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Early warning mechanism of interactive learning process based on temporal memory enhancement model

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Abstract

Interactive learning is a two-way learning method of learners independently by using computer and network technology. In the interactive relationships, interactive learning plays a role for learners to achieve the learning purpose, interactive learning has become an important effect of online learning, but it also has many problems that need to be improved. In particular, how to apply the massive data to achieve data-driven early warning needs further research. This study firstly mines the temporal series of learning behavior features, the corresponding data is collected from one online learning platform of The UK Open University, the massive data obtained from the platform have been desensitized, disclosed and shared, which might ensure the comparison and verification of research results. Secondly, we design a early warning mechanism based on temporal memory enhancement model. Through a large number of data training and testing, this method is useful to the analysis of learning behavior features, and has strong effectiveness and reliability. Thirdly, the decision and intervention mechanism are mined and predicted. The whole work is of great significance to the early warning mechanism of interactive learning process, which has strong theoretical value and practical significance.

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Data availability

The datasets used during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Thanks for the technical support provided by the laboratory of School of Software of Tsinghua University, as well as the theoretical guidance and practical reference provided by Qufu Normal University.

Funding

This study is supported by National Office for Philosophy and Social Sciences (Grant NO. BEA190107).

Author information

Authors and Affiliations

  1. Faculty of Education, Qufu Normal University, Qufu, 273165, Shandong, China

    Xiaona Xia & Wanxue Qi

  2. Chinese Academy of Education Big Data, Qufu Normal University, Qufu, 273165, Shandong, China

    Xiaona Xia & Wanxue Qi

  3. School of Computer Science, Qufu Normal University, Rizhao, 276826, Shandong, China

    Xiaona Xia & Wanxue Qi

Authors
  1. Xiaona Xia
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  2. Wanxue Qi
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Corresponding author

Correspondence to Xiaona Xia.

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Credit author statement

Xiaona Xia: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data analytics, Writing-original draft, Writing-review & editing, Visualization, Project administration.

Wanxue Qi: Conceptualization, Writing-review & editing.

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

The whole research does not involve human participants and/or animals.

Conflict of interest

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

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Xia, X., Qi, W. Early warning mechanism of interactive learning process based on temporal memory enhancement model. Educ Inf Technol 28, 1019–1040 (2023). https://doi.org/10.1007/s10639-022-11206-1

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