Providing High-Quality Formative Feedback for Database Assignments
Bibliographic record
Abstract
Abstract Automated systems such as Marmoset, WebCAT, OK, MarkUs, and many others are widely used in assessing programming assignments. Although they enable instructors to assess students' solutions at scale, the core infrastructure of these systems is not much different from a standard build and test environment, which focuses on ensuring correct solutions. However, when it comes to learning, it would be more important to assist students in correcting their misconceptions when their solutions are incorrect, i.e., provide a feedback message accurately showing them what is wrong and what they can do. The latter, which requires high-quality assessment and considerable effort in composing feedback, however, is rarely discussed, not to mention that no tools or support have been developed in these systems to assist in writing them. In this paper, we aim to fill the gap by providing guidance for assessment writers to write effective assessments and feedback for students' solutions. We present an approach to properly organizing the test cases so that automated assessments can identify students' misconceptions accurately, enabling them to provide high-quality formative feedback to rectify students' misconceptions. Following the guidance outlined, we developed assessments for a database course. By comparing student performance with and without the high-quality formative feedback, we observed an overall improvement in RA of $21\%$, with a $73\%$ improvement in query creation and an $11\%$ improvement in ER, with a $32\%$ improvement in composing new relationship sets and/or specializations.
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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 | 0.017 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".