Why Does Digital Learning Matter? Digital Competencies, Social Justice and Critical Pedagogy in Initial Teacher Education
Bibliographic record
Abstract
Digital tools and spaces are becoming prevalent in schools across the world requiring the development of digital skillsets for student-teachers. Digital technology, in enabling education to extend beyond the space and time boundaries of the conventional classroom (Seifert, T., Sheppard, B. Wakeham, M., 2015) , brings the digital landscape into the classroom and firmly into the frame of reference for those preparing student-teachers to enter the profession. For Initial Teacher Education (ITE) programmes which foreground social justice, the digital (technology which is linked to the internet) goes far beyond a skillset or a discrete subject. Engaging with digital learning encompasses the 21st century context - both local and global - in which student-teachers and their future pupils are situated. Developing a critical pedagogic approach involves understanding the context in which one lives and enabling learners to challenge or change it (Freire, 1996) . For those working in ITE a postdigital lens provides a means to understand the context in which they are situated. Critical pedagogy enables student-teachers to understand that context and challenge the inequities which persist, preparing them not simply to navigate the digital landscape, but to engage with it critically. Reflecting on student-teacher learning this article explores the digital dimension, highlighting the importance of digital learning when engaging with critical pedagogy and social justice in ITE.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".