L’usage des technologies de l’information et de la communication par les professeurs de l’Université du Québec à Trois-Rivières lors de leurs activités d’enseignement
Bibliographic record
Abstract
As part of this research study we were interested in the analysis of the use of ICTs with a specific emphasis on the different uses of ICTs by teachers while teaching and in their exchanges with international students.To collect information on teachers' perceptions regarding the contribution of ICTs in their teaching activities, we used asurvey.The main objective of this research is to understand the perception of teachers regarding the use of ICTs during their teaching activities and during their interactions with international students.In order to do that, we will identify the technologies used by teachers, present the constraints that teachers face in the use of ICTs, and analyze the impact of the use of ICTs on teaching activities and interactions between teachers and international students.Our results highlight the crucial role of ICTs in the adaptation of international students, therefore it is essential to recognize the need for better support and training adapted to the needs of teachers and international students.An In-depth research of how teachers and educators can make the most of technology is needed.This could imply deeper training strategies for teachers on the effective use of digital tools, as well as innovative teaching approaches that would balance both digital and traditional methods.In conclusion, despite the limitations of this research, our results provide new information and contribute to the debate on the use of ICTs in teaching.They highlight the importance of taking into account the different uses of ICTs by teachers and the adaptation of international students to these new technologies.In the future, it would be interesting to study other countries or organizations that have adopted balanced approaches to the integration of technologies in education.This would allow lessons to be learned and more in-depth recommendations to be made for an effective use of ICTs that truly benefits teachers and international students in their teaching and learning activities.As part of this research, we seek to answer the following questions: What is the impact of the use of ICTs by teachers during their teaching activities?What ICTs are used and how are they integrated into these activities?What processes should be put in place by university managers?
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".