Investigating the Learning Environment in Canadian Mathematics and Science Classrooms in Which Laptop Computers Are Used
Bibliographic record
Abstract
There is increasing pressure to incorporate information technology into schools and increasing interest in evaluating the effects of this technology on students. There has been a growing literature on the assessment of the success of using information technology in schools. This study is timely and potentially valuable because it investigated psychosocial factors in the learning environment where laptop computers are used in the study of mathematics and science. The study combined qualitative and quantitative data collection methods (Tobin & Fraser, 1998) to describe and compare students' perceptions of the actual and preferred learning environments and to explore students' attitudes towards mathematics and science classrooms where laptop computers are used. It has been previously found that positive students' perceptions of their learning environment are linked with their attitude toward and achievement in mathematics and science (Fraser, 1994, 1998) . Of particular interest in our study were the differences between male and female students and between subject disciplines of mathematics and science. Because there has been little research reported on the effect of using laptop computers on students' perceptions of their learning environments, this study pioneered the use and validation of a learning environment instrument in laptop schools in Canada. (Contains 37 references.) (Author/MM) Reproductions supplied by EDRS are the best that can be made from the original document.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".