Algorithmic Bias in Education

Algorithmic Bias in Education

This Wiki summarizes the current evidence surrounding Algorithmic Bias in Education: which groups are impacted, and in which contexts.

For a relatively recent review on this topic, see Baker, R.S., Hawn, M.A. (in press) Algorithmic Bias in Education. To appear in International Journal of Artificial Intelligence and Education (pdf)

By Group Impacted

• Race and Ethnicity
  • Black/African-American Learners in North America
  • Latino/Latina/Latinx/Hispanic Learners in North America
  • Asian/Asian-American Learners in North America
  • White Learners in North America
  • Indigenous Learners in North America
  • Research on Race and Ethnicity Conducted Outside of North America
• Gender: Male/Female
• Gender: Non-Binary and Transgender Learners
• Sexual Orientation
• Linguistic Origin
• National Origin or National Location
• International Students
• Native Language and Dialect
• Learners with Disabilities
• Age
• Urbanicity
• Parental Educational Background
• Socioeconomic Status
• Military-Connected Status
• Children of Migrant Workers
• Religion and Religious Background
• Public or Private K-12 School
• Intersectional Research

By Algorithm Application

• At-risk/Dropout/Stopout/Graduation Prediction
• Course Grade and GPA Prediction
• National and International Examination
• Short-term Performance and Learning Gains Prediction
• Automated Essay Scoring
• Speech Recognition for Education
• Other NLP Applications of Algorithms in Education
• Student Knowledge Modeling
• Engagement and Affect Detection
• Self-regulated Learning