LISA: Library and Information Science Abstracts

Moksliniai tyrimai intelektualaus transporto nevienalyčių paslaugų teikimo srityje yra labai aktualūs sparčiai besikeičiančios infrastuktūros automobilių transporte. Ypač tokių paslaugų teikimo galimybės išaugo, pasitelkiant belaides technologijas ir ryšių komunikacines priemones. Dabartinė informacinių komunikacinių technologijų (IKT) plėtra ir intelektinių transporto platformų bei paslaugas teikiančių sistemų integracija leidžia žymiai pagerinti eismo saugą ir sumažinti avarijų kelyje tikimybę. Tačiau, norint teikti mobiliąsias-intelektualiąsias paslaugas, reikia sukurti gana sudėtingą infrastruktūrą, skirtą daugiakompozicinėms paslaugoms palaikyti. Šio mokslinio straipsnio tikslas yra išanalizuoti duomenų perdavimo galimybes, skirtas nevienalytėms, daugialypėms (heterogeninėms) paslaugoms. Daugialypių mobiliųjų paslaugų teikimo galimybės autotransporto komunikacijos tinkluose teikti, kartu užtikrinant kelių eismo saugumą. Darbe nagrinėjami uždaviniai, kaip įvertinti tokių mobiliųjų paslaugų integracijos galimybes autotransporto priemonių infrastruktūroje. Svarbiu uždaviniu tampa duomenų pralaidumo nustatymas, įgalinant daugiakompozicines paslaugas proginiuose (Ad-Hoc) belaidžiuose tinkluose. Demonstruojami rezultatai iliustruoja belaidžių tinklų galimybes, atliekant tokių paslaugų imitacinį modeliavimą, transporto paslaugas generuojančioje aplinkoje. Straipsnyje parodyta, kad, norint teikti kokybiškas nevienalytes paslaugas, reikia naujų belaidžių tinklų sąveikos protokolų, tokių kaip sesijos inicializavimo protokolas (SIP), GPRS funkcinių galimybių, maršrutizavimo protokolų ir prieigos prie kanalų būdų dideliam palaikomųjų paslaugų komponentų kiekiui užtikrinti.

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.

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.

Straipsnyje nagrinėjamas vaizdų klasifikavimo uždavinys, kuriuo siekiama klasifikuoti vaizdus pagal suspaudimo algoritmo poveikį jų kokybei. Aptariami vaizdų kokybės įvertinimo matai bei vaizdų klasifikavimo metodai. Eksperimentiniu tyrimu siekiama nustatyti, kokie originalius vaizdus apibūdinantys požymiai turi būti naudojami klasifikuojant šiuos vaizdus pagal jų kokybę po suspaudimo JPEG algoritmu. Vaizdo kokybė po suspaudimo vertinama struktūrinio panašumo (SSIM) indekso metodu. Nustatyta, kad tiksliausi klasifikavimo taikant atraminių vektorių klasifikatorių rezultatai gauti, kai originalūs vaizdai yra apdorojami kraštų aptikimo filtrais.

The paper aims to present case study of teaching and learning using tablet devices in Lithuania. It was accomplished while implementing Creative Classrooms Lab – the flagship European project in the area. In the paper, personalised mobile learning activities using tablet devices are analysed. In CCL, the authors created and applied ontologies to interconnect these mobile learning activities with students’ learning styles, suitable mobile applications (apps), other Internet tools, and learning objects types. Teachers’ and students’ opinion on personalised mobile learning activities based on problem solving, collaboration, inquiry-based learning, and flipped class methods is analysed in the paper. Also, the quality of personalised mobile learning activities is compared against the quality of traditional learning activities based on textbooks and face-to-face knowledge transmission. A novel method of expert evaluation of mobile learning activities based on Fuzzy AHP method to establish the weights of the mobile learning activities’ quality criteria is applied. Research results show that personalised mobile learning activities could help teachers to enhance students’ creativity and motivation and thus to improve their learning results.