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Download full textLucy, Li; Demszky, Dorottya; Bromley, Patricia; Jurafsky, Dan – AERA Open, 2020
Cutting-edge data science techniques can shed new light on fundamental questions in educational research. We apply techniques from natural language processing (lexicons, word embeddings, topic models) to 15 U.S. history textbooks widely used in Texas between 2015 and 2017, studying their depiction of historically marginalized groups. We find that…
Descriptors: Textbooks, United States History, History Instruction, Textbook Content
Whitcomb, Kyle M.; Cwik, Sonja; Singh, Chandralekha – AERA Open, 2021
An analysis of institutional data to understand the outcome of obstacles faced by students from historically disadvantaged backgrounds is important in order to work toward promoting equity and inclusion. We use 10 years of institutional data at a large public research university to investigate the grades earned by students categorized on four…
Descriptors: Racial Bias, Ethnicity, Minority Group Students, Grade Point Average
Casellas Connors, Ishara – AERA Open, 2021
As racialized institutions, Hispanic-serving institutions educate large portions of racially minoritized students within organizational and policy structures that advance Whiteness. This research considers how the institution-level diversity, equity, and inclusion (DEI) plans, produced in response to state-level DEI policies in Florida,…
Descriptors: Minority Serving Institutions, Hispanic American Students, Student Diversity, Inclusion
