Work

A systematic analysis using classification machine learning algorithms to understand why learners drop out of MOOCs

Year: 2021

Type: article

Abstract: The increasing popularity of massively online open courses (MOOCs) has been attracting a lot of learners. Despite the popularity, it has been observed that there is a significant percentage of learners who discontinue courses and drop out of the platform. This is a problem that most of the MOOC courses face. The dropout probability of any student depends on his/her interaction with the platform, and the features of the course in which the student has enrolled. The research work is intended to study and analyze the dropout behavior of the students in online learning with identification of the reasons and to understand their impact. The current research accounts for the activity log of learners of 13 different online courses offered by Harvard and MIT during 2012 to 2013. The work examines the attributes which affects the student dropout rate. The research can be useful in improving the existing features of the MOOC courses and content to ensure persistence turnout of their learners.

Source: Neural Computing and Applications

DOI: 10.1007/s00521-021-06122-3

Authors: Seema Rawat, Deepak Kumar, Praveen Kumar, Chhaya Khattri

Institution: Amity University

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

OpenAlex ID: W3168896443