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1. ractitioner Perspectives of the Impact of COVID-19 on CS Education in High Schools Serving Historically Marginalized Students (Fundamental).

   McGill, M. ( January 2022 , Proceedings ASEE annual conference)

   Practitioners delivering computer science (CS) education during the COVID-19 pandemic have faced numerous challenges, including the move to online learning. Understanding the impact on students, particularly students from historically marginalized groups within the United States, requires deeper exploration. Our research question for this study was: \textit{In what ways has the high school computer science educational ecosystem for students been impacted by COVID-19, particularly when comparing schools that have student populations with a majority of historically underrepresented students to those that do not?} To answer this question, we used the CAPE theoretical framework to measure schools’ Capacity to offer CS, student Access to CS education, student Participation in CS, and Experiences of students taking CS \cite{fletcherwarner2021cape}. We developed a quantitative instrument based on the results of a qualitative inquiry, then used the instrument to collect data from CS high school practitioners located in the United States (n=185) and performed a comparative analysis of the results. We found that the numbers of students participating in AP CS A courses, CS related as well as non-CS related extracurricular activities, and multiple extracurricular activities increased. However, schools primarily serving historically underrepresented students had significantly fewer students taking additional CS courses and fewer students participatingmore »in CS related extracurricular activities. Student learning in CS courses decreased significantly; however, engagement did not suffer. Other noncognitive factors, like students’ understanding of the relevance of technology and confidence using technology, improved overall; however, student interested in taking additional CS courses was significantly lower in schools primarily serving historically underrepresented students. Last, the numbers of students taking the AP CS A and AP CS Principles exams declined overall.« less
2. Practitioner Perspectives of the Impact of COVID-19 on CS Education in High Schools Serving Historically Marginalized Students (Fundamental)

   McGill, M. ( January 2022 , ASEE annual conference proceedings)

   Practitioners delivering computer science (CS) education during the COVID-19 pandemic have faced numerous challenges, including the move to online learning. Understanding the impact on students, particularly students from historically marginalized groups within the United States, requires deeper exploration. Our research question for this study was: In what ways has the high school computer science educational ecosystem for students been impacted by COVID-19, particularly when comparing schools that have student populations with a majority of historically underrepresented students to those that do not? To answer this question, we used the CAPE theoretical framework to measure schools’ Capacity to offer CS, student Access to CS education, student Participation in CS, and Experiences of students taking CS. We developed a quantitative instrument based on the results of a qualitative inquiry, then used the instrument to collect data from CS high school practitioners located in the United States (n=185) and performed a comparative analysis of the results. We found that the numbers of students participating in AP CS A courses, CS related as well as non-CS related extracurricular activities, and multiple extracurricular activities increased. However, schools primarily serving historically underrepresented students had significantly fewer students taking additional CS courses and fewer students participating inmore »CS related extracurricular activities. Student learning in CS courses decreased significantly; however, engagement did not suffer. Other noncognitive factors, like students’ understanding of the relevance of technology and confidence using technology, improved overall; however, student interested in taking additional CS courses was significantly lower in schools primarily serving historically underrepresented students. Last, the numbers of students taking the AP CS A and AP CS Principles exams declined overall.« less
3. Developing Empathy and Persistence through Professional Development in New to CSA Teachers

   https://doi.org/10.1145/3446871.3469793  

   Broneak, Cassandra ; Rosato, Jennifer ( August 2021 , ICER 2021: Proceedings of the 17th ACM Conference on International Computing Education Research)

   To meet the rising demand for computer science (CS) courses, K-12 educators need to be prepared to teach introductory concepts and skills in courses such as Computer Science Principles (CSP), which takes a breadth-first approach to CS and includes topics beyond programming such as data, impacts of computing, and networks. Educators are now also being asked to teach more advanced concepts in courses such as the College Board's Advanced Placement Computer Science A (CSA) course, which focuses on advanced programming using Java and includes topics such as objects, inheritance, arrays, and recursion. Traditional CSA curricula have not used content or pedagogy designed to engage a broad range of learners and support their success. Unlike CSP, which is attracting more underrepresented students to computing as it was designed, CSA continues to enroll mostly male, white, and Asian students [College Board 2019, Ericson 2020, Sax 2020]. In order to expand CS education opportunities, it is crucial that students have an engaging experience in CSA similar to CSP. Well-designed differentiated professional development (PD) that focuses on content and pedagogy is necessary to meet individual teacher needs, to successfully build teacher skills and confidence to teach CSA, and to improve engagement with students [Darling-Hammondmore »2017]. It is critical that as more CS opportunities and courses are developed, teachers remain engaged with their own learning in order to build their content knowledge and refine their teaching practice [CSTA 2020]. CSAwesome, developed and piloted in 2019, offers a College Board endorsed AP CSA curriculum and PD focused on supporting the transition of teachers and students from CSP to CSA. This poster presents preliminary findings aimed at exploring the supports and challenges new-to-CSA high school level educators face when transitioning from teaching an introductory, breadth-first course such as CSP to teaching the more challenging, programming-focused CSA course. Five teachers who completed the online CSAwesome summer 2020 PD completed interviews in spring 2021. The project employed an inductive coding scheme to analyze interview transcriptions and qualitative notes from teachers about their experiences learning, teaching, and implementing CSP and CSA curricula. Initial findings suggest that teachers’ experience in the CSAwesome PD may improve their confidence in teaching CSA, ability to effectively use inclusive teaching practices, ability to empathize with their students, problem-solving skills, and motivation to persist when faced with challenges and difficulties. Teachers noted how the CSAwesome PD provided them with a student perspective and increased feelings of empathy. Participants spoke about the implications of the COVID-19 pandemic on their own learning, student learning, and teaching style. Teachers enter the PD with many different backgrounds, CS experience levels, and strengths, however, new-to-CSA teachers require further PD on content and pedagogy to transition between CSP and CSA. Initial results suggest that the CSAwesome PD may have an impact on long-term teacher development as new-to-CSA teachers who participated indicated a positive impact on their teaching practices, ideologies, and pedagogies.« less
4. An empirical analysis of high school students' practices of modelling with unstructured data

   https://doi.org/10.1111/bjet.13253  

   Jiang, Shiyan ; Nocera, Amato ; Tatar, Cansu ; Yoder, Michael Miller ; Chao, Jie ; Wiedemann, Kenia ; Finzer, William ; Rosé, Carolyn P. ( July 2022 , British Journal of Educational Technology)

   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.

   What is already known about this topic

   Scholarlymore »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).

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5. Comparing Access and Participation Outcomes of Schools Engaged in a Multi-school CS and Cybersecurity Intervention (Evaluation)

   McGill, M. ( January 2022 , ASEE annual conference proceedings)

   In early 2020, a cohort of 30 high schools engaged in a year-long intervention designed to increase their ability to offer Computer Science (CS) and Cybersecurity education to their students. After we performed an evaluation on the intervention’s impacts, we turned our attention to whether or not the outcomes were influenced by engagement of the schools in the cohort. In this research paper, we focus on the guiding research question: How do schools’ engagement in an intervention designed to build equitable CS and Cybersecurity education capacity impact schools’ course offerings and students’ participation in these courses? To measure equitable impact, we evaluated changes to actual CS and Cybersecurity course offerings and enrollment at the schools. We focused on the differences in participation across student gender and race/ethnicity as well as participation levels at the different schools across three years prior to the intervention and one year after the intervention. Findings indicate that, despite the disruption to schools from the COVID-19 pandemic, schools engaged in the program had very significant increases in AP CSP, AP CS A, and Cybersecurity course offerings and enrollment, particularly at schools that serve students from low-income families.