Arts Department, Heilongjiang International University, Heilongjiang, China
With the continuous development of information technology, the teaching method is not limited to offline teaching. Online teaching is on the rise, and more and more teachers use a blended teaching evaluation system that combines online and offline to carry out classroom teaching. However, during online teaching, teachers cannot remind students when students are not paying attention, resulting in low classroom quality. This paper applied artificial intelligence (AI) emotion recognition technology to the blended teaching evaluation index system. When students are studying in the course, the emotion recognition is carried out on the students; when the students are not concentrating, the students are reminded to ensure the quality of the students' learning. Through testing different classes it was found that: the application of AI emotion recognition technology to the blended teaching evaluation system can improve the quality of classrooms, and improve students' learning status and student performance. Student satisfaction had increased by 9.7%, and AI emotion recognition technology can optimize the evaluation index system of blended teaching.
Blended Teaching, Teaching Evaluation, AI Emotion Recognition, Haar-like Features
Wenpeng Lyu. Blended Teaching Evaluation Index System Based on AI Emotion Recognition. International Journal of Educational Innovation and Science (2023), Vol. 4, Issue 1: 1-13. https://doi.org/10.38007/IJEIS.2023.040101.
 Wang Y. Implications of Blended Teaching Based on Theory of Semantic Wave for Teaching English Writing in High School. Journal of Higher Education Research. (2022) 3(2): 166-168. https://doi.org/10.32629/jher.v3i2.747
 Gupta R, Sharma P. Analysis of Online Teaching During Lock Down: Blended Teaching the Way Forward. Indian Research Journal of Extension Education. (2021) 56(2020): 19-25.
 Xue Y, Chen J, Wang H. Exploration of Blended Teaching in Electrical Engineering and Electronic Technology under the Background of Higher Engineering Education. Education Teaching Forum. (2019) 8(12): 45-49.
 Han X, Cao P. A Brief Analysis of Blended Teaching in Higher Vocational Colleges under the Background of Education Informationization. Science & Technology Vision. (2019) 23(8): 177-182.
 Jungang L, Wang Y, Marxism S. A Study on Design of Blended Teaching Mode of Ideological and Political Courses in Colleges and Universities. Journal of Changchun University. (2018) 4(12): 7-10.
 Adom Abdul Hamid. A new hybrid PSO assisted biogeography-based optimization for emotion and stress recognition from speech signal. Expert Systems with Application. (2017) 69(8): 149-158. https://doi.org/10.1016/j.eswa.2016.10.035
 Jiang X, Xia K, Lin Y. Noisy speech emotion recognition using sample reconstruction and multiple-kernel learning. Journal of China Universities of Posts & Telecommunications. (2017) 9(2): 1-9. https://doi.org/10.1016/S1005-8885(17)60193-6
 Dong Y C, Song B C. Semi-supervised learning for facial expression-based emotion recognition in the continuous domain. Multimedia Tools and Applications. (2020) 79(37): 28169-28187. https://doi.org/10.1007/s11042-020-09412-5
 Zheng J, Zhang Q, Xu S. Cognition-Based Context-Aware Cloud Computing for Intelligent Robotic Systems in Mobile Education. IEEE Access. (2018) 6(23): 49103-49111. https://doi.org/10.1109/ACCESS.2018.2867880
 Demircan S, Kahramanli H. Application of ABM to Spectral Features for Emotion Recognition. Mehran University Research Journal of Engineering and Technology. (2018) 37(4): 452-462. https://doi.org/10.22581/muet1982.1804.01
 Xu J, Yan X. Research on Blended Teaching Model of Photography Courses in Universities under Outcome Based Education. (2020) 7(3): 46-51.
 Lee S H, Plataniotis K N, Yong M R. Intra-Class Variation Reduction Using Training Expression Images for Sparse Representation Based Facial Expression Recognition. IEEE Transactions on Affective Computing. (2017) 5(3): 340-351. https://doi.org/10.1109/TAFFC.2014.2346515
 Corpas A, Costero L, Botella G. Acceleration and energy consumption optimization in cascading classifiers for face detection on low‐cost ARM big. LITTLE asymmetric architectures. International Journal of Circuit Theory and Applications. (2018) 46(9): 1756-1776. https://doi.org/10.1002/cta.2552
 Abdurrahman M H, Darwito H A, Saleh A F. Face Recognition System for Prevention of Car Theft with Haar Cascade and Local Binary Pattern Histogram using Raspberry Pi. EMITTER International Journal of Engineering Technology. (2020) 8(2): 407-425. https://doi.org/10.24003/emitter.v8i2.534
 Gevaert C, Persello C, Nex F. A deep learning approach to DTM extraction from imagery using rule-based training labels. Isprs Journal of Photogrammetry & Remote Sensing. (2018) 142(7): 106-123. https://doi.org/10.1016/j.isprsjprs.2018.06.001
 Zhang D, Yi L, Tang H. Multi-scale microstructure binary pattern extraction and learning for image representation. Image Processing, IET. (2019) 13(13): 2507-2515. https://doi.org/10.1049/iet-ipr.2018.6358
 Liu N, Zhou P, Liu W. Sparse Representation Based Image Super-resolution Using Large Patches. Chinese Journal of Electronics. (2018) 27(04): 813-820. https://doi.org/10.1049/cje.2018.05.011
 Li B, Li Z, Zhou S. New Steganalytic Features for Spatial Image Steganography Based on Derivative Filters and Threshold LBP Operator. IEEE Transactions on Information Forensics and Security. (2018) 13(5): 1242-1257. https://doi.org/10.1109/TIFS.2017.2780805
 Sharma R K, Sugumaran V, Kumar H. Condition Monitoring of Roller Bearing by K-Star Classifier and K-Nearest Neighborhood Classifier Using Sound Signal. SDHM Structural Durability and Health Monitoring. (2017) 12(1): 1-16.
 Wang C, Shi Y, Fan X. Attribute reduction based on k-nearest neighborhood rough sets. Acoustic bulletin. (2019) 106(7): 18-31. https://doi.org/10.1016/j.ijar.2018.12.013