Collaborative Activities Management System Model (CAMS) for Web-based Virtual Classrooms

2017 ◽  
Vol 2 (2) ◽  
pp. 1-14
Author(s):  
Ibam E. Onwuka ◽  
Agbonifo O. Catherine ◽  
Adewale O. Sunday
Keyword(s):  
Collaborative Learning ◽  
Learning Environment ◽  
Management System ◽  
Learning Systems ◽  
Learning System ◽  
System Model ◽  
Online Collaborative Learning ◽  
Individual Participant ◽  
E Learning ◽  
Collaborative Activities

Online collaborative learning systems have emerged as one of the most valuable aspects of e-learning systems. E-learning products that lack features for online collaboration among participants are deemed to be incomplete or sub-standard. Collaboration modules within an e-learning system consist of assets for group communication and work “spaces” and facilities. Activities within the collaborative framework of an e-learning system advances collaborative social interaction and the social construction of knowledge. Participants in an e-learning environment get involved in many activities which if not well coordinated could hamper collaboration instead of enhancing it. Therefore, the need to create measurable tools (models) that can coordinate these collaborative activities and provide up-to-date information or status of individual participant and group participants in collaborative activities within an e-learning environment has become inevitable. This work seeks to presents the design of activities management system model for online collaborative learning systems. The model contains some mathematical models for determining the level of involvements of a participant or groups in online classes (class attendance), discussions, project and polls. Their levels of participation are assigned weights and their aggregate value interpreted to give up-to-date status of their involvement in collaborative activities. The model is developed using WAMP tools.

scholarly journals The role of e-engagement and flow on the continuance with a learning management system in a blended learning environment

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Tiong-Thye Goh ◽  
Bing Yang
Keyword(s):  
Learning Environment ◽  
Blended Learning ◽  
Management System ◽  
Learning Management System ◽  
Learning Systems ◽  
Learning System ◽  
Flow Experience ◽  
Learning Engagement ◽  
E Learning ◽  
Learning Management

AbstractE-learning systems are widely deployed in higher education institutions but sustaining students’ continued use of e-learning systems remains challenging. This study investigated the relationship between e-learning engagement, flow experience and learning management system continuance via a mediated moderation interaction model. The context of the study is a Moodle LMS supporting a blended learning environment. After controlling age and gender, a PLS analysis of 92 students’ samples with a reflective flow construct explained 49% of the variance in the research model. The analysis shows that flow mediates e-engagement and perceived ease of use with a direct positive impact on e-learning system continuance. Flow has an indirect impact through perceived usefulness on e-learning system continuance. However, the direct impact of flow on system continuance weakens as e-learning engagement increases. This finding may help to explain the mixed and inconsistent impact of flow in the e-learning system continuance literature. The dual effect of flow suggests that instructors must carefully balance pedagogical decisions intended to heighten flow experience to generate positive learning outcomes through e-engagement and its consequence of reduced impact on continued system use.

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 Application of E-learning System in the World of Education

2021 ◽  
Vol 1 (1) ◽  
pp. 127-133
Author(s):  
V Pratiwi ◽  
◽  
S L Rahman ◽  
Keyword(s):  
Management System ◽  
Learning Management System ◽  
Learning Systems ◽  
Learning System ◽  
The Internet ◽  
Literature Study ◽  
Positive Effects ◽  
The World ◽  
Teachers And Students ◽  
E Learning

The purpose of this study is to determine the positive effects of e-learning systems on improving student's understanding of concepts and comparing e-learning with old learning system. Nowadays, the internet is one of the way to find informations easily without reading books and attending classes. The method used in this research is literature study method by obtaining information and data from various sources. The results from this study is that e-learning can improve students' cognitive abilitiesas it is easier to access words, powerpoint, html or PDF in the application. The conclusion is that e-learning can be used as a learning innovation that can help teachers and students using the Software Learning Management System

scholarly journals Adaptive E-Learning System Using Justification Based Truth Maintenance System

2021 ◽  
Vol 4 (2) ◽  
pp. 44-48
Author(s):  
Tahir Mohammad Ali ◽  
Attique Ur Rehman ◽  
Ali Nawaz ◽  
Wasi Haider Butt
Keyword(s):  
Management System ◽  
Learning Management System ◽  
Learning Systems ◽  
Learning System ◽  
Educational Activities ◽  
Maintenance System ◽  
E Learning ◽  
Truth Maintenance ◽  
Learning Management ◽  
Customized Learning

In most E-learning systems, educational activities are presented in a static way without bearing in mind the particulars or student levels and skills. Personalization and adaptation of an E-learning management system are dependent on the flexibility of the system in providing different learning and content models to individual students based on their characteristics. In this paper, we suggest an Adaptive E-learning system which is providing adaptability with support of justification-based truth maintenance system. The system is accomplished of signifying students with suitable knowledge fillings and customized learning paths based on the student’s profile, interests, and previous results.

Virtual Co Learner

2010 ◽  
pp. 41-54 ◽  
Author(s):  
Dimitrios Georgiou ◽  
Sotirios Botsios ◽  
Georgios Tsoulouhas
Keyword(s):  
Collaborative Learning ◽  
Learning Environments ◽  
Learning Style ◽  
Learning Systems ◽  
Learning System ◽  
Cognitive Characteristics ◽  
Learning Procedure ◽  
E Learning ◽  
On Line ◽  
And Behavior

Adaptation and personalization of the information and instruction offered to the users in on-line e-learning environments are considered to be the turning point of recent research efforts. Collaborative learning may contribute to adaptive and personalized asynchronous e-learning. In this chapter authors intend to introduce the Virtual co Learner (VcL) that is a system designed on a basis of distributed architecture able to imitate the behavior of a learning companion who has suitable to the user’s cognitive and learning style and behavior. To this purpose an asynchronous adaptive collaborating e-learning system is proposed in the sense of reusing digitized material which deployed before by the users of computer supported collaborating learning systems. Matching real and simulated learners who have cognitive characteristics of the same type, one can find that learning procedure becomes more efficient and productive. Aiming to establish such VcL, one faces a number of questions. An important question is related to the user’s cognitive or learning characteristics diagnosis. Other questions are examined too.

scholarly journals APPLICATION OF E-LEARNING SYSTEM IN THE WORLD OF EDUCATION

2021 ◽  
Vol 14 (1) ◽  
pp. 47-52
Author(s):  
Senny Luckyardi ◽  
L. Rahman
Keyword(s):  
Management System ◽  
Learning Management System ◽  
Learning Systems ◽  
Learning System ◽  
The Internet ◽  
Literature Study ◽  
Positive Effects ◽  
The World ◽  
Teachers And Students ◽  
E Learning

The purpose of this study is to determine the positive effects of e-learning systems on improving student's understanding of concepts and comparing e-learning with old learning system. Nowadays, the internet is one of the way to find informations easily without reading books and attending classes. The method used in this research is literature study method by obtaining information and data from various sources. The results from this study is that e-learning can improve students' cognitive abilitiesas it is easier to access words, powerpoint, html or PDF in the application. The conclusion is that e-learning can be used as a learning innovation that can help teachers and students using the Software Learning Management System.

scholarly journals Learning Analytics for Diagnosing Cognitive Load in E-Learning Using Bayesian Network Analysis

2021 ◽  
Vol 13 (18) ◽  
pp. 10149
Author(s):  
Younyoung Choi ◽  
Jigeun Kim
Keyword(s):  
Academic Achievement ◽  
Learning Environment ◽  
Cognitive Load ◽  
Bayesian Network ◽  
Diagnostic Information ◽  
Learning Systems ◽  
Learning System ◽  
Extraneous Cognitive Load ◽  
Intrinsic Cognitive Load ◽  
E Learning

A learner’s cognitive load is highly associated with their academic achievement within learning systems. Diagnostic information about a learner’s cognitive load is useful for achieving optimal learning, by enabling the learner to manage and control their cognitive load in the e-learning environment. However, little empirical research has been conducted to obtain diagnostic information about the cognitive load in e-learning systems. The purpose of this study was to analyze a personalized diagnostic evaluation for a learner’s cognitive load in an e-learning system, using the Bayesian Network (BN) as a learning analytic method. Data from 700 learners were collected from Cyber University. A learner’s cognitive load level was measured in terms of three components: extraneous cognitive load, intrinsic cognitive load, and germane cognitive load. The BN was built by representing the relationship among the extraneous cognitive load, intrinsic cognitive load, germane cognitive load, and academic achievement. The conditional and marginal probabilities in the BN were estimated. This study found that the BN provided diagnostic information about a learner’s level of cognitive load in the e-learning system. In addition, the BN predicted the learner’s academic achievement in terms of their different cognitive load patterns. This study’s results imply that diagnostic information related to cognitive load helps learners to improve academic achievement by managing and controlling their cognitive loads in the e-learning environment. In addition, instructional designers are able to offer more appropriately customized instructional methods by considering learners’ cognitive loads in online learning.

A Model for Improving Online Collaborative Learning through Machine Learning

2015 ◽  
pp. 204-219 ◽  
Author(s):  
E. Muuro Maina ◽  
Peter W. Wagacha ◽  
Robert O. Oboko
Keyword(s):  
Machine Learning ◽  
Online Learning ◽  
Collaborative Learning ◽  
Learning Environment ◽  
Online Learning Environment ◽  
Online Collaborative Learning ◽  
Collaborative Software ◽  
Online Tools ◽  
E Learning ◽  
Collaborative Learning Environment

Online collaborative learning provides new opportunities for student collaboration in an online learning environment and at the same time spawns new challenges for teachers supporting group work. With the current Course Management Systems (CMS) such as Moodle, technology has provided online tools that include discussions forums, chat rooms, e-mails, newsgroups, workshops, etc. These tools provide a collaborative learning environment. To include constructivist learning in an online learning environment is a good collaborative strategy that is necessary since it engages learners in learning activities through interaction with their peers and teacher. A good collaborative strategy in an e-learning environment must primarily ensure that the expected interaction occurs in line with the learning mechanism being employed. This cannot merely be met by offering a set of collaborative software tools alone. It also requires the instructors' support. As the number of students studying online continues to increase, there is need to develop models that can improve online collaborative learning with minimal involvement of the instructor because the instructor might not be able to cope with increased number of students. To address this need, this chapter discusses a novel model for improving online collaborative learning that uses Machine Learning (ML) techniques.

University Enhancement System using a Social Networking Approach: Extending E-learning

10.28945/3318 ◽  
2009 ◽  
Author(s):  
Oludele Awodele ◽  
Sunday Idowu ◽  
Omotola Anjorin ◽  
Adebunmi Adedire ◽  
Victoria Akpore
Keyword(s):  
Learning Environment ◽  
Social Networking ◽  
Learning Process ◽  
Learning Systems ◽  
Learning System ◽  
The Internet ◽  
Open Collaboration ◽  
E Learning ◽  
Learning Institutions ◽  
Enhanced Learning

The proliferation of e-leaming systems in both learning institutions and companies has contributed a lot to the acquisition and application of new skills. With the growth in technology, especially the internet, e-learning systems are only getting better and having more impact on the users. This paper suggests an approach to e-learning that emphasizes active and open collaboration, and also the integration of other services that aid or contribute to the learning process. This approach aims at having an extended and enhanced learning environment that is tied or connected to other systems within the immediate environment or otherwise. We illustrate the possibility and usability of such system in a university, such that other important administrative systems are integrated into the e-learning system, and collaboration is open to both academic and non-academic personnel’s.

scholarly journals Integration of the tagging mechanism in the collaborative e-learning system

2012 ◽  
Vol 13 (1) ◽  
pp. 54-60
Author(s):  
Andrejs Lesovskis ◽  
Vladimirs Kotovs ◽  
Leonids Novickis
Keyword(s):  
Learning Environment ◽  
Semantic Web ◽  
Learning Systems ◽  
Learning System ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Combined Use ◽  
E Learning ◽  
Machine Readable

Abstract - The tagging mechanism is a technique that can be used to implement the personalized collaboration in the e-Learning systems, thereby increasing the efficiency of such a system. Semantic Web technologies can be used to enhance tags with machine-readable annotations to improve the accuracy of tag-based recommendation services. The novelty of the proposed approach is the combined use of the Semantic Web technologies and the reuse-oriented model in the development of the e-Learning environment.

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