Impact of inquiry interventions on students in e-learning and classroom environments using affective computing framework
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Impact of inquiry interventions on students in e-learning and classroom environments using affective computing framework T. S. Ashwin1
· Ram Mohana Reddy Guddeti1
Received: 11 October 2018 / Accepted in revised form: 16 December 2019 © Springer Nature B.V. 2020
Abstract 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 multimodal 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 crossvalidation, 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. Keywords Affective computing · Facial emotion recognition · Convolutional Neural Network · Affective states · Student engagement · Inquiry-based instruction · Automatic intervention
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T. S. Ashwin, R. M. R. Guddeti
1 Introduction Learning environments such as classroom, flipped classroom, e-learning and webinar are widely used. In a traditional classroom learning, the teacher is the primary disseminator of information during the class period, and the students can practice and explore more post classroom instructional hours. Flipped classroom instructional strategy is a reverse of that, where the students learn the concepts before coming to the class, and the classroom is used to explore topics in greater depth and create meaningful learning opportunities while students are initially introduced to new topics
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