Analyse du processus de construction de connaissances dans des activités de programmation à l’école
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
Abstract The introduction of programming activities in the classroom has given rise to a variety of teaching methods. Some methods focus on learning to code while others take a creative programming approach (Resnick & Rusk, 2020). This study looks at creative programming as an opportunity for students to develop their reasoning skills. Drawing from both mathematics teaching and creative programming practices, we identify the elements of informatics thinking and use them to analyze the behaviour of students during a programming task in educational robotics. We also apply certain conceptual tools from both the field of mathematics teaching and the framework of informatics thinking in the course of our study. Creative programming practices provide six factors for analyzing how the students approached situational problems, framed the problems, learned the code, and developed the programs. From a didactics perspective, we use the components of DeBlois’ model (2001, 2003) for interpreting cognitive activities to analyze elements in the knowledge-building process. Our article ends with a comparison of the benefits of each analytical framework, which could have implications for teacher training on programming in the classroom.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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