Modelling Study on Learning Affects for Classroom Teaching/Learning Auto-Evaluation
Science Journal of Education
Volume 6, Issue 3, June 2018, Pages: 81-86
Received: Jun. 19, 2018; Published: Jun. 20, 2018
Views 1032      Downloads 98
Authors
Minyu Pan, College of Information Science and Technology, Beijing Normal University, Beijing, China
Jing Wang, College of Information Science and Technology, Beijing Normal University, Beijing, China; Engineering Research Center of Virtual Reality and Applications, Ministry of Education, Beijing, China
Zuying Luo, College of Information Science and Technology, Beijing Normal University, Beijing, China; Engineering Research Center of Virtual Reality and Applications, Ministry of Education, Beijing, China
Article Tools
Follow on us
Abstract
In studies on classroom teaching auto-evaluation, we have achieved some remarkable results in Classroom Attendance Auto-management, learning attention & facial expression auto-analysis. For further utilizing learning affects to auto-evaluate classroom teaching/learning effects, we watch a large number of classroom videos. Then, based on the stimulus-response mechanism, we use learning facial expressions & attention to categorize students’ learning affects (SLA) and construct a SLA transfer model. At last, we simply describe how to use SLA analysis results to auto-evaluate the classroom teaching/learning effects. This work lays a theoretical foundation for the studies on learning facial expressions and learning affects for classroom teaching/learning auto-evaluation.
Keywords
Learning Affect, Classroom Evaluation, Affect Modelling, Facial Expression Recognition
To cite this article
Minyu Pan, Jing Wang, Zuying Luo, Modelling Study on Learning Affects for Classroom Teaching/Learning Auto-Evaluation, Science Journal of Education. Vol. 6, No. 3, 2018, pp. 81-86. doi: 10.11648/j.sjedu.20180603.12
References
[1]
Bloom etc. Educational Evaluation [M]. East China Normal University Press, 1987 edition.
[2]
Jihui Li. How to evaluate emotion, attitude and values [J]. Educational Science Research, 2006, 02:23-26.
[3]
Cai Min. A Study on the Evaluation of Elementary Student Affection in Mathematics Learning [J]. Education Science, 2010, 26(1): 26-30.
[4]
Kaifeng Liu and Xiaoguo Lv. The Conception of Mathematics Learning Emotion Evaluation Indicator System [J]. Journal of Higher Correspondence Education (Natural Sciences), 2009, 26(1): 26-30.
[5]
Huili Tang. Research on the Evaluation of Student’s Studying Emotion [D], Henan: Henan University, 2009.
[6]
Yi Shen and Yunhuo Cui. Class Observation: Towards professional listening and evaluation [M]. East China Normal University Press, 2008.
[7]
Chongsheng Zhang. Deep Learning: Principles and Application Practices [M]. Publishing House of Electronics Industry, 2016.
[8]
Chuangao Tang, Pengfei Xu, Zuying Luo, etc., Automatic Facial Expression Analysis of Students in Teaching Environment [C]. 10th Chinese Conference on Biometric Recognition (CCBR), LNCS 9482, 2015: 439-447.
[9]
Scotti S etc. Automatic quantitative evaluation of emotions in E-learning applications [C]. Engineering in Medicine and Biology Society, Annual International Conference of the IEEE, 2006: 1359-1362.
[10]
Krithika etc. Student Emotion Recognition System (SERS) for e-learning Improvement Based on Learner Concentration Metric [J]. Procedia Computer Science, 2016, 85: 767-776.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186