Assessing code automatically is a significant challenge in distance learning, especially in large online courses with limited teaching resources. Although auto-gradable programming exercises address scalability, creating enough high-quality exer-cises-particularly designing comprehensive unit tests-remains time-consuming and labor-intensive. To address this, we introduce a GPT-based feature that automates unit test generation for customized exercises. With a single button press, instructors can adapt existing exercises to meet specific teaching objectives while preserving auto-gradability. The AI-generated tests comprehensively cover potential edge cases that might otherwise be overlooked, thus reducing the need for manual oversight. An empirical evaluation with eight experienced educators showed these tests to be both thorough and time-efficient, achieving an average System Usability Scale (SUS) score of 81.79. Participants, who reported intermediate to advanced proficiency in designing manual unit tests and intermediate familiarity with AI tools like ChatGPT, praised the feature's ease of use and seamless workflow integration. Their combined expertise in teaching, coding, and AI-informed course development allowed them to provide insightful feedback on the practicality and reliability of our GPT-based solution. Our study includes a small participant pool ($\mathrm{n}=8$) and primarily focuses on Python, a language wellsupported by GPT. Future research will involve expanding the participant group, exploring additional programming languages, and assessing long-term tool performance and adaptability in diverse educational contexts. By harnessing GPT's language modeling capabilities, our approach addresses the gap between generic, limited-coverage test generation and the need for robust, domain-specific tests. Early reports from participants suggest that specialized exercises-such as those involving advanced data structures-can also benefit from automated unit test generation, though further evaluation is necessary. By leveraging artificial intelligence, this method streamlines exercise customization and enhances the overall usability and effectiveness of programming education tools. It has the potential to revolutionize auto-gradable exercise creation at scale, empowering educators to deliver high-quality instruction while tackling both the technical and pedagogical challenges in programming education.

With a rising amount of highly successful educational content on major video platforms, science communication (SciComm) can be considered mainstream. Although the success in terms of social media metrics (e.g. followers and watch time) is undoubtedly given, the learning mechanisms of these production styles is under-researched. Through a between-subject-design of 980 adult learners in a MOOC about data science, this study analyzes how much of a difference four popular SciComm production styles about relational databases make in regard to perceived quality, learning success and technical user behavior. Testing the isolated effect showed no statistical difference in the grand scheme of things. Additionally, a multivariate regression model, estimating the overall course points with robust standard errors showed six significant variables: The time spend with the material and the number of exercise submissions are particular noteworthy. Based on our results, an underlying (video) script is more relevant than the actual production style. Prioritizing the preparation of this material instead following a specific, pre-existing video production style is recommended.

The popularity of Massive Open Online Courses (MOOCs) as a means of delivering education to large numbers of students has been growing steadily over the last decade. As technology improves, more educational content is becoming readily available to the public. JupyterLab, an open-source web-based interactive development environment (IDE), is also becoming increasingly popular in education, however, it is so far primarily used in small classroom settings. JupyterLab can provide a more interactive, hands-on, and collaborative learning experience for students in MOOCs, and it is highly customizable and can be accessed from anywhere. To capitalize on these benefits, we have developed OpenJupyter, which integrates JupyterLab at scale with MOOCs, enhancing the student learning experience and providing hands-on exercises for data science courses, making them more interactive and engaging. While MOOCs provide access to education for a large number of students, one of the significant challenges is providing effective and timely feedback to learners. OpenJupyter includes an auto-assessment capability that addresses this problem in MOOCs by automating the evaluation process and providing feedback to learners in a timely manner. In this paper, we provide an overview of the architecture of OpenJupyter, its scalability in the context of MOOCs, and its effectiveness in addressing the auto-assessment challenge. We also discuss the Advantages and limitations associated with using OpenJupyter in a MOOC context and provide a reference for educators and researchers who wish to implement similar tools. Our efforts aim to foster an open educational environment in the field of programming by providing learners with an interactive learning tool and a streamlined technical setup, allowing them to acquire and test their knowledge at their own pace.

The Hasso Plattner Institute (HPI) has been successfully delivering courses on several MOOC (Massive Open Online Course) platforms for the last 10 years, offering courses on various topics in the context of Artificial Intelligence (AI), Machine Learning (ML), and Data Science. In recent years, Jupyter Notebooks have become one of the most widely used tools for data science applications, a platform for learning and practicing various programming languages. We want to integrate JupyterHub into our learning platform in order to provide students with hands-on experience in AI. We have conducted a survey with a series of research questions in order to understand the needs of instructors in their courses at different institutions. In this paper, we present a detailed analysis of our survey results and we discuss our future approach to using JupyterHub as an infrastructure to solve hands-on programming exercises on our platform. We propose the idea of creating a tool to automate server and environment creation for students to work on. This tool would give instructors a platform to operate from and allow them to customize their courses. Moreover, it would help them automate assignment submissions, grading, and provide feedback to their students.

In 2017, Geoffrey West published his book "Scale" in which he examined universal laws of scale in different contexts. Inspired by his keynote in 2021's [email protected] conference, we investigated the applicability of these laws in the context of Massive Open Online Courses and learners' behavior. We tested these laws on different learning platforms from academic, enterprise and social, and research contexts. In this paper, we examine course characteristics, such as course size, the completion rate, and the forum activity. We observed that the number of issued certificates scales almost identically on all examined platforms, while forum participation scales slightly different on each of the platforms. In the future, we will perform a deeper analysis on the forum behavior that exceeds a mere quantitative analysis.