Discovering Educational Data Mining: An Introduction
Year: 2024
Type: article
Abstract: This article introduces researchers in the science concerned with developing and studying research methods, measurement, and evaluation (RMME) to the educational data mining (EDM) community. It assumes that the audience is familiar with traditional priorities of statistical analyses, such as accurately estimating model parameters and inferences from those models. Instead, this article focuses on data mining’s adoption of statistics and machine learning to produce cutting-edge methods in educational contexts. It answers three questions: (1) What are the primary interests of EDM and RMME researchers? (2) What is their discipline-specific vocabulary? and (3) What are the similarities and differences in how the EDM and RMME communities analyze similar types of data?
DOI: 10.7275/pare.2037
Authors: Zachary Collier, Joshua Sukumar, Roghayeh Barmaki
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W6902856229