Race and gender biases have been a long-standing issue in paper textbooks, but prior research showed that online literacy development materials still carry significant biases and misrepresentations, potentially exacerbating the problem at a much larger scale. Surfacing these biases and stereotypes thus becomes crucial in flagging problematic content and ensuring the integrity of these learning materials. This study applies NLP techniques to nearly 2000 news articles used as literacy development materials by a popular K-12 digital learning platform to answer two following research questions: 1) which race and gender groups are more associated with which topics, and 2) how similar are the contexts in which different race and gender groups appear? Results from topic modeling show that different gender and race groups are associated with widely different set of topics: men are associated more with 鈥渃rime and law enforcement鈥, 鈥減olitics鈥, 鈥渉ealthcare/medicine鈥, and 鈥渨ar鈥, whereas women are associated more with 鈥渆ducation鈥, and 鈥渋nternational issues鈥; white people with 鈥減olitics鈥 and 鈥済overnment/national issues鈥; black people with 鈥渃rime and law enforcement鈥; Asian and Latinx people with 鈥渕inority鈥 and 鈥渋mmigration鈥. In addition, results from word embedding show that different race groups don鈥檛 often appear in similar contexts, indicating potentially narrow and stereotypical portrayals of minorities, consistent with prior research. Overall, this study contributes to a more nuanced understanding of race and gender misrepresentations in digital literacy development materials and highlights the importance of culturally responsive education in the digital age and the need for a more inclusive and just education for future generations.
