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Record W3013990533 · doi:10.1037/edu0000479

Understanding the relation between boredom and academic performance in postsecondary students.

2020· article· en· W3013990533 on OpenAlexaff
Jennifer A. Hunter, John D. Eastwood

Bibliographic record

VenueJournal of Educational Psychology · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsYork University
Fundersnot available
KeywordsBoredomPsychologyPostsecondary educationRelation (database)Mathematics educationAcademic achievementHigher educationPedagogySocial psychology

Abstract

fetched live from OpenAlex

Prior research has proposed that boredom and academic performance are reciprocally causal of one another. The present study sought to better understand the relationship between boredom and academic performance by, for the first time: distinguishing between boredom proneness, state boredom, and judgments of task boringness; conducting experiments in the laboratory where extraneous variables could be better controlled; and using experimental manipulation for causal conclusions. Study 1 examined the naturally occurring relationship between state boredom and performance on a word list recall task in the laboratory. Study 2 tested whether manipulating state boredom resulted in changes in word list recall, and Study 3 tested whether manipulating perceived word list recall resulted in changes in state boredom. State boredom and performance had a reciprocal relationship only for participants who memorized “interesting” word lists and only after repeated trials (Study 1); trait boredom predicted performance but state boredom did not (Study 2); and manipulating perceptions of performance had no effect on state boredom but did affect participants’ judgments of how boring the learning task was (Study 3). Thus, students seem to be able to weather changes in performance or boredom in the moment without one affecting the other. It is when the situation persists, or state boredom crystallizes into the form of a judgment (course-related boredom) or way of being (trait boredom), that problems emerge. Guidelines for educators are offered. Future research work is proposed, most pressingly work to replicate the current findings with more complex learning tasks. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.185

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 head0.244
GPT teacher head0.417
Teacher spread0.173 · 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.

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

Citations11
Published2020
Admission routes1
Has abstractyes

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