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The potential of Large Language Models (LLMs) in education is not trivial, but concerns about academic misconduct, misinformation, and overreliance limit their adoption. To address these issues, we introduce MerryQuery, an AI-powered educational assistant using Retrieval-Augmented Generation (RAG), to provide contextually relevant, course-specific responses. MerryQuery features guided dialogues and source citation to ensure trust and improve student learning. Additionally, it enables instructors to monitor student interactions, customize response granularity, and input multimodal materials without compromising data fidelity. By meeting both student and instructor needs, MerryQuery offers a responsible way to integrate LLMs into educational settings. 

Developers rarely build programming environments that help secondary teachers support student learning. We interviewed 11 K12 teachers to discover how they support students learning to program and how tools might assist their teaching practice. Based on thematic analysis and organizing teacher activities around student actions, we have derived a new framework that can be used to design a programming learning system to support teachers. Our results suggest that teachers structure their activities based on their ideals about effective programming teaching and learning, and student problem solving and help-seeking processes. Therefore, our framework relates the themes we discovered about teacher activities to ideals and student problem solving in a time-based framework that can inform the design for new programming learning systems. 

Background and Context. This innovative practice full paper describes the development and implementation of a professional development (PD) opportunity for secondary teachers to learn about ChatGPT. Incorporating generative AI techniques from Large Language Models (LLMs) such as ChatGPT into educational environments offers unprecedented opportunities and challenges. Prior research has highlighted their potential to personalize feedback, assist in lesson planning, generate educational content, and reduce teachers' workload, alongside concerns such as academic integrity and student privacy. However, the rapid adoption of LLMs since ChatGPT's public release in late 2022 has left educators, particularly at the secondary level, with a lack of clear guidance on how LLMs work and can be effectively adopted. Objective. This study aims to introduce a comprehensive, free, and vetted ChatGPT course tailored for secondary teachers, with the objective of enhancing their technological competencies in LLMs and fostering innovative teaching practices. Method. We developed a five-session interactive course on ChatGPT capabilities, limitations, prompt-engineering techniques, ethical considerations, and strategies for incorporating ChatGPT into teaching. We introduced the course to six middle and high school teachers. Our curriculum emphasized active learning through peer discussions, hands-on activities, and project-based learning. We conducted pre- and post-course focus groups to determine the effectiveness of the course and the extent to which teachers' attitudes toward the use of LLMs in schools had changed. To identify trends in knowledge and attitudes, we asked teachers to complete feedback forms at the end of each of the five sessions. We performed a thematic analysis to classify teacher quotes from focus groups' transcripts as positive, negative, and neutral and calculated the ratio of positive to negative comments in the pre- and post-focus groups. We also analyzed their feedback on each individual session. Finally, we interviewed all participants five months after course completion to understand the longer-term impacts of the course. Findings. Our participants unanimously shared that all five of the sessions provided a deeper understanding of ChatGPT, featured enough opportunities for hands-on practice, and achieved their learning objectives. Our thematic analysis underlined that teachers gained a more positive and nuanced understanding of ChatGPT after the course. This change is evidenced quantitatively by the fact that quotes with positive connotations rose from 45% to 68% of the total number of positive and negative quotes. Participants shared that in the longer term, the course improved their professional development, understanding of ChatGPT, and teaching practices. Implications. This research underscores the effectiveness of active learning in professional development settings, particularly for technological innovations in computing like LLMs. Our findings suggest that introducing teachers to LLM tools through active learning can improve their work processes and give them a thorough and accurate understanding of how these tools work. By detailing our process and providing a model for similar initiatives, our work contributes to the broader discourse on teaching professional educators about computing and integrating emerging technologies in educational and professional development settings. 

Background and Context. The importance of CS to 21st-century life and work has made it important to find ways to integrate learning CS and programming into the regular school day. However, learning CS is difficult, so teachers integrating programming need effective strategies to scaffold the learning. In this study, we analyze students’ log data and apply a novel technique to compare Parsons Problems with from-scratch programming in a middle school science class. Objectives. Our research questions aimed to investigate whether, how, and when Parsons Problems improve learning efficiency for a programming exercise within science, utilizing log data analysis and an automated progress detector (SPD). Method. We conducted a study on 199 students in a 6th-grade science course, divided into two groups: one engaged with Parsons problems, and the other, a control group, worked on the same programming task without scaffolding. Then, we analyzed differences in performance and coding characteristics between the groups. We also adopted an innovative application of SPD to gain a better understanding of how and when Parsons problems helped students make more progress on the coding task, with an objective measure of final student grades. Findings. The experimental group, with scaffolding through Parsons Problems, achieved significantly higher grades, spent significantly less time programming, and toggled less between block category tabs. Interestingly, they ran their code more frequently compared to the control group. The SPD analysis revealed that the experimental group made significantly higher progress in all four quartiles of their coding time. Implications. Our findings suggest that Parsons problems can improve learning efficiency by enhancing novices’ learning experience without negatively impacting their performance or grades, which is especially important when programming is integrated into K12 courses. 

Black women remain severely underrepresented in computing despite ongoing efforts to diversify the field. Given that Black women exist at the intersection of both racial and gendered identities, tailored approaches are necessary to address the unique barriers Black women face in computing. However, it is difficult to quantitatively evaluate the efficacy of interventions designed to retain Black women in computing, since samples of computing students typically contain too few Black women for robust statistical analysis. Using about a decade of student survey responses from an National Science Foundation–funded Broadening Participation in Computing alliance, we use regression analyses to quantitatively examine the connection between different types of interventions and Black women’s intentions to persist in computing and how this compares to other students (specifically, Black men, white women, and white men). This comparison allows us to quantitatively explore how Black women’s needs are both distinct from—and similar to—other students. We find that career awareness and faculty mentorship are the two interventions that have a statistically significant, positive correlation with Black women’s computing persistence intentions. No evidence was found that increasing confidence or developing skills/knowledge was correlated with Black women’s computing persistence intentions, which we posit is because Black women must be highly committed and confident to pursue computing in college. Last, our results suggest that many efforts to increase the number of women in computing are focused on meeting the needs of white women. While further analyses are needed to fully understand the impact of complex intersectional identities in computing, this large-scale quantitative analysis contributes to our understanding of the nuances of Black women’s needs in computing. 

In computing classrooms, building an open-ended programming project engages students in the process of designing and implementing an idea of their own choice. An explicit planning process has been shown to help students build more complex and ambitious open-ended projects. However, novices encounter difficulties in exploring and creatively expressing ideas during planning. We present Idea Builder, a storyboarding-based planning system to help novices visually express their ideas. Idea Builder includes three features: 1) storyboards to help students express a variety of ideas that map easily to programming code, 2) animated example mechanics with example actors to help students explore the space of possible ideas supported by the programming environments, and 3) synthesized starter code to help students easily transition from planning to programming. Through two studies with high school coding workshops, we found that students self-reported as feeling creative and feeling easy to communicate ideas; having access to animated example mechanics of an actor help students to build those actors in their plans and projects; and that most students perceived the synthesized starter code from Idea Builder as helpful and time-saving. 

Women remain underrepresented in cabinets, especially in high-prestige, “masculine” portfolios. Still, a growing number of states have appointed women to the finance ministry—a powerful position typically reserved for men. Drawing on the “glass cliff” phenomenon, we examine the relationship between financial crises and women’s ascension to, and survival in, this post. With an original dataset on appointments to finance ministries worldwide (1972–2017), we show that women are more likely to first come to power during a banking crisis. These results also hold for currency and inflation crises and even when accounting for the political and economic conditions that might otherwise explain this relationship. Subsequent examination of almost 3,000 finance ministers’ tenures shows that, once in office, crises shorten men’s (but not women’s) time in the post. Together, these results suggest that women can sometimes seize on crises as opportunities to access traditionally male-dominated positions. 

Many students struggle when they are first learning to program. Without help, these students can lose confidence and negatively assess their programming ability, which can ultimately lead to dropouts. However, detecting the exact moment of student struggle is still an open question in computing education. In this work, we conducted a think-aloud study with five high-school students to investigate the automatic detection of progressing and struggling moments using a detector algorithm (SPD). SPD classifies student trace logs into moments of struggle and progress based on their similarity to prior students' correct solutions. We explored the extent to which the SPD-identified moments of struggle aligned with expert-identified moments based on novices' verbalized thoughts and programming actions. Our analysis results suggest that SPD can catch students' struggling and progressing moments with a 72.5% F1-score, but room remains for improvement in detecting struggle. Moreover, we conducted an in-depth examination to discover why discrepancies arose between expert-identified and detector-identified struggle moments. We conclude with recommendations for future data-driven struggle detection systems. 

Background. Software Engineering (SE) is a new and emerging topic in secondary computer science classrooms. However, a review of the recent literature has identified an overall lack of reporting on the development of SE secondary curriculum. Previous studies also report low student engagement when teaching these concepts. Objectives. In this experience report, we discuss the development of a 9-week, project-based learning (PBL) SE curriculum for secondary students. During this curriculum, students create a socially relevant project in groups of two to three. We discuss displays of participant engagement with CS concepts through the PBL pedagogy and the SE curriculum. Method. We examine participant engagement through group artifact interviews about student experiences during a week-long, virtual summer camp that piloted activities from our curriculum. During this camp, students followed a modified SE life cycle created by the authors of the paper. Findings. Participants showed engagement with the curriculum through various aspects of PBL, such as autonomy, creativity, and personal interest in their project topic. Implications. The lessons learned from this experience report suggest that PBL pedagogy can increase student engagement when teaching CS concepts, and this pedagogy provides detail and structure for future secondary SE curriculum implementations to support educators in the classroom