Oto, a generic and extensible tool for marking programming assignments
Bibliographic record
Abstract
Abstract Marking programming assignments in programming courses involves a lot of work: each program must be tested, the source code must be read and evaluated, etc. With the large classes encountered nowadays, the feedback provided to students through marking is thus rather limited, and often late. Tools providing support for marking programming assignments do exist, ranging from support for administrative aspects through automation of program testing or support for source code evaluation based on metrics. In this paper, we introduce a tool, called Oto, that provides support for submission and marking of assignments. Oto aims at reducing the workload associated with the marking task. Oto also aims at providing timely feedback to the students, including feedback before the final submission. Furthermore, the tool has been designed to be generic and extensible, so that the marking process for a specific assignment can easily be customized and the tool can be extended with various marking components (modules) that allows it to deal with various aspects of marking (testing, style, structure, etc.) and with programs written in various programming languages. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".