A Quantitative and Qualitative Inquiry into Future Teachers’ Use of Information and Communications Technology to Develop Students’ Information Literacy Skills | Enquête quantitative et qualitative auprès de futurs enseignants portant sur l’utilisation des technologies de l’information et de la communication pour développer les compétences informationnelles des élèves
Bibliographic record
Abstract
This study aims to understand how preservice programs prepare future teachers to use ICT to develop students’ information literacy skills. A survey was conducted from January 2014 through May 2014 with 413 future teachers in four French Canadian universities. In the spring of 2015, qualitative data were also collected from 48 students in their final year of an initial teacher training program. Our findings suggest that although future teachers receive formal ICT training as part of their program, information literacy is not formally addressed. Nevertheless, information literacy is perceived to be an important skill. In addition to a lack of formal training, future teachers perceive that barriers such as time constraints and lack of access to necessary technologies in the classroom will prevent them from helping students develop information literacy skills. Based on these results, we propose some practical implications and recommendations for preservice programs and education policy makers.Cette étude vise à mieux comprendre comment la formation initiale en enseignement prépare les futurs enseignants à utiliser les TIC pour développer les compétences informationnelles des élèves. Dans un premier temps, une enquête a été réalisée entre janvier et mai 2014 auprès de 413 futurs enseignants dans quatre universités québécoises. Dans un second temps, au printemps 2015, des données qualitatives ont été recueillies auprès de 48 futurs enseignants au cours du dernier trimestre de leur formation initiale. Nos analyses suggèrent que si les futurs enseignants reçoivent une formation à l’utilisation pédagogique des TIC, les compétences informationnelles ne sont pas explicitement abordées, et ce, bien qu’elles soient perçues comme essentielles. De plus, les contraintes de temps et l’accessibilité aux outils informatiques sont pressenties comme des défis importants. Sur la base des suggestions des participants, nous dégageons des retombées pratiques pour la formation initiale et pour les détenteurs d’enjeux en éducation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".