Difference between revisions of "Latino/Latina/Latinx/Hispanic Learners in North America"

Revision as of 05:01, 10 June 2022

Anderson et al. (2019) pdf

Models predicting six-year college graduation
False negatives rates were greater for Latino students when Decision Tree and Random Forest yielded was used
White students had higher false positive rates across all models, Decision Tree, SVM, Logistic Regression, Random Forest, and SGD

Christie et al. (2019) pdf

Models predicting student's high school dropout
The decision trees showed little difference in AUC among Hispanic, White, Black, Asian, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.

Lee and Kizilcec (2020) pdf

Models predicting college success (or median grade or above)
Random forest algorithms performed significantly worse for underrepresented minority students (URM; Hispanic, American Indian, Black, Hawaiian or Pacific Islander, and Multicultural) than non-URM students (White and Asian)
The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values

Yu et al. (2020) pdf

Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
Hispanic students were inaccurately predicted to perform worse for both short-term and long-term
The fairness of models improved when either click or a combination of click and survey data, and not institutional data, was included in the model

Yu et al. (2021) pdf

Models predicting college dropout for students in residential and fully online program
Whether the socio-demographic information was included or not, the model showed worse true negative rates for students who are underrepresented minority (URM; or not White or Asian), and worse accuracy if URM students are studying in person
The model showed better recall for URM students, whether they were in residential or online program

Bridgeman et al. (2009) page

Automated scoring models for evaluating English essays, or e-rater
E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students

Jiang & Pardos (2021) pdf

Predicting university course grades using LSTM
Roughly equal accuracy across racial groups
Slightly better accuracy (~1%) across racial groups when including race in model

Zhang et al. (in press)

Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process
For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.
No racial/ethnic group consistently had best-performing detectors

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 Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]
 * Models predicting student's high school dropout
-* The decision trees showed little difference in AUC among White, Black, Hispanic, Asian, American Indian and Alaska Native, and  Native Hawaiian and Pacific Islander.
+* The decision trees showed little difference in AUC among Hispanic, White, Black, Asian, American Indian and Alaska Native, and  Native Hawaiian and Pacific Islander.
 Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]
 * Models predicting college success (or median grade or above)
-* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
+* Random forest algorithms performed significantly worse for underrepresented minority students (URM; Hispanic, American Indian, Black, Hawaiian or Pacific Islander, and Multicultural) than non-URM students (White and Asian)
 * The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values