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Background: There has been a dearth of research on intersectional identities in STEM, including the fields of computing and engineering. In computing education research, much work has been done on broadening participation, but there has been little investigation into how the field of computer science (CS) presents opportunities for students with strong intersectional identities. This study explores the strengths and connections among the unique identities and the symbiotic relationships that elementary Latina students hold in CS identity attainment. Purpose: The aim of this article is to better understand how predominantly low-income, multilingual Latina students experience identity development through the lens of diverse group membership. We examine how young Latinas, through their participation in a yearlong culturally and linguistically responsive CS curriculum, leverage their intersecting identities to rewrite the formula of what a computer scientist is and can be, leaving space to include and invite other strong identities as well. Research Design: An explanatory sequential mixed-methods design was used that analyzed data from predominantly low-income, multilingual Latinas in upper elementary grades, including pre- and post-CS identity surveys (N = 50) delivered before and after implementation of the curriculum, and eight individual semi-structured student interviews. Findings: We found that Latina students developed significantly stronger identification with the field of CS from the beginning to the end of the school year with regard to their experiences with CS, perception of themselves as computer scientists, family support for CS and school, and friend support for CS and school. Interviews revealed that perception of their CS ability greatly influenced identification with CS and that girls’ self-perceptions stemmed from their school, cultural, and home learning environments. Conclusion: Our results highlight the wealth of resources that Latinas bring to the classroom through their home- and community-based assets, which are characterized by intersecting group membership. Students did not report on the intersection between language and CS identity development, which warrants further investigation. 

Video programs are important, accessible educational resources for young children, especially those from an under-resourced backgrounds. These programs’ potential can be amplified if children are allowed to socially interact with media characters during their video watching. This paper presents the design and empirical investigation of interactive science-focused videos in which the main character, powered by a conversational agent, engaged in contingent conversation with children by asking children questions and providing responsive feedback. We found that children actively interacted with the media character in the conversational videos and their parents spontaneously provided support in the process. We also found that the children who watched the conversational video performed better in the immediate, episode-specific science assessment compared to their peers who watched the broadcast, non-interactive version of the same episode. Several design implications are discussed for using conversational technologies to better support child active learning and parent involvement in video watching. 

Abstract: Developing student interest is critical to supporting student learning in computer science. Research indicates that student interest is a key predictor of persistence and achievement. While there is a growing body of work on developing computing identities for diverse students, little research focuses on early exposure to develop multilingual students’ interest in computing. These students represent one of the fastest growing populations in the US, yet they are dramatically underrepresented in computer science education. This study examines identity development of upper elementary multilingual students as they engage in a year-long computational thinking curriculum, and follows their engagement across multiple settings (i.e., school, club, home, community). Findings from pre- and -post surveys of identity showed significant differences favoring students’ experiences with computer science, their perceptions of computer science, their perceptions of themselves as computer scientists, and their family support for computer science. Findings from follow-up interviews and prior research suggest that tailored instruction provides opportunities for connections to out-of-school learning environments with friends and family that may shift students’ perceptions of their abilities to pursue computer science and persist when encountering challenges. 

This quantitative study examined student participation in an introductory project-based engineering course offered in fully face-to-face and hybrid course modes (N = 160). This course attempted to counteract trends of decreased student motivation and high attrition rates among engineering majors. Mixed-design analysis of variance examined differences in motivational constructs including student self-efficacy, effort regulation, and interest in engineering, as well as engineering skills throughout the course and across instructional modes. None of the motivational constructs were associated with significant decreases throughout the course nor with differences across instructional modes. However, students’ engineering skills increased throughout the course with no significant differences across course modalities. Furthermore, interest in engineering and effort regulation were positively associated with course performance. The instructional modality was not significantly associated with course performance. Overall, this study provides an example of a project-based introductory engineering course which may help maintain student motivation and foster student success in engineering.

 

Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose. In particular, existing datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. Drawing on the reading education research, we introduce FairytaleQA, a dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. Our dataset is valuable in two folds: First, we ran existing QA models on our dataset and confirmed that this annotation helps assess models’ fine-grained learning skills. Second, the dataset supports question generation (QG) task in the education domain. Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.