The changes of areas of eight tree species in Lithuania during the past century were analysed. Aiming to apply the different approaches in forest studies, the Exponential smoothing method for forecasting the changes of the tree areas for the 25 years was used. The data dating from 1922 was analyzed as a time series. The descending trend was identified for Scots pine (Pinus sylvestris L.) and European ash (Fraxinus excelsior L.) and increasing trend – for Norway spruce (Picea abies (L.) H. Karst.), common oak (Quercus robur L.), birch species (Betula pubescens Ehrh. and Betula pendula Roth), black alder (Alnus glutinosa (L.) Gaertn.), European aspen (Populus tremula L.) and grey alder (Alnus incana (L.) Moench). The Exponential Trend with Multiplicative Seasonality (ET-MS) model was fitted for almost all investigated tree species with exception of European ash. For the latter species, the Damped Trend with Multiplicative Seasonality (DT-MS) model was chosen. Mean absolute percentage error of the model in all cases did not exceed 2%.

Baltic States and Finland are vibrant regions with similar sized population and historical experience. Their adaptation to the new digital era is undermined by lack of professionals. Across the EU a major reason for labour shortage in science, technology, engineering and mathematics (STEM) fields professionals lies in the insufficient supply of higher education graduates due to stagnant enrolment rates in STEM fields. The aim of this research addresses the existing trends in tertiary STEM education in Finland, Estonia, Latvia and Lithuania. This includes the tasks of analysing the quantitative trends (enrolment and graduation) in the HEIs of Baltics and Finland over the period from 2013 to 2017 as well as analysing the structural changes taking place in the respective higher education systems from 2013 to 2018. The Baltic States combined have numerical advantages in terms of young people, and young professionals (25−34 year olds) with tertiary education. However, in terms of the number of students and graduates Finland is at the forefront. The largest proportion of students enrolling in STEM fields lie within Finland (33.9%) whereas the smallest one is in Latvia (24.0%). Finland is also a leader in the share and total number of information and communication technology (ICT) graduates. In the period 2013−2017 Latvia’s results improved in two (5th and 6th) of the STEM study fields, Lithuania made a remarkable result in one STEM (6th) field by 46.9%, Estonia saw a relative increase in the 5th and 6th study field while Finland experienced a numerical decrease in all three STEM fields.

One of the most often mentioned competence in the surveyed literature is problem solving. It is also one of the key skills for the sustainable development of society. The article includes analysis of pedagogical approaches like project-based, problem-based learning and enquiry-based learning, as well as describes the use of design thinking as one of the problem solving approaches in higher education. As mathematics studies at universities play an important role in developing problem solving skills, an empirical study was carried out to identify the pedagogical approaches used by mathematics teachers and to illustrate the development of problem solving skills in mathematics studies at universities in Latvia. It includes a survey of mathematics teachers in Latvia aimed at identifying their experience in implementing different pedagogical approaches to mathematics and assessing the extent to which these methods help develop problem solving skills. The answers of mathematics teachers show that problem solving skills are developed at the middle level, which means only for the use in a certain situation (that is slightly different from previously known). The most effective approach to promoting problem-solving skills is the so-called context approach, which means focusing on the practical task related to a specialty. In the empirical study self-assessment method is used and the results are based only on respondents’ opinion.

This article examines the importance and necessity of high-level mathematical skills in engineering studies. It analyses the specific mathematical skills required for successful engineering education and their role in engineering studies. The results provide insight into how high-level mathematical skills contribute to the development of engineering competencies and enable engineers to solve complex problem scenarios. Key findings reveal that high-level mathematical skills are indispensable in engineering education, providing the tools needed to solve real-world problems and drive innovation in engineering. Mathematics is the language of engineering. It provides the analytical and problem-solving tools necessary for engineers to design, analyse, and optimize systems, ensuring that they meet safety, efficiency, and performance requirements. Engineers use math as a fundamental tool to make informed decisions and drive technological advancements across various engineering disciplines. Without math, engineering would be severely limited in its ability to design safe, efficient, and innovative solutions to the complex problems that engineers encounter in various industries. The questions contained in the article are investigated using survey data of university students. The purpose of this study is to research the relative applicability and level of knowledge of the learning material learned in secondary educational institutions in mathematics, based on the experience of school graduates and engineering students.

The use of artificial intelligence (AI) tools in university education is a phenomenon of various directions: the potential of AI tools, skills, purpose and sense of usage. Each direction is worth of working out and introducing regulatory systems and deeper investigating users’ choice and managing the process of getting, navigating and creating information by means of AI. Therefore, one of the emerging scientific challenges is students’ abilities and personalised learning experience in the use of AI. The study is focused on the usage of AI in specific courses, and namely the students of Information Technology (IT) programmes from Latvia University of Life Sciences and Technologies (LBTU) and Riga Technical University (RTU). The aim of the study is to investigate the students’ ability, need and merit to use AI in learning numerical methods, mathematics and programming. The main data collection method used is a student survey. According to the main results, it is found out that respondents when solving the programming tasks sometimes used AI, while solving mathematical tasks respondents rarely used AI. AI actually did not help to solve the mathematical tasks, while it partly helped to solve the programming tasks. The use of AI partly helped the respondents to improve the knowledge and skills of programming. Acquiring the study course Numerical Methods respondents mainly used ChatGPT, but performing practical works respondents mostly did not use AI.

One of the most important problems of modern education is low achievement in STEM (Science, Technology, Engineering and Mathematics) subjects. Poor performance in mathematics is a serious problem in many countries. In order to improve the effectiveness of mathematics studies, it is more important to improve such key factors as students’ learning self–efficacy and learning initiative. This study is devoted to students’ learning initiative and the factors influencing it. It is the insufficiency of students’ regular learning initiative in the study process that is the basis of the fact that students are not sufficiently prepared for the final exam. Based on the research results of the scientific literature, a questionnaire was created that investigated the students’ learning initiative and factors that could improve it – attitude towards mathematics studies, motivation, as well as emotional factors that are often related to Causal attributions. The research was conducted at Latvia University of Life Sciences and Technologies, surveying students of various specialties and various courses. Fisher’s exact test of independence is used to determine whether there is a significant relationship between two categorical variables – the respective factors and performance on the math completion test. The results show that one of the main causes is a low self–assessment of mathematics competence, which in turn affects motivation and attitude towards mathematics studies and slows down learning initiative.