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Xiuyu Lin; Zehui Zhan; Xuebo Zhang; Jiayi Xiong – IEEE Transactions on Learning Technologies, 2024
The attribution of learning success or failure is crucial for students' learning and motivation. Effective attribution of their learning success or failure in the context of a small private online course (SPOC) could generate students' motivation toward learning success while an incorrect attribution would lead to a sense of helplessness. Based on…
Descriptors: Learning Analytics, Learning Processes, Learning Motivation, Attribution Theory
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Shi Pu; Yu Yan; Brandon Zhang – Journal of Educational Data Mining, 2024
We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream…
Descriptors: Prediction, Success, Data Analysis, Learning Analytics
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Danhua Zhou – Interactive Learning Environments, 2024
Online scientific argumentation activities have significant value for students' cognitive ability. The purpose of this study was to solve the issues of online scientific argumentation. Firstly, the influences of online scientific argumentation were classified into "Learn to Argue" and "Argue to Learn" abilities. And then the…
Descriptors: Meta Analysis, Instructional Design, Instructional Effectiveness, Electronic Learning
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Hua-yang Zhang; Yi-xin Wang; Wei Liu – Interactive Learning Environments, 2024
During the normalization of COVID-19 control measures, more and more high school students are learning through the Internet. With the widespread use of online teaching, students' internet self-efficacy has become an important factor in measuring students' online learning ability. Although researchers have been paying greater attention to the…
Descriptors: High School Students, Internet, Self Efficacy, Influence of Technology