Classification model of students' performance based on interaction on massive open online courses (MOOCs) - Mohd Fahmi bin Husin
Interaction is central to any learning experience, especially in the online learning method. Massive Open Online Courses (MOOCs) is one of the learning platforms to support open leaming education as well as classroom teaching to enhance student teacher interaction during the teaching and learning process. Studies show that the lack of interaction between students with the course taken has an impact on the students' performance i.e. whether they pass, fail or drop the course. Therefore, the objective of this study is to analyse the significant attributes that may affect their interaction performance and learning behaviour. This study also proposes a classification model for the performance of students using MOOCs. For this purpose, the research follows the Knowledge Discovery in Database (KDD) methodology starting with data collection, data pre-processing, data analysis by using feature selection method, classification algorithm, and finally evaluating or interpreting the knowledge gained. The dataset that has been used in this study is obtained from the Open University Leaming Analytics Dataset (OULAD). The datasets are tested by applying six classifiers namely Naive Bayes (NB), Nearest Neighbour (NN), Multi-Class Classifier (MCC), Rules One R (OR), 148, and Random Tree (RT). The results show that the proposed classification model developed can classify students according to their academic achievement and interaction with the MOOCs. It is also found the 148 Classifier is the most appropriate model to classify the performance, 148 Classifier shows the highest result among the six models that have been applied to the OULAD data sets with an accuracy of96.8%. It also found that the attributes namely studentfinal result has a significant relationship with sum click, date submitted, id assessment, imd band, age band, gender, studied credit, highest education and assessment date which can contribute to the effectiveness of the students' performance. In conclusion, this result is vital as the educators can focus on developing the underperforming students once they have been successfully identified by using such attributes in the MOOCs platform.