The Use of the Recommended Learning Path in the Personalized Adaptive E-Learning System

2020 ◽  
pp. 280-294
Author(s):  
Vija Vagale ◽  
Laila Niedrite ◽  
Svetlana Ignatjeva
Keyword(s):  
Learning System ◽  
Learning Path ◽  
E Learning

Generating Personalized Navigation Learning Path Based on Knowledge Map

2014 ◽  
Vol 4 (2) ◽  
pp. 1-17
Author(s):  
Xiang Jun Huang ◽  
Chao Zhang ◽  
Qing Hua Zheng
Keyword(s):  
User Satisfaction ◽  
Information Overload ◽  
Rapid Development ◽  
Learning System ◽  
Knowledge Map ◽  
Learning Efficiency ◽  
Learning Path ◽  
Learning Instruction ◽  
E Learning ◽  
On Line

With a rapid development of Internet, E-Learning is becoming a new learning mode. E-Learning is not limited by time and space. It also has a large number of on-line learning resource. However, it has many disadvantages for students, such as information overload, disorientation, low learning efficiency, low user satisfaction and so on. Our aim is to improve learning efficiency and user satisfaction by overcoming information overload and disorientation of E-Learning system. This paper proposes an algorithm by combining Spreading-Activation Theory and techniques of classifying and sorting knowledge. The algorithm can generate a near optimal navigation learning path(NLP) based on a student's target knowledge unit(TKU) and knowledge map(KM) which it belongs to. NLP provides students an appropriate learning instruction to effectively eliminate disorientation during the process when they are learning interested knowledge units. The essential tasks of the algorithm is to filter redundant information and sort candidate knowledge units. So its realization process can be divided into three phrases: first, generating candidate complement map to overcome information overload. Because the candidate complement map only contains essential candidate knowledge units and learning dependencies among them to master TKU. Second, constructing learning features to discrete the candidate complement map to implement techniques of sorting knowledge conveniently. Final, sorting candidate knowledge units to get an appropriate NLP by using a Secondary Sort Strategy(SSS). The experimental results have shown that our method is sound for improving learning efficiency and users' satisfaction.

Learning Path Recommendation System for Programming Education Based on Neural Networks

2020 ◽  
Vol 18 (1) ◽  
pp. 36-64 ◽  
Author(s):  
Tomohiro Saito ◽  
Yutaka Watanobe
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recommendation System ◽  
Current Knowledge ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Programming Education ◽  
Learning Path ◽  
E Learning ◽  
Trained Teachers

Programming education has recently received increased attention due to growing demand for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting this demand. One way to compensate for a shortage of trained teachers is to use machine learning techniques to assist learners. This article proposes a learning path recommendation system that applies a recurrent neural network to a learner's ability chart, which displays the learner's scores. In brief, a learning path is constructed from a learner's submission history using a trial-and-error process, and the learner's ability chart is used as an indicator of their current knowledge. An approach for constructing a learning path recommendation system using ability charts and its implementation based on a sequential prediction model and a recurrent neural network, are presented. Experimental evaluation is conducted with data from an e-learning system.

scholarly journals A New Learning Path Model for E-Learning Systems

2021 ◽  
Vol 19 (2) ◽  
pp. 20-40
Author(s):  
David Brito Ramos ◽  
Ilmara Monteverde Martins Ramos ◽  
Isabela Gasparini ◽  
Elaine Harada Teixeira de Oliveira
Keyword(s):  
Student Behavior ◽  
Group Formation ◽  
Learning Systems ◽  
Learning System ◽  
Path Model ◽  
New Approach ◽  
Learning Path ◽  
E Learning ◽  
Learning Paths ◽  
Collaborative Activities

This work presents a new approach to the learning path model in e-learning systems. The model uses data from the database records from an e-learning system and uses graphs as representation. In this work, the authors show how the model can be used to represent visually the learning paths, behavior analysis, help to suggest group formation for collaborative activities, and thus assist the teacher in making decisions. To validate the practical utility of the model, the authors created two tools, one to visualize the learning paths and another to suggest groups of students for collaborative activities. Both tools were tested in a real environment, presenting useful results. The authors carried experiments with students from three programs: physics, electrical engineering, and computer science. Experiments show that it is possible to use the proposed learning path to analyze student behavior patterns and recommend group formation with positive results.

scholarly journals The choice of customisation strategies in training: An overview of parameters and their systematisation

2021 ◽  
pp. 170-186
Author(s):  
Marina Anikieva
Keyword(s):  
Learning Process ◽  
Learning System ◽  
Learning Goals ◽  
Individual Student ◽  
Educational Strategies ◽  
Learning Materials ◽  
Educational Activities ◽  
Learning Path ◽  
Large Numbers ◽  
E Learning

This paper discusses the factors that determine the customisation of e-learning programmes. The process of customisation depends on many parameters, such as the objectives of the programme, the quantity and order of the learning materials, the personality and abilities of the student, and the resources within the learning system. Curriculum developers are able to put together these parameters in varying combinations, reflecting differing educational strategies. Because of this possibility it has become important to study how one can determine an appropriate strategy or learning path for any individual student. This is becoming particularly relevant because curriculum developers have to consider large numbers of already developed learning courses, modules, and technologies. One of the approaches to addressing this problem is the classification, or taxonomy, of customisation parameters. This paper reviews published material from highly-rated journals dealing with customisation of learning. As a result of this review the groups of customisation parameters are identified and a generalised scheme of grouped parameters, and their sequence, corresponding to the inner logic of the learning process are developed. This taxonomy allows the educational activities to be arranged so that learners can achieve their learning goals more efficiently.

scholarly journals Optimal Hierarchical Learning Path Design With Reinforcement Learning

2020 ◽  
Vol 45 (1) ◽  
pp. 54-70
Author(s):  
Xiao Li ◽  
Hanchen Xu ◽  
Jinming Zhang ◽  
Hua-hua Chang
Keyword(s):  
Reinforcement Learning ◽  
Learning System ◽  
Learning Material ◽  
Optimal Learning ◽  
Model Free ◽  
Learning Path ◽  
The Hierarchical Structure ◽  
E Learning ◽  
Efficient Learning ◽  
Skills Learning

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.

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

2011 ◽  
Vol 21 (1) ◽  
pp. 69-82 ◽  
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.

scholarly journals Application of the Reccommended Learning Path in the Personalized Adaptive E-learning System

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Vija Vagale ◽  
Laila Niedrite ◽  
Svetlana Ignatjeva
Keyword(s):  
Learning System ◽  
Learning Path ◽  
E Learning

scholarly journals Recommending learning material in Intelligent Tutoring Systems

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>

scholarly journals An Adaptive E-learning System for Teaching Mobile Applications

2021 ◽  
Vol 10 (9) ◽  
pp. 80-87
Author(s):  
George Pashev
Keyword(s):  
Mobile Applications ◽  
Learning Systems ◽  
Learning System ◽  
Software Implementation ◽  
Learning Goals ◽  
Abstract Model ◽  
Scientific Publications ◽  
Learning Path ◽  
E Learning ◽  
Software Prototype

Support for various aspects of personalization and adaptation of the reviewed tools is not at the desired level of abstraction - very often depends on the specific way in which it is assumed that a learning object will be combined, to build a learning path for going through an e-course, or to achieve learning goals. This document describes an innovative and more abstract model and related software system for adaptive e-learning. A software prototype for adaptive teaching of the disciplines "Mobile Applications" and "Programming applications for mobile devices", taught at Plovdiv University "Paisii Hilendarski” is presented. Objectives are discussed, based on an extensive overview in the field of adaptive e-learning systems and the software implementation of the e-learning tool is presented. Results of specific tests with study activities will be presented in future scientific publications.

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