User/tutor optimal learning path in e-learning using comprehensive neuro-fuzzy approach

E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners’ current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners’ hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners’ learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.

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A method for learning scenario determination and modification in intelligent tutoring systems

International Journal of Applied Mathematics and Computer Science ◽

10.2478/v10006-011-0005-2 ◽

2011 ◽

Vol 21 (1) ◽

pp. 69-82 ◽

Cited By ~ 6

Author(s):

Adrianna Kozierkiewicz-Hetmańska ◽

Ngoc Nguyen

Keyword(s):

Learning Styles ◽

Intelligent Tutoring Systems ◽

Intelligent Tutoring ◽

Educational Process ◽

Learning System ◽

Optimal Learning ◽

Tutoring Systems ◽

Learning Path ◽

E Learning ◽

Using Data

A method for learning scenario determination and modification in intelligent tutoring systemsComputers have been employed in education for years. They help to provide educational aids using multimedia forms such as films, pictures, interactive tasks in the learning process, automated testing, etc. In this paper, a concept of an intelligent e-learning system will be proposed. The main purpose of this system is to teach effectively by providing an optimal learning path in each step of the educational process. The determination of a suitable learning path depends on the student's preferences, learning styles, personal features, interests and knowledge state. Therefore, the system has to collect information about the student, which is done during the registration process. A user is classified into a group of students who are similar to him/her. Using information about final successful scenarios of students who belong to the same class as the new student, the system determines an opening learning scenario. The opening learning scenario is the first learning scenario proposed to a student after registering in an intelligent e-learning system. After each lesson, the system tries to evaluate the student's knowledge. If the student has a problem with achieving an assumed score in a test, this means that the opening learning scenario is not adequate for this user. In our concept, for this case an intelligent e-learning system offers a modification of the opening learning scenario using data gathered during the functioning of the system and based on a Bayesian network. In this paper, an algorithm of scenario determination (named ADOLS) and a procedure for modifying the learning scenario AMLS with auxiliary definitions are presented. Preliminary results of an experiment conducted in a prototype of the described system are also described.

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Recommending learning material in Intelligent Tutoring Systems

Annales Universitatis Mariae Curie-Sklodowska sectio AI – Informatica ◽

10.17951/ai.2016.16.1.1 ◽

2016 ◽

Vol 16 (1) ◽

pp. 1

Author(s):

Jarosław Bernacki

Keyword(s):

Learning Styles ◽

Intelligent Tutoring Systems ◽

Intelligent Tutoring ◽

Psychological Research ◽

Learning System ◽

Learning Material ◽

Optimal Learning ◽

Tutoring Systems ◽

Learning Path ◽

E Learning

<p>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.</p>

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