Thomas Staubitz

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.

We report on a systematic review of the landscape of peer assessment in massive open online courses (MOOCs) with papers from 2014 to 2020 in 20 leading education technology publication venues across four databases containing education technology–related papers, addressing three research issues: the evolution of peer assessment in MOOCs during the period 2014 to 2020, the methods used in MOOCs to assess peers, and the challenges of and future directions in MOOC peer assessment. We provide summary statistics and a review of methods across the corpus and highlight three directions for improving the use of peer assessment in MOOCs: the need for focusing on scaling learning through peer evaluations, the need for scaling and optimizing team submissions in team peer assessments, and the need for embedding a social process for peer assessment.

Massive Open Online Courses (MOOCs) have opened new educational possibilities for learners around the world. Numerous providers have emerged, which usually have different targets (geographical, topics or language), but most of the research and spotlight has been concentrated on the global providers and studies with limited generalizability. In this work we apply a multi-platform approach generating a joint and comparable analysis with data from millions of learners and more than ten MOOC providers that have partnered to conduct this study. This allows us to generate learning analytics trends at a macro level across various MOOC providers towards understanding which MOOC trends are globally universal and which of them are context-dependent. The analysis reports preliminary results on the differences and similarities of trends based on the country of origin, level of education, gender and age of their learners across global and regional MOOC providers. This study exemplifies the potential of macro learning analytics in MOOCs to understand the ecosystem and inform the whole community, while calling for more large scale studies in learning analytics through partnerships among researchers and institutions.

This work presents insights about the long-term effects and retention rates of knowledge acquired within MOOCs. In 2015 and 2017, we conducted two introductory MOOCs on object-oriented programming in Java with each over 10,000 registered participants. In this paper, we analyze course scores, quiz results and self-stated skill levels of our participants. The aim of our analysis is to uncover factors influencing the retention of acquired knowledge, such as time passed or knowledge application, in order to improve long-term success. While we know that some participants learned the programming basics within our course, we lack information on whether this knowledge was applied and fortified after the course's end. To fill this knowledge gap, we conducted a survey in 2018 among all participants of our 2015 and 2017 programming MOOCs. The first part of the survey elicits responses on whether and how MOOC knowledge was applied and gives participants opportunity to voice individual feedback. The second part of the survey contains several questions of increasing difficulty and complexity regarding course content in order to learn about the consolidation of the acquired knowledge. We distinguish three programming knowledge areas in the survey: First, understanding of concepts, such as loops and boolean algebra. Second, syntax knowledge, such as specific keywords. Third, practical skills including debugging and coding. We further analyzed the long-term effects separately per participant skill group. While answer rates were low, the collected data shows a decrease of knowledge over time, relatively unaffected by skill level. Application of the acquired knowledge improves the memory retention rates of MOOC participants across all skill levels.

With the increasing use of graded team-based assignments on our MOOC platforms-openHPI, openSAP, and mooc.house-we see the need to consult the opinion of our course participants about their perception of these tasks and the sufficiency of the platform support. Since we introduced the feature in May 2016, seven courses that included team-based assignments have been conducted on our platforms. In four of these courses, we have conducted qualitative and quantitative surveys among the participants. The paper at hand presents and discusses the results of these surveys.

Distance education gained considerable attention with the rise of Massive Open Online Courses (MOOCs). Given the significant role collaboration plays in practical computer science education on campus, it becomes evident that nowadays online course platforms mostly lack the necessary collaborative capabilities. We present a solution to support collaborative programming through video conferencing for practical exercises employed in MOOC contexts. Two user surveys show that although users value the possibilities, privacy concerns remain. We therefore propose to additionally use video conferencing technology to face another challenge: MOOCs usually are conceptualized and produced to a large extent before the actual course runtime. Reaction on current events within the course is possible but requires insights on students' problems. Course conductors can use the tutoring mode in our WebIDE to understand struggling students and potentially uncover topics that lack additional background material or need additional training exercises.

Programming tasks are an important part of teaching computer programming as they foster students to develop essential programming skills and techniques through practice. The design of educational problems plays a crucial role in the extent to which the experiential knowledge is imparted to the learner both in terms of quality and quantity. Badly designed tasks have been known to put-off students from practicing programming. Hence, there is a need for carefully designed problems. Cellular Automata programming lends itself as a very suitable candidate among problems designed for programming practice. In this paper, we describe how various types of problems can be designed using concepts from Cellular Automata and discuss the features which make them good practice problems with regard to instructional pedagogy. We also present a case study on a Cellular Automata programming exercise used in a MOOC on Test Driven Development using JUnit, and discuss the automated evaluation of code submissions and the feedback about the reception of this exercise by participants in this course. Finally, we suggest two ideas to facilitate an easier approach of creating such programming exercises.