Who's Learning? Using Demographics in EDM Research

Item

Title

Who's Learning? Using Demographics in EDM Research

Abstract/Description

The growing use of machine learning for the data-driven study of social issues and the implementation of data-driven decision processes has required researchers to re-examine the often implicit assumption that data-driven models are neutral and free of biases. The careful examination of machine-learned models has identified examples of how existing biases can inadvertently be perpetuated in fields such as criminal justice, where failing to account for racial prejudices in the prediction of recidivism can perpetuate or exasperate them, and natural language processing, where algorithms trained on human languages corpora have been shown to reproduce strong biases in gendered descriptions. These examples highlight the importance of thinking about how biases might impact the study of educational data and how data-driven models used in educational contexts may perpetuate inequalities. To understand this question, we ask whether and how demographic information, including age, educational level, gender, race/ethnicity, socioeconomic status (SES), and geographical location, is used in Educational Data Mining (EDM) research. Specifically, we conduct a systematic survey of the last five years of EDM publications that investigates whether and how demographic information about the students is reported in EDM research and how this information is used to 1) investigate issues related to demographics, 2) use the information as input features for data-driven analyses, or 3) to test and validate models. This survey shows that, although a majority of publications reported at least one category of demographic information, the frequency of reporting for different categories of demographic information is very uneven (ranging from 5% to 59%), and only 15% of publications used demographic information in their analyses.

Author/creator

Paquette, Luc

Ocumpaugh, Jaclyn

Li, Ziyue

Andres, Alexandra

Baker, Ryan S. J. d.

Date

2020

In publication

Journal of Educational Data Mining

Volume

12

Issue

3

Pages

1-30

Resource type

en Research/Scholarly Media

Resource status/form

en Published Text

Scholarship genre

en Synthesis/Overview

Language

en

Open access/full-text available

en Yes

Peer reviewed

en Yes

ISSN

2157-2100

DOI

10.5281/zenodo.4143612

URL

Official Publisher's Webpage (Zenodo)

Citation

Paquette, L., Ocumpaugh, J., Li, Z., Andres, A., & Baker, R. (2020). Who’s Learning? Using Demographics in EDM Research. Journal of Educational Data Mining, 12(3), Article 3. https://doi.org/10.5281/zenodo.4143612

Abbreviation

JEDM

Rights

Copyright (c) 2020 Luc Paquette, Jaclyn Ocumpaugh, Ziyue Li, Alexandra Andres, Ryan Baker

Item sets

Handbook Chapter 20 Citations

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