The intake of computer science faculty has rapidly increased with simultaneous reductions to course personnel. Presently, the economy is recovering slightly, and students are entering the working life already during their studies. These reasons have fortified demands for flexibility to keep the target graduation time the same as before, even shorten it. Required flexibility is created by increasing distance learning and MOOCs, which challenges students’ self-regulation skills. Teaching methods and systems need to evolve to support students’ progress. At the curriculum design level, such learning analytics tools have already been taken into use. This position paper outlines a next-generation, course-scope analytics tool that utilises data from both the learning management system and Gitlab, which works here as a channel of student submissions. Gitlab provides GitOps, and GitOps will be enhanced with machine learning, thereby transforming as MLOps. MLOps that performs learning analytics, is called here LAOps. For analysis, data is copied to the cloud, and for that, it must be properly protected, after which models are trained and analyses performed. The results are provided to both teachers and students and utilised for personalisation and differentiation of exercises based on students’ skill level.

Open-source projects commonly receive new feature requests from different types of users from layperson end users to developers, who actively contribute code to the project. However, the submission of new feature requests and the processes adopted for handling them is not always clear. In this work, we aim at investigating the availability of the contribution information, and in particular on the new feature requests, on 66 out of the 100 most starred GitHub projects. We examined the contribution guidelines and other documentation from those 66 projects. We particularly searched for whether the projects openly welcomed new contributions, such as feature requests. Our finding shows that even the most starred GitHub projects are often not reporting information on how to contribute and, in particular, how new feature requests are managed.

Traditional methods of managing concurrent processes are difficult and prone to errors. We propose that actions can provide a much simpler approach to the problem. In this paper, we use Temporal Logic of Actions to define an execution system that can be used to concurrently execute programs created with action languages. Important features of the system include naturally concurrent execution of actions, automated management of mutual exclusion, complete avoidance of low-level deadlocks, and implicit means of synchronization.

Many commonly used systems, for example event-based systems, can be considered action systems. Traditionally, action systems have only been visualized behaviorally using state diagrams, which requires translating the system into a state machine and discarding structural information. We introduce an action-based diagram and associated formalism that depict both the behavior and the structure of the system as actions, objects, and participations. The diagrams are useful for visualizing action systems in a native format and can provide an action-oriented viewpoint for other systems as well.

To instruct students on the use of visualizations most beneficially the teacher needs to know who the students are that use the visualizations, for what they use them, and what kind of problems they have. Empirical research has been carried out on the educational impact of visualizations in test situations, but this survey presents the students' normal way of studying when the use of visualizations is voluntary.

Programming is related to several fields of technology, and many university students are studying the basics of it. Unfortunately, they often face difficulties already on the basic courses. This work studies the difficulties in learning programming in order to support developing learning materials for basic programming courses. The difficulties have to be recognized to be able to aid learning and teaching in an effective way.An international survey of opinions was organized for more than 500 students and teachers. This paper analyses the results of the survey. The survey provides information of the difficulties experienced and perceived when learning and teaching programming. The survey results also provide basis for recommendations for developing learning materials and approaches.

Assessment provides means for influencing students' learning results and strategies on university courses. However, supporting students to learn practical programming skills requires carefully designed assignments and their assessment. This paper studies challenges and approaches for assessing homework programming assignments. We present and discuss a semi-automatic assessment system for supporting programming assignments on large courses.