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Technological advancements, particularly in artificial intelligence, significantly transform our society and work practices. Computational thinking (CT) has emerged as a crucial 21-century skill, enabling individuals to solve problems more effectively through an automation-oriented perspective and fundamental concepts of computer science. To ensure the effective integration of CT into educational curricula, it is crucial to develop efficient assessment frameworks that allow teachers to measure and promote student CT proficiency. Therefore, our aim is to develop a short test to measure CT among undergraduate students. To this end, we consider two performance tests: the Computational Thinking test (CTt) and the Algorithmic Thinking Test for Adults (ATTA). We use items from both instruments to compile a short test. Based on a sample of 290 second-year non-computer science undergraduate students, we provide evidence on the quality of our test. Besides classical test theory, we apply item response theory, namely Rasch modeling, and confirmatory factor analysis. Our test shows favorable properties, e.g., Cronbach's alpha > .75, and may be suitable for the efficient assessment of CT across higher education programs.

Josef Guggemos, Roman Rietsche, Stephan Aier, Jannis Strecker and Simon Mayer. 2024. Measuring Computational Thinking - Developing A Short Performance Test For Higher Education. In Proceedings of the 21st International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2024), October 26-28, Zabreg, Croatia.

Social media platforms play a significant role in shaping public opinion and societal norms. Understanding this influence requires examining the diversity of content that users are exposed to. However, studying filter bubbles in social media recommender systems has proven challenging, despite extensive research in this area. In this work, we introduce SOAP (System for Observing and Analyzing Posts), a novel system designed to collect and analyze very large online platforms (VLOPs) data to study filter bubbles at scale. Our methodology aligns with established definitions and frameworks, allowing us to comprehensively explore and log filter bubbles data. From an input prompt referring to a topic, our system is capable of creating and navigating filter bubbles using a multimodal LLM. We demonstrate SOAP by creating three distinct filter bubbles in the feed of social media users, revealing a significant decline in topic diversity as fast as in 60min of scrolling. Furthermore, we validate the LLM analysis of posts through an inter-and intra-reliability testing. Finally, we open source SOAP as a robust tool for facilitating further empirical studies on filter bubbles in social media.

Luka Bekavac, Kimberly Garcia, Jannis Strecker, Simon Mayer, and Aurelia Tamò-Larrieux. 2024. From Walls to Windows: Creating Transparency to Understand Filter Bubbles in Social Media. In NORMalize 2024: The Second Workshop on the Normative Design and Evaluation of Recommender Systems. 12 pages.

Establishing rapport between authors and readers of scientific texts is essential for supporting readers in understanding texts as intended, facilitating socio-discursive practices within disciplinary communities, and helping in identifying interdisciplinary links among scientific writings.We propose a Reader-aware Congruence Assistant (RaCA), which supports writers to create texts that are adapted to target readers. Similar to user-centered design which is based on user profiles, RaCA features reader-centered writing through reader profiles that are dynamically computed from information discovered through academic search engines. Our assistant then leverages large language models to measure the congruence of a written text with a given reader profile, and provides feedback to the writer. We demonstrate our approach with an implemented prototype that illustrates how RaCA exploits information available on the Web to construct reader profiles, assesses writer-reader congruence and offers writers color-coded visual feedback accordingly. We argue that our approach to reader-oriented scientific writing paves the way towards the more personalized interaction of readers and writers with scientific content, and discuss how integration with Semantic Web technologies and Adaptive User Interface design can help materialize this vision within an ever-growing Web of scientific ideas, proof, and discourse.

Ge Li, Danai Vachtsevanou, Jérémy Lemée, Simon Mayer, and Jannis Strecker. 2024.2024. Reader-aware Writing Assistance through Reader Profiles. In 34th ACM Conference on Hypertext and Social Media (HT ’24), September 10–13, 2024, Poznań, Poland. ACM, New York, NY, USA, 7 pages.