engleski

Adaptive intelligent agent for e-learning: First report on enabling technology solutions

Because of the global COVID-19 pandemic, online learning has become the dominant teaching method. Moreover, a wide range of e-learning pedagogies are rapidly gaining importance, and in some cases emerging as the preferred approach in education over the traditional methods and techniques of classroom teaching. However much has to be done to efficiently assess student engagement and the learning curve. In this regard, we have proposed construction of an intelligent agent for personalized and adaptive assessment of learning performance based on methods for automated estimation of attention and emotion. We report on the first progress towards the development of the intelligent agent. Three classifiers were used in parallel to detect information about the progress of student engagement. Object detection in video is accomplished with YOLOv3, emotion detection from facial expressions using PAZ software library, and detection of head, arms, and upperbody orientation and position with OpenPose system. NimStim facial expression database, WIDER Attribute Dataset, and UPNA Head Pose Database were used for experimental validation of the individual classifiers. Our system attained the highest precision and recall of 79.13% and 94.15%, respectively, and the highest success rate of 59.56% in recognition of 6 discrete emotions from facial expressions.

digital learning ; adaptive learning ; emotion recognition ; pose estimation ; object detection

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