What We Talk About When We Talk About K-12 Computing Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
K-12 computing education research is a rapidly growing field of research, both driven by and driving the implementation of computing as a school and extra-curricular subject globally. In the context of discipline-based education research, it is a new and emerging field, drawing on areas such as mathematics and science education research for inspiration and theoretical bases. The urgency around investigating effective teaching and learning in computing in school alongside broadening participation has led to much of the field being focused on empirical research. Less attention has been paid to the underlying philosophical assumptions informing the discipline, which might include a critical examination of the rationale for K-12 computing education, its goals and perspectives, and associated inherent values and beliefs. In this working group, we conducted an analysis of the implicit and hidden values, perspectives and goals underpinning computing education at school in order to shed light on the question of what we are talking about when we talk about K-12 computing education. To do this we used a multi-faceted approach to identify implicit rationales for K-12 computing education and examine what these might mean for the implemented curriculum. Methods used include both traditional and natural language processing techniques for examining relevant literature, alongside an examination of the theoretical literature relating to education theory. As a result we identified four traditions for K-12 computing education: algorithmic, design-making, scientific and societal. From this we have developed a framework for the exemplification of these traditions, alongside several potential use cases. We suggest that while this work may provoke some discussion and debate, it will help researchers and others to identify and express the rationales they draw on with respect to computing education.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it