Using digital technologies to support Self‐Directed Learning for preservice teacher education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it