Digital literacies and competencies: Examining teacher candidates’ achievement, engagement, attitudes, and personalized learning in technology enhanced environments
Bibliographic record
Abstract
Teachers and students are increasingly operating in classrooms where the presence of digital technology impacts both parties across numerous facets related to teaching and learning. In an attempt to comprehend this dynamic, the current research aimed to understand the relationship from the perspective of the educator, acknowledging that today’s teachers are tasked with guiding students to become global, 21st century citizens who are capable of appropriately engaging with the opportunities and challenges put forth by digital technology. Therefore, this research aimed to examine teacher candidates’ (TCs’) a) attitudes towards digital technology, b) ability to manifest personalized learning experiences, and c) personal engagement and achievement outcomes. To address these aims, the research utilized secondary data related to digital competencies and digital technology experiences of TCs in a teacher education program at a Canadian university. Both quantitative (surveys) and qualitative (interviews, coursework) data were analyzed to determine overall impact. Findings suggest that TCs’ experiences with digital technology positively affected both their attitudes toward and uses of digital technology. Additionally, TCs’ levels of engagement with subject content was heightened when combined with digital technology, as well as their abilities to foster personalized learning, and enhanced achievement through knowledge construction and knowledge mobilization.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".