The model of e-learning systems used for the improvement of students’ cognitive achievements

U današnje vreme sve više obrazovnih institucija kao što su fakulteti koji nude e-obrazovanje. U nekim slučajevima učenje na daljinu je ukombinovano sa tradicionalnim oblicima učenja, dok se u drugim ono odvija u potpunosti samostalno putem internata. U svakom slučaju da bi učenje na daljinu moglo da se realizuje i da bi njime moglo da se upravlja potrebno je da postoji posebno okruženje u kom će se ono organizovati. U većini slučajeva Sistemi za obrazovanje na daljinu – (Learning management system – LMS) obavljaju ovaj zadatak. LMS obezbeđuje raznovrsne alate za podršku profesorima u kreiranju, administriraju i upravljanju online kursevima. S druge strane oni uglavnom ne uzimaju u obzir individualne razlike studenata i tretiraju sve studente na isti način bez obzira na njihove lične potrebe i karakteristike. U našoj literaturi ne postoji puno radova koji se bave temom adaptivnog elektronskog obrazovanja, naročito ne sa aspekta izrade i implementacije modela adaptivnog elektronskog obrazovanja. Predmet ove doktorske disertacije je implementacija sistema za elektronsko obrazovanje koji je kreiran po modelu adaptivnog elektronskog obrazovanja i koji obezbeđuje za isto vreme veće neposredno znanje korisnika i pozitivno utiče na trajnost znanja, nego standardni neadaptivni sistem za elektronsko obrazovanje. U radu su kombinovane prednosti LMS-a sa adaptivnim sistemima i na taj način je proširena funkcija LMS-a tako što su integrisani stilovi učenja i obezbeđena je adaptivnost sistema. Adaptivni model elektronskog obrazovanja koji je razvijen u radu je implementiran i procenjivan korišćenjem Moodle sistema. Ova doktorska disertacija imala je za cilj da na osnovu kreiranja, implementacije i korišćenja modela adaptivnog elektronskog obrazovanja ukaže na statistički značajnu mogućnost podizanja sveobuhvatnog nivoa i kvaliteta obrazovnog procesa.Nowadays the majority of universities offer e-learning to their students. Sometimes distance learning is combined with traditional education, while in other cases it functions on its own by using the Internet. However, distance learning requires special surroundings where it can be organized. Learning management systems – LMSs are used in most of the cases for distance learning. LMS provides professors with various tools for creation, administration, and management of online courses. On the other hand, LMSs don’t usually consider individual differences of students and treat all students in the same way, disregarding their personal needs and characteristics. In our literature, there are very few studies that analyze adaptive e-learning systems, especially the creation and implementation of adaptive e-learning models. The goal of this doctoral thesis has been creation and implementation of an adaptive model of the e-learning system which provides students with wider knowledge that lasts a longer period of time comparing to the knowledge acquired with standard (non-adaptive) systems of e-learning. The thesis has expanded the function of LMS by combining LMS with adaptive systems and incorporating students’ learning styles into it. The adaptive model that is developed in the thesis has been implemented and evaluated by using Moodle system. The aim of the doctoral thesis has been to point out at statistically significant probability of improving the level and quality of the educational process by creating, implementing and using the adaptive model of e-learning

Functional inferences over heterogeneous data

Inference enables an agent to create new knowledge from old or discover implicit relationships between concepts in a knowledge base (KB), provided that appropriate techniques are employed to deal with ambiguous, incomplete and sometimes erroneous data. The ever-increasing volumes of KBs on the web, available for use by automated systems, present an opportunity to leverage the available knowledge in order to improve the inference process in automated query answering systems. This thesis focuses on the FRANK (Functional Reasoning for Acquiring Novel Knowledge) framework that responds to queries where no suitable answer is readily contained in any available data source, using a variety of inference operations. Most question answering and information retrieval systems assume that answers to queries are stored in some form in the KB, thereby limiting the range of answers they can find. We take an approach motivated by rich forms of inference using techniques, such as regression, for prediction. For instance, FRANK can answer “what country in Europe will have the largest population in 2021?" by decomposing Europe geo-spatially, using regression on country population for past years and selecting the country with the largest predicted value. Our technique, which we refer to as Rich Inference, combines heuristics, logic and statistical methods to infer novel answers to queries. It also determines what facts are needed for inference, searches for them, and then integrates the diverse facts and their formalisms into a local query-specific inference tree. Our primary contribution in this thesis is the inference algorithm on which FRANK works. This includes (1) the process of recursively decomposing queries in way that allows variables in the query to be instantiated by facts in KBs; (2) the use of aggregate functions to perform arithmetic and statistical operations (e.g. prediction) to infer new values from child nodes; and (3) the estimation and propagation of uncertainty values into the returned answer based on errors introduced by noise in the KBs or errors introduced by aggregate functions. We also discuss many of the core concepts and modules that constitute FRANK. We explain the internal “alist” representation of FRANK that gives it the required flexibility to tackle different kinds of problems with minimal changes to its internal representation. We discuss the grammar for a simple query language that allows users to express queries in a formal way, such that we avoid the complexities of natural language queries, a problem that falls outside the scope of this thesis. We evaluate the framework with datasets from open sources