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Rethinking the Development of Technology-Enhanced Learning and the Role of Cognitive Computing

Author

Listed:
  • Yihang Cheng

    (Tianjin University, China)

  • Xi Zhang

    (Tianjin University, China)

  • Xiaojiong Wang

    (Institutes of Science and Development, Chinese Academy of Sciences, China)

  • Hongke Zhao

    (Tianjin University, China)

  • Yao Yu

    (Tianjin University, China)

  • Xianhai Wang

    (Tianjin University, China)

  • Patricia Ordoñez de Pablos

    (The University of Oviedo, Spain)

Abstract

Technology-enhanced learning (TEL) is important in social web. Recently, cognitive computing became significant to analyze sentiment and improve effectiveness in TEL field. So analyzing the development of cognitive computing, what and how its abilities improve TEL are necessary. For solving these issues, this study used systematic review approach based on technology view and enhancement view of TEL. Specifically, this study used topic search results in computer science field of “cognitive computing” and “anticipatory computing” in Web of Science database to do map analysis. Besides development footprints, the manuscript describes three development stages and key technologies of cognitive computing through burst study and step-by-step clustering. Finally, this study proposed influencing framework of cognitive computing on TEL and some research trends. This work provides an advanced background of TEL and a systemic review of cognitive computing, contributing to theory development and application of cognitive computing in TEL.

Suggested Citation

  • Yihang Cheng & Xi Zhang & Xiaojiong Wang & Hongke Zhao & Yao Yu & Xianhai Wang & Patricia Ordoñez de Pablos, 2021. "Rethinking the Development of Technology-Enhanced Learning and the Role of Cognitive Computing," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 17(1), pages 67-96, January.
    • Handle: RePEc:igg:jswis0:v:17:y:2021:i:1:p:67-96
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    Cited by:

    1. Guangzhen Zhang & Wangyang Jiang, 2023. "Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 19(1), pages 1-16, January.

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