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- W2981752657 abstract "The capacity to predict learner's academic performance accurately is critical for online learning platforms. Based on obtained predictions, platform managers can formulate appropriate policies for different students to improve the online learning experience for all online learners. The real-world data from Peking University's open research data platform contained e-learning data such as e-learning behaviors of students, are used in this study. According to the dataset, we apply four decision tree methods and factorization machine (FM) method to create prediction models. The experimental results show that students are categorized into two types of learning, namely interactive learning, and autonomous learning. Qualified students and outstanding students have different performances in the two types of learning. Furthermore, the experiments revealed that considering the interaction between e-learning behaviors can effectively improve the predictive ability of the model. We believe that our research results can provide effective support for further development in e-learning and our subsequent research work." @default.
- W2981752657 created "2019-11-01" @default.
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- W2981752657 date "2019-07-01" @default.
- W2981752657 modified "2023-09-25" @default.
- W2981752657 title "Prediction of Learners' Academic Performance Using Factorization Machine and Decision Tree" @default.
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- W2981752657 doi "https://doi.org/10.1109/ithings/greencom/cpscom/smartdata.2019.00024" @default.
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