Perspectives from Students: How to Tame the Chaos and Harness the Power of Technology for Learning
Bibliographic record
Abstract
Technology continues to form an important part of the educational landscape, although the value of portable devices as learning tools is still being explored and debated. In light of the technology-based teaching methods suddenly brought into effect in response to the COVID-19 pandemic, the deliberate use of technology for learning is increasingly significant. The purpose of this article is to highlight student perspectives of learning with portable devices to inform the use of portable technology in the Canadian school system going forward. To gather student perceptions, the research team surveyed 704 students in grades 6 to 9 about their use of iPads in the classroom during a 1:1 technology initiative. While students were enthusiastic about the presence of portable technology, they also shared mixed feelings about the use of such technology as a learning tool. Key themes fell into three categories—engagement, inclusivity, and learning—as students shared their insight into the academic, social, and physical barriers that exist as a result of the technology. In the discussion, we identify lessons learned, especially in the area of self-regulation, and make recommendations on how to harness the power of this multi-faceted learning tool and minimize the chaos it can create when not utilized deliberately and carefully.
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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.012 |
| 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".