Web-adaptive training system based on cognitive student style

International Journal of Research in Education and Science, 2018

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The Bologna Process changes the educational paradigm, to be focus on the student and in the learning outcomes. The majority of e-learning platforms are used as mere repositories of content, based on the classroom paradigm and don't support the individualism of each student learning process. Through the integration of new pedagogical methodologies based on students learning styles, we present an approach to intelligent tutoring systems in order to improve the learning process. This article is focused on the importance of learning styles to create the student model in intelligent tutoring systems and what was the student's feedback about the adaptation of the system to each learning experience.

International Journal of …, 2011

Personalized adaptive systems rely heavily on the learning style and the learner's behavior. Due to traditional teaching methods and high learner/teacher ratios, a teacher faces great obstacles in the classroom. In these methods, teachers deliver the content and learners just receive it. Moreover, teachers can't cope with the individual differences among learners. This weakness may be attributed to various reasons such as the high number of learners accommodated in each classroom and the low teaching skills of the teacher himself/herself, Therefore, identifying learning styles is a critical step in understanding how to improve the learning process. This paper presented an automatic tool for identifying learning styles based on the Felder-Silverman learning style model in a learning environment using a social book marking website such as www.tagme1.com. The proposed tool used the learners' behaviour while they are browsing / exploring their favorite web pages in order to gather hints about their learning styles. Then the learning styles were calculated based on the gathered indications from the learners' database. The results showed that the proposed tool recognition accuracy was 72% when we applied it on 25 learners with low number of links per learner. Recognition accuracy increased to 86.66% when we applied it on 15 learners with high number of links per learner.

Anais do 7. Congresso Brasileiro de Redes Neurais, 2016

This paper presents a novel Hybrid Intelligent Tutoring System based on traditional and connectionist Artificial Intelligence. It is adaptive and reactive and has the ability to offer customized and dynamic tuition. Features of apprentice's psychological profile or learning style are employed as basic elements for customization, and they are complemented by (human) expert rules. These rules are represented by probability distributions. The proposed system is implemented on web environment to take advantages such as wide reach and portability. Three types of navigation (on course contents) are compared based on user performances: free (user has full control), random (user is controlled by chance) and intelligent (navigation is controlled by the proposed system: neural network combined with expert rules). Descriptive and inferential analysis of data indicate that the application of proposed techniques is adequate, based on (significant at 5%) results. The main aspects that have been studied are retention ("learning improvement") normalized gain, navigation total user interaction time and number of steps (length of visited content). Both customizations (by psychological profiles and learning styles) have shown good results and no significant difference has been found between them.

Adaptive Educational Hypermedia Systems among the emerging technologies serves Knowledge and Learning Systems. Aiming to improve pervasiveness and efficiency of asynchronous e-learning we give emphasis to the development of diagnostic tools capable to recognize certain learner’s characteristics to the purpose of providing learning material tailored to the learner’s specific needs. Learning Style diagnosis can be approached either by the use of probabilistic expert systems or by the use of fuzzy expert systems. In order to establish a probabilistic Learning Style’s diagnostic expert system we propose a method based on Bayesian Networks. Our system analyzes information supplied by the system’s descendant users, as well as providing a stochastic analysis of the problem. It is a system that utilizes what appear to be reasoning capabilities so as to reach Learning Style’s estimations. In order to classify learners in a predefined set of classes, the proposed system takes under consideration learners’ answers to a certain questionnaire, as well as a classification of learners who have been previously examined. As it was expected, our method takes advantage of previously accumulated knowledge and proves to be more accurate than Learning Style’s direct estimation, i.e. an estimation based only on user responses. Further to this, we propose a Fuzzy Cognitive Maps technique, which modifies the Learning Style into appropriate quantitative characteristics. The importance of Fuzzy Cognitive Map’s technique becomes even greater since this method takes into consideration the previously gained experience on learners’ style diagnoses.

There are many learning management systems (LMSs) around us. Of them WebCT, Blackboard, and Moodle are widely and successfully used when e-education is concerned. They help teachers in creating and conducting online courses though the individual differences of learners are not considered. The learners possess different characteristics and requirements like motivation, learning styles, prior knowledge and cognitive abilities. Some of the characteristics such as learning styles, their effect on learning abilities can be supported by some of the recent learning systems. It makes learning easier according to some educational theories. Considering these facts, we focus on dynamically changed learning styles of students to provide them with a better learning material. This system will hopefully enhance the learning ability and quality of learners. Finally we propose to find differences between learning style detected by ILS questionnaires and detected those dynamically.

The world of information is becoming increasingly complicated and it gains more dynamic face these days. In this study, not only learning styles in web-based instruction are taken into account, but also the studies that give information about the effects of learner control are investigated. The styles are Kolb, Cognitive, McCarthy learning styles and etc according to a set of criteria of work relevancy their internal architecture techniques are discussed. Our work addresses the issue of software engineering usage through these methods using in e-learning efficient systems. A classification considering the main goal of the methods has been made. For each category, a discussion of the suitability of learning techniques is proposed in cognition of requirements in inception phase of software engineering. The results of researches conducted indicate that in most cases, these methods very efficacious in web-based instruction and education.

International Journal of Database Theory and Application, 2016

This paper is a part of ongoing research that tries to approve our hypothesis about adaptive personalization in web-based learning systems; we are debating that building online learning environments with the ability to detect learners learning style dynamically by observing their behaviour and then presenting learning material based on the detected learning style is more effective than using ILS questionnaire. In an earlier work, we provided a dynamic technique that identifies learners VAK learning styles according to their behaviour in the learning environment influenced by literature approach, in this paper we have modified our technique and re-proposed it. First, we connected behavioural patterns (time, visits and answer patterns) to the features (contents, outlines, group forums, examples, case studies, exercises and assessments), then we defined the effect each VAK learning style will have on each pattern. Next, we described three general rules and three algorithms that should detect the learning style. Now we are in the process of building two online learning environments to test and confirm the differences between dynamic learning style detection _ based on our technique _ and the traditional ILS questionnaire environments; we choose a course called 'Computer Skills for medicine faculty students', which is provided from the faculty of Information Technology at the University of Jordan; the results of the planed experiment will be saved and analysed using SPSS.

Universal Access in Human-Computer …, 2011