James H. Cross II is a Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering, with prior affiliations at the University of Nevada Las Vegas' Business School and Drexel University's College of Medicine. His research focuses on software engineering education, program visualization, reverse engineering, and educational technology. Key Roles : Software Engineering Educator, Java Pedagogy Innovator, Program Visualization Researcher His work emphasizes enhancing code comprehension through dynamic data structure visualizations (e.g., jGRASP IDE), reverse engineering methodologies, and interdisciplinary applications in software maintenance and curriculum development. Recent trends in his publications (2019–2024) include FAIR data principles, ontological frameworks, and token-based incentives for scholarly contributions. Notable collaborations include T. Dean Hendrix, Larry A. Barowski, and David A. Umphress. Affiliated with institutions like Auburn University, University of Nevada Las Vegas, and Drexel University's College of Medicine, his career spans software engineering research, educational tool development, and academic leadership.
Quintin I. Cutts is a Professor in the School of Computing Science within the College of Science and Engineering at the University of Glasgow. He serves as a leading researcher in computing education with extensive contributions to programming pedagogy, educational frameworks development, and assessment methodologies across multiple educational levels from primary school through undergraduate programs. His research interests focus on computing education , particularly the development of educational frameworks like the ABC Framework for orienting learners in programming classes, spatial skills development in computational thinking, code comprehension strategies, and early-years computing education. His work bridges cognitive psychology with practical educational interventions, examining how spatial reasoning, argumentative reasoning, and physical activities impact programming learning outcomes. Analysis of his recent publications reveals a strong emphasis on developing structured educational approaches for novice programmers, with particular attention to how spatial skills correlate with computational thinking development, how to design effective early programming experiences, and how to address participation inequalities in computer science education. His work increasingly focuses on practical frameworks that can be implemented in real classroom settings across different educational stages. Cutts has established significant collaborative networks through organizing UKICER (United Kingdom and Ireland Computing Education Research) conferences and participating in international computing education research communities. His mentoring approach is evident through frequent co-authorship with junior researchers including Jack Parkinson, Maria Kallia, and Joseph Maguire, who have become established researchers in the computing education field themselves. He has contributed to building research infrastructure through initiatives like Works-in-Progress workshops that support emerging scholars.
Prof. Dr. Ralf Romeike is a Professor of Computer Science Education specializing in Didactics of Computer Science at the Free University of Berlin, within the Department of Mathematics and Computer Science. His office is located at Königin-Luise-Str. 24-26, Room 019, 14195 Berlin, where he holds consultation hours every Wednesday from 12-1 p.m. by appointment. Romeike's research focuses on innovative approaches to computer science education, with particular emphasis on artificial intelligence education, data literacy development, and constructionist learning methodologies. His work bridges theoretical educational frameworks with practical classroom applications, especially in teacher training contexts. He has developed numerous educational frameworks including AI-PACK, which adapts the DPACK model for AI-related digital competencies for teachers. His publication record shows a clear trend toward AI education in K-12 settings, teacher professional development in AI and data literacy, and the integration of constructionist principles in computing education. The research spans multiple educational contexts from primary through higher education, with particular attention to how non-computer science students and teachers can develop meaningful AI competencies. His scientific contributions include significant work on: AI literacy frameworks for school education Data literacy as a component of AI education Constructionist approaches to AI/ML learning Debugging education methodologies Agile methods in computer science education Romeike leads or participates in multiple significant research projects including ENKIS (Establishment of sustainable AI-related study programs), Digi4All (Interdisciplinary Digital Education), AMI (Agile Methods for Computer Science Education), and TrainDL (Teacher training for Data Literacy & Computer Science competences), demonstrating his leadership in shaping computer science education policy and practice in Germany and internationally.
Prof. Dr. Tilman Michaeli is a Professor of Computer Science Education at the Technical University of Munich (TUM), affiliated with the TUM School of Social Sciences and Technology. His academic career includes a doctorate from Friedrich-Alexander University of Erlangen-Nuremberg (2020) and prior research roles at Erlangen and the Free University of Berlin under Prof. Ralf Romeike. He leads the Professorship for Computer Science Education, focusing on didactics, debugging pedagogy, AI/ML literacy, quantum computing education, and digital education transformation. Education Background: Studied Computer Science Teaching at FAU until 2017, completed state examinations, and earned his doctorate in 2020. Appointed to TUM in 2021. Research Interests: Prioritizes empirical and design-based research in computing education. Key areas include debugging strategies in classrooms, AI literacy for K-12, collaborative programming, and integrating quantum computing into curricula. His work emphasizes practical, student-centered approaches. Publications: Over 50 peer-reviewed articles since 2017, focusing on educational technology, teacher training, and curriculum development. Notable works include studies on debugging frameworks, MOOCs for AI literacy, and quantum computing in schools. Grants & Awards: Recipient of the 2019 Doctoral Award from the German Informatics Society for contributions to computer science didactics. Active in securing research grants for projects like DigiProMIN and the AI Training Initiative. Teaching: Leads courses such as Didactics of Computer Science 1/2 and Theoretical Computer Science for Vocational Education. Supervises advanced seminars and doctoral candidates. Teams: Directs a research group including employees like Sven Baumer, Luisa Gebhardt, and Heike Wachter, focusing on collaborative projects like InstaClone and Smerge tools.
Georgiana Haldeman is an active computer science educator and researcher specializing in programming education, particularly for introductory courses (CS1). Her work focuses on improving code quality, program decomposition, and developing educational tools that enhance student learning experiences in computer science education. Her research interests center around CS1 pedagogy, autograding systems, and code quality assessment. She has developed frameworks for teaching program decomposition and created educational tools like RAVIC (Runtime Analysis Visualizer for Introductory Courses) to help students visualize program execution. Her work often addresses both procedural and conceptual knowledge dimensions in programming education, recognizing the importance of developing both skill sets in novice programmers. Analysis of her publication record shows a strong focus on practical educational interventions in computer science. Her work spans multiple dimensions of CS education including assessment methods, educational tool development, classroom activities for improving code quality, and gender diversity in computer science. She frequently collaborates with researchers like Monica Babes-Vroman, Andrew Tjang, and Thu D. Nguyen, suggesting established research partnerships in the CS education community. Her recent publications (2023-2025) indicate active research in program decomposition frameworks, notional machines for databases, and synthesized teaching models that balance procedural and conceptual learning. This demonstrates continued innovation in computer science pedagogy and a commitment to addressing fundamental challenges in teaching introductory programming.
Gordon Fraser is a Professor and holds the Chair of Software Engineering II at the Faculty of Computer Science and Mathematics, University of Passau, since 2017. His academic journey includes a doctorate from Graz University of Technology and research experience at Saarland University and the University of Sheffield. Research Interests: His work centers on software analysis, software development, and programming education. Key themes include preventing software errors through automated testing, improving developer productivity using AI-based tools, and enhancing programming training—especially for beginners and children. He actively explores the role of gamification, large language models, and search-based techniques in software testing and education. Recent Research Trends: His recent publications (2023–2024) show a strong focus on flaky tests in Python, automated test generation for Android and block-based environments (Scratch), gamification of testing in IDEs, and educational studies on pair programming, gender dynamics, and feedback mechanisms in primary computing education. The integration of AI and machine learning in software testing is a recurring theme, especially in the context of large language models and neuroevolution. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: He leads several research projects, including DFG-funded TYPES4STRINGS and DeepMBT , and the DeepCode project which explores AI for code quality. He mentors a large number of students and researchers, particularly in the areas of automated testing and programming education. His advising spans PhD and Master’s students, many of whom are co-authors on his publications. Labs and Teams: He leads a research group at the University of Passau focused on software testing, analysis, and education. The team develops tools like Pynguin for automated Python testing and Code Defenders for gamified software testing education. They also work on LitterBox , a linter for Scratch programs, and various gamification plugins for IDEs and educational platforms.
Franz Jetzinger is a researcher at the Professorship for Computer Science Education (Lehrstuhl für Informatikdidaktik) at the Technical University of Munich (TUM), working under the direction of Prof. Dr. Tilman Michaeli. He is based at Marsstr. 20-22(2907)/IV, 80335 Munich, with his office in room 2907.04.446. Jetzinger teaches courses in the Computer Science Education program, including "digi4all - Kompetenzen für das Unterrichten in einer digitalen Welt" and "Übung zur Didaktik der Informatik" for both winter and summer semesters of 2024/25 and 2025. His research focuses on several key areas within computer science education, with particular emphasis on Artificial Intelligence and Machine Learning education for K-12 settings. He has developed and delivered professional development programs for computer science teachers, especially regarding AI integration into compulsory computer science classrooms. His work spans teacher training, curriculum development, and practical implementation strategies for bringing AI education to secondary school levels. Jetzinger's publications reveal a strong focus on AI education in K-12 settings, with multiple publications in 2023-2025 addressing scalable professional development for teachers and evidence-based approaches to teaching AI. His work also includes substantial contributions to German computer science textbooks for Bavarian high schools, covering topics from object-oriented modeling to AI education. Developed professional development programs for AI education Contributed to multiple computer science textbooks for Bavarian high schools Conducted research on debugging processes in learning environments Participated in the TRR419 SHARP research project Worked on the "Von Bits zu QuBits" quantum computing education initiative Through his work with the Bavarian Ministry of Education and various workshops, Jetzinger has been instrumental in implementing AI education across Bavarian schools, delivering numerous training sessions throughout 2024 and 2025 on both basic and advanced AI topics for teachers.
Colleen M. Lewis is a Professor at the University of Illinois Urbana-Champaign specializing in computer science education research with a strong focus on equity, inclusion, and pedagogical innovation. Her work spans K-12 through higher education computing contexts, examining systemic barriers and developing evidence-based interventions to broaden participation in computing. Her research interests include: Equity and inclusion in computer science education Microaggressions and bias in computing environments Physical and tangible models for teaching abstract programming concepts Goal-congruity theory applications in computing education Organizational change strategies for diversity initiatives Dr. Lewis's recent work demonstrates a sophisticated multi-institutional approach to studying critical issues in computing education. Her publications reveal a consistent focus on intersectional analyses of student experiences, with particular attention to how gender, race, and other identity factors interact with learning environments. She has developed innovative teaching approaches including physical Java models and syntax exercises for Python, while simultaneously addressing systemic issues like the 'diversity-hire narrative' that can undermine inclusion efforts. Her scientific contributions include: Pioneering research on goal-congruity interventions in computing education In-depth analysis of microaggressions in CS classrooms Development of the Teaching Practices Game for TA training Multi-institutional studies of computing self-efficacy across identity groups Dr. Lewis actively shapes the field through leadership roles in SIGCSE, ICER, and RESPECT conferences, often organizing panels and workshops on broadening participation in computing. Her collaborative work across numerous institutions demonstrates her commitment to generating generalizable knowledge that can transform computing education nationwide.
Sohum Sohoni is a Professor in the School of Computing and Augmented Intelligence within the Ira A. Fulton Schools of Engineering at Arizona State University. His research spans computer science education, programming pedagogy, computer architecture, and image quality assessment with GPU acceleration. He has developed innovative educational approaches for computer architecture courses using the PLP instruction set architecture and has contributed significantly to understanding programming education through analysis of error messages and embedded questions. His research interests focus on Computer Science Education , particularly programming education techniques, computer architecture pedagogy, and engineering education methods. He has made substantial contributions to understanding how students learn programming concepts, develop effective debugging skills, and comprehend computer architecture principles. His work bridges theoretical computer science concepts with practical educational applications, emphasizing hands-on learning experiences and innovative assessment methods. His publication trends reveal a strong focus on computer science education research with particular emphasis on programming education (2015-2018), computer architecture education using PLP (2014-2017), and image quality assessment with GPU acceleration (2012-2018). His work consistently combines theoretical computer science concepts with practical educational applications, demonstrating his commitment to improving how computing concepts are taught and learned. His advising work includes mentoring students like Christopher Mar, Harsha B. M. Kadekar, Shaowen Lu, Thien D. Phan, and Vignesh Kannan, who have co-authored publications with him across various computing education topics. His research has been supported through various educational technology projects focused on online learning environments, software engineering education, and computer architecture instruction. He has been actively involved in developing virtualized learning environments for IoT education, creating innovative approaches for software engineering education, and designing effective methods for teaching computer architecture concepts. His work demonstrates a consistent commitment to improving computing education through evidence-based pedagogical approaches and technological innovation.
Qi Xin is an Associate Professor at the School of Computer Science, Wuhan University. He is a member of the Centre of Software Testing, Analysis and Reliability (CSTAR) and serves on program committees for major software engineering conferences including ASE, ISSTA, and ICSE. Dr. Xin received his Ph.D. from Brown University under the supervision of Dr. Steven Reiss and was previously a Postdoctoral Researcher at Georgia Institute of Technology working with Dr. Alex Orso. His research focuses on software engineering, particularly on developing automated and semi-automated approaches to improve software development processes and enhance software quality, security, and reliability. His recent work centers on automated program repair, software debugging, and program debloating. Dr. Xin has published extensively in top-tier software engineering venues, with research that frequently addresses practical challenges in real-world software systems. Dr. Xin's publication record shows a consistent focus on improving software reliability through automated techniques. His work spans from foundational program analysis to practical tool development, with several publications focusing on Android applications and addressing challenges specific to mobile software development. His recent work has begun exploring the application of large language models to software engineering problems, as evidenced by his 2025 paper on conversational LLM-based repair. ACM SIGSOFT Distinguished Paper Award for fault localization research Guest editor for Automated Software Engineering (AUSE) Special Issue Reviewer for ACM TOSEM, IEEE TSE, and EMSE journals Dr. Xin actively mentors students and serves the academic community through conference organization and journal reviewing. He teaches courses including Compiler Design and Software Testing and Practice at Wuhan University, bridging his research expertise with classroom instruction to train the next generation of software engineers.
Greg L. Nelson is a Professor at the University of Maine, specializing in computing education research and visualization. His work focuses on program tracing, educational pedagogy, and assessment methods in computer science education. Previously affiliated with the University of Washington, his research spans topics like adaptive learning systems, prerequisite skill analysis, and the integration of AI in education. Research Interests: His primary areas include improving introductory programming instruction, developing diagnostic assessments, and exploring how prerequisite skills affect advanced learning. He also investigates visualization design constraints and the role of agency in self-directed learning environments. Publications: Nelson has authored/co-authored over 15 papers in venues like ICER, SIGCSE, and IEEE TVCG, emphasizing practical pedagogical strategies and empirical studies in computing education. Notable works include frameworks for program tracing pedagogy and the application of generative AI in creative media courses. Awards: He has received prestigious recognitions including the Herbrand Award and John Henry Award for contributions to computing education theory and practice. Contributions: Collaborates with institutions globally, focusing on curriculum development and educational technology innovation. His work bridges theoretical frameworks with real-world classroom applications, particularly in fostering equitable and effective learning environments.
Laurie C Murphy is a researcher at Pacific Lutheran University, Tacoma, WA, USA, specializing in computer science education and pedagogy. Her work focuses on novice programming, debugging strategies, recursive algorithms, and student mindset theories. Her research explores student-centered learning , code explanation techniques , and assessment methodologies in programming education. Key topics include debugging strategies , self-theories in computing , and learning style comparisons between programming and mathematics. Recent publications (2017–2012) highlight trends in student-centered classroom practices debugging error analysis recursive/iterative comprehension code literacy assessments She collaborates with institutions like Grand Valley State University and College of Charleston on studies about instructional practices and programming education challenges.
Barbara Ericson is a Professor in the Computer Science and Engineering department at the University of Michigan, Ann Arbor. Her research focuses on computer science education, programming pedagogy, and broadening participation in computing. She has developed innovative educational tools including interactive ebooks and Parsons problems for teaching programming concepts. Dr. Ericson's research interests span multiple areas of computer science education including adaptive learning systems, CS teacher professional development, and broadening participation in computing, particularly for underrepresented groups. She has pioneered work on Parsons problems as learning tools and has extensively researched how to make computer science education more accessible and effective. Her recent work has focused on AI literacy and the integration of large language models in programming education. Her publication record shows a clear trend toward integrating AI technologies into computer science education while maintaining a strong focus on equity and accessibility. The majority of her recent articles address how to effectively use AI tools like large language models in programming education, develop AI literacy among students, and create more inclusive computing classrooms. Her work bridges educational theory with practical classroom applications. Dr. Ericson has received recognition for her work in broadening participation in computing, particularly through initiatives like Sisters Rise Up and Project Rise Up 4 CS which support underrepresented students in Advanced Placement Computer Science courses. She has been instrumental in developing the Runestone Interactive platform for free, open-source computer science ebooks and has conducted extensive research on how teachers and students use these resources. Her work on adaptive Parsons problems has influenced programming pedagogy across multiple institutions.
Felix Ziemann is a Scientific Assistant at the Chair of Didactics of Computer Science within the Faculty of Computer Science at the University of Duisburg-Essen, working under the supervision of Prof. Dr. Torsten Brinda. He contributes to research, teaching, and outreach in computer science education, with a strong focus on pedagogical methods, teacher training, and inclusive learning technologies. Education: Master of Science in Computational Science, University of Potsdam (2021–2024) Bachelor of Arts in Computer Science, Social Sciences, and Educational Sciences, University of Duisburg-Essen (2018–2021) Theodor-Körner-Schule, Bochum/NRW (2010–2018) Felix Ziemann’s research centers on improving computer science education through innovative teaching strategies and tools. His work investigates systematic debugging instruction using cognitive apprenticeship models, language-sensitive teaching in diverse classrooms, and the implementation challenges of computer science curricula in primary schools. He also explores personalized digital learning pathways to support displaced students, particularly Ukrainian academics seeking to continue their studies in Germany. His research combines qualitative user studies, instructional design, and educational technology development. His recent publications reflect a strong trend in computing education research, focusing on teacher development, inclusive pedagogy, and the design of effective learning environments. The articles explore debugging tools for undergraduates, linguistic aspects of CS teaching, and adaptive systems for refugee integration into higher education—highlighting a commitment to equity and practical pedagogical innovation. Professional Memberships: German Informatics Society (GI) GI Specialist Group "Computer Science Education" GI Specialist Group "Information Technology Education in NRW" GI Specialist Group "Computer Science Education in Berlin and Brandenburg" Felix is actively involved in teaching courses on heterogeneity and inclusion in computer science education, supervising student theses, and leading outreach activities through the Computer Science Student Lab. He has previously worked as a research assistant at the Hasso-Plattner-Institute and the Center for Quality Development in Teaching and Learning at the University of Potsdam, contributing to educational quality and student academies. No grants or awards are mentioned in the provided text. He is engaged in multiple outreach and teacher training initiatives, including the Computer Science Student Lab, where he supports school students through programs like App Inventor and BBC micro:bit workshops. He also contributes to professional development for in-service teachers in North Rhine-Westphalia.
Thomas Degueule is a researcher at CNRS (Centre National de la Recherche Scientifique) in France, actively contributing to software engineering research since 2015. He serves on program committees for major conferences including ASE, ICSE, and SLE, with primary research interests in Software Evolution, Empirical Software Engineering, and Domain-Specific Languages. His work focuses on breaking change analysis in APIs and libraries, client-library compatibility testing, and dependency management. He develops practical tools like Roseau for source-based breaking change detection and investigates semantic versioning impacts in ecosystems like Maven Central. His empirical approach leverages large-scale repository analysis to address real-world software maintenance challenges, particularly in Java ecosystems. Recent publications (2023-2025) show consistent contributions to breaking change analysis and compatibility testing, appearing in top venues like ASE, ICSE, and ISSTA. His research bridges theoretical insights with practical tooling for software evolution challenges, demonstrating strong empirical methodology and tool-oriented contributions. No scientific awards are documented in the available information. Degueule has advised no publicly listed students and holds no mentioned research grants. His organizational roles include Program Co-Chair for SLE 2023 and committee positions across multiple conferences, reflecting significant service to the software engineering community.
