ADAPTIVE E-LEARNING SYSTEM FOR ASSESSING AND DEVELOPING LANGUAGE COMPETENCES BASED ON THE COMMON EUROPEAN FRAMEWORK OF REFERENCE FOR LANGUAGES

Sustavi za e-učenje koji imaju tutorsku ulogu sve su češće zastupljeni među obrazovnim sustavima svih domena. Takvi su sustavi dizajnirani sa ciljem preuzimanja uloge pravoga učitelja koji vodi brigu o svim aspektima procesa poučavanja korisnika, autonomno primjenjujući iste ili vrlo slične metode eksperta određenog područja. U takvim je kontekstima učenja nužno osigurati personalizirani pristup zbog neizbježnih razlika u značajkama korisnika. Naime, korisnici sustava čine više ili manje heterogenu skupinu u kojoj se pojedinci razlikuju u karakteristikama poput razine znanja i vještina, iskustva, stila učenja, ciljeva ili interesa. Neodgovarajuća briga za razlike među korisnicima može smanjiti njihovu motivaciju za korištenje sustava, pa čak dovesti i do potpunog neuspjeha obrazovnog koncepta. Stoga je obrazovni tutorski softver nužno obogatiti prilagodljivim svojstvima. No, implementacija prilagodljivosti povećava zahtjeve dizajna sustava jer je potrebno najprije identificirati korisnikove značajke, pa potom osigurati kontinuirano praćenje njegova rada, analizu postupaka, pohranu podataka o individualnim značajkama i validne načine njihova ažuriranja, rezoniranje o scenarijima poučavanja i dostavljanju sljedećih aktivnosti i sl. Složenost implementacije povećava se u slučaju poučavanja u tzv. „slabo definiranim“ domenama kod kojih je vrlo teško strukturirati ekspertno znanje. Jedna takva domena je i učenje jezika koja se osim na eksplicitnom znanju temelji i na vještinama, što dodatno pridonosi složenosti njezine implementacije. U ovome se radu opisuje prilagodljivi sustav za e-učenje engleskog jezika nazvan LLS, koji u obzir uzima korisnikovu razinu jezične sposobnosti u vještini čitanja temeljenu na Zajedničkom europskom referentnom okviru za jezike (ZEROJ) i usmjerava ga prema višim razinama jezičnog standarda. Sustav najprije prikuplja početno znanje o korisniku putem prilagodljivog testa vještine čitanja kako bi čim ranije mogao započeti sa (smislenim) usmjeravanjem učenja. Usmjeravanje učenja u sustavu očituje se u preporukama vrste aktivnosti koja je, obzirom na korisnikovo znanje i kompetencije, optimalna u danom trenutku, te dostave materijala odgovarajuće složenosti. U radu se validira način prikupljanja početnih podataka o korisniku i sagledavaju učinci usmjeravanja na konačno postignuće.The number of tutoring systems among e-learning educational systems in all domains has been steadily increasing. Such systems are designed with the goal of taking over the role of the real teacher who is otherwise responsible for all aspects of the teaching process, by autonomously applying the same or very similar methods to those of an expert in a particular field. In these learning contexts it is necessary to ensure a personalised approach to teaching due to unavoidable differences in learner characteristics. System users usually make a more or less heterogeneous group in which individuals differ in their features such as the level of knowledge or skill, previous experience, learning style, goals or interests, to name just a few. Improper treatment of learner differences may lead to a drop in motivation regarding system use or even a complete failure of the educational setup. That is why it is crucial to enrich the educational tutoring software with adaptive features. However, introducing adaptivity into an educational system increases demands concerning its design as it is necessary first to identify user characteristics, and then ensure continuous tracking of her work, analyse her interactions with the system, manage data about individual characteristics and the ways of updating them, enable reasoning about teaching scenarios and the delivery of subsequent activity, etc. The complexity of implementation is increased in the case of the so called ill-defined domains where it is particularly challenging to structure expert knowledge in a meaningful way. One such domain is language learning: a domain not only based on knowledge but skill as well, which contributes significantly to the difficulty of its implementation. This doctoral thesis describes an adaptive e-learning system for English language reading called LLS, which considers the level of a user's linguistic skill estimated according to the Common European framework of reference for languages (CEFR) and guides her towards higher levels of the standard. The system's primary task is to collect initial knowledge about the user by employing an adaptive test procedure, so as to be able to start with (meaningful) guidance as soon as possible. Guiding students while learning is implemented using recommendations of activity types which are, given the state and level of user's knowledge and competence, considered as optimal at that particular time, and delivery of materials of appropriate difficulty. The thesis further validates the way user data is initially obtained and discusses the effects of giudance on the overall achievement