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Senthil Kumaran, V.; Malar, B. – Interactive Learning Environments, 2023
Churn in e-learning refers to learners who gradually perform less and become lethargic and may potentially drop out from the course. Churn prediction is a highly sensitive and critical task in an e-learning system because inaccurate predictions might cause undesired consequences. A lot of approaches proposed in the literature analyzed and modeled…
Descriptors: Electronic Learning, Dropouts, Accuracy, Classification
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Shaheen, Muhammad – Interactive Learning Environments, 2023
Outcome-based education (OBE) is uniquely adapted by most of the educators across the world for objective processing, evaluation and assessment of computing programs and its students. However, the extraction of knowledge from OBE in common is a challenging task because of the scattered nature of the data obtained through Program Educational…
Descriptors: Undergraduate Students, Programming, Computer Science Education, Educational Objectives
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Wanxue Zhang; Lingling Meng; Bilan Liang – Interactive Learning Environments, 2023
With the continuous development of education, personalized learning has attracted great attention. How to evaluate students' learning effects has become increasingly important. In information technology courses, the traditional academic evaluation focuses on the student's learning outcomes, such as "scores" or "right/wrong,"…
Descriptors: Information Technology, Computer Science Education, High School Students, Scoring
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Li, Cheng-Hsuan; Wu, Huey-Min; Kuo, Bor-Chen; Yang, Yu-Mao; Lin, Chin-Kai; Wang, Wei-Hsiang – Interactive Learning Environments, 2018
The purpose of this study is to explore the validity of the assessment tool. The purposive sampling method is applied in this research on a total of 551 preschool children between 4 and 6 years old. Their ages range from 46 to 81 months, with an average age of 63.9 months (SD = 7.58). The assessment tool used in this research is the…
Descriptors: Test Validity, Chinese, Psychomotor Skills, Preschool Children