Understanding the relation between boredom and academic performance in postsecondary students.
Bibliographic record
Abstract
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)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".