Developing Course-Level Learning Goals for Basic Data Structures in CS2
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
Establishing learning goals for a course allows instructors to design course content to address those goals, helps students to focus their learning appropriately, and enables researchers to assess learning of those goals. In this work, we propose six learning goals for a topic prevalent in CS2 courses: Basic Data Structures. These learning goals arise from reviewing several CS2 courses at a variety of institutions, surveying faculty experts who commonly teach CS2, and meeting and working closely with these experts. We outline our process for creating learning goals, identify important topics underlying these goals, and provide examples of how the goals developed on the path to consensus. We also document that the term "CS2" does not have a unified interpretation within the CS education community and describe how this hurdle influenced our decision to focus on Basic Data Structures.
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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 | 0.002 | 0.001 |
| 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