Love vs Learning: Examining the Negative Psychological Effects of Romantic Distraction on Academic Performance
Bibliographic record
Abstract
This study examines the impact of romantic distraction on students' academic performance, analyzing how emotional and cognitive engagement in romantic relationships interferes with academic success—a quantitative, cross-sectional research design utilized survey data from 300 university students. Pearson correlation, regression analysis, and ANOVA were conducted to assess the relationship between romantic distraction and GPA. The results indicate a strong negative correlation (r = -0.89, p < 0.001) between romantic distraction and academic performance. Regression analysis confirms that romantic distraction significantly predicts GPA (β = -0.465, p < 0.001), accounting for 78.6% of the variance. ANOVA results suggest that relationship status alone does not substantially impact academic outcomes (F = 1.93, p = 0.165). The findings highlight the need for universities to implement student support programs that enhance self-regulation skills, enabling students to manage personal relationships while maintaining academic focus. This study provides empirical evidence supporting Self Regulation Theory in an academic setting, emphasizing the cognitive burden imposed by romantic involvement and its impact on academic success. Future research should explore moderating factors such as emotional intelligence and time management strategies to mitigate the adverse effects of romantic distraction.
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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 | 0.003 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".