Do Computer Science Students Understand Their Programming Task?—A Case Study of Solving the Josephus Variant Problem
Bibliographic record
Abstract
The ability of students to problem solve begins with interpreting the problem. When they interpret the problem inaccurately, they will likely use ineffective strategies or fail to solve the problem. Studies reported students are often incapable of identifying and articulating the problem goal, requirements/constraints, and expected output. In other words, students lack self-regulation skills, especially related to task understanding. In this study, two male and two female senior computer science students from Utah State University, USA, were recruited as research participants to learn more about their task understanding skills while engage in programming tasks. The participants were asked to answer five programming problems while thinking aloud, and their responses were video- and audio-recorded. This report focuses on one of the problems, which was a variant of the Josephus problem. Three research questions were used to guide the analysis: (a) what were the participants’ initial task understanding; (b) how did it change during the problem-solving endeavor; and (c) why did it change. All participants identified the problem goal inaccurately and as a result, selected ineffective problem-solving strategies. The analysis suggested their inaccurate task interpretations were caused by their confidence bias (i.e., a systematic cognitive error), in which they drew knowledge and strategies from irrelevant experience. Out of four participants, only one was able to defeat the confidence bias and acquired an accurate task understanding; the influencing factors and possible interventions to overcome confidence bias are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".