Analysis of MOOCs Design based on Herzberg's Theory: A Literature Review

Abstract

The COVID-19 pandemic has changed all types of activities, including in the education sector. As an online education platform, MOOCs are one of the innovations in distance learning. The number of MOOCs users during the COVID-19 pandemic has increased rapidly, but the drop-out rate for MOOC users has also increased. Many MOOCs users do not complete the course they have chosen on MOOCs. One of the things that causes the high drop-out rate and low retention of MOOCs participants is the level of understanding of MOOCs participants towards the design and the interest and satisfaction of participants in existing MOOCs features or elements. Based on these problems, this research will analyze the design pattern based on the existing element classification using Two Factor Theory. The elements available in the MOOC will be classified into two factors, namely hygiene-factor and motivation-factor. Hygiene-factor is a MOOCs element that must be present, while motivation-factor is a MOOCs element that can increase MOOCs user satisfaction. The method used in the classification of these two factors is sentiment-analysis by utilizing NLP (Natural Language Processing) technology. The expected results in this literature review are references to elements in the MOOC based on previous research. The results of the identification of these elements will later be used as initialization data in the analysis of the MOOC design pattern using NLP technology.