Design and Evaluation of a Conversational Agent for Formative Assessment in Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, there have been attempts to design and use conversational agents for educational assessments (i.e., conversation-based assessments: CBA).To address the limited research on CBA, we designed a CBA to serve as a formative assessment of higher-education students' knowledge and scaffold their learning by providing support and feedback.CBA was designed using Rasaan artificial intelligence-based tooland shared with students via Google Chat.The conversation data showed that CBA produced high standard accuracy measures and confidence scores.The findings suggest that ensuring the accuracy of CBA with constructed-response items is more challenging than CBA with selected-response items.In addition, a cognitive walkthrough of CBA provided preliminary evidence for the use of CBA as an interactive assessment tool.According to survey responses, most of the participating students reported positive attitudes toward CBA and its use to improve their assessment experience and learning.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 it