Explaining computer use among preservice teachers: towards the development of a richer conceptual model incorporating experience, demographic, motivation, personality, and learning style clusters of variables
Bibliographic record
Abstract
Despite the professional training that North American teachers receive, many believe they are not well prepared to implement computer technology in their classrooms (Industry Canada, 2003; The CEO Forum, 2001). Educational computing research has failed to provide conceptually integrated frameworks and theories that can best predict or explain the factors that facilitate computer use, whether in a computer course or for general purposes. The conceptual framework that emerged in this study incorporated specific determinants of computer use---demographics, experience, learning style, motivation, and personality---for new teachers that represent prominent themes in theories of human motivation and decision making. However, among the twenty-one variables that constituted these five clusters, experience, intrinsic motivation, program of study, gender, familiarity with computer terminology, and educational level were the only significant predictors of computer use. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .Z642. Source: Dissertation Abstracts International, Volume: 67-07, Section: A, page: 2455. Thesis (Ph.D.)--University of Windsor (Canada), 2006.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".