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@article{146011,
        author = {M Sivashankar and M.sreedevi},
        title = {Identifying At-Risk Students for Early Interventions�A Time-Series Clustering Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {11},
        pages = {1051-1057},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146011},
        abstract = {the purpose of this study is to identify at-risk online students earlier, more often, and with greater accuracy using time-series clustering. The case study showed that the proposed approach could generate models with higher accuracy and feasibility than traditional frequency aggregation approaches. The best performing model can start to capture at-risk students from week 10. In addition, the four phases in student’s learning process detected holiday effect and illustrates at-risk students’ behaviors before and after a long holiday break. The findings also enable online instructors to develop corresponding instructional interventions via course design or student-teacher communications},
        keywords = {Clustering, classification, and association rules, Feature extraction or construction, Mining methods and algorithms, Time-Series analysis, LMS, predictive modeling},
        month = {},
        }