Comparative Performance Analysis of Machine Learning Techniques for Engagement Assessment in Education
Bibliographic record
Abstract
Enhancing education techniques and strategies is crucial to building and developing student knowledge and skills, as it fosters a more engaging learning environment, can accommodate diverse learning styles, and prepares students to meet the demands of a rapidly changing world. Student engagement in classroom discussions and lectures significantly affects the learning outcomes achieved in a course, and with the common ratio of one teacher to a classroom of students, it is challenging for a teacher to determine if all students are actively participating in the learning material. Machine learning can be utilized to adjust and create new learning techniques to capture and hold student attention by detecting and tracking student engagement through social robots. Social Robots have been successfully implemented in many job sectors, including healthcare, therapy, and, most recently, education. To begin integrating social robots into the classroom, a study is needed on the different machine-learning techniques used to train and test the robot’s ability to detect engagement. This study aims to investigate the effectiveness of using a social robot to assess and monitor student engagement during learning activities. For this purpose, this study investigates the performance of various machine learning techniques on publicly available datasets for engagement assessment, which social robots can utilize. The results showed that the dataset is crucial for the machine learning model's improved performance, and CNN models with the selection of the correct activation function performed better.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".