Student Misconceptions of Dynamic Programming
Why this work is in the frame
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Bibliographic record
Abstract
Dynamic Programming (DP) is considered to be one of the most difficult topics for students to understand in theoretical CS. Prior work suggests that misconceptions arise even when students have completed a course in which there is considerable focus on learning how to solve DP problems. We conducted think-aloud interviews with students who have completed the DP portion of the Algorithms course at a top North American research university. We report on three themes and their misconceptions discovered through this process. The first theme delves into students' struggles defining the notion of a subproblem and identifying particular subproblems. The second theme focuses on the understanding and usage of DP solution techniques compared to other algorithmic approaches. The third theme is composed of misconceptions related to defining and using recurrences. Analysis of each misconception provides insight into student thinking and offers ideas for improving the education of DP to university students.
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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 | 0.000 | 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 | 0.001 | 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