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Modelling Study on Learning Affects for Classroom Teaching/Learning Auto-Evaluation

Received: 19 June 2018     Published: 20 June 2018
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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.

Published in Science Journal of Education (Volume 6, Issue 3)
DOI 10.11648/j.sjedu.20180603.12
Page(s) 81-86
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Learning Affect, Classroom Evaluation, Affect Modelling, Facial Expression Recognition

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.
Cite This Article
  • APA Style

    Minyu Pan, Jing Wang, Zuying Luo. (2018). Modelling Study on Learning Affects for Classroom Teaching/Learning Auto-Evaluation. Science Journal of Education, 6(3), 81-86. https://doi.org/10.11648/j.sjedu.20180603.12

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    ACS Style

    Minyu Pan; Jing Wang; Zuying Luo. Modelling Study on Learning Affects for Classroom Teaching/Learning Auto-Evaluation. Sci. J. Educ. 2018, 6(3), 81-86. doi: 10.11648/j.sjedu.20180603.12

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    AMA Style

    Minyu Pan, Jing Wang, Zuying Luo. Modelling Study on Learning Affects for Classroom Teaching/Learning Auto-Evaluation. Sci J Educ. 2018;6(3):81-86. doi: 10.11648/j.sjedu.20180603.12

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  • @article{10.11648/j.sjedu.20180603.12,
      author = {Minyu Pan and Jing Wang and Zuying Luo},
      title = {Modelling Study on Learning Affects for Classroom Teaching/Learning Auto-Evaluation},
      journal = {Science Journal of Education},
      volume = {6},
      number = {3},
      pages = {81-86},
      doi = {10.11648/j.sjedu.20180603.12},
      url = {https://doi.org/10.11648/j.sjedu.20180603.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjedu.20180603.12},
      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.},
     year = {2018}
    }
    

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    AU  - Jing Wang
    AU  - Zuying Luo
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    JF  - Science Journal of Education
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    AB  - 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.
    VL  - 6
    IS  - 3
    ER  - 

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Author Information
  • College of Information Science and Technology, Beijing Normal University, Beijing, China

  • College of Information Science and Technology, Beijing Normal University, Beijing, China

  • College of Information Science and Technology, Beijing Normal University, Beijing, China

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