Predicting Student Performance in Massive Open Online Courses (MOOCs) Using Big Data Analysis and Convolutional Neural Network

Authors : Ibrahim Aboualgasem O Alrmah, Dr. Asiah Lokman

Volume/Issue : Volume 5 - 2020, Issue 3 - March

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One of the understanding analytics in MOOC is to determine and forecast students' performance based on different antecedents that gathered as records from the system for the engagement activities of the students. Along with the visibility of big data, the usage of artificial intelligent methods can easily supply effective results in forecasting the students' standing and performance. This study aims to provide an artificial neural network design for forecasting students pass/fail status along with their band performance based on MOOC big data analysis. The data collection utilized is the one collected and discharged through Harvard and MIT "HarvardXMITx -Course Dataset AY2013" in May, 2014. MATLAB Convolutional Neural Networks (CNN) is used as a platform for simulating the proposed design. For the data set, the total cases were 641138 cases; the filtered cases with complete field were 58453 cases. The initial design has eight possible input variables, which is tested as a preliminary step to determine its importance to the model. The final design for predicting learners’ performance and level have four inputs and two outputs. Predicting accuracy of success status (pass/fail) shows that 91.6% of learners’ success status can be predicted by using testing data. Predicting accuracy of success Level (Band 1 to Band 5) shows that 82.6% of learners’ success status can be predicted by using testing data. The proposed data mining model has four input variables and the precedence for its importance are day’s activity, followed by played videos, then events number, and finally chapters opened.

Keywords : MOOC, Data Analytics, CNN, Student Performance, Educational Data Science.


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