Studying situated learning in a constructionist programming camp
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
Computationally generated data have increasingly been used to provide insights into individual students' learning in constructionist learning environments. However, such studies have either missed examining the influence of local, physical environments, or they have taken students out of the situated scenarios to study them in isolation. In this paper, we explore an expanded methodological approach in order to examine how computationally generated data insights can potentially be informed or expanded with a microgenetic approach. To achieve that, we examine one ten-year-old novice girl's learning of programming in a week-long Scratch camp, applying a microgenetic approach to analysis across multiple forms of data, from traditional observational and artifact documentation to frequent, computationally generated save data. The findings highlight the utility of this approach in identifying Mila's growing engagement with coding, as well as the iterative and social nature of her learning experiences with Scratch.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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