Construction of Affective Education in Mobile Learning: The Study Based on Learner’s Interest and Emotion Recognition

Haijian Chen1, Yonghui Dai2, Yanjie Feng2, Bo Jiang3, Jun Xiao1 and Ben You2

  1. Shanghai Engineering Research Center of Open Distance Education, Shanghai Open University
    Shanghai 200433, China
    {xochj, xiaoj}@shtvu.edu.cn
  2. Management School, Shanghai University of International Business and Economics
    Shanghai 201620, China
    {daiyonghui, fengyanjie}@suibe.edu.cn, youben022@gmail.com
  3. School of Information Management and Engineering, Shanghai University of Finance and Economics
    Shanghai 200433, China
    jiangbo@sui.edu.cn

Abstract

Affective education has been the new educational pattern under modern ubiquitous learning environment. Especially in mobile learning, how to effectively construct affective education to optimize and enhance the teaching effectiveness has attracted many scholars attention. This paper presents the framework of affective education based on learner’s interest and emotion recognition. Learner’s voice, text and behavior log data are firstly preprocessed, then association rules analysis, SO-PMI (Semantic Orientation-Pointwise Mutual Information) and ANN-DL (Artificial Neural Network with Deep Learning) methods are used to learner’s interest mining and emotion recognition. The experimental results show that these methods can effectively recognize the emotion of learners in mobile learning and satisfy the requirements of affective education.

Key words

Affective education, mobile learning, learner’s interest, emotion recognition

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS170110023C

Publication information

Volume 14, Issue 3 (September 2017)
Advances in Information Technology, Distributed and Model Driven Systems
Year of Publication: 2017
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Chen, H., Dai, Y., Feng, Y., Jiang, B., Xiao, J., You, B.: Construction of Affective Education in Mobile Learning: The Study Based on Learner’s Interest and Emotion Recognition. Computer Science and Information Systems, Vol. 14, No. 3, 685–702. (2017), https://doi.org/10.2298/CSIS170110023C