Information and Communication Technologies: Views of Canadian College Students and “Excellent” Professors
Bibliographic record
Abstract
We explored students’ perspectives about their professors’ use of information and communication technologies (ICTs) and compared these to the views of professors deemed by their students to be excellent in their use of ICTs. 311 students completed an online questionnaire and nominated up to three of their professors who used technology in a way that worked well for them. We conducted semi-structured interviews with 114 of the nominated professors, who also completed a checklist of technologies used in their teaching. There are some technologies that students said worked well for them that not many professors used in their teaching, such as online tests / quizzes, podcasts, and clickers. However, there were some technologies that both students and professors agreed did not facilitate learning, such as digital text books, blogs and chat rooms. Finally, there was also agreement among professors and students about technologies that did help with learning, such as e-mails, videos and online submission of assignments.Both student and professor perspectives need to be considered when evaluating what technologies work in teaching. Future research should examine why students prefer certain technologies. In addition, reasons for the discrepancies between professors and student views needs further investigation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".