Final Exam Prediction Analysis for Students Performance in Massive Open Online Learning (MOOC) Online Classes Using Optimization Method (S/O 14148)

• Main Authors: Abu Bakar, Nashirah, Abu Bakar, Azizi
• Format: Monograph
• Language: English
• Published: UUM 2021
• Subjects: L Education (General)
• Online Access: https://repo.uum.edu.my/id/eprint/30217/1/14148.pdf; https://repo.uum.edu.my/id/eprint/30217/

The objective of this research is to develop robust estimation of student performance in Massive Open Online Course (MOOC) using fuzzy logic approach. A massive open online course (MOOC) is an online course aimed at unlimited participation and open access via the web. This evaluation for MOOC was implemented using online assessment marks and online self-learning time. Data were collected from 30 students who were participated in online course. Data for online assessment marks was represented using trapezoidal membership function. Meanwhile, data for online self-learning time was represented using triangular membership function. Output data for this analysis using final examination marks with gaussian membership function. Fuzzy logic procedure involved in this study using three procedures namely fuzzification of all inputs, fuzzy inference process using rule base and defuzzification to get output values. Results indicated higher value online assessment marks and higher value of online self-learning time contributed to higher performance in final examination. The findings of this study will help educators to forecast student performance in final examination with considering online input variables namely online assessments marks and online self-learning time. This study also will help students to adjust their self-learning time in obtaining required expected result in final examination.