Personal-professional Interconnections: Contextualizing Teachers' Use of Information and Communication Technologies in the Classroom
Bibliographic record
Abstract
Teachers' use of information and communication technologies (ICT) in the classroom has largely been studied to date with a focus on what is lacking. An important aspect that seems to have been understudied is how teachers' own histories and experiences of ICT connects to their teaching practices - in essence, focussing on what is 'present' and not what is 'missing'. This study examines five teachers working in the same school, but working very differently with ICT in their classrooms. The narratives of their lived experiences highlight the acquisition of their beliefs, attitudes and their in situ behaviours about the role of ICT in the classroom. A critical look at the factors responsible for shaping such behaviours suggests the value of understanding individuals' experiences when planning for technology-utilization, rather than prescribing a "teacher-proof" (Connelly & Clandinin, 1988) approach to technology uptake.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".