Digital Literacy Development in Teacher Education: A Case Study
Bibliographic record
Abstract
With increasing technological advancement, developing citizens’ digital literacy is more crucial than ever before in supporting Canada’s societal and economic future. Teachers hold a critical role in fostering their students’ digital literacy development. Using case study methodology, the objective of this research was to gain a deeper understanding from the perspectives of an administrator and five instructors on how pre-service teachers understand and develop digital literacy with the central research question of: How is digital literacy developed within a Design-based Thinking course in a teacher education program? The research question was investigated through collecting data on opportunities in one teacher education program for pre-service teachers in developing digital literacy in a Design-based Thinking course. Data were collected using individual interviews, focus group interviews, and document analysis. The collected data were analyzed through thematic analysis and two cycles of coding to identify emergent themes of participants’ understanding and perceptions of digital literacy development within the context of the Design-based Thinking course within the teacher education program. Four key findings emerged from this research study. First, instructors’ openness (or risk-taking) and modeling the usage of digital technologies in courses within the teacher education program encourage pre-service teachers to use digital technologies. Second, opportunities for feedback in support of pre-service teachers’ digital literacy development can be provided through learning tasks and assessments. Third, teacher education programs need to consider establishing program goals focused on developing digital literacy and provide professional development opportunities to support instructors’ in designing and facilitating pre-service teachers’ digital literacy development. Fourth, instructors need to have an understanding of digital literacy to design authentic and embedded learning tasks for pre-service teachers focused on supporting digital literacy development.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".