Authors : Rabie Moussa Houssin and Mohammed Nazeh Abdulwahid
Abstract: This study investigated the gap of how the differences of a personal profile, subjects and culture may moderate the learner's performance in Massive Open Online Course (MOOC). One of the learning analytics in MOOC is assessing and predicting learner's performance, dropout and completion. The dataset consists of 641,138 observations of 20 variables. Based on literature review and analysis of the dataset variables twelve variables are chosen. For measurement purposes, two variables "grade" and "certification" which account for learning outcomes are available to the researcher. Five hypothesis are examining direct relations while another four set of relations are examining the moderation affect. The model summary measures show that this accounts for only 4.9% of variance in the spelling grades. Interaction log forum log and chapters log associated hypothesis are rejected. Interaction days shows a significant standardize regression coefficients value (Beta = 0.219) in the model and the hypothesis is accepted. Video log shows a significant standardize regression coefficients value (Beta = 0.217) in the model where hypothesizes are discussed and results are figured out.
Rabie Moussa Houssin and Mohammed Nazeh Abdulwahid, 2018. Learning Analytics in Massive Open Online Courses (MOOCS)-Assessing Participant's Performance. Agricultural Journal, 13: 107-113.