Fuzzy contrastive learning for online behavior analysis

Yang, Jie; Huang, Gang; Ma, Jun; Howard, Sarah; Ciao, Matthew; Gao, Junbin

doi:10.1109/fuzz45933.2021.9494588

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The development of information technology has been promoting continuous changes in modern teaching models and learning forms [1][2][3][4][5][6]. In this context, how to scientifically and properly organize and guide learners to learn actively and efficiently has become a research subject for domestic and foreign scholars [7][8][9][10][11][12]. To achieve this goal, it is particularly important to analyze and study the features and patterns of learners' learning behaviours [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Features of Group Online Learning Behaviours Based on Data Mining

Zhu¹,

Wang²,

Wang³

2022

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

With the development of information technology, how to scientifically and properly organizing and guiding learners to learn actively and efficiently has become a research subject for domestic and foreign scholars. However, existing research on online learning behaviours studied little about learning attitudes, learning preferences, student-student interaction, teacher-student interaction and so on. To this end, this paper studies the features of group online learning behaviours based on data mining. In this paper, a K-means-based group online learning behaviour feature selection model and an AdaBoost-based group online learning behaviour classification model were constructed, and the processing methods, execution processes and algorithm functions of the two models were described in detail. Finally, the effectiveness of the constructed models was verified through an experiment.

show abstract

Section: Introductionmentioning

confidence: 99%

Features of Group Online Learning Behaviours Based on Data Mining

Zhu¹,

Wang²,

Wang³

2022

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

Early Prediction of Students’ Performance Using a Deep Neural Network Based on Online Learning Activity Sequence

Xiao

2023

Applied Sciences

View full text Add to dashboard Cite

Predicting students’ performance is one of the most important issues in educational data mining. In this study, a method for representing students’ partial sequence of learning activities is proposed, and an early prediction model of students’ performance is designed based on a deep neural network. This model uses a pre-trained autoencoder to extract latent features from the sequence in order to make predictions. The experimental results show that: (1) compared with demographic features and assessment scores, 20% and wholly online learning activity sequences can achieve a classifier accuracy of 0.5 and 0.84, respectively, which can be used for an early prediction of students’ performance; (2) the proposed autoencoder can extract latent features from the original sequence effectively, and the accuracy of the prediction can be improved more than 30% by using latent features; (3) after using distance-based oversampling on the imbalanced training datasets, the end-to-end prediction model achieves an accuracy of more than 80% and has a better performance for non-major academic grades.

show abstract

Fuzzy contrastive learning for online behavior analysis

Cited by 2 publications

References 12 publications

Features of Group Online Learning Behaviours Based on Data Mining

Features of Group Online Learning Behaviours Based on Data Mining

Early Prediction of Students’ Performance Using a Deep Neural Network Based on Online Learning Activity Sequence

Contact Info

Product

Resources

About