Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
Search Results
Item Affective database for e-learning and classroom environments using Indian students’ faces, hand gestures and body postures(Elsevier B.V., 2020) Ashwin, T.S.; Guddeti, R.M.R.Automatic recognition of the students’ affective states is a challenging task. These affective states are recognized using their facial expressions, hand gestures, and body postures. An intelligent tutoring system and smart classroom environment can be made more personalized using students’ affective state analysis, and it is performed using machine or deep learning techniques. Effective recognition of affective states is mainly dependent on the quality of the database used. But, there exist very few standard databases for the students’ affective state recognition and its analysis that works for both e-learning and classroom environments. In this paper, we propose a new affective database for both the e-learning and classroom environments using the students’ facial expressions, hand gestures, and body postures. The database consists of both posed (acted) and spontaneous (natural) expressions with single and multi-person in a single image frame with more than 4000 manually annotated image frames with object localization. The classification was done manually using the gold standard study for both Ekman's basic emotions and learning-centered emotions, including neutral. The annotators reliably agree when discriminating against the recognized affective states with Cohen's ? = 0.48. The created database is more robust as it considers various image variants such as occlusion, background clutter, pose, illumination, cultural & regional background, intra-class variations, cropped images, multipoint view, and deformations. Further, we analyzed the classification accuracy of our database using a few state-of-the-art machine and deep learning techniques. Experimental results demonstrate that the convolutional neural network based architecture achieved an accuracy of 83% and 76% for detection and classification, respectively. © 2020 Elsevier B.V.Item Impact of inquiry interventions on students in e-learning and classroom environments using affective computing framework(Springer Science and Business Media B.V. editorial@springerplus.com, 2020) Ashwin, T.S.; Guddeti, R.M.R.Effective teaching strategies improve the students’ learning rate within academic learning time. Inquiry-based instruction is one of the effective teaching strategies used in the classrooms. But these teaching strategies are not adapted in other learning environments like intelligent tutoring systems, including auto tutors. In this paper, we propose an automatic inquiry-based instruction teaching strategy, i.e., inquiry intervention using students’ affective states. The proposed model contains two modules: the first module consists of the proposed framework for predicting the unobtrusive multi-modal students’ affective states (teacher-centric attentive and in-attentive states) using the facial expressions, hand gestures and body postures. The second module consists of the proposed automated inquiry-based instruction teaching strategy to compare the learning outcomes with and without inquiry intervention using affective state transitions for both an individual and a group of students. The proposed system is tested on four different learning environments, namely: e-learning, flipped classroom, classroom and webinar environments. Unobtrusive recognition of students’ affective states is performed using deep learning architectures. After student-independent tenfold cross-validation, we obtained the students’ affective state classification accuracy of 77% and object localization accuracy of 81% using students’ faces, hand gestures and body postures. The overall experimental results demonstrate that there is a positive correlation with r= 0.74 between students’ affective states and their performance. Proposed inquiry intervention improved the students’ performance as there is a decrease of 65%, 43%, 43%, and 53% in overall in-attentive affective state instances using the inquiry interventions in e-learning, flipped classroom, classroom and webinar environments, respectively. © 2020, Springer Nature B.V.