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Purpose This paper aims to deal with the previously unknown prediction accuracy of students’ activity pattern in a blended learning environment. Design/methodology/approach To extract the most relevant activity feature subset, different feature-selection methods were applied. For different cardinality subsets, classification models were used in the comparison. Findings Experimental evaluation oppose the hypothesis that feature vector dimensionality reduction leads to prediction accuracy increasing. Research limitations/implications Improving prediction accuracy in a described learning environment was based on applying synthetic minority oversampling technique, which had affected results on correlation-based feature-selection method. Originality/value The major contribution of the research is the proposed methodology for selecting the optimal low-cardinal subset of students’ activities and significant prediction accuracy improvement in a blended learning environment.

Purpose This paper aims to show the implementation in the terrestrial trunked radio (TETRA)-based sensor network. The publicly available data show that, in Serbia, the annual damage caused by hailstorms in the past seven years has been estimated almost at an average level of 40m of euros. As the amount of hail was not changed, the hail suppression system of the Republic of Serbia has to be improved, both technically and organizationally, to get better efficiency and protection and to reduce the damage. Design/methodology/approach In this paper, the authors show the implementation and improvements in the modern terrestrial trunked radio (TETRA)-based sensor network, and they propose the scientific use of sensors for remote control of automatic hail suppression rocket stations. Findings The authors’ idea is that TETRA should be used as an operational and official telecommunicating system for hail suppression activities units. A number of sensors, connected in a network, are used to maintain a high-quality functioning of this digital radio system, managed remotely and controlled either by operators or automatically. Originality/value The presented study with a real example attempts to explain as to how the system functions and how it can improve hail suppression activities.

The paper introduces a novel Generative Learning Object (GLO) model, the Stage-Based Model (SBM) to specify the learning content. New capabilities of the model are the content automatic generation and adaptation. Externally, our model has a similar structure as the known two-level generic models (i.e. metadata and content implementation). The internal structure, however, is quite different in both parts. The use of the external parameterization technology based on pre-programming predefines the internal structure. Furthermore, the structure is derived from the initial parameterized GLO model using the refactoring tool. The technology we use in both models is based on the parameter-function relationship so that to perform manipulations on parameters. Parameters represent metadata, while the relationship implements the content variability by pre-programming the possible changes in advance so that to create the space for adaptation. The SBM implements deep internal staging by allocating parameters and functions (further objects) into predefined stages according to the given context. For example, pedagogically related parameters (objectives, teaching model, etc.) have the highest priority and appear at the top stage while the others - at the remaining stages. Typically, objects in the initial GLO specification are active, i.e. are ready to perform the prescribed role when interpreted by adequate tools. In SBM, the top stage objects are active while the remaining are passive (not able to serve the prescribed role there). The essence of the approach is the stage-based de-activation and activation of the objects within the pre-programmed specification. That ensures the automatic stage-based generation and flexibility for adaptation. In this paper, we analyze the SBM capabilities, present a case study and extended results of using the approach to the robot-oriented teaching in computer science. We also provide the pedagogical evaluation of the approach.

Distance, e-education supported by technological development have enhanced educational processes by combining achievements from various scientific fields. This techno-educational enhancement has happened within the last decade. Virtual learning has grown in popularity as a new way of learning that gives preference to learn in convenient time and places, using specially prepared learning materials. The progression of technological developments implies the rise of user demands. It also aspires to look for flexible, but qualitative, training and learning ways. One of the outcomes is adaptive and intelligent learning environments that show to become a common tool used for virtual learning. This paper focuses on the key feature elements of adaptive learning environments (ALE), also researching about intelligent agents and their behaviour in ALEs. The method to provide individual learning syllabus according student knowledge in rational way is proposed. It is based on latent semantic indexing algorithm and is featured with an ability to update syllabus according individual learning outcomes of the student. This is yet another tool in a teacher's tool box. The description of results obtained during the exploration of proposed method is presented as well., Nuotoliniame ir el. švietime dėka technologijų taikymo ir spartaus technologijų vystymosi, ugdymo procesas yra praturtintas įvairių mokslo sričių pasiekimais. Šis techno-edukacinis šuolis ivyko praėjusio dešimtmečio eigoje. Virtualusis mokymas(is) tampa vis populiaresnis kaip naujas mokymosi būdas, sukuriantis prielaidas studijoms patogiu laiku ir vietoje naudojant specialiai parengtą mokymo medžiagą. Technologijų pažanga sąlygoja ir vartotojų poreikių augimą. Pastaroji taip pat ikvepia lanksčų, bet kokybiškų, ugdymo ir mokymosi būdų paieškai. Vienas tokių būdų yra išmaniai prisitaikančios mokymo(si) aplinkos, kurios pamažu turėtų tapti iprastu irankiu virtualiam mokymui(si) teikti. Straipsnyje analizuojamos adaptyvios mokymosi aplinkos (AMA), jų galimybės, agentai ir jų elgesys tokiose sistemose. Aprašomas semantinio indeksavimo algoritmu grindžiamas racionalaus mokymo(si) medžiagos pateikimo pagal studento žinių lygį, o dėstytojui atnaujinti mokymo turinį atsižvelgiant į besimokanˇci uj u mokymosi rezultatus, metodas. Tai dar vienas mokytojo/d˙estytojo irankis. Pristatomi tyrimo, atlikto pritaikant pasiūlytą metodą, rezultatai.