openHPI benefits from our many years of research activities and a large catalog of top-notch publications in the field of MOOCs, whereby the team focuses on the following topics.
Collaborative Learning, Team Work and Peer Assessment in MOOCs
Generally, participants are learning on their own in MOOCs. On openHPI, we have implemented several approaches to allow more collaboration between the participants in courses. The most general feature in this context is the course forum, where participants can discuss with other participants and the teaching team. More sophisticated features are the Collab Spaces, the Team Builder, and the Peer Assessment. The Collab Spaces allow the participants to create small groups within the general course population. Teaching teams can use the Team Builder to automate the process of creating such groups or teams according to special criteria, such as the participants' time zone or time commitment. The teaching teams can also provide open-end tasks to individual participants or such teams, which in the end can be graded via so-called Peer Assessments, where participants are reviewing and grading the work of their peers. All of these features have been provided successfully in several courses on our platforms.
Learning Analytics and Self-Regulated Learning in MOOCs
The vast amount of data that MOOCs produce on the learning behavior and success of thousands of students provides the opportunity to study human learning and develop approaches to support learning and teaching with data-driven insights. For openHPI, a learning analytics architecture to collect, process, and analyze event-driven learning data based on schema-agnostic pipelining has been introduced. On this basis, a dashboard for learners has been developed to support self-regulated learning, in particular to enable learners to evaluate and plan their learning activities, progress, and success by themselves. Additionally, personalized learning objectives are provided to better connect learners' success to their intentions, offer guidance, and align the data-driven insights about their learning progress. Furthermore, a dashboard for teachers has been realized to enable the monitoring of their courses with thousands of learners, identify potential issues, and take informed action. These are key tools and integral parts of the learning and teaching experience on the HPI MOOC platform.
Mobile (Seamless) Learning in MOOCs
As mobile devices have become ubiquitous in our daily lives, learning with MOOCs no longer needs to be tied to a stationary learning environment. Learners can access the learning material provided whenever and wherever they want. Therefore, an appropriate learning environment must be provided on mobile devices. The learning experience has to be adapted to the shorter usage times of mobile devices. This includes leaner interaction patterns compared to those of a full-featured web application, and the development of learning experiences that work well on smaller screens and even with multiple screens simultaneously. By using proactive interventions (e.g., via push notifications), short learning activities can be triggered and thus enable learning on the go. While mobile devices offer learners an additional degree of freedom in designing their learning process, providing appropriate learning experiences also presents new challenges. Mobile devices can also be used in places with no or poor internet connection. In these cases, the learning material must be made available on mobile devices—manually or with smart automatic approaches.
Gameful Learning in MOOCs
Gameful learning describes the use of games or game mechanics for learning purposes. Applying gameful learning designs to a learning scenario can foster three effects: the students' understanding, their motivation, and the social interaction between learners. In this research, we aim at finding out how gameful learning designs can be used at the HPI MOOC platform, especially to foster ice-breaking situations and improve social interaction.
Automated Evaluation of Programming Exercises
The focus of our platform is on IT topics, which include programming courses in different programming languages. An important element of these courses is 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. Therefore, we have investigated and developed two tools 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. In our ongoing research, we investigate various assistance features for learners (such as contextual tips or tailored help offers) and introduce novices to software engineering practices (e.g., a linter, pair programming, or debugging techniques). Further, we analyze the requirements of course instructors to create auto-gradable programming exercises, explore practical tooling support for them to ease the exercise creation, and thus lower the barrier to using them.
Dialogue-Based Systems for Learners and Instructors in MOOCs
Dialogue-based systems, also known as chatbots, offer a diverse toolbox that goes beyond a simple question-and-answer system. This research explores various elements that can be integrated into the chatbot to facilitate learning and teaching with MOOCs for learners and instructors. Important elements include personalized support, integrated quizzes, recommendations for further learning and reading materials, as well as courses. The extent to which this toolbox helps users is measured, among other things, by the number of support tickets and feedback in the chatbot dialog.
Synchronous Learning through Live Streaming in MOOCs
With increasing popularity and viewership, live streaming has quickly found its place in online pop culture and informal learning environments. The synchronous classroom is also a viable application: Interacting with learners in real-time, clarifying issues in online office hours, or offering academic content to a public audience are all possible use cases. In the context of MOOCs, live streaming can also foster a sense of community and emphasize learning as a social process. Research questions include the benefits of synchronous learning environments as measured by pre- and post-session communication and the level of interaction within a live session (e.g., the average number of participants, number of chat messages).
Exploring Learner Activity Data to Provide Insights for Teacher Analytics in MOOCs
The HPI MOOC platform uses independent Learning Tools Interoperability (LTI) providers such as H5P as an extension tool for conducting evaluation and exercise inside the platform. We aim to explore how the learners' activities can benefit the teachers by examining the collected learning activity data, structuring the different types of interaction, and providing the teachers with an analytics interface. To build a meaningful analytics tool, we apply a user-centered process to understand the requirements and deliver functionalities to aid teachers in evaluating their course content and exercises.
Integrating JupyterHub and a Traffic Management System in MOOCs
The past few years have witnessed significant growth in the number of users attending online courses. Currently, there is an enormous amount of interest in machine learning (ML) and artificial intelligence (AI) and what these new technologies can create for the present and future. To truly learn the necessary skills to explore the world of AI, one needs the right set of learning tools and more importantly, resources. JupyterHub and Jupyter notebooks have exploded in popularity since late 2014. Moreover, it has become a very useful learning tool to program and solve assignments in a well-structured, supportive environment. When it comes to MOOCs, thousands of users could attend a certain course and as resources are not infinite, creating a dynamic scheduler for resource allocation and traffic management would help create a balanced learning environment where each student has access to a learning tool but also enough resources to practice.
