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Xiao Wen; Hu Juan – Interactive Learning Environments, 2024
To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows:…
Descriptors: MOOCs, Dropouts, Dropout Characteristics, Dropout Research
Mizumoto, Atsushi – Language Learning, 2023
Researchers often make claims regarding the importance of predictor variables in multiple regression analysis by comparing standardized regression coefficients (standardized beta coefficients). This practice has been criticized as a misuse of multiple regression analysis. As a remedy, I highlight the use of dominance analysis and random forests, a…
Descriptors: Predictor Variables, Artificial Intelligence, Evaluation Methods, Multiple Regression Analysis
Asselman, Amal; Khaldi, Mohamed; Aammou, Souhaib – Education and Information Technologies, 2020
Recently, tracking student behavior has become a very important phase for constructing adaptive educational systems. Several researchers have developed various methods based on machine learning for better tracing students' knowledge. Most of these methods have shown an effective estimation of student features and an accurate prediction of future…
Descriptors: Scaffolding (Teaching Technique), Predictor Variables, Student Behavior, Academic Achievement
Soland, James; Domingue, Benjamin; Lang, David – Teachers College Record, 2020
Background/Context: Early warning indicators (EWI) are often used by states and districts to identify students who are not on track to finish high school, and provide supports/interventions to increase the odds the student will graduate. While EWI are diverse in terms of the academic behaviors they capture, research suggests that indicators like…
Descriptors: Identification, At Risk Students, Potential Dropouts, High School Students
Madhyastha, Tara M.; Tanimoto, Steven – International Working Group on Educational Data Mining, 2009
Most of the emphasis on mining online assessment logs has been to identify content-specific errors. However, the pattern of general "consistency" is domain independent, strongly related to performance, and can itself be a target of educational data mining. We demonstrate that simple consistency indicators are related to student outcomes,…
Descriptors: Web Based Instruction, Computer Assisted Testing, Computer Software, Computer Science Education
Anderson, Joan L. – College and University, 2006
Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…
Descriptors: Graduate Students, Grade Point Average, Predictor Variables, Success