Eugenijus Kurilovas

Interoperability, Standards and Metadata for E-Learning

The main research object of the paper is investigation and proposal of interoperability recommendations for e-learning system components - Learning Objects (LOs) and Virtual Learning Environments (VLEs). The main problem in e-learning is not the identification of suitable standards and specifications, but the adoption of these standards and specifications and their application in e-learning practice. Approaches concerning Learning Object Metadata (LOM) standard application profiles are the main topics investigated here because they could provide more quick and convenient LOs search possibilities for the users. Interoperability issues are also analyzed here as significant topics for e-learning systems components quality evaluation.

New MCEQLS AHP method for evaluating quality of learning scenarios

Abstract The aim of the paper is to present a new MCEQLS AHP method for the expert evaluation of quality of learning scenarios. A special attention is paid to suitability of scenarios to particular learner groups (styles). Solution of learning scenarios quality evaluation and optimisation problem could help educational institutions to select suitable scenarios for particular learning styles. The research results will be implemented in iTEC – a four-year, largest pan-European e-learning R&D project focused on the design of the future classroom funded by 7th Framework Programme. A novel method of consecutive triple application of AHP is explored in more detail. Suitability of several iTEC scenarios to particular learner groups is also analysed in the paper. A number of multiple criteria decision analysis principles are applied to create a comprehensive quality model (criteria tree) for evaluating quality of scenarios. Several optimisation methods are explored and applied to optimise learning scenarios in co...

Evaluation of quality and personalisation of VR/AR/MR learning systems

ABSTRACT The paper aims to analyse the problem of quality evaluation and personalisation of virtual reality/augmented reality/mixed reality (VR/AR/MR). First of all, systematic review of relevant scientific literature on the research topic was conducted. After that, findings of the systematic review concerning evaluation of quality and personalisation of VR/AR/MR learning environments are presented. The author’s VR/AR/MR learning systems/environments quality evaluation and personalisation framework is also presented in the paper. Evaluation of quality of VR/AR/MR platforms/environments should be based on (a) applying both expert-centred (top-down) and user-centred (bottom-up) quality evaluation methods and (b) separating ‘internal quality’ criteria, and ‘quality in use’ criteria in the set of quality criteria (model). Personalisation of VR/AR/MR platforms/environments should be based on learners’ models/profiles using students’ learning styles, intelligent technologies, and Semantic Web applications.

New MCEQLS fuzzy AHP methodology for evaluating learning repositories: a tool for technological development of economy

AbstractThe 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. LOR 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 p...

Creation of Web 2.0 tools ontology to improve learning

Systematic review on ontology development tools.Establishing interconnections between learning styles, preferred learning activities and related Web 2.0 tools.Creating Web 2.0 tools ontology to interconnect learning activities with relevant Web 2.0 tools. The aim of the paper is to present systematic review results on ontology development tools, to establish interconnections between learning styles, preferred learning activities and related Web 2.0 tools, and also to create Web 2.0 tools ontology to interconnect learning activities with relevant Web 2.0 tools. This ontology is necessary for learners to semantically search for suitable Web 2.0 tools while learning in virtual learning environments (VLEs). Suitability of Web 2.0 tools depends on preferred types of learning activities which in its turn depend on preferred learning styles. The research results include: (1) systematic review results on ontology development tools and ontology representation language/formats; (2) established interconnections between learning styles, preferred learning activities, and relevant Web 2.0 tools using sets portrait method, and (3) creating Web 2.0 tools ontology to interconnect preferred learning activities with relevant Web 2.0 tools in VLE. The research results will be implemented in iTEC - pan-European research and development project focused on the design of the future classroom funded by EU 7FP. The research results presented are absolutely novel in scientific literature, and this makes the current study distinct from all other works in the area.

New MCEQLS TFN method for evaluating quality and reusability of learning objects

AbstractThe 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 exa...

Application of Scientific Approaches for Evaluation of Quality of Learning Objects in eQNet Project

The paper is aimed to analyse the application of several scientific approaches, methods, and principles for evaluation of quality of learning objects for Mathematics subject. The authors analyse the following approaches to minimise subjectivity level in expert evaluation of the quality of learning objects, namely: (1) principles of multiple criteria decision analysis for identification of quality criteria, (2) technological quality criteria classification principle, (3) fuzzy group decision making theory to obtain evaluation measures, (4) normalisation requirement for criteria weights, and (5) scalarisation method for learning objects quality optimisation. Another aim of the paper is to outline the central role of social tagging to describe usage, attention, and other aspects of the context; as well as to help to exploit context data towards making learning object repositories more useful, and thus enhance the reuse. The applied approaches have been used practically for evaluation of learning objects and metadata tagging while implementing European eQNet and te@ch.us projects in Lithuanian comprehensive schools in 2010.

On Suitability Index to Create Optimal Personalised Learning Packages

The paper aims to present a novel probabilistic method to creating personalised learning packages. The method is based on learning components’ suitability to students needs according to their learning styles. In the paper, the authors use Felder-Silverman Learning Styles Model and an example of Inquiry Based Learning (IBL) method. Expert evaluation method based on trapezoidal fuzzy numbers is applied in the research to obtain numerical values of suitability of learning styles and learning activities. Personalised learning packages should consist of learning components (learning objects, learning activities and learning environments) that are optimal (i.e. the most suitable) to particular students according to their learning styles. “Optimal” means “having the highest suitability index”. Original probabilistic method is applied to establish not only students’ learning styles but also probabilistic suitability of learning activities to students’ learning styles. An example of personalised learning package using IBL activities is presented in more detail.

On Personalised Learning Units Evaluation Methodology

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.

APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO SUPPORT PERSONALISED LEARNING

The paper aims to analyse possible application of artificial neural networks (ANNs) to support learning personalisation and optimisation in terms of enhancing learning quality and effectiveness. ANNs are referred here as a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs. Information that flows through the network affects the structure of the ANN because a neural network changes – or learns, in a sense – based on that input and output. An ANN has several advantages but one of the most recognised of these is the fact that it can actually learn from observing data sets. In the paper, first of all, systematic review was performed in Clarivate Analytics (formerly Thomson Reuters) Web of Science database. The following research question has been raised to perform systematic literature review: “What are existing ANN methods, tools, and techniques applied to support personalised learning?” During XXI century (2001-2017), 100 articles in English were found in Web of Science database on the topic “TS=(artificial neural network* AND education)”. After applying Kitchenham’s systematic review methodology, on the last stage 20 suitable articles were identified to further detailed analysis on possible application of ANN to support personalised learning both in general and Higher education. Systematic review has shown that ANNs are already quite actively used in both school and University education to solve different problems e.g. academic assessment, predicting students’ success and dropout, predicting instructional effectiveness of virtual learning environments, performance evaluation of online teaching and learning, improving students’ motivation, analysing emotional social and cognitive competencies, modelling student cognitive processes, cognitive diagnostic, course timetabling etc. At the same time, ANNs are still rarely used to personalise learning according to students’ needs, and future research is needed in the area. After that, the author’s original learning personalisation methodology based on identifying students’ learning styles and other needs is presented in more detail. The last but not the least, some insights on possible application of ANN to support personalised learning are provided. This should be helpful to enhance learning quality and effectiveness.