The German University of Digital Science is a new online-only university catering to a world-wide audience. Coding Camp 1: Python was one of a first set of micro-degree programs offered in October 2024. It covered basic Python, Django, open-source development, version control, and agile project management. After introducing the basics, the program followed a project-based learning (PBL) approach. The learners had to choose between designing and developing an application of their own from scratch or contributing to the Open edX open-source project. Experts from two of Open edX' main stakeholders supported the learners to accommodate for the steeper learning curve. The paper at hand discusses the intentions, goals and views of the learners and the other involved stakeholders.

Massive Open Online Courses (MOOCs) are a popular form of online education that often attracts a huge and heterogeneous group of learners with diverse interests and backgrounds. However, most MOOCs follow a one-size-fits-all approach, providing a fixed order of learning materials and expecting all learners to follow this recommended path. Thus, they neither motivate nor support their learners in adapting the courses to their individual preferences. In the work at hand, we tackle this issue by introducing and evaluating the concept of flexible learning paths in MOOCs. We, therefore, establish a network of dependencies between course content, omit intermediate deadlines, and thereby rethink the way learners interact with the course. By presenting learners with a non-linear course format, we encourage them to create their individual learning paths based on instructor-defined dependencies and their personal interests. Our evaluation of flexible learning paths within a programming MOOC shows that learners chose many different learning paths. Despite achieving similar results in individual tasks compared to learners using the traditional course structure, they engaged with less course content, resulting in a slight decrease in their overall performance. This may indicate a lack of self-regulatory learning skills, with learners struggling to organise their work without instructor-given deadlines. However, the flexible course format significantly increased the motivation of learners. By introducing and evaluating the concept of flexible learning paths in MOOCs, this work provides valuable insights into the individualisation of online education.

Der MOOChub ist eine Webseite, die weit über 700 Massive Open Online Courses (MOOCs) aus dem deutschsprachigen Raum von insgesamt neun unterschiedlichen Partner:innen listet. Damit eine solche Seite automatisiert aufgebaut werden kann, ist es notwendig, dass alle Partner:innen die Metadaten der Kurse in gleicher Weise beschreiben und verfügbar machen. Dieser Artikel beschreibt zunächst die Entstehung der Idee eines gemeinsamen Standards und wie dieser im Anschluss entwickelt worden ist. Das Ergebnis ist einerseits ein offen lizenzierter Quasi-Standard, der sich an üblichen Standards orientiert, und ein erster Prototyp, der sogenannte MOOChub, auf dem nun alle Kurse auffindbar und durchsuchbar sind. Abschließend wird über die nächsten möglichen und auch notwendigen Entwicklungen berichtet, die die Schnittstelle weiter optimieren sollen.

Massive Open Online Courses (MOOCs) are delivering rich learning content to a variety of audiences. Next to the learning material, the discussion forums play a major role in the success of MOOCs. A healthy climate in the discussions is of great importance for the motivation of the instructors and the participants. We have employed a sentiment analyzer to observe the development of the discussions in several of our courses. We expect to obtain a better understanding of the development in the discussions, the influence of the instructors' interventions on this behavior, and to some extent the dropout and course completion development.

Massive Open Online Courses, kurz MOOCs, sind Online-Kurse mit einer großen Zahl an Teilnehmer:innen, die zumeist auf speziellen Plattformen kostenlos zur Verfügung gestellt werden. Mit dem Kurs zur Künstlichen Intelligenz von Sebastian Thrun mit über 160.000 Lernenden fanden MOOCs zunehmend Verbreitung. Spätestens seit der COVID19-Pandemie sind sie nicht mehr aus unserem universitären Hochschulalltag wegzudenken und heute zum Teil integraler Bestandteil von Lehrveranstaltungen. Durch vielfältige Einsatzmöglichkeiten werden so Weiterbildungen, Workshops oder joint lectures unterstützt. Das aktuelle Themenheft rief zu Beiträgen rund um MOOCs auf und erlaubt dadurch einen Einblick in die facettenreichen Entwicklungen. In der aktuellen Ausgabe finden Sie hierzu spannende Beiträge mit Erfahrungsberichten, neuesten Erkenntnissen, Weiterentwicklungen und didaktischen Einsatzmöglichkeiten. Wir laden Sie also herzlich ein, mit uns gemeinsam dieses innovative, zukunftsträchtige und auch nachhaltige Thema weiter zu vertiefen.

Nowadays, there are many online courses like MOOCs (Massive Open Online Courses) available from different providers (e.g. edX, Coursera, openHPI, OpenWHO, iMOOX). To support learners, aggregators like Class Central or MOOChub were established. These aggregators hold catalogs with the offerings of the providers making them a central entry point for the users. Such catalogs are based on metadata, which needs to be formatted in a proper way. This metadata can then be used for filtering courses and recommendation engines also. With more and more emerging AI-based recommendation services for learning opportunities and learning path assistants, the need for well-maintained and meaningful metadata is growing massively. In this paper, we report on our research about different systems for categorizing the fields of study, topic, or subject, which can be used to enhance existing metadata formats. An overview of field of study categorization systems of different entities (e.g. international, national, and private organizations) is given. The systems are compared regarding their usefulness in metadata formats for the description of courses. The results are utilized to refine our own metadata format and represent a further step towards a standardized metadata format for courses and automatically generated metadata.

Delivering content in a course on the Massive Open Online Courses (MOOCs) platform is becoming more interactive as technology evolves, especially with the availability of various tools that can be integrated into the platform. However, learners' interactions with interactive content in MOOCs are frequently overlooked and under-analyzed. This is a missed opportunity, as numerous aspects can be investigated to provide insights into the course's growth and success based on the interactive content. To address this issue, a Learning Record Store (LRS) was created as an extension of the existing Learning Analytics in MOOCs. The LRS is designed for instructors and allows them to collect, store, and analyze learner activity data within interactive content. This information can be used to monitor learners' progress, identify areas for improvement, and tailor learning experiences. The design process, concept, and objectives of the LRS system development are presented in this work. The LRS system was evaluated to test its effectiveness in capturing learner activity and providing insights into the use of interactive content. The results showed that the LRS successfully recognized the benefits of using interactive content early on and evaluated the extent to which instructors used different interactive content for different course use cases.

This paper presents the Smart Learning Assistant on the German MOOC platform AI-Campus. It is an AI-powered, dialog-based system that provides learners with personalized learning support and features for enhanced learning. The paper presents the original motivation and planned features of the Smart Learning Assistant as well as its technical implementation, architecture, and status quo. An initial evaluation was conducted using event logs from the dialog system and an integrated feedback loop. It shows that learners start to use the learning assistant and are also engaged in long conversations. Frequent topics of conversation are related to the user's profile, more information about AI-Campus, information about AI, or reporting an error. However, the initial analysis has also shown that users are not yet satisfied with the first assistant and that the list of topics and functions as well as the conversation guidance need to be expanded. The paper concludes with a discussion of future directions for AI development and integration in MOOC platforms, including the need for continued evaluation and refinement of AI-enabled educational technologies. In addition, an overview of future functionalities and opportunities for improvement is provided. Overall, this paper contributes to the understanding of the potential benefits and challenges of AI in education and sheds light on the role of a Smart Learning Assistant in enhancing personalized learning experiences on MOOC platforms.

World Health Organization's (WHO) emergency learning platform OpenWHO provided by Hasso Plattner Institut (HPI) delivered online learning in real-time and in multiple languages during the COVID-19 pandemic. The challenge was to move from manual transcription and translation to automated to increase the speed and quantity of materials and languages available. TransPipe tool was introduced to facilitate this task. We describe the TransPipe development, analyze its functioning and report key results achieved. TransPipe successfully connects existing services and provides a suitable workflow to create and maintain video subtitles in different languages. By the end of 2022, the tool transcribed nearly 4,700 minutes of video content and translated 1,050,700 characters of video subtitles. Automated transcription and translation have enormous potential as a public health learning tool, allowing the near-simultaneous availability of video subtitles on OpenWHO in many languages, thus improving the usability of the learning materials in multiple languages for wider audiences.

Massive Open Online Courses (MOOCs) remarkably attracted global media attention, but the spotlight has been concentrated on a handful of English-language providers. While Coursera, edX, Udacity, and FutureLearn received most of the attention and scrutiny, an entirely new ecosystem of local MOOC providers was growing in parallel. This ecosystem is harder to study than the major players: they are spread around the world, have less staff devoted to maintaining research data, and operate in multiple languages with university and corporate regional partners. To better understand how online learning opportunities are expanding through this regional MOOC ecosystem, we created a research partnership among 15 different MOOC providers from nine countries. We gathered data from over eight million learners in six thousand MOOCs, and we conducted a large-scale survey with more than 10 thousand participants. From our analysis, we argue that these regional providers may be better positioned to meet the goals of expanding access to higher education in their regions than the better-known global providers. To make this claim we highlight three trends: first, regional providers attract a larger local population with more inclusive demographic profiles; second, students predominantly choose their courses based on topical interest, and regional providers do a better job at catering to those needs; and third, many students feel more at ease learning from institutions they already know and have references from. Our work raises the importance of local education in the global MOOC ecosystem, while calling for additional research and conversations across the diversity of MOOC providers.

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 Learning@Scale 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.

Many participants in Massive Open Online Courses are full-time employees seeking greater flexibility in their time commitment and the available learning paths. We recently addressed these requirements by splitting up our 6-week courses into three 2-week modules followed by a separate exam. Modularizing courses offers many advantages: Shorter modules are more sustainable and can be combined, reused, and incorporated into learning paths more easily. Time flexibility for learners is also improved as exams can now be offered multiple times per year, while the learning content is available independently. In this article, we answer the question of which impact this modularization has on key learning metrics, such as course completion rates, learning success, and no-show rates. Furthermore, we investigate the influence of longer breaks between modules on these metrics. According to our analysis, course modules facilitate more selective learning behaviors that encourage learners to focus on topics they are the most interested in. At the same time, participation in overarching exams across all modules seems to be less appealing compared to an integrated exam of a 6-week course. While breaks between the modules increase the distinctive appearance of individual modules, a break before the final exam further reduces initial interest in the exams. We further reveal that participation in self-paced courses as a preparation for the final exam is unlikely to attract new learners to the course offerings, even though learners' performance is comparable to instructor-paced courses. The results of our long-term study on course modularization provide a solid foundation for future research and enable educators to make informed decisions about the design of their courses.

The COVID-19 pandemic generated an unprecedented global demand for learning about the disease and how to manage it. This paper draws on theWorld Health Organization (WHO)'s experience of COVID-19 knowledge-transfer to a worldwide audience of millions of learners registered on OpenWHO, WHO's massive open online course platform. It aims to illustrate the technological solutions that WHO, in collaboration with the Hasso Plattner Institute (HPI), OpenWHO's platform provider, employed in response to the unique challenges this surge in demand for learning engendered. Data on OpenWHO use, including geographic patterns and certificate attainment, were extracted from OpenWHO's internal and external reporting systems. Descriptive analysis was employed to identify trends and compare OpenWHO use with COVID-19 caseload in each WHO region. Data on the OpenWHO system load were obtained from the OpenWHO load balancer (HAProxy). The OpenWHO team responded to the need for trustworthy, evidence-based knowledge on COVID-19 via three main avenues: increased scale, targeting the needs of affected and underserved communities, and prioritising multilingualism. Each approach brought novel problems, which WHO and HPI leveraged their collaboration to meet by employing technology. This included increasing server bandwidth, expanding support teams, adding new language capabilities, and deploying functions to streamline workflows and boost learner experience. In doing so, the ability to effectively and efficiently harness technology became a critical step towards empowering learning's life-saving potential during the COVID-19 pandemic. © 14th International Conference on ICT, Society, and Human Beings, ICT 2021, 18th International Conference on Web Based Communities and Social Media, WBC 2021 and 13th International Conference on e-Health, EH 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021. All rights reserved.

The Hasso Plattner Institute (HPI) successfully operates a MOOC (Massive Open Online Course) platform since 2012. Since 2013, global enterprises, international organizations, governments, and research projects funded by the German ministry of education are partnering with us to operate their own instances of the platform. The focus of our platform instance is on IT topics, which includes programming courses in different programming languages. An important element of these courses are graded hands-on programming assignments. MOOCs, even more than traditional classroom situations, depend on automated solutions to assess programming exercises. Manual evaluation is not an option due to the massive amount of users that participate in these courses. The paper at hand presents two of the tools developed in this context at the HPI: CodeOcean—an auto-grader for a variety of programming languages, and CodeHarbor, a tool to share auto-gradable programming exercises between various online platforms.