scholarly journals Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

2022 ◽  
Vol 40 (3) ◽  
pp. 1127-1144
Author(s):  
K. Abhirami ◽  
M. K. Kavitha Devi
Keyword(s):  
Student Behavior ◽  
Learning Experiences ◽  
Personalized Learning ◽  
Learning System ◽  
Behavior Modeling ◽  
E Learning

scholarly journals Multi-Agent Adaptive e-Learning System Based on Learning Styles

2021 ◽  
Vol 4 (1) ◽  
pp. 1-12
Author(s):  
Faith Ngami Kivuva ◽  
Elizaphan Maina ◽  
Rhoda Gitonga
Keyword(s):  
Learning Styles ◽  
Learning Process ◽  
Intelligent Agents ◽  
Personalized Learning ◽  
Learning Systems ◽  
Learning System ◽  
Personalized Recommendation ◽  
Information Agent ◽  
E Learning ◽  
Multi Agent

Most traditional e-learning system fails to provide the intelligence that a learner may require during their learning process. Different learners have different learning styles but the current e-learning systems are not able to provide personalized learning. In this paper, we discuss how intelligent agents can aid learners in their learning process. Three agents have been developed namely, learner agent, information agent, and tutor agents that will be integrated into a learning management system (Moodle). Learners are provided with a personalized recommendation based on the learning styles.

scholarly journals Designing and integrating reusable learning objects for meaningful learning: Cases from a graduate programme

2017 ◽  
Author(s):  
Joyce Hwee Ling Koh
Keyword(s):  
Learning Experiences ◽  
Meaningful Learning ◽  
Learning Objects ◽  
Learning System ◽  
Learning Activities ◽  
Five Dimensions ◽  
Reusable Learning Objects ◽  
Programme Design ◽  
E Learning ◽  
Learning With Technology

E-learning quality depends on sound pedagogical integration between the content resources and lesson activities within an e-learning system. This study proposes that a meaningful learning with technology framework can be used to guide the design and integration of content resources with e-learning activities in ways that promote learning experiences, characterised by five dimensions: active, constructive, intentional, authentic, and collaborative. The pedagogical uses of these meaningful learning dimensions to support the design and integration of reusable learning objects as content resources will be explicated and exemplified through three cases related to the instruction of theories, principles, and professional skills respectively in a graduate programme. Design notes and surveys of students’ perception of learning experiences are used as data sources to understand how the five meaningful learning dimensions are being implemented by instructors and perceived by students. The strategies for supporting meaningful learning with reusable learning objects in higher education contexts are discussed.

scholarly journals Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 6637-6644
Author(s):  
H. El Fazazi ◽  
M. Elgarej ◽  
M. Qbadou ◽  
K. Mansouri
Keyword(s):  
Reinforcement Learning ◽  
Learning Style ◽  
Learning Process ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
E Learning ◽  
Multi Agent

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.

scholarly journals The Latent of Student Learning Analytic with K-mean Clustering for Student Behaviour Classification

2018 ◽  
Vol 4 (2) ◽  
pp. 156
Author(s):  
Andi Besse Firdausiah Mansur ◽  
Norazah Yusof
Keyword(s):  
Student Performance ◽  
Time Management ◽  
Student Behavior ◽  
Learning System ◽  
Cluster Number ◽  
Data Set ◽  
Research Areas ◽  
Great Achievement ◽  
Future Data ◽  
E Learning

Since the booming of “big data” or “data analytic” topics, it has drawn attention toward several research areas such as: student behavior classification, video surveillance, automatic navigation and etc. This paper present k-mean clustering technique to monitor and assess the student performance and behavior as well as give improvement toward e-learning system in the future. Data set of student performance along with teacher attributes are collected then analyzed, it was filtered into 6 attributes of teacher that may potentially affect the student performance. Afterwards, k-mean clustering applied into the filtered data set to generate particular cluster number. The result reveal that Teacher1 statistically hold the highest density (0.27) and teachers with good speech/lectures tend to have strong correlation with another factor such as: commitment of teacher on preparing lecture material and time management utilization. If this synergy between teacher and student running flawlessly, it will be great achievement for e-learning system to the society.

scholarly journals Design and Development of a Web-based Student Screening Module as Part of a Personalized Learning System

TEM Journal ◽  
2021 ◽  
pp. 1454-1460
Author(s):  
Pavel Zlatarov ◽  
Ekaterina Ivanova ◽  
Galina Ivanova ◽  
Julia Doncheva
Keyword(s):  
Personalized Learning ◽  
Learning Systems ◽  
Learning System ◽  
Practical Implementation ◽  
Design Development ◽  
Web Based ◽  
The Past ◽  
Development Level ◽  
Recent Developments ◽  
E Learning

Various researchers, institutions and companies have been increasingly working on and using e-learning systems in the past. However, with the recent developments, the demand for learning systems that can adapt to learners’ need and development level has risen considerably. A lot of learning from a distance requires new approaches in teaching, It is more important than ever for teachers to be able to accurately test students’ knowledge, determine the appropriate level of difficulty and adjust content accordingly. This paper describes the design, development and use of a web-based application used to prepare tests for students and determine their level as a module of an integrated personalized learning system. Results from a practical implementation of the system are also discussed.

Sequence generation for learning: a transformation from past to future

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fareeha Rasheed ◽  
Abdul Wahid
Keyword(s):  
Design Methodology ◽  
Learning Experiences ◽  
Personalized Learning ◽  
Path Generation ◽  
Learning Sequence ◽  
Content Type ◽  
Broad Category ◽  
Sequence Generation ◽  
E Learning

Purpose The purpose of this paper is to identify the different sequence generation techniques for learning, which are applied to a broad category of personalized learning experiences. The papers have been classified using different attributes, such as the techniques used for sequence generation, attributes used for sequence generation; whether the learner is profiled automatically or manually; and whether the path generated is dynamic or static. Design/methodology/approach The search for terms learning sequence generation and E-learning produced thousands of results. The results were filtered, and a few questions were answered before including them in the review. Papers published only after 2005 were included in the review. Findings The findings of the paper were: most of the systems generated non-adaptive paths. Systems asked the learners to manually enter their attributes. The systems used one or a maximum of two learner attributes for path generation. Originality/value The review pointed out the importance and benefits of learning sequence generation systems. The problems in existing systems and future areas of research were identified which will help future researchers to pursue research in this area.

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.

Behavioral Tracking in E-Learning by Using Learning Styles Approach

2017 ◽  
Vol 8 (1) ◽  
pp. 17 ◽  
Author(s):  
Amira Fatiha Baharudin ◽  
Noor Azida Sahabudin ◽  
Adzhar Kamaludin
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Student Behavior ◽  
User Behavior ◽  
Learning System ◽  
Learning Needs ◽  
Learning Materials ◽  
Behavioral Tracking ◽  
E Learning ◽  
The Cost

Currently, e-learning is becoming an option as it can save the cost of education, time, and more flexible in its implementation. The main problem that arises is how to create e-learning content that is interesting and really fit the needs of the users. One way that can be done to optimize the content of e-learning is to analyze the user behavior. This study aims to analyze user (student) behavior in KALAM UMP, based on logs report (activity history), which is often called as behavioral tracking. First, the learning style of the students is determined based on Honey and Mumford Learning Styles Model by using Learning Styles Questionnaire. The analysis is done using SPSS 16.0 for Windows. The results shows that student with Reflector and Theorist learning styles access e-learning materials the most. From Spearman Correlation analysis, the relationship between learning styles and students’ behavior in e-learning is found to be very weak (r<sub>s</sub>=.276, p=.000), but statistically significant (p&lt;0.05). In other words, students’ learning styles and behavior in e-learning have significant impacts on the improvement or degradation of students’ performance. Therefore, from the results of this study, an adaptive KALAM e-learning system which can suits the learning styles of UMP students is proposed. In adaptive e-learning system, students can access learning materials that match the students' learning needs and preferences.

Career Goal-based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan

2018 ◽  
Vol 10 (3) ◽  
pp. 23-37 ◽  
Author(s):  
Xueying Ma ◽  
Lu Ye
Keyword(s):  
Collaborative Filtering ◽  
Cold Start ◽  
Personalized Learning ◽  
Learning System ◽  
Career Goals ◽  
Learning Goals ◽  
Background Information ◽  
Learning Path ◽  
E Learning ◽  
Cold Start Problem

This article describes how e-learning recommender systems nowadays have applied different kinds of techniques to recommend personalized learning content for users based on their preference, goals, interests and background information. However, the cold-start problem which exists in traditional recommendation algorithms are still left over in e-learning systems and a few of them have seriously affected the learning goals of users. Thus, an intelligent e-learning system have been developed which can recommend professional and targeted courses according to their career goals. First, an enhanced collaborative filtering (CF) approach is proposed considering users' career goals and background information. Then, the relevance between career goals and courses are calculated to alleviate the cold-start problem and recommend specialized courses for users. Finally, a PrefixSpan algorithm is combined with the above methods to generate a personalized learning path step by step. Some experiments are carried out with real users of different professions to test the performance of the hybrid algorithm.

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