · 2017
Typearticle
Languageen
FieldPsychology
TopicLearning Styles and Cognitive Differences
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLink (geometry)Learning stylesComputer scienceMathematics educationPsychologyComputer network

Abstract

fetched live from OpenAlex

The existence of individual learnings styles is a hot topic in contemporary education theory and practice. There is an on-going debate on whether learners benefit from teaching methods that are tailored to their perceived learning styles. The majority of educators believe that such styles do exist and a striving industry benefits from this concept. However, experts conclude that the evidence on this matter provides no support for the utility of learning styles. In this manuscript we briefly review this debatable topic and focus on a key flaw in the analyses of learning styles. We indicate that the current models for the evaluation of such styles call for different instruction methods and a uniform test. However, the possibility that learner styles correlate to different performance in different types of examination has not been experimentally addressed. We discuss this discrepancy and propose methodologies that can identify such preferred performance styles.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.239

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.307
Teacher spread0.286 · 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