Bibliographic record
Abstract
With the increasingly frequent appearance of mobile phones in college classrooms, there have been growing concerns regarding their negative aspects including distractive off-task multitasking. In this work, we design and evaluate Let’s FOCUS, a software-based intervention service that assists college students in self-regulating their mobile phone use in classrooms. Our preliminary survey study (with 47 professors and 283 students) reveals that it is critical to encourage voluntary participation by framing intervention as a learning tool and to raise awareness regarding appropriate mobile phone usage by establishing social norms in colleges. Let’s FOCUS introduces a virtual limiting space for each class (or a virtual classroom) where the students can explicitly restrict their mobile phone use voluntarily. Furthermore, it promotes students’ willing participation by leveraging social facilitation and context-aware reminders associated with virtual classrooms. We conducted a campus-wide campaign for approximately six weeks to evaluate the feasibility of the proposed approach. The results confirm that 379 students used the app to limit 9,335 hours of mobile phone usage over 233 classrooms. Let’s FOCUS was used in diverse learning contexts and for different purposes and its social learning and context-awareness features significantly motivated prolonged participation. We present the design considerations of software-based intervention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.021 |
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".