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Record W4391064600 · doi:10.5539/jel.v13n2p1

The Association of Personality Characteristics with Learning Strategy Preferences

2024· article· en· W4391064600 on OpenAlexvenueaboutno aff
Gary J. Conti, Rita C. McNeil

Bibliographic record

VenueJournal of Education and Learning · 2024
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPersonalityPreferenceBig Five personality traitsAssociation (psychology)TraitMetacognitionSocial psychologyCognitive psychologyCognitionDevelopmental psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to describe the association between the learning strategy preference of the learners as identified by Assessing The Learning Strategies of AdultS (ATLAS) and the individual personality traits as defined by the Myers-Briggs Type Indicator (MBTI). The sample was 553 adults in Canada and the United States. Two types of analyses were used to investigate the association between learning strategy preferences and personality traits. First, discriminant analysis explored the interaction of personality traits with the learning strategy preference. Second, analysis of variance measured the association of each personality trait with the learning-strategy-preference groups separately. The findings provided several explicit personality traits associated with each learning-strategy-preference group. These findings support the conclusion that a strong association exists between personality traits and learning-strategy-preference characteristics. Learning strategy preferences and personality traits complement each other. Each clarifies and enriches the other. As a result, teachers have two indicators that can help them personalize the teaching-learning environment for each student. Teachers can use the learning-strategy-group descriptions as guides for organizing each learner's instructional activities and plans and as a cognitive framework for uncovering and monitoring student behaviors and alerting teachers to potential learning difficulties for some students. Students can apply the descriptions of the learning-strategy-preference groups to facilitate self-assessment and metacognition. Theory can be enhanced by considering the two concepts of learning strategy preferences and personality traits coupled and by conducting quantitative and qualitative research to test and expand the generalizability of the learning-strategy-group descriptions. (Permission is granted to use Assessing The Learning Strategies of AdultS and the Personality Identity Estimator in practice and research. Links to printable copies and online completion are appended.)

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.393
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes2
Has abstractyes

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