Research

Foci

Learning Technologies research is an interdisciplinary field that investigates how technology can enhance learning and teaching. It involves the design, development, and evaluation of tools, platforms, and strategies that support learning and related educational processes. This research explores a wide array of technologies —from learning platforms and mobile applications to artificial intelligence (AI), virtual reality (VR), and digital games.

Learning Analytics (LA) research is a interdisciplinary subfield of learning technologies research that focuses on collecting, analyzing, and interpreting data from educational settings to understand and improve learning processes and environments. It uses theories and methods of data science and artificial intelligence, but also draws from educational theory and didactics to provide insights into student behaviors, performance, and learning outcomes. This research helps educators, administrators, and learners make informed decisions that can lead to more effective and personalized learning experiences.

Computer Science Education (CSEd) research is a field focused on studying how people learn and teach computer science (CS). It involves investigating teaching methods, learning environments, curricula, tools, and policies that support effective computer science education at all levels —from elementary school to higher education and professional development. The goal of CSEd research is to understand the challenges in teaching CS, improve educational practices, and ensure equitable access to CS knowledge and skills.

Research in Digital Learning and Education is the study of how digital technologies impact teaching, learning, and educational contexts. It focuses on understanding the ways in which digital tools, resources, and platforms can enhance or transform educational experiences and outcomes for learners, educators, and institutions. This research spans various domains, including eDidactics and technology-enhanced learning (TEL).

Projects

The eduLAB is an outreach program fostering computer science competencies of students between grade 2 and matura. We offer an array of workshops and events on various topics, e.g.,  AI, algorithmics, and encoding. With access to a diverse set of audiences, eduLAB provides a valuable research environment to study the design and impact of educational interventions. Our research involves the study of students understanding and processing of algorithms and AI, their computational thinking skills and AI literacy, as well as their general attitude and interests to computer science.

In this project, we are developing the interactive learning platform Dev0land, which aims at teaching children and teenagers the ways of thinking in informatics through playful tasks and challenges. The objective is to create a user-friendly, browser-based learning application that is both didactically and technically appealing to the audiences. Furthermore, we will develop interactive workshops in which the platform is combined with unplugged activities to develop and practice ways of thinking in informatics.

Funding is provided by the Vienna Business Agency (Wirtschaftsagentur Wien).

STEAM – STEM – stART’em
 
This research is centered around the doctoral program STE[A+]M and aims at identifying the challenges of inclusive, meaningful and effective STEM teaching and approaching them through the innovative possibilities of the fields of computer science/technology and art/art education. Particular attention is paid to computer science lessons, which are often taught by teachers from outside the subject area, make an important contribution to digitization, but sometimes only insufficiently reach and motivate students.The individual dissertation topics first record the current status in the Austrian educational and school landscape and then derive new interventions with the help of innovative processes from computer science, art and pedagogy in order to evaluate the further potential of various approaches.

Funding is provided by Die Innovationsstiftung für Bildung

When using (decentralized) version control systems (VCS) like Git in project courses and for programming tasks, students leave digital traces of their interactions. Platforms like GitLab or GitHub even extend the functionality of VCS by project management and collaboration features which are used by students in group projects and larger software projects. By gathering the data from these platforms in the context of learning analytics (LA), instructors can gain insights into students’ interactions with the VCS and their performance in the projects overall. In this research, we explore the data available in VCS and transform it to xAPI statements for data analysis and visualization. Furthermore, we aim to combine this data with related learner data from learning management systems and other educational environments.

In this research, we investigate the interdisciplinary nature of the GALA (Games and Learning Alliance) conference series through bibliometric and thematic analyses, revealing key trends, influential contributors, and emerging themes in the field. This study highlights how the GALA community integrates diverse academic perspectives and practices to advance the understanding of serious games and their educational potential. It uses the community of practice theory to map and understand community patterns over time.

This research explores game-based learning approaches to teach end-users about dark patterns, deceptive design strategies commonly used in digital interfaces, and effective countermeasures. Through interactive and engaging gameplay, the study aims to raise awareness, equip users with the skills to recognize manipulation tactics, and empower them to make informed choices online.

Publications

  • Risley, K., Röpke, R.: A Growing Community of Practice on Games and Learning: A Literature Review with Bibliometric and Thematic Analyses. In: Games and Learning Alliance (GALA). Springer, Cham (2024) accepted for publication
  • Lehner, L., Landman, M.: Unplugged Decision Tree Learning – A Learning Activity for Machine Learning Education in K-12. In: Creative Mathematical Sciences Communication (CMSC). pp. 50–65. Springer, Cham (2024). https://doi.org/10.1007/978-3-031-73257-7_4
  • Heinemann, B., Görzen, S., Röpke, R., Ehlenz, M.: Nuts & Bolts: Bildungstechnologien offen und transparent gestalten. Die Technik hinter den Kulissen der digitalen Bildungsforschung. In: Workshopband der 22. Fachtagung Bildungstechnologien (DELFI). pp. 111–117. Gesellschaft für Informatik e.V., Bonn (2024). https://doi.org/10.18420/delfi2024-ws-15
  • Roepke, R., Judel, S., Schroeder, U. (2024). Study Path Analyses for Quality Assurance and Support of Study Planning – Approaches and Advancements in the AIStudyBuddy Project. Informatik Spektrum. https://doi.org/10.1007/s00287-024-01574-y
  • Salmen, F., Röpke, R., Schroeder, U.: WebWriter: Authoring and Remixing Explorables. In: Technology Enhanced Learning for Inclusive and Equitable Quality Education (EC-TEL). pp. 247–253. Springer, Cham (2024). https://doi.org/10.1007/978-3-031-72312-4_35
  • Görzen, S., Röpke, R., Schroeder, U. (2024). BuddyAnalytics: A Dashboard and Reporting Tool for Study Program Analysis and Student Cohort Monitoring. In: 22. Fachtagung Bildungstechnologien (DELFI). pp. 527–531. Gesellschaft für Informatik e.V., Bonn. https://doi.org/10.18420/delfi2024_53
  • Kiesler, N., Röpke, R., Schiffner, D., Schulz, S., Strickroth, S., Ehlenz, M., Heinemann, B., Wilhelm-Weidner, A. (2024). Towards Open Science at the DELFI Conference. In: 22. Fachtagung Bildungstechnologien (DELFI). pp. 251–265. Gesellschaft für Informatik e.V., Bonn. https://doi.org/10.18420/delfi2024_22
  • Landman, M. & Kohn, T. (2024). “Something that Happens Each Day” – Students’ Explanations of What Algorithms Are. In ITiCSE 2024: Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1 (pp. 199–205). Association for Computing Machinery (ACM). https://doi.org/10.1145/3649217.3653531
  • Lehner, L. (2024). A Mental Leap: Impact of Teaching the Math Behind Machine Learning Techniques in K-12. In ITiCSE 2024: Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 2 (pp. 844–845). Association for Computing Machinery (ACM). https://doi.org/10.1145/3649405.3659479
  • Landman, M. (2024): Probleme algorithmisch lösen lernen. OCG Journal, 49(1), 32–33 (2024). http://hdl.handle.net/20.500.12708/197824