Exploring teacher knowledge and actions supporting technology-enhanced teaching in elementary schools: Two approaches by pre-service teachers
Bibliographic record
Abstract
<span>Two approaches to teaching with technology to highlight practice-based teacher knowledge and actions for teaching technologically enhanced lessons are presented. Participants were two elementary pre-service teachers teaching during practicum. Qualitative data sources included verbatim transcripts of participant interviews, field notes of planning and support sessions, and classroom observations. Teacher lesson plans and student work samples triangulated data. Cross case analysis revealed that </span><em>content-centric</em><span> pedagogy - focusing lesson design on a specific content learning outcome, rather than technical skill - promoted student engagement and learning of both content and technical skill. Additionally, some pedagogical knowledge characteristics, reflected in specific teacher actions related to planning and implementation of technology-enhanced lessons, were fundamental across the two subject areas investigated. For novice elementary teachers, explicit communication of generic technology pedagogical knowledge characteristics, supported by concrete examples of teacher actions, may contribute to teachers experiencing a degree of success during their initial attempts at teaching with technology.</span>
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".