Work

Generating descriptive model for student dropout: a review of clustering approach

Year: 2017

Type: article

Abstract: The implementation of data mining is widely considered as a powerful instrument for acquiring new knowledge from a pile of historical data, which is normally left unstudied. This data driven methodology has proven effective to improve the quality of decision-making in several domains such as business, medical and complex engineering problems. Recently, educational data mining (EDM) has obtained a great deal of attention among educational researchers and computer scientists. In general, publications in the field of EDM focus on understanding student types and targeted marketing, using both descriptive and predictive models to maximize student retention. Inspired by previous attempts, this paper aims to establish the clustering approach as a practical guideline to explore student categories and characteristics, with the working example on a real dataset to illustrate analytical procedures and results.

Source: Human-centric Computing and Information Sciences

DOI: 10.1186/s13673-016-0083-0

Authors: Natthakan Iam-On, Tossapon Boongoen

Institutions: Mae Fah Luang University, Navaminda Kasatriyadhiraj Royal Thai Air Force Academy

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $1,870