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Khor, Ean Teng – International Journal of Information and Learning Technology, 2022
Purpose: The purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance. Design/methodology/approach: For the first step, the author performed exploratory data analysis to analyze the dataset. The process was then…
Descriptors: Prediction, Low Achievement, Algorithms, Artificial Intelligence
Zanellati, Andrea; Macauda, Anita; Panciroli, Chiara; Gabbrielli, Maurizio – Research on Education and Media, 2023
Within scientific debate on post-digital and education, we present a position paper to describe a research project aimed at the design of a predictive model for students' low achievements in mathematics in Italy. The model is based on the INVALSI data set, an Italian large-scale assessment test, and we use decision trees as the classification…
Descriptors: Foreign Countries, Artificial Intelligence, Models, Algorithms
Hyun-Bin Hwang – Language Learning, 2025
This study explored the effects of practice schedule on the processing of new second language (L2) vocabulary and resulting knowledge. Participants were 107 low-achieving adolescents attending a vocational high school in Korea. They were randomly assigned to one of three practice groups and completed a L2 English-L1 Korean paired-associates…
Descriptors: Low Achievement, Adolescents, Second Language Learning, Vocabulary Development
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