@article{nocera_newton_jiang_2024, title={"They created segregation with the economy": Using AI for a student-driven inquiry into redlining in the social studies classroom}, ISSN={["2163-1654"]}, DOI={10.1080/00933104.2024.2331466}, abstractNote={This article investigates students' engagement with a historical inquiry into redlining—a practice of discriminatory lending that originated in the 1930s as part of the New Deal. The authors developed and implemented a week-long curricular intervention for high school sophomores using StoryQ—an Artificial Intelligence (AI) textual modeling platform designed for high school students without technical expertise—to examine hundreds of neighborhood descriptions produced for the Home Owners Loan Corporation's "residential security maps" in the late 1930s. In this article, we ask: What kinds of historical and present-day racial awareness do high school students demonstrate through instruction focused on AI-assisted analysis of patterns in redlining? Analyzing field notes, interviews, and student-generated digital work showed that many students were drawn to structural explanations of racism and worked to unpack the way primary sources presented Whiteness through "coded language." We argue that it is not only possible for teachers to construct historical inquiries that aim to identify patterns in a large set of primary sources with the aid of AI, but this approach to inquiry offers students an important avenue to engage with alternatives to individual conceptions of racial oppression.}, journal={THEORY AND RESEARCH IN SOCIAL EDUCATION}, author={Nocera, Amato and Newton, Victoria and Jiang, Shiyan}, year={2024}, month={Apr} }
 @article{nocera_2023, title={"May We Not Write Our Own Fairy Tales and Make Black Beautiful?" African American Teachers, Children's Literature, and the Construction of Race in the Curriculum, 1920-1945}, volume={63}, ISSN={["1748-5959"]}, DOI={10.1017/heq.2022.41}, abstractNote={Abstract This article examines children's literature written by African American teachers during the first part of the twentieth century. Drawing on theories of racialization, I analyze children's books written by two African American teachers: Helen Adele Whiting (1885-1959) and Jane Dabney Shackelford (1895-1979). I argue that their books represented more than an effort toward greater Black representation in schools; they also served as a contribution to a larger discourse on Blackness and identity that emerged during the “New Negro” movement. In this view, African American teachers were not mere passive recipients of an outside Black culture, but rather intellectual actors involved in the production of racial identity during the interwar period.}, number={1}, journal={HISTORY OF EDUCATION QUARTERLY}, author={Nocera, Amato}, year={2023}, month={Feb}, pages={32–58} }
 @article{jiang_nocera_tatar_yoder_chao_wiedemann_finzer_rose_2022, title={An empirical analysis of high school students' practices of modelling with unstructured data}, ISSN={["1467-8535"]}, DOI={10.1111/bjet.13253}, abstractNote={Abstract To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making. Practitioner notes What is already known about this topic Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models. While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data. There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data. What this paper adds Results show that students developed nuanced understandings of models learning patterns in data for automated decision making. Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models. Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in. Implications for practice and/or policy It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies. Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources. To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).}, journal={BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY}, author={Jiang, Shiyan and Nocera, Amato and Tatar, Cansu and Yoder, Michael Miller and Chao, Jie and Wiedemann, Kenia and Finzer, William and Rose, Carolyn P.}, year={2022}, month={Jul} }
 @article{nocera_2022, title={Fugitive Pedagogy: Carter G. Woodson and the Art of Black Teaching}, volume={62}, ISSN={["1748-5959"]}, DOI={10.1017/heq.2021.63}, abstractNote={An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.}, number={1}, journal={HISTORY OF EDUCATION QUARTERLY}, author={Nocera, Amato}, year={2022}, month={Feb}, pages={127–130} }
 @article{nocera_2022, title={The Development of Southern Public Libraries and the African American Quest for Library Access, 1898-1963}, volume={127}, ISSN={["1937-5239"]}, DOI={10.1093/ahr/rhac310}, abstractNote={Dallas Hanbury’s title says a great deal about this unflashy, descriptive, and concise history. The Development of Southern Public Libraries and the African American Quest for Library Access, 1898–1963 is a book about public library systems in the South developed for white southerners and how African Americans struggled for library access during the first part of the twentieth century. Hanbury’s title may cue the reader into another important feature: this book is driven by its subject matter, not broader meaning-making or historical interpretation. Hanbury does an amiable job in covering significant historical ground, even featuring some original primary source research, but missing from the picture is an interpretative framework that might provide greater resonance to Hanbury’s narrative. Indeed, he does little to guide his readers. Historical interventions and analysis are largely cast aside. This book is a well-researched and detailed portrayal of Black America’s engagement with public libraries, but Hanbury largely falls flat on scholarly impact.}, number={3}, journal={AMERICAN HISTORICAL REVIEW}, author={Nocera, Amato}, year={2022}, month={Nov}, pages={1520–1521} }