The Experts below are selected from a list of 87 Experts worldwide ranked by ideXlab platform
Robert Keight - One of the best experts on this subject based on the ideXlab platform.
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Detecting At-Risk Students With Early Interventions Using Machine Learning Techniques
IEEE Access, 2019Co-Authors: Raghad Al-shabandar, Abir Hussain, Panos Liatsis, Robert KeightAbstract:Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility of the teaching environment, the number of participants is rapidly growing. However, extensive research reports that the high attrition rate and low completion rate are major concerns. In this paper, the early identification of Students who are at risk of withdrew and failure is provided. Therefore, two models are constructed namely At-Risk Student model and learning achievement model. The models have the potential to detect the Students who are in danger of failing and withdrawal at the early stage of the online course. The result reveals that all classifiers gain good accuracy across both models, the highest performance yield by GBM with the value of 0.894, 0.952 for first, second model respectively, while RF yield the value of 0.866, in At-Risk Student framework achieved the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to At-Risk Students.
Raghad Al-shabandar - One of the best experts on this subject based on the ideXlab platform.
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Detecting At-Risk Students With Early Interventions Using Machine Learning Techniques
IEEE Access, 2019Co-Authors: Raghad Al-shabandar, Abir Hussain, Panos Liatsis, Robert KeightAbstract:Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility of the teaching environment, the number of participants is rapidly growing. However, extensive research reports that the high attrition rate and low completion rate are major concerns. In this paper, the early identification of Students who are at risk of withdrew and failure is provided. Therefore, two models are constructed namely At-Risk Student model and learning achievement model. The models have the potential to detect the Students who are in danger of failing and withdrawal at the early stage of the online course. The result reveals that all classifiers gain good accuracy across both models, the highest performance yield by GBM with the value of 0.894, 0.952 for first, second model respectively, while RF yield the value of 0.866, in At-Risk Student framework achieved the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to At-Risk Students.
Anthony D. Molina - One of the best experts on this subject based on the ideXlab platform.
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Style Over Substance Reconsidered: Intrusive Intervention and At-Risk Students with Learning Disabilities
NACADA Journal, 2002Co-Authors: Robert Abelman, Anthony D. MolinaAbstract:In two recent publications, we reported that the academic intervention process, not the specific intervention content, was responsible for a short-and long-term influx in At-Risk Student performanc...
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Style Over Substance Revisited: A Longitudinal Analysis of Intrusive Intervention
NACADA Journal, 2001Co-Authors: Robert Abelman, Anthony D. MolinaAbstract:In a recent report, the authors showed that the academic intervention process, rather than the specific intervention content, was responsible for a short-term influx in At-Risk Student performance ...
Panos Liatsis - One of the best experts on this subject based on the ideXlab platform.
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Detecting At-Risk Students With Early Interventions Using Machine Learning Techniques
IEEE Access, 2019Co-Authors: Raghad Al-shabandar, Abir Hussain, Panos Liatsis, Robert KeightAbstract:Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility of the teaching environment, the number of participants is rapidly growing. However, extensive research reports that the high attrition rate and low completion rate are major concerns. In this paper, the early identification of Students who are at risk of withdrew and failure is provided. Therefore, two models are constructed namely At-Risk Student model and learning achievement model. The models have the potential to detect the Students who are in danger of failing and withdrawal at the early stage of the online course. The result reveals that all classifiers gain good accuracy across both models, the highest performance yield by GBM with the value of 0.894, 0.952 for first, second model respectively, while RF yield the value of 0.866, in At-Risk Student framework achieved the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to At-Risk Students.
Abir Hussain - One of the best experts on this subject based on the ideXlab platform.
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Detecting At-Risk Students With Early Interventions Using Machine Learning Techniques
IEEE Access, 2019Co-Authors: Raghad Al-shabandar, Abir Hussain, Panos Liatsis, Robert KeightAbstract:Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility of the teaching environment, the number of participants is rapidly growing. However, extensive research reports that the high attrition rate and low completion rate are major concerns. In this paper, the early identification of Students who are at risk of withdrew and failure is provided. Therefore, two models are constructed namely At-Risk Student model and learning achievement model. The models have the potential to detect the Students who are in danger of failing and withdrawal at the early stage of the online course. The result reveals that all classifiers gain good accuracy across both models, the highest performance yield by GBM with the value of 0.894, 0.952 for first, second model respectively, while RF yield the value of 0.866, in At-Risk Student framework achieved the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to At-Risk Students.