The presented study aspires to utilize the gradually validated immense potential of collaborative immersive virtual environments (CIVEs) in higher education when designing and conducting geography lessons. These particular lessons focused on hypsography. A Research through Design approach and relevant qualitative methodology were used as we engaged two groups of domain experts (experienced geography teachers) to validate both the learning scenarios and the virtual environment we used. The lessons were administered via eDIVE—a novel platform for collaborative learning and teaching in virtual reality of our own design. The teachers underwent a hypsography virtual lesson and were randomly divided into two groups to be compared, which differed in the level of structure given to the lesson (one group received detailed instructions on what task they were to solve, while the other was given a free hand in exploring the environment and activities it afforded). The teachers’ experiences were then summarized in a post-lesson reflection and a subsequent focus group following the tasks. The participants’ expertise allowed insights to be gained into their first-hand experience as students, as well as their expert view of the lesson from an educational point of view. Virtual reality’s implementation into teaching practice was the key topic of the discussion.

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This study focuses on the analysis of changes in the digital competence profile of students of Information and Library Studies at Masaryk University in Czechia. As a research tool, we used the DigComp self-assessment questionnaire that students were asked to fill in after completing the course. Our research shows that students are insufficiently prepared for work as highly qualified information specialists. At the same time, we found that their competence profile remained very stable between 2018 and 2020. This finding indicates that students do not readily respond to new societal changes at the level of individual competences. The research results are based on data collected from 152 students during three runs of a compulsory course at the university. Information Science and Library Science students have long perceived their competences to be strongest in the domains of information and data literacy and communication and collaboration. Programming is the weakest competency among the competences, followed by solving technical problems and engaging in active citizenship through digital technologies. These findings can be used to innovate the curriculum to meet the demands of digitally competent information workers.

The paper analyzes the possibilities of using learning analytics outside of LSM, for example in educational environments that are not associated with common large systems such as Moodle or Blackboard, but which can only be analyzed through web analytics tools. This topic has been marginalized in the discourse by the educational public for a long time. My research and methodological attention show that the data that can be obtained from these environments about the learning and information behaviour of students can be of interest to the design of the whole educational environment and can help with significant changes in the design of courses, measuring the attractiveness or clarity of individual pages etc. In this paper, we focus primarily on the combination of Google Analytics and Smartlook or Hotjar-tools for creating heatmaps.

We consider the quadratic optimization problem max x ∈C x T Qx + q T x, where C ⊆ R n is a box and r ≔ rank(Q) is assumed to be O(1) (i.e., fixed). We show that this case can be solved in polynomial time for an arbitrary Q and q. The idea is based on a reduction of the problem to enumeration of faces of a certain zonotope in dimension O(r). This paper generalizes previous results where Q had been assumed to be positive semidefinite and no linear term was allowed in the objective function. Positive definiteness was a strong restriction and it is now relaxed. Generally, the problem is NP-hard; this paper describes a new polynomially solvable class of instances, larger than those known previously.