Problem-based scenarios with laptops: an effective combination for cross-curricular learning in mathematics, science and language
Bibliographic record
Abstract
72 800x600 Normal 0 21 false false false FR-CA X-NONE X-NONE Many educational systems consider using one-to-one access to the laptop as a way to improve teaching and learning. A two-year action research project on the use of laptop computers by New Brunswick (Canada) grade 7 and 8 Francophone students aimed to better understand the impact of laptops on learning. Two problem-based learning (PBL) interdisciplinary scenarios (math, science, language arts) were implemented in eight experimental classes to measure and document students’ actual learning process, particularly in terms of their ability to scientifically investigate authentic problems, to reason mathematically, and to communicate. On-site observations, video-recording, journals, samples of students’ work, and interviews were used to collect qualitative data. Based on our findings, we argue that laptops in and of themselves may not automatically lead to better results on standardized tests, but rather create opportunities to enrich learning with more open-ended, constructive, collaborative, reflective, and cognitively complex learning tasks.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".