Embedding Digital Pedagogy in Pre-Service Higher Education to Better Prepare Teachers for the Digital Generation
Bibliographic record
Abstract
In preparing pre-service teachers for their professional practice in the information age, we need to impress upon them that the children in their classrooms will be Digital Natives, with skills for digital fluency rather than skills in the orthodoxy 3Rs developed with talk, chalk and board; paper, pencil and pen. Since most of our pre-service teachers belong to the pre-digital generation without the skills for 21 st century digital fluency, there is a great need for us as higher education practitioners, to prepare them well for the new classrooms they will work in so as to prevent a mismatch between them and their students where they could be seen as illiterate teachers trying to teach literate children. Embedding digital pedagogy in the skilling of these teachers is urgently needed to help them appreciate the role of technology in the teaching of pedagogy and content knowledge (TPACK). Fortunately enough, a wide range of apps are available for use on iPads, Androids, eTablets, Smart Phones and other platforms, which our pre-service teachers could apply in their teaching. One example of how this was achieved in higher education with two cohorts of 2 nd year B.Ed pre-service teachers is discussed in this paper. The paper demonstrates that social media digital tools can be embedded in pre-service higher education to help train pre-service teachers so they appreciate the TPACK model. The paper concludes that it is incumbent upon higher education providers, to ensure that graduates are well prepared to be effective teachers for the digital generation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".