Using digital technologies to support Self‐Directed Learning for preservice teacher education
Bibliographic record
Abstract
This article begins with the perspective that teacher education programmes are cultural institutions and are thus compelled to respond to the societal push for teachers to be conversant in so‐called twenty‐first‐century skills, grounded primarily in the ability to use digital technologies for pedagogical purposes. The results of an attempt to provide teacher candidates with an opportunity to engage in a sustained self‐directed learning experience using digital technologies are presented. Findings indicate that candidates selected tasks for themselves for both personal and pragmatic reasons, and that external pressures played a significant role in candidates' ability to see their tasks through to satisfactory completion. The focus on self‐directed learning provided me with an opportunity to address some pragmatic concerns raised by the perceived need to teach technology skills in a teacher education course while, more importantly, providing an atypical learning experience that encouraged teacher candidates to engage in metacognitive talk about their experiences learning with and through technology. The article concludes by suggesting that self‐directed learning experiences are worthwhile in teacher education, although experiences framed explicitly around digital technologies may tacitly reinforce a positive bias toward using technology for teaching.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".