Implementing Technology in the Classroom: Assessing Teachers’ Needs Through the Use of a Just-In-Time Support System
Bibliographic record
Abstract
Given the importance of computer technology in classrooms today, it is crucial to identify the types of supports that will facilitate teachers’ effective implementation of technology. Ten teachers (four kindergarten, four grade one, and two grade one/two) received just-in-time support while introducing a reading software program in their class. An additional 12 teachers (four kindergarten, seven grade one, and one grade two) were exposed to the software, but did not receive just-in-time support. Both quantitative and qualitative analyses of instructional sessions were conducted in order to determine the kinds of support that teachers required throughout the intervention. Results provided an in-depth look at how the software was integrated within the classrooms. Analysis of the just-in-time support indicated that the greatest number of support requests pertained to computer software related issues, followed by computer hardware related issues, and a smaller number of requests for support regarding classroom management issues, reading related issues, and “other” issues. The greatest level of support was required at the initial stage of implementation, with the number of support requests declining over time; however, the types of support requested did not differ across the stages of implementation. Outcomes based on teachers’ self-report responses suggested no significant differences between teachers in the just-in-time support and minimal support only control conditions with respect to computer use, comfort with computers, integration, views on computers, and views on the software program specifically. Student performance indicated that the software program was successful in facilitating the development of reading and pre-reading skills.\nResults provide a summary of the kinds of supports required of teachers when planning and implementing a new software program. This study provides instruction for future training in order to ease the transition to computer based 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".