TY - GEN
T1 - Learning Analytics Based on Multilayer Behavior Fusion
AU - Yang, Yu
AU - Cao, Jiannong
AU - Shen, Jiaxing
AU - Yang, Ruosong
AU - Wen, Zhiyuan
N1 - Funding Information:
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).
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - At-risk student prediction
KW - Automatic text scoring
KW - Learning analytics
KW - Multilayer behavior extraction
UR - http://www.scopus.com/inward/record.url?scp=85089224076&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-51968-1_2
DO - 10.1007/978-3-030-51968-1_2
M3 - Conference article published in proceeding or book
AN - SCOPUS:85089224076
SN - 9783030519674
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 15
EP - 24
BT - Blended Learning. Education in a Smart Learning Environment - 13th International Conference, ICBL 2020, Proceedings
A2 - Cheung, Simon K.S.
A2 - Li, Richard
A2 - Phusavat, Kongkiti
A2 - Paoprasert, Naraphorn
A2 - Kwok, Lam-For
PB - Springer
T2 - 13th International Conference on Blended Learning, ICBL 2020
Y2 - 24 August 2020 through 27 August 2020
ER -