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Direct linkMengjiao Yin; Hengshan Cao; Zuhong Yu; Xianyu Pan – International Journal of Web-Based Learning and Teaching Technologies, 2024
This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a…
Descriptors: Predictor Variables, Artificial Intelligence, Performance, Cluster Grouping
Morais, Jorge E.; Forte, Pedro; Silva, Antonio J.; Barbosa, Tiago M.; Marinho, Daniel A. – Research Quarterly for Exercise and Sport, 2021
Purpose: The aims of this study were to classify, identify and follow-up young swimmers' performance and its biomechanical determinants during two competitive seasons (in seven different moments of assessment--M), and analyze the individual variations of each swimmer. Method: Thirty young swimmers (14 boys: 12.70 ± 0.63 years-old; 16 girls:…
Descriptors: Aquatic Sports, Performance, Biomechanics, Reliability
Barata, Gabriel; Gama, Sandra; Jorge, Joaquim; Gonçalves, Daniel – IEEE Transactions on Learning Technologies, 2016
State of the art research shows that gamified learning can be used to engage students and help them perform better. However, most studies use a one-size-fits-all approach to gamification, where individual differences and needs are ignored. In a previous study, we identified four types of students attending a gamified college course, characterized…
Descriptors: Prediction, Performance, Profiles, Games
