Using Technology in Secondary Education to Support Engagement and Learning: Students’ Perspectives
Bibliographic record
Abstract
When examining the use of technology in secondary school classes, students’ perspectives have been little investigated in the scientific literature, notwithstanding the importance of their perceptions on how technology benefits their engagement and learning. The objective of this study was to investigate how secondary school students perceive the use of technology in their classes and how it supports their engagement and learning, as well as related success factors. The study followed a descriptive and qualitative research design, through semi-structured individual interviews with 40 students enrolled in 16 different secondary schools in Quebec (Canada). Data were analyzed using a general inductive approach with the aim of meeting the overall objective of this study, while categorizing the main uses of technology in secondary school classes according to the Interactive-Constructive-Active-Passive (ICAP) framework. Our findings suggest that there are technology uses promoting student engagement and learning in each mode of the ICAP framework, depending on the specific context. Students view technologies as tools whose usefulness depends on their respective affordances in different teaching and learning situations. The findings also suggest that students need to be educated in matters of digital technology and choices need to be provided regarding the use of technology or paper and pencil to overcome barriers to their engagement and learning.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".