In the context of programming education, so-called auto-graders allow learners to receive automated feedback on their submissions. Because assessing learners' code typically involves executing the learners' untrusted code, this commonly used mechanism poses a significant security risk for these systems. Since auto-graders are mostly employed in the context of large-scale learning environments, such as universities or Massive Open Online Courses (MOOCs), security considerations are especially important. In this paper, we first introduce our auto-grader CodeOcean, which is regularly used in MOOCs with thousands of active learners, and in university contexts. As the execution of untrusted code can entail severe security implications, ensuring that the application contains no security vulnerabilities is essential. Hence, we partnered with a security consultancy to assess our auto-grader system landscape through a professional penetration test. This work presents the findings and countermeasures resulting from the performed security analysis for CodeOcean. We contextualize overarching enhancements for three main categories of threat vectors to auto-grader systems. Implementing these in any auto-grader system can improve the security and prevent learners from manipulating the assessment of their code. We also discuss the potential consequences of hardening an auto-grader, such as a reduced system performance. Therewith, we provide valuable recommendations for educators, researchers, and system designers to improve the security of auto-graders in the future, supporting their usage in even larger settings or in the context of exams.

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.

Podcasts are a widely-used medium for communication and learning. One advantage of them is the possibility to pursue other activities while listening. Contrasting, Massive Open Online Courses (MOOCs) employ video-based teaching methods. Current research, however, challenges the interactivity and variation of teaching content in established MOOCs. This manuscript presents an experiment conducted with a podcast series deployed alongside a MOOC on cybersecurity. In our Static-Group Comparison, we identified a significant increase in learning success in weekly graded exercises (6.3%) and the course's final examination (6.4%) for learners exposing themselves to the podcast. Our first study results are promising in favor of multimedia learning. Hence, we present ideas for additional analysis and briefly outline which aspects of the results should be discussed in more depth.

Massive Open Online Courses (MOOCs) are usually attended by several thousand learners who barely get to know each other during the course period. Being unaware of fellow learners often results in a low sense of community. In addition, many MOOC learners are afraid of using the course forum, which often is the only participation opportunity in social course activities apart from forming smaller learning groups. Thus, learners can easily be frustrated with the course content when feeling alone. To improve social presence and the sense of community, course instructors can use ice-breaking games. First, this paper evaluates which kind of ice-breaking games can be used in MOOCs. Afterward, we present the results from a first experiment where we use “self-reflection sociograms as an icebreaking activity. Most learners perceived the implemented Self-Reflection Questionnaires” (SRQ) ice-breaker as a positive course feature (68.35%). SRQs increased the sense of community, and learners were satisfied (91.06%) with their perceived community sense level. The SRQs were also helpful for the teaching teams. Our results indicate that further investigation of SRQs is beneficial to explore the provided value for course instructors and their influence on individual MOOC learners and community-building.

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.

Learners in Massive Open Online Courses offering practical programming exercises face additional challenges next to the actual course content. Beginners have to find approaches to deal with misconceptions and often struggle with the correct syntax while solving the exercises. The paper at hand presents insights from offering contextual tips in a web-based development environment used for practical programming exercises. We measured the effects of our approach in a Python course with 6,000 active students in a hidden A/B test and additionally used qualitative surveys. While a majority of learners valued the assistance, we were unable to show a direct impact on completion rates or average scores. We however noticed that users requesting tips took significantly longer and made more use of other assistance features of the platform than users in our control group. Insights from our study can be used to target beginners with more specific hints and provide additional, context-specific clues as part of the learning material.

An increasing number of high school teachers use existing Massive Open Online Courses (MOOCs) concerning programming education. Most MOOCs focus on teaching the basics of a programming language and common concepts or patterns. MOOC platforms usually provide their own code execution environments and thus have full control over the features and appearance available to learners. However, only a subset of tools available to professional software engineers is used in introductory programming MOOCs. While the reduction of features is helpful to ease navigation for novices, we assume that learners benefit from more advanced features at a later stage in the learning process. To help students minimize bugs and conceptual mistakes, we intend to evaluate how pair programming could be enabled for remote peers in MOOCs with a synchronized editor and an additional communication channel. Further, we plan to use static program analysis to get more insights about the code written by learners and to provide early feedback about the coding style. One of our contributions will be to identify possibilities to integrate professional tools and methods in MOOCs supported by an evaluation from learners.

This Research Full Paper presents insights from digital worksheets with embedded interactive programming exercises tailored for high-school students new to programming. Computer science teachers often incorporate existing videos, quizzes, and practical programming exercises from Massive Open Online Courses (MOOCs). However, teachers' options to adapt the content to their specific needs are currently limited. Based on a qualitative survey with thirteen teachers, we developed a software prototype which allows teachers to create their own interactive worksheets consisting of texts, videos, quizzes, and practical programming exercises. Additionally, teachers can embed and further customize existing exercises from MOOCs. Further, we enable teachers to gain deeper insights by providing results from automated submission analysis, thus uncovering knowledge gaps and fostering content-driven in-class discussions. Our evaluation shows that the concept was well received by students and teachers alike: Teachers noticed the possibility of a shift in their role from a lecturing instructor to an individual tutor, as students are enabled to learn at their own pace and receive specific, direct feedback based on automated unit tests. Interactive worksheets, as an integrated part of digital education, thus foster informed teacher interventions as part of an individualized student learning process.

E-commerce marketplaces are highly dynamic with constant competition. While this competition is challenging for many merchants, it also provides plenty of opportunities, e.g., by allowing them to automatically adjust prices in order to react to changing market situations. For practitioners however, testing automated pricing strategies is time-consuming and potentially hazardously when done in production. Researchers, on the other side, struggle to study how pricing strategies interact under heavy competition. As a consequence, we built an open continuous time framework to simulate dynamic pricing competition called Price Wars. The microservice-based architecture provides a scalable platform for large competitions with dozens of merchants and a large random stream of consumers. Our platform stores each event in a distributed log. This allows to provide different performance measures enabling users to compare profit and revenue of various repricing strategies in real-time. For researchers, price trajectories are shown which ease evaluating mutual price reactions of competing strategies. Furthermore, merchants can access historical marketplace data and apply machine learning. By providing a set of customizable, artificial merchants, users can easily simulate both simple rule-based strategies as well as sophisticated data-driven strategies using demand learning to optimize their pricing strategies.

Modern e-commerce platforms pose both opportunities as well as hurdles for merchants. While merchants can observe markets at any point in time and automatically reprice their products, they also have to compete simultaneously with dozens of competitors. Currently, retailers lack the possibility to test, develop, and evaluate their algorithms appropriately before releasing them into the real world. At the same time, it is challenging for researchers to investigate how pricing strategies interact with each other under heavy competition. To study dynamic pricing competition on online marketplaces, we built an open simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and handles large numbers of competing merchants and arriving consumers. It allows merchants to deploy the full width of pricing strategies, from simple rule-based strategies to more sophisticated data-driven strategies using machine learning. Our platform enables analyses of how a strategy's performance is affected by customer behavior, price adjustment frequencies, the competitors' strategies, and the exit/entry of competitors. Moreover, our platform allows to study the long-term behavior of self-adapting strategies.

IoT devices usually are battery-powered and directly connected to the Internet. This makes them vulnerable to so-called path-based denial-of-service (PDoS) attacks. For example, in a PDoS attack an adversary sends multiple Constrained Application Protocol (CoAP) messages towards an IoT device, thereby causing each IoT device along the path to expend energy for forwarding this message. Current end-to-end security solutions, such as DTLS or IPsec, fail to prevent such attacks since they only filter out inauthentic CoAP messages at their destination. This demonstration shows an approach to allow en-route filtering where a trusted gateway has all necessary information to check the integrity, decrypt and, if necessary, drop a message before forwarding it to the constrained mote. Our approach preserves precious resources of IoT devices in the face of path-based denial-of-service attacks by remote attackers.