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
Introduction:The emergence and widespread adoption of generative AI (GenAI) chatbots such as ChatGPT, and programming assistants such as GitHub Copilot, have radically redefined the landscape of programming education.This calls for replication of studies and reexamination of findings from pre-GenAI CS contexts to understand the impact on students.Objectives: Achievement Goals are well studied in computing education and can be predictive of student interest and exam performance.The objective in this study is to compare findings from prior achievement goal studies in CS1 courses with new CS1 courses that emphasize the use of human-GenAI collaborative coding.Methods: In a CS1 course that integrates GenAI, we use linear regression to explore the relationship between achievement goals and prior experience on student interest, exam performance, and perceptions of GenAI.Results: As with prior findings in traditional CS1 classes, Mastery goals are correlated with interest in computing.Contradicting prior CS1 findings, normative goals are correlated with exam scores.Normative and mastery goals correlate with students' perceptions of learning with GenAI.Mastery goals weakly correlate with reading and testing code output from GenAI.
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 | 0.000 | 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