Complicated Adaptive Systems as a Way to Improve Higher Education

The aspiration to constant economic wellbeing and intensive transformation of the scientific world detects a problem of adaptation in high professional education (HPE). The HPE system built in the socio-economic structure reacts with a delay to changes in the environment. In the article we claim that HPE systems have features of bio-systems: self-preservation and adequate response to external stimuli. It is supposed that this delay can be overcome if we insert into the system the elements which can improve its adaptability. As the quantitative evaluation of the system gives no grounds to the model construction, our study offers a new concept of high professional education model building. To make a diagnostics of the HPE system and to create a model of HPE adaptation to the environmental challenges we use the method of fuzzy logics, comprising a base of rules and membership functions. The proposed algorithm of fuzzy output is based on the expert assessment data of the Russian Association of Engineering Universities received during the accreditation of engineering specialties in 10 universities of Russia

Complicated Adaptive Systems as a Way to Improve Higher Education

The aspiration to constant economic wellbeing and intensive transformation of the scientific world detects a problem of adaptation in high professional education (HPE). The HPE system built in the socio-economic structure reacts with a delay to changes in the environment. In the article we claim that HPE systems have features of bio-systems: self-preservation and adequate response to external stimuli. It is supposed that this delay can be overcome if we insert into the system the elements which can improve its adaptability. As the quantitative evaluation of the system gives no grounds to the model construction, our study offers a new concept of high professional education model building. To make a diagnostics of the HPE system and to create a model of HPE adaptation to the environmental challenges we use the method of fuzzy logics, comprising a base of rules and membership functions. The proposed algorithm of fuzzy output is based on the expert assessment data of the Russian Association of Engineering Universities received during the accreditation of engineering specialties in 10 universities of Russia

A fuzzy logic application in virtual education

Traditionally, the teaching and learning process uses the problems resolving for fixing, transmitting and evaluating concepts and knowledge about a subject. Learning is the process of acquiring relative permanent changes in understanding, attitude, knowledge, information, capacity and ability through experience. A change can be decided or involuntary, to better or worsen learning. The learning process is an internal cognitive event. To help this teaching and learning process, it is important the use of a computer tool able to stimulate these changes. Also, it is important that it can function as validation and helping tool to the student. These functions are performed by computer systems called Intelligent Tutoring Systems. This paper describes the use of artificial intelligence techniques as a teaching support tool. Using Intelligent Tutoring Systems e fuzzy logic, this work shows, throgh eletronic ways, teaching will be more efficient and more adapted to students necessities, in group or individually

ANALYSIS OF THE APPLICATION OF THE ANALYTICAL NETWORK PROCESS (ANP) METHOD IN E-LEARNING SYSTEMS

Cilj je ovim radom istražiti pojavnost korištenja metode analitičkog mrežnog procesa u sustavima za e-učenje kao metode za donošenje odluke temeljem više kriterija. Pretražene su znanstvene baze Scopus i WOS (Web of Science) prema odabranim ključnim riječima: „analytic network process“ i „e-learning system“, radovi koji su u otvorenom pristupu i u cijelosti na engleskom jeziku. Pronađena su 23 članka, od čega su dva članka bila indeksirana u obje baze pa se analiza temelji na 21 članku. Radovi su analizirani prema godini objave, državi prebivališta autora, broju izvora korištenih u pisanju radova, prosječnom broju autora koji su sudjelovali u izradi rada, publikaciji objave članaka te je izvršena kvalitativna analiza sadržaja radova o primjeni metode analitičkog mrežnog procesa ANP (engl. Analytic Network Process) u sustavima za e-učenje. Najviše je objavljenih radova u 2018. i 2020. godini, najčešća je država prebivališta autora Tajvan, a prosječan je broj autora po jednom radu 4. Kvalitativnom analizom sadržaja izdvojenih radova utvrđeno je da ni u jednom sustavu metoda ANP nije ugrađena u sam sustav već se navedena metoda koristi u istraživanjima vezanim uz evaluaciju sustava ili identifikaciju parametara potrebnih za njihovu izgradnju ili u nekom dijelu istraživanja koje uključuje sustav za e-učenje.The Scopus and Web of Science scientific databases were searched according to the selected keywords: "analytical network process" and "e-learning system", papers that are in open access and entirely in English. 23 articles were found, of which two articles were indexed in both databases, so the analysis is based on 21 articles. The papers were analyzed according to the year of publication, country of residence of the author, number of sources used in writing the paper, average number of authors who participated in the preparation of the paper, publication of articles and qualitative analysis of the content of papers on the application of Analytical Network Process) methods in e-learning systems. The most published papers are in 2018 and 2020, the most common country of residence of the authors is Taiwan, and the average number of authors per paper is 4. Qualitative analysis of the content of selected papers found that in no system ANP method is built into the system itself. in research related to system evaluation or identification of parameters required for their construction or in some part of research involving an e-learning system

A MULTI-LAYERED TAXONOMY OF LEARNING ANALYTICS APPLICATIONS

Digital technologies have become immersed in education systems and the stakeholders have discovered a pervasive need to reform existing learning and teaching practices. Among the emerging educational digital technologies, learning analytics create a disruptive potential as it enables the power of educational decision support, real-time feedback and future prediction. Until today, the field of learning analytics is rapidly evolving, but still immature and especially low on ontological insights. Little guidance is available for educational designers and researchers when it comes to studies applied learning analytics as a method. Hence, this study offers a well-structured multi-layered taxonomy of learning analytics applications for deeper understanding of learning analytics

Recommending learning material in Intelligent Tutoring Systems

Nowadays, intelligent e-learning systems which can adapt to learner's needs and preferences, became very popular. Many studies have demonstrated that such systems can increase the eects of learning. However, providing adaptability requires consideration of many factors. The main problems concern user modeling and personalization, collaborative learning, determining and modifying learning senarios, analyzing learner's learning styles. Determining the optimal learning scenario adapted to students' needs is very important part of an e-learning system. According to psychological research, learning path should follow the students' needs, such as (i.a.): content, level of diculty or presentation version. Optimal learning path can allow for easier and faster gaining of knowledge. In this paper an overview of methods for recommending learning material is presented. Moreover, a method for determining a learning scenario in Intelligent Tutoring Systems is proposed. For this purpose, an Analytic Hierarchy Process (AHP) method is used

Data Analytics in Higher Education: An Integrated View

Data analytics in higher education provides unique opportunities to examine, understand, and model pedagogical processes. Consequently, the methodologies and processes underpinning data analytics in higher education have led to distinguishing, highly correlative terms such as Learning Analytics (LA), Academic Analytics (AA), and Educational Data Mining (EDM), where the outcome of one may become the input of another. The purpose of this paper is to offer IS educators and researchers an overview of the current status of the research and theoretical perspectives on educational data analytics. The paper proposes a set of unified definitions and an integrated framework for data analytics in higher education. By considering the framework, researchers may discover new contexts as well as areas of inquiry. As a Gestalt-like exercise, the framework (whole) and the articulation of data analytics (parts) may be useful for educational stakeholders in decision-making at the level of individual students, classes of students, the curriculum, schools, and educational systems

On Predicting Learning Styles in Conversational Intelligent Tutoring Systems using Fuzzy Decision Trees

Intelligent Tutoring Systems personalise learning for students with different backgrounds, abilities, behaviours and knowledge. One way to personalise learning is through consideration of individual differences in preferred learning style. OSCAR is the name of a Conversational Intelligent Tutoring System that models a person's learning style using natural language dialogue during tutoring in order to dynamically predict, and personalise, their tutoring session. Prediction of learning style is undertaken by capturing independent behaviour variables during the tutoring conversation with the highest value variable determining the student's learning style. A weakness of this approach is that it does not take into consideration the interactions between behaviour variables and, due to the uncertainty inherently present in modelling learning styles, small differences in behaviour can lead to incorrect predictions. Consequently, the learner is presented with tutoring material not suited to their learning style. This paper proposes a new method that uses fuzzy decision trees to build a series of fuzzy predictive models combining these variables for all dimensions of the Felder Silverman Learning Styles model. Results using live data show the fuzzy models have increased the predictive accuracy of OSCAR-CITS across four learning style dimensions and facilitated the discovery of some interesting relationships amongst behaviour variables