Should Laptops Be Allowed in the Classroom? Two Viewpoints
Bibliographic record
Abstract
This Point/Counterpoint article discusses the pros and cons of deploying one aspect of instructional technology in dental education: the use of laptops in the classroom. Two opposing viewpoints, written by different authors, evaluate the arguments. Viewpoint 1 argues that laptops in classrooms can be a catalyst for rapid curricular advancement and prepare dental graduates for the digital age of dentistry. As dental education is not limited to textual information, but includes skill development in spatial relationships and hands-on training, technology can play a transformative role in students' learning. Carefully implemented instructional technology can enhance student motivation when it transforms students from being the objects of teaching to the subjects of learning. Ubiquitous access to educational material allows for just-in-time learning and can overcome organizational barriers when, for instance, introducing interprofessional education. Viewpoint 2 argues that, in spite of widespread agreement that instructional technology leads to curricular innovation, the notion of the use of laptops in classrooms needs to be deconstructed and rethought when effective learning outcomes are sought. Analyzing the purpose, pedagogy, and learning product while applying lessons learned from K-12 implementation leads to a more complex picture of laptop integration in dental classrooms and forms the basis for questioning the value of such usage. For laptop use to contribute to student learning, rather than simply providing opportunity for students to take notes and access the Internet during class, this viewpoint emphasizes that dental educators need to think carefully about the purpose of this technology and to develop appropriate pedagogical strategies to achieve their objectives. The two viewpoints agree that significant faculty development efforts should precede any introduction of technology into the educational process and that technology alone cannot change education. While the first viewpoint emphasizes the pivotal role of technology in bringing dental education into the contemporary digital world, the second viewpoint focuses on challenges surrounding laptop usage in the classroom including the alignment of instructional methods with learning objectives.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".