Multi-Modal Affective Computing: An Application in Teaching Evaluation Based on Combined Processing of Texts and Images
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
Conventional teaching evaluation emphasizes students' knowledge mastery over their affections.Multi-modal Affective Computing (MAC) can analyze versatile information of students in the classroom, including their facial expressions, gestures, and text feedback, in a comprehensive way, thereby helping teachers discover problems with students' affections in a timely manner, so that they could adjust the teaching methods and strategies accordingly.However, the available MAC technology might make unstable or wrong judgement when dealing with complex affective expressions, then the inaccurate evaluation results of students' affection state might adversely affect the teaching evaluation results.To tackle these issues, this study innovatively applied MAC in teaching evaluation based on combined processing of texts and images.The input texts were divided into two parts: main body and the hash tag, which were subjected to feature extraction respectively.The image features were extracted from two angles: object and scene, since the two angles can give image information of different levels.The MAC model was divided into modal sharing tasks and modal private tasks to attain better adaptability in case of new teaching evaluation scenarios.The effectiveness of the proposed method was verified by experimental results.
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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