Article

Discovering Educational Data Mining: An Introduction

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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?

Keywords: Education Measurement and Evaluation, Educational Data Mining, Disciplinary Research Comparison, Causal Inference, Prediction

How to Cite:

Collier, Z., Sukumar, J. & Barmaki, R., (2024) “Discovering Educational Data Mining: An Introduction”, Practical Assessment, Research, and Evaluation 29(1): 11. doi: https://doi.org/10.7275/pare.2037