Difference between revisions of "Algorithmic Bias in Education"
m |
m |
||
(One intermediate revision by the same user not shown) | |||
Line 12: | Line 12: | ||
This wiki can be cited as | This wiki can be cited as | ||
Penn Center for Learning Analytics (*current year*) | Penn Center for Learning Analytics (*current year*) Empirical Evidence for Algorithmic Bias in Education: The Wiki. | ||
Philadelphia, PA: Penn Center for Learning Analytics. | Philadelphia, PA: Penn Center for Learning Analytics. | ||
Retrieved *current date* from https://www.pcla.wiki/index.php/Algorithmic_Bias_in_Education | Retrieved *current date* from https://www.pcla.wiki/index.php/Algorithmic_Bias_in_Education | ||
Line 27: | Line 27: | ||
* [[Gender: Non-Binary and Transgender Learners]] | * [[Gender: Non-Binary and Transgender Learners]] | ||
* [[Sexual Orientation]] | * [[Sexual Orientation]] | ||
* [[National Origin or National Location]] | * [[National Origin or National Location]] | ||
* [[International Students]] | * [[International Students]] |
Latest revision as of 14:59, 15 February 2023
Empirical Evidence for Algorithmic Bias in Education: The Wiki
This Wiki summarizes the current peer-reviewed published 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)
Note: Within this Wiki, we recommend that page editors use the group labels originally used within the publications being cited, to best represent the articles included here. We also request that each page center the members of the group the page is about.
This wiki can be cited as Penn Center for Learning Analytics (*current year*) Empirical Evidence for Algorithmic Bias in Education: The Wiki. Philadelphia, PA: Penn Center for Learning Analytics. Retrieved *current date* from https://www.pcla.wiki/index.php/Algorithmic_Bias_in_Education
By Group Impacted
- Race and Ethnicity
- Gender: Male/Female
- Gender: Non-Binary and Transgender Learners
- Sexual Orientation
- 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
- Task/Activity Quit Prediction
- Social Network Link Prediction