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| Abstract: |
| The dynamics of student engagement and emotional states significantly influence learning outcomes. Positive emotions resulting from successful task completion stand in contrast to negative affective states that arise from learning struggles or failures. Effective transitions to engagement occur upon problem resolution, while unresolved issues lead to frustration and subsequent boredom. This study proposes a Convolutional Neural Networks (CNN) based approach utilizing the Multi-source Academic Affective Engagement Dataset (MAAED) to categorize facial expressions into boredom, confusion, frustration, and yawning. This method provides an efficient and objective way to assess student engagement by extracting features from facial images. Recognizing and addressing negative affective states, such as confusion and boredom, is fundamental in creating supportive learning environments. Through automated frame extraction and model comparison, this study demonstrates reduced loss values with improving accuracy, showcasing the effectiveness of this method in objectively evaluating student engagement. Monitoring facial engagement with CNN using the MAAED dataset is essential for gaining insights into human behaviour and improving educational experiences. |
| Key words: emotion recognition student engagement facial expressions academic affective engagement MAAED |
| DOI：10.11916/j.issn.1005-9113.2024026 |
| Clc Number:TP391,G442 |
| Fund: |
