Assessing Procedural Knowledge in Free-Text Answers through a Hybrid Semantic Web Approach
Bibliographic record
Abstract
Several techniques have been proposed to automatically grade students' free-text answers in e-learning systems. However, these techniques provide no or limited support for the evaluation of acquired procedural knowledge. To address this issue, we propose a new approach, named ProcMark, specifically designed to assess answers containing procedural knowledge. It requires a teacher to provide the ideal answer as a semantic network (SN) that is used to automatically score learners' answers in plain text. The novelty of our approach resides mainly in three areas: a) the variable granularity levels possible in the SN and the parameterizing of ontology concepts, thus allowing the students free expression of their ideas, b) the new similarity measures of the grading system that give refined numerical scores, c) the language-independence of the grading system as all linguistic information is given as data files or dictionaries and is distinct of the semantic knowledge of the SN. Experimental results in a Computer Algorithms course show that the approach gives marks that are very close to those of human graders, with a very strong (0.70, 0.79, and 0.79) positive correlation.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".