Listening to Teacher Candidates and Teacher Educators: Revising Educational Technology Courses in a Canadian Teacher Education Program
Bibliographic record
Abstract
Educational technology courses in teacher education programs are critical as they equip teacher candidates (TCs) with the necessary knowledge, skills, and attitudes to incorporate technology into their teaching. Given the rapid technological advancements, it is essential that these courses implement research-informed and current practices to promote TCs’ preparedness in using educational technologies. Accordingly, the instructional team of the educational technology course in the teacher education program at Brock University—Canada initiated a rigorous process to revise this course. This process included exploring the evolving needs of TCs and their feedback on previous course iterations and consulting with teacher educators who lead other courses in the program to ensure curriculum alignment. This paper aims to achieve the following: (1) document the course revision process, with a focus on how TCs and teacher educators were involved; (2) explore TCs’ evolving needs in educational technology; (3) present the revised educational technology course. The paper presents the findings of a survey administered to 116 TCs, focus groups with TCs, and a survey administered to 14 teacher educators. Findings from TCs’ survey showed high levels of their self-assessment of digital competence and intention to use technology in their future teaching. However, TCs believed that they had not received adequate training to do so, suggesting improvements in the design and delivery of the educational technology course. Drawing on the Voice Theory and instructional design models relevant to educational technology courses, this research offers valuable insights into TCs’ digital competence, the scholarship of teaching and learning, and universities’ response to change. Implications for teacher education research and practice are also discussed based on the course revisions and the adopted process.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".