Digital Visions: Developing 21st century skills and competencies with the Digital Media Academy
Bibliographic record
Abstract
This thesis proposes the need for a comprehensive digital literacy program in Ontario \nschools. A K-12 digital literacy program is essential so that students can grow up with a set of \n21st century skills and competencies that prepare them for life in an increasingly complex and \ndigital world. The lack of unified digital literacy instruction in Ontario schools has led to an \ninvestigation of a US based Science, Technology, Engineering, and Math (STEM) academy called \nthe Digital Media Academy. The Digital Media Academy offers programs for students, teachers, \nand adult learners in range of digital media disciplines. A qualitative study was designed to \nextract insights from the Digital Media Academy to establish a digital literacy framework worthy \nof the Ontario classroom. An ethnographic study was performed and eight interviews were \nconducted with eight curriculum staff from the Digital Media Academy. The results formed the \nbasis of a comprehensive digital literacy program synthesized through the critical lens of an \nOntario educator. The Ontario classroom would benefit from a digital literacy program that \nencompasses a creation-based learning platform that is intertwined with a human-centred \ndesign approach and teaches students to adopt a growth mindset, tell digital stories, learn to \ncode, and make use of relatively inexpensive technologies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".