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Record W2057469192 · doi:10.5539/jel.v3n2p92

Measuring Student Preferences for Stimulus-Response (Rote) Learning

2014· article· en· W2057469192 on OpenAlexvenueno aff
Robert A. Peters, Raymond J. Higbea

Bibliographic record

VenueJournal of Education and Learning · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCreativityGraduate studentsMathematics educationCritical thinkingReplicateStimulus (psychology)Rote learningTeaching methodPedagogySocial psychologyCognitive psychologyCooperative learning

Abstract

fetched live from OpenAlex

The study developed and distributed a survey to measure students’ preference for stimulus-response learning.The responses of undergraduate and graduate students suggest the desire to maximize grades fosters a strongpreference for instructors who tell students what they need to know and exam questions that incorporate termsand keywords similar to those used in course materials. Although graduate students exhibit a strong partiality foradditional elements of stimulus-response learning, they are less likely than undergraduates to prefer courses inwhich complex assignments are accompanied by step-by-step instructions and most of the required readings arecovered by lectures. They also are less prone to focus their exam preparation on items discussed in class. Giventhe students’ predisposition to replicate information and problem solving strategies conveyed to them, thedevelopment of creativity and critical thinking is dependent on students assuming greater responsibility forlearning. Instructional strategies for achieving the outcome are discussed.

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)0.005
metaresearch head score (Gemma)0.017
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 score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.094
GPT teacher head0.437
Teacher spread0.343 · 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".

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Citations1
Published2014
Admission routes1
Has abstractyes

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