Using ICT-based Instructional Technologies to Teach Science: Perspectives from Teachers in Trinidad and Tobago.
Bibliographic record
Abstract
The purpose of this study was to investigate how science teachers in Trinidad and Tobago use ICT-based instructional technologies in classroom science teaching. The participants were 30 secondary school science teachers who completed their Postgraduate Diploma in Education within the last 2 years from the University of the West Indies in Trinidad and Tobago. The teachers were asked to prepare lesson plans which demonstrate their use of instructional technologies to teach science topics within their term’s schemes of work. They were subsequently asked to explain their reasons for using the selected instructional technologies. The findings revealed that PowerPoint was the most widely used ICT-based instructional technology in the lesson plans analyzed. Animations and hands-on practical activities were the least used ICT-based instructional technologies. Virtual labs, computer-aided simulations and smartboards, were other ICT-based instructional technologies used by a few teachers. Textbooks and whiteboards were the non-ICT-based instructional technologies teachers used.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".