Block-based programming puzzle games are popular as engaging, visual tools for introducing novice learners to programming. Many incorporate 3D environments, where players navigate and solve spatial challenges. However, little research has examined how spatial reasoning skills interact with game features to shape player experience. This study investigates the interplay between game features, spatial ability, in-game performance, and perceived difficulty within BOTs: 3D programming game where players plan spatial movements to solve puzzles. In an online study with 60 players, we examined how feature changes affected performance and perceived difficulty. Spatial skills strongly predicted performance but did not predict perceived difficulty. Larger or more complex layouts increased performance costs, with backward-facing player characters producing the largest spike in performance demands. Loops reliably increased perceived difficulty. Our findings highlight concrete needs for early spatial scaffolds, clearer support for mental-model shifts, and better cues for recognizing repetition and abstraction.

Figure 1: EclipseMonitor workflow diagram.It shows how the student's coding session data from Eclipse development environment has been collected through the EclipseMonitor.

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.

Background and Context: Open-ended programming projects encourage novice students to choose and pursue projects based on their own ideas and interests, and are widely used in many introductory programming courses. However, novice programmers encounter challenges exploring and discovering new ideas, implementing their ideas, and applying unfamiliar programming concepts and APIs. Code examples are one of the primary resources students use to apply code usage patterns and learn API knowledge, but little work has investigated the effect of having access to examples on students’ open-ended programming experience.

Villanelle is an interactive fiction authoring tool designed to support autonomous non-player characters, or “char-acter AI.” Character AI is notoriously challenging for interactive fiction authors to develop, especially for authors approaching interactive fiction from a writing rather than programming background. This paper describes a participatory design process in which we assess the author experience with Villanelle and build a new tool iteration to support their needs. The results of our first user study demonstrate the strong potential of Villanelle's incorporation of behavior trees as an easy-to-Learn computational model for character AI, but they also indicate syntax challenges for inexperienced programmers. Consequently, we developed a block-based programming interface for Villanelle and recruited a new set of study participants to evaluate this iteration using the same study instruments. The results indicate improvements in Villanelle's usability and creativity support for inexperienced programmers.