Revisiting the media generation: Youth media use and computational literacy instruction
Bibliographic record
Abstract
An ongoing challenge of 21st century learning is ensuring everyone has the requisite skills to participate in a digital, knowledge-based economy. Once an anathema to parents and teachers, digital games are increasingly at the forefront of conversations about ways to address student engagement and provoke challenges to media pedagogies. While advances in game-based learning are already transforming educative practices globally, with tech giants like Microsoft, Apple and Google taking notice and investing in educational game initiatives, there is a concurrent and critically important development that focuses on “game construction” pedagogy as a vehicle for bringing computational literacy to middle and high school students. Founded on Seymour Papert’s constructionist learning model and developed over nearly two decades, there is compelling evidence that game construction can increase confidence and build capacity in science, technology, engineering and mathematics. This project is a research-based challenge to the by now widely questioned but surprisingly persistent presumption that students in today's classrooms are all by default “digitally native” and that those “digitally native” children are learning just by playing digital games. Through a survey of 60+ students at a largely immigrant middle school in Toronto, Canada, we present some important updates on youth’s media and technology competence and its relationship to baseline knowledge of computer programming and performance in a computational literacy game-based curriculum.
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.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".