Educational terminology in computing remains fragmented across geographic regions and educational traditions. Identical terms may refer to different concepts in different regions, while equivalent concepts are often termed differently in different traditions, creating barriers to the exchange of pedagogical practices. A globally shared terminology resource will help bridge this inconsistency in usage. This working group will explore the use of generative AI (GenAI) to support the aggregation, validation, and curation of terminology from diverse sources. Generative AI will serve in a directed co-development role while domain experts will still retain responsibility for the final product. The working group will evaluate the benefits and limitations of this GenAI-in-the-loop approach with particular attention to accuracy, bias, and coverage.

Technical and ethical aspects of Computer Science (CS) are interdependent. Many CS departments teach ethical and social implications of technology in separate standalone courses. However, prior research shows that ethical issues are better taught in tandem with their related technical content as an integral required skill in CS curricula. In this experience report, we share our experience with embedding ethics assignments in 3 CS courses at different levels: a CS2 course in software design and data structures, a CS3 course in data structures and algorithms, and a Software Engineering capstone course, all taught at Virginia Tech (a large public R1 institution) in Spring 2024. Students from the 3 courses were surveyed at the beginning and end of Spring 2024. By comparing results from the pre and post surveys, we found that the embedded assignments for the CS2 and CS3 courses improved students' confidence in their knowledge about how ethical issues may come into play in their career, their confidence in their ability to address ethical issues arising from applying technology in real contexts, and their confidence in communicating and defending their positions on how to address these issues. For all 3 courses, students gave positive feedback on how the assignments were engaging and relevant to the course, and how it improved their ability in raising, and reasoning about, ethical implications of technology. We believe that the practices and results of our experience will be helpful to other CS instructors thinking of injecting ethical content into their technical courses.

Studying abroad can be a life-changing experience that can help students develop skills, make friends, and gain a global perspective. However, Computer Science (CS) students rarely encounter opportunities to participate in a study abroad program. We interviewed students who participated in our institution’s first CS Study Abroad program-focused on software engineering-to understand participants’ experiences and how the program impacted the students’ personal and computing identities. We found that students faced unique challenges, such as working with teammates who have different cultural backgrounds and programming styles. Through overcoming those challenges, students were able to strengthen their computing identity and gain confidence that they could work in diverse software engineering teams. Based on the lessons that we learned, we provide guidelines to enhance future CS Study Abroad experiences.

In this Innovative Practice full paper, we introduce Gurukul, an innovative coding platform designed to support teaching Data Structures and Algorithm (DSA) course by integrating advanced Large Language Models (LLMs). LLMs have emerged as powerful tools in Computer Science Education (CSEd), offering unparalleled opportunities for enhancing student comprehension and engagement. However, their use in educational settings presents challenges, including tendencies toward hallucination, contextual inaccuracies, and the risk of undermining critical thinking by providing explicit solutions. To address these challenges, and to explore how specialized LLMs can bolster learner engagement, we present Gurukul, a platform featuring dual innovations: Retrieval-Augmented Generation (RAG) and Guardrails. Gurukul offers a hands-on practice feature where students can solve DSA problems within a code editor, supported by a dynamically Guardrailed LLM that prevents the delivery of explicit solutions. Additionally, the platform's study feature utilizes RAG, drawing from OpenDSA as a trusted source, to ensure accurate and contextually relevant information is provided. To assess the platform's effectiveness, we conducted a User Study with students, and a User Expert Review with faculty from a U.S. public state university specializing in DSA courses. Our analysis of student usage patterns and perceptions, along with insights from instructors, reveal that Gurukul positively impacted student engagement and learning in DSA, demonstrating the potential of specialized LLMs to enhance educational outcomes in this field.

This research full paper describes a hybrid approach based on data, questionnaire answers, and machine learning algorithms to predict usability scores in Learning Management Systems (LMSs) to improve student learning and satisfaction. Students need to achieve their learning goals by interacting with LMSs. To attain these goals, usability evaluation ensures effectiveness (task completion), efficiency (time measurement), and satisfaction (positive attitude). Usability evaluation usually follows questionnaires, user testing of the LMS, and expert reviews. Although these methods are widely used due to several benefits, they face challenges related to trying these software systems multiple times until the system satisfies student needs, human subjectivity perception, and lack of software system adaptability. We propose this hybrid approach to face these challenges, promote student engagement with the system, and create a better design in the LMS courses. The aim is to identify features extracted from the LMS to predict usability scores with machine learning techniques. We evaluated this strategy through a case study with data collected from undergraduate students at a public university in the United States. The students' tasks were answering a quiz, posting in a forum, and uploading an assignment. These activities in the LMS allow the extraction of ten features into the machine learning algorithms. These attributes are time quiz, time forum, time assignment, grade quiz, word count message post, file size, file type, clicks module quiz, clicks module forum, and clicks module assignment. The four targets are from scores of the System Usability Scale and UseLearn questionnaires. Random Forest produces the best performance of average mean square error and root mean square error among machine learning algorithms. The results are promising, though there are alternatives for improvements. Our proposed approach contributes to the engineering and computing education field by providing a predictive tool for usability scores to improve the student learning experience and the components of the LMS.

Capstone courses are an integral part of undergraduate and postgraduate degrees in the computing fields. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self reflection as they transition into the real world. Prior research on capstone courses has primarily focused on the experiences of the students. The perspectives of instructors who teach these capstone courses has not been explored much. However, an instructor's motivation and expectancy can have a significant effect on a capstone course quality. In this working group, we plan to use a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation will be examined through a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across multiple continents. This global perspective will be used to develop a guiding framework on the different pedagogical approaches that can be used to enhance engagement and motivation for both staff and students in computing courses.

Computer Science programs have seen high enrollments in recent years, which contributed to widening the capacity gap. One way to address this problem is to hire more teaching-focused faculty at both research and non-doctoral granting institutions. Although this kind of hiring has already been taking place in several institutions, PhD-granting CS departments have not been able to produce enough PhDs to meet the increasing demand, especially for PhD holders with interest in - and capacity for - teaching. In this paper, we describe our experience with the initial phase of building a training program within our (large, land grant, R1) institution, targeting graduate students interested in pursuing an academic teaching-focused career in CS. Through a semester-long set of meetings, conversations, and activities, we worked with participants on improving their teaching skills and applying effective pedagogies in the classroom. At the end of the semester, we surveyed participants about the value of those meetings to them, ideas for improvement, and perspectives for future directions. Most participants rated the meetings positively in terms of content relevance and usefulness, and the opportunity to connect and interact with other participants and invited faculty members. We also discuss the lessons learned and best practices, which can be widely applied by other departments looking to better prepare their graduate students for a CS teaching-focused faculty position.

Recent developments in Artificial Intelligence (AI) have shifted the software development paradigm. Past studies demonstrated how effective AI can generate code for programming purposes. However, to our knowledge, no prior study has been done to evaluate the effectiveness of SQL queries generated by AI. We utilized nine AI assistants to generate SQL queries. Our results reveal that most AI assistants generate inaccurate SQL queries, and based on the results, we provide possible implications for SQL developers.

Computer science (CS) has the potential to be one of the most diverse fields when it comes to education and employment opportunities, yet there still exists an equity gap in the field when it comes to accessibility and empowerment. To mitigate that, conferences that celebrate diversity in CS were founded and are becoming major yearly technology events. For years, our department offered scholarships for students to attend a number of these major diversity conferences. A few years ago, we started working on pre-conference preparation, during-conference activities, and post-conference reflections, in order to provide a deeper and a more comprehensive experience for the attendees. In 2021, we started offering a diversity-focused course for students attending the Tapia and Grace Hopper Conferences, with the goal of incorporating their conference experiences into a CS course that discusses diversity and inclusion topics. This paper presents how we planned and implemented this course, and students' reception and feedback. We also provide lessons learned that can work - with the course description - as guidelines to help structure and plan diversity-focused courses for CS departments whose students attend diversity conferences.

Debugging is one of the most expensive and time-consuming processes in software development. To support programmers, researchers, and developers have introduced a wide variety of debuggers or tools to automatically find errors in code, to make this process more efficient. However, there is a gap between industry developers and students regarding the skillful use of debuggers. We aim to understand this gap by studying barriers that hinder new programmers from using debuggers. We conducted a survey involving 73 students with various extents of programming experience and performed qualitative analysis. The goal was to extract insights into why students do not develop debugger usage skills. Our results suggest the general lack of academic course focus on debuggers is one of the primary reasons for avoidance. At the same time, complex user interfaces and a lack of visualization also seem intimidating for many students, making using a debugger unappealing. Based on the results, we provide guidelines to motivate future debugger designs and education materials to improve debugger usage. Our survey results summary is publicly available at https://github.com/minhyukko/vlhcc23

Students are facing various difficulties in courses like Programming and Data Structure through undergraduate programs, which is why failure rates and dropouts in these courses are high. Identifying students at risk of failure at an early stage of a semester is a serious challenge in higher education, so predicting students' academic performance is one of the most essential research topics to reduce failure rates, and to improve the performance of students by the end of a semester. We are developing a predictive model based on a deep artificial neural network to predict students' academic performance of upcoming courses based on their grades in previous courses of the first academic year. We have used one of the most common resampling methods, which is the SMOTE approach to handle the problem of the imbalanced dataset. The preliminary results show that our proposed model has achieved 86% accuracy. To put our system in context, we compared our results to some traditional machine learning techniques such as Decision Tree, K-Nearest Neighbor, and Random Forest, which were applied to an online dataset that has been widely used in some previous works. The comparison showed that our experimental results have achieved better results than the other traditional models. In future work, we will use a larger dataset to improve the accuracy of our models. Using other oversampling techniques such as SVM-SMOTE and Random Over Sampler will be used to evaluate their performance in comparison to the algorithms used in our work.

Research suggests that different teaching styles and multiple exposures of different styles to material can aid in the learning process. While there are guidelines for identifying the best teaching style for material, new and evolving areas can present unique challenges. The emerging area of mobile software development, which combines aspects of software, hardware, and interpersonal interaction, captures many such challenges; e.g., understanding how to develop for multiple screen sizes, designing for GPS time lag, dealing with unreliable sensor data. Teaching these challenging materials seemed well suited for multiple approaches that leveraged different learning styles. This paper examines three teaching approaches employed in ten teaching modules across two semesters of a mobile software development course. The approaches included lectures, interactive tutorials, and Pair Programming. Lectures were used to introduce topics and explore underlying theories of development. The lectures included time for questions from and for the students, but otherwise did not have an active learning component. Two active learning approaches used in the class were interactive tutorials and Pair Programming. Interactive tutorials presented applied development approaches, then explored their use in an individual-based hands-on demos. Pair Programming is an agile software development practice, used in both industry and education, which enforces a role-based approach to learning new programming concepts. Homeworks were used to assess learning, and surveys reflected student satisfaction. Results show areas of promise and of concern with regard to the learning styles. It seems that repetition of topics is important for mastery of the topics. Foundational theories seem well suited for lectures, while programming concepts work better in active learning situations. Additional learning took place through office hours, online question forums, and individual and group online exploration. The findings suggest specific approaches to teaching challenging and unique mobile software development topics as well as a general approach to identifying ways to distribute learning objectives across lectures, interactive tutorials, and Pair Programming sessions.

Pair Programming is an agile practice that has numerous studies showing its benefits for education. The emergence of mobile software design education raises questions about the effectiveness of Pair Programming in this evolving field. Developing for mobile is different than regular desktop/web development in three major areas: having more than one screen to work on (i.e. computer screen and mobile device screen), connectivity issues (dealing with Bluetooth, GPS and location data, smart watches, and sensors), and User Experience (UX) issues. Our research probes unique challenges for Pair Programming when used in mobile software design classes, focusing on five mobile design topics: dealing with interface and data managements (as in fragments), using camera, handling multi-device connectivity, using sensors and collecting GPS data, and using microphones and speakers. The study highlights successes and challenges for Pair Programming for mobile applications, with the objective of providing a set of recommendations for instructors considering using Pair Programming in their mobile development classes.

Pair Programming has demonstrated benefits for education, but unique concerns of mobile software design raise questions about the effectiveness of Pair Programming in this evolving field. This paper probes unique challenges for Pair Programming when used in mobile software design classes, focusing on five mobile design topics: dealing with interface and data management, using camera, handling multi-device connectivity, using sensors and collecting GPS data, and using microphones and speakers. The paper highlights successes and challenges for Pair Programming and mobile applications, concluding with recommendations on building assignments, managing student interaction, and implementing Pair Programming for instructors considering using it in their mobile development classes.

Today's Computer Science (CS) students may not give enough attention for the importance of the User Interfaces (UIs) they design for class projects, which becomes even more critical when they design for mobile applications. They also lack the required organization skills that help them manage how they work together. Although agile methods proponents and UI experts follow different guidelines for each to achieve their goals, integrating agile and UI design practices seems to be a promising combination that can help CS students (and developers in general) to better design for various mobile devices (smartphones, tablets, smart watches etc.) as well as to follow a semi-structured development approach to help them manage their programming work. Our research is concerned with studying the various approaches that can be used to combine agile practices with UI design guidelines for designing mobile applications. We are designing a framework that uses Pair Programming (PP) -- as an agile practice to guide the mobile UI design and application development processes. Unlike the previous studies that applied agile methods in classrooms, we are concerned with the special requirements of mobile devices as well as the regular development tasks. Moreover, we are focusing on applying certain practices that we believe to be easier to follow than the broader agile guidelines. Our current work aims at providing CS educators with a new adaptive teaching approach that is more student-oriented instead of the traditional task assignment approaches. Our research will then be extended to include teams from software development companies that are working on mobile application development. We believe that our practice-oriented framework that integrates agile with mobile UI design and development practices has much to do with industry as well as classrooms.