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ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation

Author

Listed:
  • Yi-Zeng Hsieh

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan
    Institute of Food Safety and Risk Management, National Taiwan Ocean University, Keelung City 202, Taiwan
    Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan)

  • Shih-Syun Lin

    (Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan)

  • Yu-Cin Luo

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan)

  • Yu-Lin Jeng

    (Department of Information Management, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan)

  • Shih-Wei Tan

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan)

  • Chao-Rong Chen

    (Department of Electrical Engineering, National Taipei University of Technology, Taipei 106, Taiwan)

  • Pei-Ying Chiang

    (Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106, Taiwan)

Abstract

Under the vigorous development of global anticipatory computing in recent years, there have been numerous applications of artificial intelligence (AI) in people’s daily lives. Learning analytics of big data can assist students, teachers, and school administrators to gain new knowledge and estimate learning information; in turn, the enhanced education contributes to the rapid development of science and technology. Education is sustainable life learning, as well as the most important promoter of science and technology worldwide. In recent years, a large number of anticipatory computing applications based on AI have promoted the training professional AI talent. As a result, this study aims to design a set of interactive robot-assisted teaching for classroom setting to help students overcoming academic difficulties. Teachers, students, and robots in the classroom can interact with each other through the ARCS motivation model in programming. The proposed method can help students to develop the motivation, relevance, and confidence in learning, thus enhancing their learning effectiveness. The robot, like a teaching assistant, can help students solving problems in the classroom by answering questions and evaluating students’ answers in natural and responsive interactions. The natural interactive responses of the robot are achieved through the use of a database of emotional big data (Google facial expression comparison dataset). The robot is loaded with an emotion recognition system to assess the moods of the students through their expressions and sounds, and then offer corresponding emotional responses. The robot is able to communicate naturally with the students, thereby attracting their attention, triggering their learning motivation, and improving their learning effectiveness.

Suggested Citation

  • Yi-Zeng Hsieh & Shih-Syun Lin & Yu-Cin Luo & Yu-Lin Jeng & Shih-Wei Tan & Chao-Rong Chen & Pei-Ying Chiang, 2020. "ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation," Sustainability, MDPI, vol. 12(14), pages 1-17, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5605-:d:383520
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    References listed on IDEAS

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    1. Yi-Hsing Chang & Pei-Rul Lin & You-Te Lu, 2020. "Development of a Kinect-Based English Learning System Based on Integrating the ARCS Model with Situated Learning," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    2. Miltiadis D. Lytras & Vijay Raghavan & Ernesto Damiani, 2017. "Big Data and Data Analytics Research: From Metaphors to Value Space for Collective Wisdom in Human Decision Making and Smart Machines," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(1), pages 1-10, January.
    3. Miltiadis D. Lytras & Anna Visvizi, 2018. "Who Uses Smart City Services and What to Make of It: Toward Interdisciplinary Smart Cities Research," Sustainability, MDPI, vol. 10(6), pages 1-16, June.
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    Cited by:

    1. Shih-Wei Tan & Sheng-Wei Huang & Yi-Zeng Hsieh & Shih-Syun Lin, 2021. "The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm," Energies, MDPI, vol. 14(15), pages 1-21, July.
    2. Yutong Fang & Jianzhi Deng & Fengming Zhang & Hongyan Wang, 2023. "An Intelligent Question-Answering Model over Educational Knowledge Graph for Sustainable Urban Living," Sustainability, MDPI, vol. 15(2), pages 1-18, January.

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