Physical and psychosocial aspects of the learning environment in information technology rich classrooms
Bibliographic record
Abstract
This paper reports on a study of environments in emerging Internet classrooms. At issue for this study is to what extent these 'technological classrooms' are providing a positive learning environment for students. To investigate this issue, this study involved an evaluation of the physical and psychosocial environments in computerized school settings through a combination of questionnaires and inventories that were later cross-referenced to case studies on a subset of these classrooms. Data were obtained from a series of physical evaluations of 43 settings in 24 school locations in British Columbia, Canada and Western Australia. Evaluations consisted of detailed inventories of the physical environment using the Computerised Classroom Environment Inventory (CCEI): an instrument developed specifically for this study. Data on psychosocial aspects of the environment were obtained with the What is Happening in this Class? (WIHIC) questionnaire administered to 1404 high school students making routine use of these computerized classrooms. Potential deficiencies in the physical environment of these locations included problems with individual workspaces, lighting and air quality, whereas deficiencies in the psychosocial environment were confined to the dimension of Autonomy. Further analysis of these classroom environment data indicated that student Autonomy and Task orientation were independently associated with students' Satisfaction with learning and that many physical (e.g. lighting and workspace dimensions) and psychosocial factors (e.g. students' perceptions of Co-operation and Collaboration) were also associated. The results provide a descriptive account of the learning environment in 'technology-rich' classrooms and, further, indicate that ergonomic guidelines used in the implementation of IT in classrooms may have a positive influence on the learning environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".