Smartphone Addiction among University Students and Its Relationship with Academic Performance
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Smartphone use is almost universally relied on among college students. Whether smartphone addiction among college students has a negative predictive effect on academic performance is hardly studied. Previous research found an apparent association between smartphone use and academic achievement partly explained by the nature of the task the student is engaged in when using a smartphone. This study aims to assess the relationship between smartphone addiction and students’ academic performance controlling for important potential confounding variables.METHODS: A sample of 688 undergraduate students was randomly selected from Notre Dame University, Lebanon. Students were asked to fill out a questionnaire that included a) questions on variables related to socio-demographics, academics, smartphone use, and lifestyle behaviors; and b) a 26-item Smartphone Addiction Inventory (SPAI) Scale. Multiple logistic regression was performed to assess the independent association between smartphone addiction and cumulative grade point average (GPA).RESULTS: 49% reported smartphone use for at least 5 hours during a weekday. Controlling for confounding effects in the model, the association between total SPAI score and GPA did not reach statistical significance, whereas alcohol drinking (OR= 2.10, p=0.026), age at first use of smartphone (OR=1.20, p=0.042), use of smartphone for study-related purposes (OR=0.31, p=0.000), class (OR=0.35 (senior vs. sophomore standing), p=0.024), and faculty (ORs of 0.38 and 0.35 (engineering and humanities, respectively, vs. business students)) were found to be independent predictors of reporting a GPA of < 3.CONCLUSION: Findings from our study can be used to better inform college administrators and faculty about most-at- risk groups of students who shall be targeted in any intervention designed to enhance low academic performance.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it