Learning Analytics Based on Multilayer Behavior Fusion

Yu YANG, Jiannong CAO*, Jiaxing SHEN, Ruosong YANG, Zhiyuan WEN

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)


Learning analytics is the measurement, collection, and analysis of data about learners and their contexts for the purposes of understanding and optimizing the process of learning and the underlying environment. Due to the complex nature of the learning process, existing works mostly focus on the modeling and analysis of single learning behavior and thus bears limited capacity in achieving good performance and interpretability of predictive tasks. We propose a research framework for learning analytics based on multilayer behavior fusion which achieves significantly better performance in various tasks including at-risk student prediction. Results of extensive evaluation on thousands of students demonstrate the effectiveness of multilayer behavior fusion. We will report the insights about mining learning behaviors at different layers including physical, social and mental layers from the data collected from multiple sources. We will also describe the quantitative relationships between these behaviors and the students’ learning performance.

Original languageEnglish
Title of host publicationBlended Learning. Education in a Smart Learning Environment - 13th International Conference, ICBL 2020, Proceedings
EditorsSimon K.S. CHEUNG, Richard LI, Kongkiti PHUSAVAT, Naraphorn PAOPRASERT, Lam-For KWOK
PublisherSpringer, Cham
Number of pages10
ISBN (Electronic)9783030519681
ISBN (Print)9783030519674
Publication statusPublished - 2020
Externally publishedYes
Event13th International Conference on Blended Learning, ICBL 2020 - Bangkok, Thailand
Duration: 24 Aug 202027 Aug 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Blended Learning, ICBL 2020

Bibliographical note

The work is supported by Human-computer fusion cloud computing architecture and software definition method (project code: 2018YFB1004801). It is also supported by Learning Analytics and Educational Data Mining: Making Sense of Big Data in Education (project code: 1.61.xx.9A5V) and Multi-stage Big Data Analytics for Complex Systems: Methodologies and Applications (RGC No.: C5026-18G).


  • At-risk student prediction
  • Automatic text scoring
  • Learning analytics
  • Multilayer behavior extraction


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