Challenge-based and Competency-based Assessments in an Undergraduate Programming Course
Bibliographic record
Abstract
In this work, we investigate an optimal assessment strategy to measure student learning in the first-year undergraduate engineering course at X-Department at X University. Specifically, we evaluate and compare challenge-based and competency-based assessment strategies. In the challenge-based approach, the students are required to design a C++-based application that meet the required design objectives. The competency-based assessment involves assessing learning by asking a variety of pointed questions pertaining to a single or a small group of concepts. After studying the performance of 207 students, we found that in the challenge-based assessment, due to the complex nature of the questions that assess numerous concepts simultaneously, students who are not very thorough with even one or two concepts fared very poorly since they were unable to finish the challenge and present a functional prototype of the program. On the other hand, the competency-based assessment allowed for a more balanced approach in which the students’ learning was reflected more accurately by their performance in the various assessments.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".