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The paper aims to present application of Educational Data Mining and particularly Case-Based Reasoning (CBR) for students profiling and further to design a personalised intelligent learning system. The main aim here is to develop a recommender system which should help the learners to create learning units (scenarios) that are the most suitable for them. First of all, systematic literature review on application of CBR and its possible implementation to personalise learning was performed in the paper. After that, methodology on CBR application to personalise learning is presented where learning styles play a dominate role as key factor in proposed personalised intelligent learning system model based on students profiling and personalised learning process model. The algorithm (the sequence of steps) to implement this model is also presented in the paper.

The aim of the paper is to present methodology to personalise learning using learning analytics and to make further decisions on suitability, acceptance and use of personalised learning units. In t...

The paper aims to analyse the problem of personalisation and evaluation of quality of Augmented Reality (AR) learning systems. AR is often used in education to enhance students’ motivation by visualizing learning content and activities. In the paper, first of all, systematic review of relevant scientific literature on the research topics was conducted. The author’s AR learning systems quality personalisation and evaluation frameworks are presented in the paper. Evaluation of quality of AR learning systems should be based on applying both expert-centred (top-down) and user-centred (bottom-up) quality evaluation methods consisting of creating quality models (systems of criteria) and evaluation methods. AR-based learning systems including learning content (i.e. learning objects) and activities should be suitable, acceptable and usable for particular learners. Personalisation of AR learning systems should be based on learners’ models/profiles using students’ learning styles (bottom-up method), and educational data mining (top-down method). AR personalisation method is aimed to personalise learning by applying well-known learning styles models, educational data mining methods and techniques, and intelligent technologies, and thus to ensure that suitable AR-based learning systems should be selected for particular users to improve their learning motivation and thus—quality and efficiency. The method of identifying students preferring to actively use AR-based learning systems is based on identification of probabilistic suitability indexes to choose the most suitable AR-based learning systems for particular students. Experimental research is also performed, and its results are presented in the paper. The research is multidisciplinary, including computer science, education, operations research, and educational psychology areas.

Effective quality education is one of the most efficient tools to empower individuals and help them out of poverty and exclusion. The paper is aimed to present and analyse data-driven decision making methodology to evaluate suitability, acceptance and use of innovative technologies to enhance the quality of education. Analysed example of those innovative technologies is Augmented Reality (AR). AR is known as one of the most recent technological advances that can be used as an educational tool to enhance the quality of education. In the paper, user-centered methodology (i.e. model and method) to evaluate suitability, acceptance and use of AR applications in education is presented. Many recent studies have suggested that differences between individuals influence acceptance and use of Information Technology (IT). AR suitability, acceptance and use evaluation methodology presented in the paper is based on (1) well-known principles of Multiple Criteria Decision Analysis for identifying evaluation criteria; (2) Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model, and (3) probabilistic suitability indexes to identify AR applications' suitability to particular students' needs i.e. learning styles. In the paper, there is also an example of implementing the methodology using different weights of evaluation criteria. This methodology is a typical case of data-driven decision-making for quality education. It is applicable in real life situations where teachers have to help students to apply IT that is the most suitable for their needs and thus to improve learning motivation, and consequently, its quality and effectiveness.

The aim of the paper is to present application of the methodology (i.e. model and method) to evaluate suitability, acceptance and use of information technology (IT) tools for particular students as well as a case study of evaluating two IT tools widely used while studying human-computer interaction (HCI) design at Lithuanian Universities. These IT tools are “Axure” and “Balsamiq Mockups”. High-quality IT tools should be optimised to particular students according to their personal needs, e.g. learning styles. In the paper, optimised IT tools should have the highest probabilistic suitability indexes for particular students according to Felder-Silverman learning styles model. Personalised IT tools' evaluation methodology presented in the paper is based on (1) well-known principles of Multiple Criteria Decision Analysis for identifying quality evaluation criteria; (2) Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model, and (3) probabilistic suitability indexes to identify technology's suitability to particular students' needs according to their learning styles. In the paper, there is also a case study of implementing the methodology in Lithuania. This methodology is applicable in real life situations where teachers have to help students to apply IT tools that are most suitable, acceptable and useable for their needs and thus to improve students' motivation, which in its turn creates the conditions for better and more efficient education. The case study aims at analysing the results of evaluating HCI tools using this novel methodology. The methodology is one of the examples of application of Technology Enhanced Learning Research in Higher Education.

In this article, the authors suggest a methodology to adapt learning units to the needs and talents of individual students using an intelligent learning system. Learning personalisation is done based on several factors. Felder and Silverman Learning Styles model is used to create student's profile with conjunction of data mining technologies and previously recorded behaviour of the student. Firstly, the authors perform systematic review of application of intelligent software agents in teaching throughout Clarivate Analytics Web of Science database. Secondly, they present methodologies to personalise learning by means of intelligent technologies. They analyse preferences of students according to Soloman-Felder Learning Styles questionnaire. The resulting model of a student is used in the creation of a personalised learning unit. The model of an adaptive intelligent teaching system based on application of aforementioned technologies is presented in more detail.

Recommending suitable learning paths according to learners' preferences.Experimental research results.Learners behaviour and learning styles. The paper deals with the problem of personalising learning units with the main focus on finding personalised learning paths in learning units. Finding suitable learning paths is based on students' needs in terms of their learning styles. It has been shown that learning path in static and dynamic learning units can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model, mainly by adapting ant colony optimisation method based on collaboration and pheromones. In the paper, experimental results of applying the proposed approach in practise are presented. The results of empirical experiment have shown that learning in the proposed prototype of e-learning system applying created recommending method improves students' learning results and saves their learning time. This fact indicates that the developed adaptive method for personalising learning units is practically applicable in e-learning and enhances the learning quality.

The paper presents semantic clustering and artificial neural network (ANN) based learning analytics software agent for personalised adaptive multi-agent learning system. First of all, systematic literature review on application of ANN to personalise learning in Web of Science database was performed. After that, methodology of application of ANN and semantic clustering of learning material in personalised adaptive multi-agent learning system is presented. In the paper, personalisation in multi-agent learning system is based on learning styles model that requires the use of psychological questionnaire to determine student's learning styles. The results of filling in the questionnaire could be incorrect since some students may answer the questionnaire dishonestly, irresponsibly, or make mistakes in self-diagnosis. This results in creation of an incorrect student's model. This causes the system to provide suboptimal learning objects and scenarios to the student. The authors present a model of ANN based learning analytics agent to be used in the system. Proposed agent uses ANN to associate students' learning styles with their real behaviour within the learning environment. The key factor describing the behaviour of students within the system is the learning content they seek out independently. Clustering the visited documents based on semantic content categorizes students into groups. Belonging to a semantic cluster is one of the inputs that can be used to train ANN agent. After training, the agent could identify potentially faulty student models by looking for anomalous behaviour for those learning styles. Such problems can be resolved by providing alternative learning objects or scenarios to the students and observing their choices.1

The aim of this paper is to present the methodology (i.e., the model and method) for evaluating the acceptance, use and suitability of personalized learning units/scenarios for particular students. Learning units/scenarios are referred to here as the methodological sequences of learning components (learning objects, learning activities and the learning environment). High-quality learning units should consist of learning components optimized for particular students in accordance with their personal needs, e.g., learning styles. In the paper, optimized learning units mean learning units composed of the elements having the highest probabilistic suitability indexes for particular students in accordance with the Felder-Silverman learning styles model. The personalized learning unit evaluation methodology, presented in the paper, is based on (1) the well-known principles of the Multiple Criteria Decision Analysis for identifying evaluation criteria; (2) the Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on a well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model and (3) probabilistic suitability indexes for identifying learning component suitability to the needs of particular students, all in accordance with their learning styles. The methodology for evaluating the acceptance, use and suitability of personalized learning units for particular students, which is presented in the paper, is absolutely new in scientific literature. In the paper, there are also examples of implementing the methodology using different weights of evaluation criteria. This methodology is applicable in real life situations, where teachers have to help students create and apply learning units that are most suitable for their needs and thus to improve education quality and efficiency.

The paper aims to suggest a method of using artificial neural for learning personalisation. Learning personalisation consists form learning style identification then linking it to learning activities and objects which correspond to learning style. But the problem is that learning style identified by questionnaire cannot guarantee adequacy and objectivity. In order to overcome this issue we can use artificial neural network which could analyse learner in learning activities and interactionwith learning objects and correct learning style and scenarios characteristics according to collected information.

The paper aims to analyse application trends of intelligent multi-agent systems to personalise learning. First of all, systematic literature review was performed. Based on the systematic review analysis, the main trends on applying multi-agent systems to personalise learning were identified. Second, main requirements and components for an educational multi-agent system were formulated. Third, based on these components a model of intelligent personalized system is proposed. The system employs five intelligent agents: (1) learning styles identification software agent, (2) learner profile creation software agent, (3) pedagogical suitability software agent, (4) optimal learning units/scenarios creation software agent, and (5) learning analytics/educational data mining software agent.

The paper aims to present a methodology (i.e. model and method) to evaluate suitability, acceptance and use of personalised learning scenarios. High-quality learning scenarios should consist of the learning components (i.e. learning objects, learning activities, and learning environment) optimised to particular students according to their personal needs, e.g. learning styles. In the paper, optimised learning scenarios mean learning scenarios composed of the components having the highest probabilistic suitability indexes to particular students according to Felder–Silverman learning styles model. Personalised learning scenarios evaluation methodology presented in the paper is based on (1) probabilistic suitability indexes to identify learning components suitability to particular students needs according to their learning styles, and (2) Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model.

What learning content, methods and technologies are the most suitable to achieve better learning quality and efficiency? In Lithuania, we believe that there is no correct answer to this question if we don't apply personalised learning approach. We strongly believe that “one size fits all” approach doesn't longer work in education. It means that, first of all, before starting any learning activities, we should identify students' personal needs: their preferred learning styles, knowledge, interests, goals etc. After that, teachers should help students to find their suitable (optimal) learning paths: learning methods, activities, content, tools, mobile applications etc. according to their needs. But, in real schools practice, we can't assign personal teacher for each student. This should be done by intelligent technologies. Therefore, we believe that future school means personalisation plus intelligence. In this chapter, Lithuanian Intelligent Future School project is presented aimed at implementing learning personalisation and educational intelligence.

The aim of the paper is to present a methodology (i.e. model and method) to evaluate suitability, acceptance and use of personalised learning units/scenarios. Learning units/scenarios are referred here as methodological sequences of learning components (learning objects, learning activities, and learning environment). High-quality learning units should consist of the learning components optimised to particular students according to their personal needs, e.g. learning styles. In the paper, optimised learning scenarios mean learning scenarios composed of the components having the highest probabilistic suitability indexes to particular students according to Felder-Silverman learning styles model. Personalised learning units evaluation methodology presented in the paper is based on (1) well-known principles of Multiple Criteria Decision Analysis for identifying evaluation criteria; (2) Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model, and (3) probabilistic suitability indexes to identify learning components’ suitability to particular students’ needs according to their learning styles. The methodology to evaluate personalised learning units presented in the paper is absolutely new in scientific literature. This methodology is applicable in real life situations where teachers have to help students to create and apply learning units that are most suitable for their needs and thus to improve education quality and efficiency.

The aim of the paper is to present and compare so-called bottom-up and top-down approaches for evaluating quality and reusability of learning objects (LOs). The paper proposes bottom-up methodology that outlines the central role of learners' individual and social behaviour while working with LOs. This includes social tagging and some parameters of interaction for measuring context to describe LOs usage, attention, and other aspects of the context as well as helps to exploit context data towards making LOs repositories more useful, and thus enhance the reuse. The paper also presents top-down methodology for the expert evaluation of LOs quality and reusability. This methodology consists of consecutive application of several scientific approaches, methods, and principles. The authors investigate how these two different, however complimentary approaches for evaluating LOs quality and reusability can be better applied for the aims of eQNet project in order to select reusable (''travel well'') LOs for implementing in different educational contexts and countries. These approaches could be considered as suitable applications of information and communication technologies (ICT's) for development of human capital. The examples of practical application of these approaches for evaluating LOs quality and reusability in eQNet project are also presented in the paper.

The paper aims at research on Web 3.0 - based personalisation of learning objects (LOs) while learning in virtual learning environments. Learning personalisation is analysed in terms of suitability of LOs and VLEs to particular learning styles. The novel sets portrait analysing interconnections between students' learning styles, their preferred learning activities, relevant teaching/learning methods, and LOs types is presented in more detail. The well-known standardised vocabularies of teaching/learning methods and LOs types were used to establish these interconnections. The sets portrait of these interconnections is followed by the appropriate ontology. The ontology is considered as an appropriate tool to create learners' personalised learning environments consisting of LOs, suitable teaching/learning methods and activities according to their preferred learning styles. The ontology should help the learner to find suitable LOs according to preferred learning methods/activities, and vice versa, and thus to personalise learning. The presented Web 3.0 - based approaches are the typical cases of information and communication technologies (ICT's) application for development of human capital.

The paper aims to analyse Educational Data Mining/Learning Analytics application trends to personalise learning. First of all, systematic literature review was performed. Based on the systematic review analysis, the main trends on applying educational data mining methods to personalise learning were identified. Second, three main tendencies on educational data mining/learning analytics application in education were formulated. They are: (a) Educational Data Mining/Learning Analytics support self-directed autonomous learning; (b) Educational Data Mining/Learning Analytics systems become essential tools of educational management; and (c) most teaching is delegated to computers, and Educational Data Mining/Learning Analytics based recommendations become better and more reliable than those that can be produced by even the best-trained teachers.

In the paper, learning scenarios (units) quality evaluation and optimisation problems are analysed. Learning scenarios optimisation is referred here as its personalisation according to learners needs. In the paper, comparative analysis of two popular optimisation methods based on Fuzzy numbers theory and Analytic Hierarchy Process is performed, aiming to measure what method is the most suitable to evaluate the quality of personalised learning scenarios. Learning scenarios quality is referred here as its suitability to learners needs. Research results show that Fuzzy numbers theorybased methods are more suitable to evaluate the quality of personalised learning scenarios.

The paper is aimed to analyse the problem of personalisation of Virtual Reality/Augmented Reality/Mixed Reality (VR/AR/MR) based learning systems. Research results are two-fold: first, the results of systematic literature review are presented, and, second, VR/AR/MR-based learning systems personalisation framework is proposed. First of all, systematic literature review on research topic was conducted in Thomson Reuters Web of Science database and applying Semantic Scholarsearch tool. The review revealed that strides are being made in education using VR/AR/MR, although much needs to be done. The possibilities of VR/AR/MR application in education seem to be endless and bring many advantages to students of all ages. Few are creating content that may be used for educational purposes, with most advances being made in the entertainment industry, but many understand and realise the future and importance of education applying VR/AR/MR. Manystudies argue that new VR/AR/MR-based learning systems are more effective in comparison with traditional ones. Teachers and students like learning content and activities provided by VR/AR/MR technologies. On the other hand, although the concept of VR/AR/MR has already been proposed more than 20 years ago, most applications are still limited to simple visualisation of virtual objects onto spatially limited scenes, and the developed systems did not pass the barrier of demonstration prototypes. Many authors agree that personalisation of VR/AR/MR-based learning platforms should be further analysed. Original personalisation framework of VR/AR/MR-based learning systems is also presented in the paper. According to the framework, personalisation of VR/AR/MR learning systems should be based on applying learners models and intelligent technologies e.g. expert evaluation, ontologies, recommender systems, software agents etc. This pedagogically sound personalisation framework is aimed to improve learning quality and effectiveness.

The paper aims to present a methodology to personalise learning and a model of personalised intelligent multi-agent learning system for engineering courses based on students' learning styles and another personal characteristics and needs. The main technologies used to create the system are Semantic Web, ontologies, recommender system, and intelligent software agents. First of all, the authors performed systematic review on intelligent software agents' application in education. After that, they have analysed students' preferences to certain learning styles according to Felder and Silverman Learning Styles Model which is widely recognised the most suitable for engineering disciplines. This analysis is necessary to further creating personalised learning units / scenarios optimised for particular learners in conformity with their learning styles and other preferences. These learning units should consist of suitable learning components (learning objects, learning activities, and learning environments) optimal for particular students. Scientific methodology to creating optimised learning units for particular learners is based on expert evaluation method and application of intelligent technologies - ontologies, recommender systems, and intelligent software agents. The novel model of personalised intelligent learning system for engineering students based on application of intelligent software agents is presented in more detail. The main success factors of this approach are application of pedagogically sound vocabularies of the learning components used to create personalised learning units, and the experts' collective intelligence.

The paper aims to present a new methodology to evaluate the quality of features and functionality of learning object repositories (LORs). The quality of features and functionality of LORs is analysed in terms of engaging LOR users and content producers. Thus, it can be referred to as quality-in-use of LORs. This methodology consists of creation and consequent application of methods and the model for the quality-in-use of LORs. The model of the quality-in-use of LORs is presented in this paper. The methodology for evaluating the quality-in-use of LORs is based on the general MCEQLS (Multiple Criteria Evaluation of the Quality of Learning Software) approach to evaluate the quality of learning software. The essential part of the novel methodology is the application of improved Fuzzy AHP method to establish criteria weights of the quality-in-use of LORs. It is shown that the created methodology is suitable and stable for evaluating the quality of LOR features and its functionality. A more detail presentation is given on the results of the expert evaluation of the quality-in-use of three LORs that are most popular in Lithuania against the proposed methodology. The novelty of the presented research is achieved through the innovative instrument consisting of the model of the quality-in-use of LORs and the Fuzzy AHP method. The presented methodology could serve as a technological tool for decision making in education as well as in different areas of economy. First published online:03 Nov 2015

This paper analyses and presents the new scientific models and methods for the expert evaluation of quality of learning objects (LOs) paying special attention to LOs reusability level. Currently all existing approaches in the area are quite subjective and depend only on the experience of the decision-makers. The authors analyse several scientific methods and principles to minimise the subjectivity level in the expert evaluation of LOs quality. They are: (a) the principles of multi-criteria decision analysis for identification of quality criteria, (b) technological quality criteria classification principle, (c) fuzzy group decision making theory to obtain evaluation measures, (d) normalisation of the weights of criteria, and (e) scalarisation method for LOs quality optimisation. The authors demonstrate that the complex application of these approaches could significantly improve the quality of the expert evaluation of LOs and noticeably reduce the level of the expert evaluation subjectivity. The paper also presents the example of practical application of these approaches for evaluation of LOs for Mathematics subject.

The paper is aimed to analyse the external expert evaluation results of eContentplus programme's iCOPER (Interoperable Content for Performance in a Competency-driven Society) project's deliverables, especially quality control and Web 2.0 technologies report. It is a suitability report for better practice concerning the use of Web 2.0 technologies and associated quality con- trol mechanisms within the iCOPER best practice network. It aims to outline the key topics and associated standards and specifications found in this community. These illustrate a set of best prac- tice issues for developing educational resources open for remixing and repurposing, tailored to a European dimension. Information relating to both the evidence and experience of using standards and specifications for the delivery of Web 2.0 tools in the community has also been captured. This includes an indication of the most popular technical platforms for content development, sharing and reuse with respect to the new media as well as an indication of quality control methods for them if used. The paper is also aimed to analyse the first results of Lifelong Learning Programme's (LLP) te@ch.us project. The project plans to set up a web community for teachers interested in integrating Web 2.0 in classes at school.

The paper aims to analyse several scientific approaches how to evaluate, implement or choose learning content and software suitable for personalised users/learners needs. Learning ob- jects metadata customisation method as well as the Method of multiple criteria evaluation and optimisation of learning software represented by the experts' additive utility function are analysed in more detail. The value of the experts' additive utility function depends on the learning soft- ware quality evaluation criteria, their ratings and weights. The Method is based on the software engineering Principle which claims that one should evaluate the learning software using the two different groups of quality evaluation criteria - 'internal quality' criteria defining the general soft- ware quality aspects, and 'quality in use' criteria defining software personalisation possibilities. The application of the Method and Principle for the evaluation and optimisation of learning soft- ware is innovative in technology enhanced learning theory and practice. Application of the method of the experts' (decision makers') subjectivity minimisation analysed in the paper is also a new as- pect in technology enhanced learning science. All aforementioned approaches propose an efficient practical instrumentality how to evaluate, design or choose learning content and software suitable for personalised learners needs.

In this paper, we present a methodology for personalizing learning in accordance with the needs of individual students by using an intelligent, multi-agent learning system and data mining. Learning personalization is implemented on the basis of several methods. The Felder and Silverman Learning Styles model is used to create student profiles, and the probabilistic suitability indexes are identified to interlink learning components (i.e., learning objects, learning activities and learning environments) with the learning styles of individual students. Other technologies, which were proposed for creating the learning system, are ontologies, recommender system, intelligent software agents and educational data mining/learning analytics. Personalized learning units are referred to here as learning units composed of the learning components that have the highest probabilistic suitability indexes for particular students. In the paper, first, a systematic review on the application of intelligent software agents in learning is performed using the Clarivate Analytics Web of Science database. Second, we present the methods for personalizing the intelligent technologies of learning application, which are used to create optimized learning units for individual students. The developed student profiles and personalized learning units are further corrected by applying the methods and tools of data mining. The model of an intelligent, multi-agent learning system, based on the application of the aforementioned technologies, is presented in more detail. The principal success factors of the proposed methodology are the pedagogically sound vocabularies of learning components, an expert evaluation of the learning components in terms of their suitability for particular students as well as the application of ontologies, recommender systems, intelligent software agents and data mining.

The main scientific problems investigated in this paper deal with the problem of multiple criteria evaluation of the quality of the main components of e-learning systems, i.e., learning objects (LOs) and virtual learning environments (VLEs). The aim of the paper is to analyse the existing LO and VLE quality evaluation methods, and to create more comprehensive methods based on learning individualisation approach. LOs and VLEs quality evaluation criteria are further investigated as the optimisation parameters and several optimisation methods are explored to be applied. Application of the experts' additive utility function using evaluation criteria ratings and their weights is explored in more detail. These new elements make the given work distinct from all the other earlier works in the area.

Spręsti optimalaus mokyklos tvarkaraščio sudarymo problemą yra labai svarbu mokyklos darbo efektyvumo didinimui. Užduotis yra sudaryti patogiausią tvarkaraštį iš visų galimų. Visi nepatogumai yra vertinami baudos taškais. Šio tyrimo tikslas yra išanalizuoti optimizavimo atžvilgiu keletą Lietuvoje paplitusių komercinių mokyklos tvarkaraščio sudarymo programų bei palyginti jas su šiuo metu veikiančia Matematikos ir informatikos instituto (MII) Optimizavimo skyriaus programa. Mūsų tyrimo objektas yra šių programų optimizavimo algoritmų kokybės tyrimas ir palyginimas. Vienas uždavinių yra nustatyti esamas rinkoje mokyklos tvarkaraščio sudarymo programas, kurios tiktų Lietuvos profiliuotoms mokykloms. Kitas uždavinys yra MIMOSA, aSc Timetables ir MII tvarkaraščio sudarymo programų tyrimas ir eksperimentinis palyginimas optimizavimo atžvilgiu. Programų tyrimą vykdėme trimis etapais: 1) tvarkaraštis sudaromas be apribojimų, t.y. leidžiama sudėti iš eilės du vienodus dalykus, maksimalus pamokų skaičius per dieną – 10, mokytojai dirba be laisvų dienų; 2) maksimalus pamokų skaičius per dieną sumažinamas iki 9; 3) realios Vilniaus vidurinės mokyklos tvarkaraščio sudarymas ir optimizavimas. Solution of the problem of optimal school scheduling is very important to make the work of comprehensive schools more effective. Here the sequence of teaching subjects, regarded as tools, can be changed. One needs to reduce the sum of gaps (“empty” hours) in the teacher schedules. There should be no gaps for students. Different classes are considered as different tasks. The classrooms, including the computer and physics rooms and studies, are the limited resources. The most difficult example is scheduling of profiled school. Here eleventh and twelfth grade students are choosing several subjects from the list of available ones. This means that each student works by his own schedule. We search for the most convenient feasible schedule. The inconveniences are evaluated by penalty points. The purpose of the study is to analyze several commercial scheduling programs which are prevalent in Lithuania at the moment and to compare them with existing timetable version of Optimization Department of Lithuanian Institute of Mathematics and Informatics (IMI) from optimal scheduling point of view. Therefore our research object is to study and compare the quality of optimization algorithms of these programs. One of the tasks is to identify existing commercial scheduling programs which might be right for Lithuanian profiled comprehensive schools. Another task is to perform the tentative comparison of scheduling programs MIMOSA, aSc Timetables and IMI timetable version from the point of view of quality of optimal scheduling. We used three stages for study of these programs: 1) scheduling without any confines – it’s allowable to consecutively add two equal subjects, the ultimate number of lessons per day is 10 and teachers are working without day offs; 2) reduction of ultimate number of lessons to 9 lessons per day; 3) optimal scheduling of real Vilnius secondary school.

The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners' preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors - by helping them to monitor, refine, and improve e-learning modules and courses according to the learners' behaviour.

Šio darbo tikslas pristatyti antrosios kartos saityno priemonių naudojimo mokymosi personalizavimui tyrimo rezultatus. Darbe pateiktas personalizuotas antrosios kartos saityno priemonių parinkimo metodas, kai atsižvelgiama į mokymosi tikslus, norimą įgyvendinti mokymosi veiklą, mokymosi turinio pobūdį, bendravimo formas, siekiant padėti besimokančiajam greičiau ir tiksliau rasti tinkamą mokomąją priemonę. Visų pirma, tyrime buvo ištirti personalizuoto el. mokymosi technologiniai ypatumai: rekomendavimo sistemų taikymo personalizuotam mokymuisi galimybės, šių sistemų komponentai. Antra, atlikta antrosios kartos saityno priemonių, tinkamų mokymosi veikloms įgyvendinti, parinkimo metodų analizė. Taip pat buvo sukurtas pasiūlytą metodą realizuojančios sistemos prototipas. Sukurtojo metodo ekspertinis aprobavimas parodė, kad metodas yra taikytinas realiame kontekste: jis yra tinkamas, tikslus ir našus laiko atžvilgiu. The paper aims to present research results on using Web 2.0 tools for learning personalisation. In the work, personalised Web 2.0 tools selection method is presented. This method takes into account student’s learning preferences for content and communication modes tailored to the learning activities with a view to help the learner to quickly and accurately find the right educational tools, and to implement this method in prototype of knowledge-based recommender system. In the research, first of all, personalised e-learning technological peculiarities i.e. recommender systems applications for learning personalisation and those systems components were investigated. After that, selection methods for Web 2.0 tools suitable for implementing learning activities were analysed. The novel method of integrating Web 2.0 tools into personalised learning activities according to students learning styles was created, and prototype of the recommender system that implements the method proposed was developed. Finally, the expert evaluation of the developed system prototype that implements the method proposed was performed.

The aim of the paper is to present a new simple to use and efficient MCEQLS (Multiple Criteria Evaluation of the Quality of Learning Software) TFN (Trapezoidal Fuzzy Numbers) method for the expert evaluation of the quality and reusability of learning objects (LOs). MCEQLS and TFN methods are analysed, improved, and practically applied to present the decision analysis process for selecting LOs suitable to reuse in different pedagogical situations and in different education systems. The research results are implemented in eQNet – a three-year strategic pan-European project focused on reusability of LOs. A novel method of consecutive application of Fuzzy Numbers theory to establish the weights of LOs quality criteria and MCEQLS approach to establish final evaluation results are explored in more detail. A number of multiple criteria decision analysis principles are applied to create a comprehensive quality model (criteria system) for evaluating the quality and reusability of LOs. Several practical examples of LOs evaluated against the proposed MCEQLS TFN method are presented in the paper. The research results have shown that the proposed method is quite objective, exact, simple to use, and efficient for selecting qualitative reusable LOs alternatives in the market.

This chapter analyzes the quality of XML learning object repositories. Special attention is paid to the models and methods to evaluate the quality of learning repositories. Multiple criteria decision analysis and optimization methods are explored to be applied for evaluating the quality of learning repositories. This chapter also presents the results of several large-scale projects co-funded by EU research programs that have been implemented in the area of learning repositories. Learning repositories’ technological quality model (system of criteria) and novel comprehensive model for evaluating the quality of user interfaces of learning repositories are presented in more detail. The general MCEQLS (Multiple Criteria Evaluation of Learning Software) approach is presented in this chapter. It is shown that the MCEQLS approach is suitable for evaluating the quality of learning repositories. The author believes that research results presented in the chapter will be useful for all educational stakeholder groups interested in developing learning repositories.

The goal of this paper is to determine whether the VLE Moodle 2.2 is a sufficient for successful learning inresponse to appropriate learning activities suitable to particular learning style. The survey showed that the majorityof these tools are applied in Moodle. The problem is that it is quite difficult for the learner to identify all these tools while working in VLE, and use them in a proper way. Therefore, it is recommended to constructand install semanticsearch engine for learning toolsinto VLE. The engine will facilitate the search processby optimizing workloads, thereby improving learner’s satisfaction and improving the efficiency andeffectiveness of the learning process.

The paper is aimed to analyse the problems of evaluation of quality of learning scenarios. Multiple criteria analysis principles are applied to form learning scenarios quality model. Several alternativesof learning scenarios that are widely used in Europe are analysed in the paper. These alternatives are evaluated with the help of the created quality model and application of triangular and trapezoidalfuzzy numbers methods. Research results show that both these methods are applicable in practice while evaluating quality of learning scenarios alternatives, and their application shows in principlesimilar results while comparing different alternatives of learning scenarios.

The main research objective of the chapter is to provide an analysis of the technological quality evaluation models and make a proposal for a method suitable for the multiple criteria evaluation (decision making) and optimization of the components of e-learning systems (i.e. learning software), including Learning Objects, Learning Object Repositories, and Virtual Learning Environments. Both the learning software ‘internal quality’ and ‘quality in use’ technological evaluation criteria are analyzed in the chapter and are incorporated into comprehensive quality evaluation models. The learning software quality evaluation criteria are further investigated in terms of their optimal parameters, and an additive utility function based on experts’ judgements, including multicriteria evaluation, numerical ratings, and weights, is applied to optimize the learning software according to particular learners’ needs.

Several distance learning courses adaptation aspects are analyzed in the paper. Adaptation characteristics of virtual learning environments and distance learning courses are considered very significant quality factors in scientific literature. These factors warrant the better adaptability of distance learning courses to the different learners’ needs, and therefore, the better learning quality. While creating distance learning courses according to “Computer training systems” teaching program for VGTU IV course students, there was an additional course adaptation requirement for the students. The aim of this paper is to analyze the students’ works from courses’ adaptation point of view. The paper is also aimed to analyze which course sequencing rules are the mostly clear and often used by the students.

The paper is aimed to analyse the learning objects quality evaluation model taking into account learning objects reusability principles. Multiple criteria analysis methods and quality evaluation criteria identification principles are used to create the learning objects quality evaluation model. The portrait of the sets of the learning objects reusability principles and the learning objects quality evaluation criteria is presented in the paper. This portrait shows the bilateral cohesion of theseprinciples and criteria. The presented learning objects quality evaluation model is usable in education institutions while selecting learning objects in the market or free-of-charge repositories.