TY - JOUR
T1 - Detecting at-risk students with early interventions using machine learning techniques
AU - Al-Shabandar, Raghad
AU - Hussain, Abir Jaafar
AU - Liatsis, Panos
AU - Keight, Robert
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - area under curve
KW - Machine learning
KW - massive open online courses
KW - receiver operator characteristics
UR - http://www.scopus.com/inward/record.url?scp=85077739446&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2943351
DO - 10.1109/ACCESS.2019.2943351
M3 - Article
AN - SCOPUS:85077739446
SN - 2169-3536
VL - 7
SP - 149464
EP - 149478
JO - IEEE Access
JF - IEEE Access
M1 - 8847304
ER -