Abstract
This paper represents our newly developed software for emotion recognition from facial expressions. Besides allowing emotion recognition from image files and recorded video files, it uses webcam data to provide real-time, continuous, and unobtrusive facial emotional expressions. It uses FURIA algorithm for unordered fuzzy rule induction to offer timely and appropriate feedback based on learners’ facial expressions. The main objective of this study was first to validate the use of webcam data for a real-time and accurate analysis of facial expressions in e-learning environments. Second, transform these facial expressions to detected emotional states using the FURIA algorithm. We measured the performance of the software with ten participants, provided them with the same computer-based tasks, requested them a hundred times to mimic specific facial expressions, and recorded all sessions on video. We used the recorded video files to feed our newly developed software. We then used two experts’ opinions to annotate and rate participants’ recorded behaviours and to validate the software’s results. The software provides accurate and reliable results with the overall accuracy of 83.2%, which is comparable to the recognition by humans. This study will help to increase the quality of e-learning.
Highlights
1.1 Emotion and e-learningEmotions are a significant influential factor in the process of learning [58]
We present a new methodology of webcam-based emotion recognition, along with a full technical implementation that was used for its validation
This study presented an analysis for establishing the accuracy of facial emotion recognition based on a fuzzy logic model
Summary
1.1 Emotion and e-learningEmotions are a significant influential factor in the process of learning [58]. Current instructional methods for online learning increasingly address emotional dimensions by Multimedia Tools and Applications (2019) 78:18943–18966 accommodating challenges, excitement, ownership, and responsibility among other things in the learning environment [25, 80]. While online learning has expanded radically over the past years, there is a renewed interest in adaptive methods and personalization that adjust the instruction and support explicitly to the learners’ mental states and requirements. Such personalization is conventionally based on producing and maintaining a model of the learner, which is mainly based on individual characteristics and validated performances [13, 14]. That technology is about to be capable of automatically recognising the learners’ emotional states, learner models could readily include emotions and thereby improve the quality of personalization
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