Cultivating Classroom Connectivity: Navigating the Role of Cell Phones in Education
Bibliographic record
Abstract
The proliferation of mobile technology, particularly cell phones, in educational settings has sparked substantial debate in recent years. Academic studies have extensively examined the challenges associated with student cell phone use during class time, highlighting issues such as decreased learning, achievement, and participation. Despite the negative impacts, students express a strong desire to retain the right to use their devices, underscoring the need for policies that consider student perspectives. Research suggests that involving students in the establishment of cell phone policies can lead to increased compliance and engagement. Educators play a crucial role in navigating the role of cell phones in education by fostering open communication, collaboration and empowering students to make responsible technology decisions. Strategies such as the 3Cs - construct, collaborate, and create - serve as effective means to engage students in meaningful learning activities while mitigating distractions. Additionally, research reveals that cell phones also have productive uses for conducting research, communicating, and accessing information. By embracing technology as a tool for learning and hearing students’ perspectives, educators can create inclusive and supportive learning environments that maximize the benefits of cell phones while addressing individual student needs and concerns.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".