NoSQL data models such as document databases and wide-column databases have gained popularity in industry and education in the last two decades. The database design principles and query languages of different NoSQL systems differ from those of the relational model and SQL, which can cause challenges in transitioning from relational systems to NoSQL systems. In this mixed-methods study, we explore the challenges novices experience with database design and querying with two NoSQL systems: MongoDB and Cassandra. Our results show that NoSQL database design is perceived more difficult than relational due to access-pattern-first design patterns, old habits, and the limits imposed by database distribution. Querying, however, is not perceived as more difficult or easier due to similarities of query languages to previous experiences with SQL and programming. These results are applicable in the classroom in determining the teaching order of novel data models, and in focusing teaching on the challenging aspects in document and wide-column databases.

The enrollment of computer science students continues to increase, with record enrollment numbers in the course ”Data Structures and Algorithms” in fall 2022. As a result, teaching methods and systems must evolve to support student progress in the face of scarce teaching resources. This paper examines the shift from manual to peer-reviewed and auto-graded grading processes and investigates students’ perceptions of different grading styles. The developed automatic graders utilize data from both the Plussa LMS and GitLab, which serves as the channel for student submissions and provides feedback for formative assessments. The results indicate that peer-reviews are accepted as an exercise for the reviewer, but not as a means to grade the reviewee. Autograders are well-received due to their instant feedback, ability to allow for multiple submissions and iterate towards more efficient solutions, which helps foster a growth mindset.

In this article, the ongoing research on collaborative prototype development between university and enterprises is presented. The study of project featured numerous pilot cases and prototypes, executed in collaboration with organizations to address real-world challenges. This article assesses the appropriateness of the Descriptive Model for Prototyping Process (DMPP) for research project applications. We delve into two primary facets: the synergy between universities and enterprises, and the potential for artifact reusability within the DMPP. The article presents various pilot cases from the KIEMI project, highlighting the DMPP’s role in each. Furthermore, the paper evaluates the model, sets forward the challenges faced, and, finally, discusses topics for future research.

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.

Finnish university pedagogues are keen on applying flipped learning techniques to improve education and learning outcomes. Flipped learning implies the transfer of assessment in a more formative direction and targeted feedback that is frequently delivered. On the contrary, teaching resources are decreasing. Increasing the portion of self-, peer- and automatic assessment partially helps solving this dilemma. Currently, Tampere University is in the midst of the process of combining two separate campuses together. Both campuses offer major-specific computer science studies. This paper presents a case study of merging basic level web technology courses, and in particular their different assessment practices together. The courses are targeted for the second- and third-year students, and the number of participants is about 200 in the studied course (enrolled N=324 / completed N=178). The merged course was run in two learning management systems (LMSs), called Plussa and WETO. The switch from one LMS to another happened in the middle of the course. LMSs employed different assessment practices for weekly exercises: Plussa assessed them automatically, whereas WETO exploited peer-reviews. This provided a unique opportunity to compare these two assessment methods and the study addresses the pedagogical opportunities and challenges of both.