Soil microorganisms and their activities are essential for maintaining soil health and fertility. Microorganisms can be negatively affected by application of herbicides. Although effects of herbicides on microorganisms are widely studied, there is a lack of information for chloroacetamide herbicide dimethachlor. Thus, dimethachlor and well known linuron were applied to silty-loam luvisol and their effects on microorganisms were evaluated during112 days long laboratory assay. Dimethachlor and linuron were applied in doses 1.0 kg ha⁻¹ and 0.8 kg ha⁻¹ corresponding to 3.33 mg kg⁻¹ and 2.66 mg kg⁻¹ respectively. Also 100-fold doses were used for magnification of impacts. Linuron in 100-fold dose caused minor increase of respiration, temporal increase of soil microbial biomass, decrease of soil dehydrogenase activity, and altered microbial community. Dimethachlor in 100-fold dose significantly increased respiration; microbial biomass and decreased soil enzymatic activities. Microbial composition changed significantly, Proteobacteria abundance, particularly Pseudomonas and Achromobacter genera increased from 7 to 28th day. In-silico prediction of microbial gene expression by PICRUSt2 software revealed increased expression of genes related to xenobiotic degradation pathways. Evaluated characteristics of microbial community and activity were not affected by herbicides in recommended doses and the responsible use of both herbicides will not harm soil microbial community.

Computational thinking is a highly appreciated skill by mathematicians. It was forecasted that, in the next few years, half of the jobs in science, mathematics, technology and engineering (abbreviated as STEM, including arts as STEAM) will use some kind of computation. It is therefore necessary to enhance the learning of mathematics by collaborative problem-solving activities focused on both learning mathematics and developing computational thinking. The problems in science offer a reasonable context in which to investigate the common overarching concepts (e.g., measuring the length). An interdisciplinary STEAM collaborative problem-solving activity was designed and piloted with 27 lower secondary students aged 13.07 ± 1.21 years. Different levels of willingness to use the technology were observed and the factors influencing it were identified. We found that strong background knowledge implies high demands when controlling the used device. On the other hand, when a nice and user-friendly application was used, students did not need to perceive any control over it. After the intervention, the students’ views on the tablet changed and they reported more STEAM-related functions of the device.

Metacognitive knowledge and mathematical intelligence were tested in a group of 280 pupils of grade 7 age 12–13 years in the Czech Republic. Metacognitive knowledge was tested by the tool MAESTRA5-6+. Mathematical intelligence is understood as an important criterion of a learner’s ability to solve mathematical problems and defined as the specific sensitivity to the six particular phenomena: causality, patterns, existence and uniqueness of solution, geometric imagination, functional thinking, and perception of infinity. The main objective of the research is to explore relationships and links between metacognitive knowledge and mathematical intelligence of the learners and discover the scope of impacts of their metacognitive knowledge on the school success rate. Based on the collected answers and nearly zero correlation (r = 0.016) between the researched domains, a two-dimensional model considering the correlations between metacognitive knowledge and mathematical intelligence was designed. The developed model enables to describe an impact of the domains on the learner’s school performance within the selected school subjects, and concurrently, it emphasizes their importance within the educational practice as such.

Graphs can be considered as useful mathematical models. Graph algorithms are a common part of undergraduate courses in discrete mathematics. Even though they have been successfully implemented in secondary curricula, little research has been dedicated to the analysis of students’ work. Within a discrete mathematics course for university students, several graph algorithms were introduced via their applications. At the end of the course, the students took a test focused, inter alia, on applications of the algorithms. The mistakes that occurred in 127 students’ solutions of three problems (the Chinese postman problem, the shortest path problem, and the minimum spanning tree problem) were categorized and compared. Surprisingly, no mistakes were identified in the mathematization of situations or in the interpretation of results with respect to the wording of the problem. The categories of errors varied regardless of the problem types. Hierarchical cluster analysis grouped together the students’ solutions for the Chinese postman problem and the minimum spanning tree problem. By means of nonparametric item response theory analysis, the Chinese postman problem was identified as the most problematic for students. Possible sources of this difficulty are discussed in more detail herein.