External help resources are frequently used by novice programmers solving classwork in undergraduate computing courses. Traditionally, these tools consisted of web resources such as tutorial websites and Q&A forums. With the rise of Generative AI (GenAI), there has been increasing concern and research about how external resources should be used in the classroom. However, little work has directly contrasted student beliefs and perceptions of web resources with GenAI, has grounded these beliefs in prior psychological theory, and has investigated how demographic factors and student backgrounds influence these beliefs and intentions. We administered a vignette-style survey across two courses required for a CS major at an R1 University, a freshman (n = 152) and senior capstone course (n = 44). Students responded to likert questions aiming to measure behavioral factors related to these tools, such as intention to use, perceived attitudes, peer perceptions, and their own perceived tool competency. We primarily investigate the results of an introductory course, finding that novices have a wide range of opinions on both resources, but overall find them slightly useful and have a tendency to prefer web-search. We compare this with seniors, who have more positive perceptions of these tools, and discuss possible reasons and implications for this difference. We constructed two path models to investigate which factors strongly influence novices’ intention to use resources and find the primary factor to be their general attitudes in how these tools will result in a positive or negative outcome (e.g. perceived benefits, justifiability). We also measure the effects of student background on intention to use these resources. Finally, we discuss implications and suggestions on how instructors can use this information to approach, address, and influence resource usage in their classrooms.

Background and Context. Research software in the Computing Education Research (CER) domain frequently encounters issues with scalability and sustained adoption, which limits its educational impact. Despite the development of numerous CER programming (CER-P) tools designed to enhance learning and instruction, many fail to see widespread use or remain relevant over time. Previous research has primarily examined the challenges educators face in adopting and reusing CER tools, with few focusing on understanding the barriers to scaling and adoption practices from the tool developers’ perspective.

External help resources are frequently used by novice programmers solving classwork in undergraduate computing courses. Traditionally, these tools consisted of web-based resources such as tutorial websites and Q&A forums. With the rise of AI code-generation and explanation tools, understanding how students use external resources and their roles in classroom have become especially relevant. Despite this, little research has directly investigated the extent to which students intent to use these tools and what factors influence their beliefs. It is unknown when students think it is appropriate to use these tools and what features they find valuable. Understanding these beliefs would allow instructors and researchers to better focus their efforts on what aspects of pedagogy and tool usage should be addressed. We administered a pilot vignette-style survey to introductory programming classes at an R1 University (n=45), giving students scenarios of external resource usage while questioning their attitudes, subjective norms, and their perceived behavioral control on using these external resources. We share preliminary findings on free response data, showcasing the variety of beliefs and opinions that novice programming students have on when and how much external resource usage is acceptable in the classroom. Some students felt that AI tools can provide more exact solutions than searching for help online, but also expressed that this exactness could be detrimental to their learning. Others expressed awareness that professionals use these resources, and expressed a desire to learn how to use them in a way to help their educational and career goals.

Web-based help-seeking -- finding and utilizing websites to solve a problem -- is a critical skill during programming in both professional and academic settings. However, little work has explored how students, especially novices, engage in web-based help-seeking during programming, or what strategies they use and barriers they face. This study begins to investigate these questions through analysis of students' web-search behaviors during programming. We collected think-aloud, screen recording, and log data as students completed a challenging programming task. Students were encouraged to use the web for help when needed, as if in an internship. We then qualitatively analyzed the data to address three research questions: 1) What events motivate students to use web search? 2) What strategies do students employ to search for, select, and learn from web pages? 3) What barriers do students face in web search, and when do they arise? Our results suggest that that novices use a variety of web-search strategies -- some quite unexpected -- with varying degrees of success, suggesting that web search can be a challenging skill for novice programmers. We discuss how these results inform future research and pedagogy focused on how to support students in effective web search.

Machine learning (ML) is one of the fastest growing subfields in Computer Science, and it is important to identify ways to improve ML education. A key way to do so is by understanding the common errors that students make when writing ML programs, so they can be addressed. Prior work investigating ML errors has focused on an instructor perspective, but has not looked at student programming artifacts, such as projects and code submissions to understand how these errors occur and which are most common. To address this, we qualitatively coded over 2,500 cells of code from 19 final team projects (63 students) in an upper-division machine learning course. By isolating and codifying common errors and misconceptions across projects, we can identify what ML errors students struggle with. In our results, we found that library usage, hyperparameter tuning, and misusing test data were among the most common errors, and we give examples of how and when they occur. We then provide suggestions on why these misconceptions may occur, and how instructors and software designers can possibly mitigate these errors.

Open-ended programming increases students' motivation by allowing them to solve authentic problems and connect programming to their own interests. However, such open-ended projects are also challenging, as they often encourage students to explore new programming features and attempt tasks that they have not learned before. Code examples are effective learning materials for students and are well-suited to supporting open-ended programming. However, there is little work to understand how novices learn with examples during open-ended programming, and few real-world deployments of such tools. In this paper, we explore novices' learning barriers when interacting with code examples during open-ended programming. We deployed Example Helper, a tool that offers galleries of code examples to search and use, with 44 novice students in an introductory programming classroom, working on an open-ended project in Snap. We found three high-level barriers that novices encountered when using examples: decision, search, and integration barriers. We discuss how these barriers arise and design opportunities to address them.

This poster outlines the design and results of a course entitled "Computational Thinking in Music." The course teaches computational thinking principles as a general education objective to undergraduate students, using data-driven investigation to inform musical composition. Students compose a song to imitate an artist of their choice by analyzing data extracted from a corpus of crowd-sourced pop song transcriptions. Students learn principles of abstraction, decomposition, and algorithmic thinking; no coding experience is required. Quantitative and qualitative results indicate that students are learning and applying computational thinking principles. Since the course is designed and taught by a musician and is run in the music department, students also learn a significant amount of music theory and composition, including harmonic structures and harmonization principles, melodic organization, consonance and dissonance, aural analysis of formal structures and meter, and influence of rhythm and timbre to create desired sounds.