With the push towards broader computer science education, there is a never-ending need for introductory computer science resources. This work shows the first steps towards a Computer Science Topics Ontology that aims to help computer science educators and curriculum developers alike. This ontology is meant to provide a framework for CS1 vocabulary with use cases ranging from lesson repositories to a standardized CS1 topic language. Our ontology differs from other computer science education ontologies by focusing on the standards many educators must follow in their curriculum and showing the connections between those standards and the lessons they use. We show our approach using the KNARM methodology when constructing such an ontology, as well as the rationale for changes made in the process. In its current state, the ontology is queryable showing the relation between synthetic lesson data and the 2023 ACM Computer Science Curricula.

Identifying students who struggle during programming tasks remains a persistent challenge in computer science education, often resulting in delayed interventions and untimely support. Keystroke data analysis has emerged as an approach for capturing students' coding behaviors and identifying those who might have difficulties. This is made possible due to the granular nature of the data. By examining the keystroke dynamics such as typing speed, frequency of deletions, pauses and code reconstruction, it becomes possible to make an inference of the level of confidence, frustration and hesitations students experience during programming tasks. Nevertheless, while keystroke data can reveal insightful patterns, the interpretation of the results is complex and requires complementary data sources to validate inferences about students' struggles or confidence. This study introduces TrackIt a rule-based system designed to detect struggling programming students by analyzing keystroke data in conjunction with self-reported survey responses. By applying a series of predefined thresholds and scoring rules, TrackIt computes a struggling score that categorizes students into five struggle levels. The system was validated in an introductory Python course involving 40 students with data collected from 547 labs and 130 homework keystroke logs, alongside structured post-assignment surveys. Results revealed that TrackIt effectively identified key struggle behaviors such as long pauses, frequent deletions, low insert-delete ratios and copy-paste events. These indicators closely aligned with students' self-reports, particularly for challenging assignments

The continuous rising of digital learning platforms has unarguably brought about a surge in the amount of data obtained from different learning environments, which presents a great opportunity for computer science teachers to gain an understanding of students' coding processes. This is vital for enhancing student support as teachers can gain insights into students' thought process, strategies and identify areas where students might be struggling while tackling programming tasks. Traditional assessment methods such as feedback after homework submissions or completed lab assignments often results in late and untimely intervention that would prevent early dropouts and massive failures as the students' learning journey is neglected. We leverage students' keystroke data obtained from Python and Java-based introductory programming courses delivered through CODIO learning platform - covering 3 semesters and having 13 programming assignments each, to design an interactive code visualization platform using Streamlit. The application features an interface that reproduces students' code snippets with JavaScript enabled syntax highlighting. It includes a combination of dropdown menu and an adjustable slider which enables an instructor to navigate through the timestamps and have a detailed view of students' coding processes. The intervention ultimately fosters a more supportive learning environment and helps boost students' confidence.

In the United States 1 in 5 people live in a rural area, and many other countries also have large rural populations. These rural populations face many challenges that make them less likely to engage with computer science education. Studies with specific rural populations have demonstrated that those students have less access to and engagement with educational opportunities involving computer science, and that these disparities are even greater for intersectional populations who are both rural and from a recognized group underrepresented in computer science. Moreover recent sociological research has suggested that rural populations share a complex and cohesive group identity that goes beyond "living in a rural area". While many universities and institutions are making inroads in understanding and engaging our rural populations, these efforts are often in their infancy and there is much for us to learn. In this session, we will facilitate a discussion of the challenges facing our varied rural populations and how the members of this community are seeking to address them. We invite anyone who wants to learn and/or share about ongoing efforts, identify challenges (and potential research questions), disseminate best practices, and forge new collaborations to join us.

As K-12 computer science education grows, so does the imperative for universities to prepare highly qualified computer science teachers. An increasing number of SIGCSE participants either have developed programs at their universities to credential CS teachers in their states, or are interested in doing so. This BOF provides an opportunity to build community, share best practices, and address challenges in creating CS teacher preparation programs. We particularly welcome participants from Colleges of Education.

We have developed a visualization tool that displays the run-time memory state of an executing program in a fashion similar to Python Tutor as an extension to the popular VS Code IDE. Our approach combines the benefits of a diagrammatic visualization emphasizing the stack frame and heap memory model adopted by Python Tutor with a professional and extensible IDE that has made inroads into the educational community. Moreover, our tool leverages the Debug Adapter Protocol adopted by Visual Studio Code, allowing it to be used for any language supported by the IDE with minimal configuration.

This paper describes our experiences developing and teaching two different interventions focused on computational thinking and computer science at a yearly STEM outreach program hosted by a local school district. We describe the creation of our lesson plans, how we worked with experienced and pre-service teachers alike to deliver the lessons, and how we assessed the effectiveness of each intervention. We will discuss our successes and failures, and provide information on our future plans to incorporate more formalized education theory, pedagogy, and research methodology in future years to further this project. Based on our assessment results, we observed statistically significant gains in student self-efficacy with creating computer programs that perform a variety of operations. In addition, students reported a significantly higher understanding of how computer programming can be used in daily life. Our survey also highlighted differences in student self-efficacy between the two interventions, and we discuss possible sources for that result. We discuss observed results based on student groups with various backgrounds, previous STEM experiences, and socioeconomic status.

This paper details a series of pre-professional development interventions to assist teachers in utilizing computational thinking and programming as an instructional tool within other subject areas (i.e. music, language arts, mathematics, and science). It describes the lessons utilized in the interventions along with the instruments used to evaluate them, and offers some preliminary findings.