TY - CHAP A1 - Prather, James A1 - Leinonen, Juho A1 - Kiesler, Natalie A1 - Benario, Jamie Gorson A1 - Lau, Sam A1 - MacNeil, Stephen A1 - Norouzi, Narges A1 - Opel, Simone A1 - Pettit, Virginia A1 - Porter, Leo A1 - Reeves, Brent N. A1 - Savelka, Jaromir A1 - Smith, David H. A1 - Strickroth, Sven A1 - Zingaro, Daniel T1 - How Instructors Incorporate Generative AI into Teaching Computing T2 - Proceedings of the 2024 on Innovation and Technology in Computer Science Education Vol. 2 N2 - Generative AI (GenAI) has seen great advancements in the past two years and the conversation around adoption is increasing. Widely available GenAI tools are disrupting classroom practices as they can write and explain code with minimal student prompting. While most acknowledge that there is no way to stop students from using such tools, a consensus has yet to form on how students should use them if they choose to do so. At the same time, researchers have begun to introduce new pedagogical tools that integrate GenAI into computing curricula. These new tools offer students personalized help or attempt to teach prompting skills without undercutting code comprehension. This working group aims to detail the current landscape of education-focused GenAI tools and teaching approaches, present gaps where new tools or approaches could appear, identify good practice-examples, and provide a guide for instructors to utilize GenAI as they continue to adapt to this new era. Y1 - 2024 U6 - https://doi.org/10.1145/3649405.3659534 SP - 771 EP - 772 PB - ACM CY - New York, NY, USA ER - TY - CHAP A1 - Azaiz, Imen A1 - Kiesler, Natalie A1 - Strickroth, Sven T1 - Feedback-Generation for Programming Exercises With GPT-4 T2 - Proceedings of the 2024 on Innovation and Technology in Computer Science Education Vol. 1 N2 - Ever since Large Language Models (LLMs) and related applications have become broadly available, several studies investigated their potential for assisting educators and supporting students in higher education. LLMs such as Codex, GPT-3.5, and GPT 4 have shown promising results in the context of large programming courses, where students can benefit from feedback and hints if provided timely and at scale. This paper explores the quality of GPT-4 Turbo's generated output for prompts containing both the programming task specification and a student's submission as input. Two assignments from an introductory programming course were selected, and GPT-4 was asked to generate feedback for 55 randomly chosen, authentic student programming submissions. The output was qualitatively analyzed regarding correctness, personalization, fault localization, and other features identified in the material. Compared to prior work and analyses of GPT-3.5, GPT-4 Turbo shows notable improvements. For example, the output is more structured and consistent. GPT-4 Turbo can also accurately identify invalid casing in student programs' output. In some cases, the feedback also includes the output of the student program. At the same time, inconsistent feedback was noted such as stating that the submission is correct but an error needs to be fixed. The present work increases our understanding of LLMs' potential, limitations, and how to integrate them into e-assessment systems, pedagogical scenarios, and instructing students who are using applications based on GPT-4. Y1 - 2024 U6 - https://doi.org/10.1145/3649217.3653594 SP - 31 EP - 37 PB - ACM CY - New York, NY, USA ER - TY - CHAP A1 - Kiesler, Natalie A1 - Röpke, René A1 - Schiffner, Daniel A1 - Schulz, Sandra A1 - Strickroth, Sven A1 - Ehlenz, Matthias A1 - Heinemann, Birte A1 - Wilhelm-Weidner, Arno ED - Schulz, Sandra ED - Kiesler, Natalie T1 - Towards Open Science at the DELFI Conference T2 - 22. Fachtagung Bildungstechnologien (DELFI) N2 - Despite the increasing awareness of Open Science within the educational technology community, conferences, such as DELFI, do not yet foster the publication of research data including software. To address this, we conducted a survey eliciting the community’s needs, perspectives, and publication preferences. The analysis of 24 valid responses reveals a variety of research data formats used, and several uncertainties, e. g., regarding data ownership. Associated barriers comprise legal concerns and lacking resources to publish data. Nonetheless, researchers seem open for new publication formats. Moreover, we analyzed author’s intentions to publish data related to their DELFI submissions in 2023 (n=66). Many researchers assume not to have data to share (n=28), or no intention to publish data in the future (n=16). Overall, the results imply a lack of awareness and recognition of data publications, so that further efforts and incentives are required to move toward Open Science practices in the DELFI community. KW - Open Science , Open Data , Data publication , FAIR , DELFI , Educational Technology Y1 - 2024 U6 - https://doi.org/10.18420/delfi2024_22 SP - 251 EP - 265 ER -