Online learning has become more popular in higher education since it adds convenience and flexibility to students' schedule. But, it has faced difficulties in the retention of the continuity of students and ensure continual growth in course. Dropout is a concerning factor in online course continuity. Therefore, it has sparked great interest among educators and researchers to offer many models and strategies to reduce online course students' dropout by analyzing students' behavior and their individual and academic information. However, online education platforms still face challenges of high dropout rate and the difficulty of accurate prediction to reduce it. The key aim of the present study is to construct a predictive model to early predict students who are at-risk of dropout. This model is useful to the course instructors to make effective and timely interventions. We have noticed that dropout prediction is basically a sequence labeling or time series prediction problem. For these reasons, we proposed two models; the Logistic Regression by adding a regularization term and the Input-Output Hidden Markov Model (IOHMM). Results showed that the proposed models achieved an accuracy of 84% compared to the baseline of Machine Learning Models for prediction of the students at-risk of dropping out.