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Using Machine Learning to Predict UK and Japanese Secondary Students' Life Satisfaction in PISA 2018
Zexuan Pan; Maria Cutumisu – British Journal of Educational Psychology, 2024
Background: Life satisfaction is a key component of students' subjective well-being due to its impact on academic achievement and lifelong health. Although previous studies have investigated life satisfaction through different lenses, few of them employed machine learning (ML) approaches. Objective: Using ML algorithms, the current study predicts…
Descriptors: Artificial Intelligence, Secondary School Students, Life Satisfaction, Foreign Countries
Buyukatak, Emrah; Anil, Duygu – International Journal of Assessment Tools in Education, 2022
The purpose of this research was to determine classification accuracy of the factors affecting the success of students' reading skills based on PISA 2018 data by using Artificial Neural Networks, Decision Trees, K-Nearest Neighbor, and Naive Bayes data mining classification methods and to examine the general characteristics of success groups. In…
Descriptors: Classification, Accuracy, Reading Tests, Achievement Tests
Hu, Jie; Peng, Yi; Chen, Xiao – IEEE Transactions on Learning Technologies, 2023
The prevalence of information and communication technologies (ICTs) has brought about profound changes in the field of reading, resulting in a large and rapidly growing number of young digital readers. The article intends to identify key contextual factors that synergistically differentiate high and low performers, high and average performers, and…
Descriptors: Decoding (Reading), Educational Technology, Information Technology, Reading Skills