Dynamic Group Formation based on a Natural Phenomenon

2016 ◽  
Vol 14 (4) ◽  
pp. 13-26 ◽  
Author(s):  
Amina Zedadra ◽  
Yacine Lafifi ◽  
Ouarda Zedadra
Keyword(s):  
Collaborative Learning ◽  
Group Formation ◽  
Winter Season ◽  
Learning Systems ◽  
Learning System ◽  
Natural Phenomenon ◽  
Cognitive Profiles ◽  
New Approach ◽  
Homogeneous Groups ◽  
Grouping Algorithm

This paper presents a new approach of learners grouping in collaborative learning systems. This grouping process is based on traces left by learners. The goal is the circular dynamic grouping to achieve collaborative projects. The proposed approach consists of two main algorithms: (1) the circular grouping algorithm and (2) the dynamic grouping algorithm (used to update groups). The circular grouping is a novel algorithm to group learners based on their learning and collaborative traces. So, the aim is to form heterogeneous groups based on their profiles. The dynamic grouping algorithm is based on the behavior of penguins when they are moving in the winter season to secure their lives. The new proposed approach used the same behavior of penguins' colony. The proposed approach was applied on a collaborative learning system called LETline 2.0 (http://www.labstic.com/letline/). The developed system was experimented at an Algerian university. After the experiment, the authors observed that their system groups automatically the learners into homogeneous groups and improves their cognitive profiles.

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.

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.

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 Evaluation of Intelligent Grouping Based on Learners’ Collaboration Competence Level in Online Collaborative Learning Environment

2016 ◽  
Vol 17 (2) ◽  
Author(s):  
Maina Elizaphan Muuro ◽  
Robert Obwocha Oboko ◽  
Peter Waiganjo Wagacha
Keyword(s):  
Collaborative Learning ◽  
Learning Environment ◽  
Group Formation ◽  
Learning Experiences ◽  
Online Collaborative Learning ◽  
Competence Level ◽  
Collaborative Learning Environment ◽  
Formation Method ◽  
Grouping Algorithm ◽  
Random Method

<p>In this paper we explore the impact of an intelligent grouping algorithm based on learners’ collaborative competency when compared with (a) instructor based Grade Point Average (GPA) method level and (b) random method, on group outcomes and group collaboration problems in an online collaborative learning environment.  An intelligent grouping algorithm has been added in a Learning Management System (LMS) which is capable of forming heterogeneous groups based on learners’ collaborative competency level. True experiment design methodology was deployed to examine whether there is any association between group formation method and group scores, learning experiences and group problems.  From the findings, all groups had almost similar mean scores in all group tests, and shared many similar group collaboration problems and learning experiences. However, with the understanding that GPA group formation method involves the instructor, may not be dynamic, and the random method does not guarantee heterogeneity based on learner’s collaboration competence level, instructors are more likely to adopt our intelligent grouping method as the findings show that it has similar results. Furthermore, it provides an added advantage in supporting group formation due to its guarantee on heterogeneity, dynamism, and less instructor involvement.</p>

scholarly journals Forming Suitable Groups in MCSCL Environments

2021 ◽  
Vol 17 (1) ◽  
pp. 42-56
Author(s):  
Sofiane Amara ◽  
Fatima Bendella ◽  
Joaquim Macedo ◽  
Alexandre Santos
Keyword(s):  
Learning Environments ◽  
Real World ◽  
Group Formation ◽  
Computer Supported Collaborative Learning ◽  
Mobile Computer ◽  
Homogeneous Groups ◽  
Cluster Distance ◽  
Mixed Groups ◽  
World Environment ◽  
Grouping Algorithm

Given the peculiarities of mobile computer-supported collaborative learning (MCSCL) environments, forming suitable groups in such learning environments represents a hard and time-consuming task. This is because many conditions related to mobile learners, devices, and environment should be considered. Unlike the existing solutions, the present paper shows a grouping approach that allows a customizable formation of (1) homogeneous groups, (2) heterogeneous groups, and (3) mixed groups. The proposed solution does not only help instructors to dynamically form appropriate MCSCL groups, but it also allows to continually control the learners' learning, psychological, and social developments. To assess the effectiveness of the proposed solution, three metrics were used: (1) comparison between the characteristics of the existing group formation tools, (2) average intra-cluster distance of each grouping algorithm, and (3) an experimental evaluation in a real world environment. The obtained results show a great superiority of the proposed solution compared to the existing ones.

A Web-Based Collaborative Learning System Using Concept Maps

2016 ◽  
pp. 916-936 ◽  
Author(s):  
Anal Acharya ◽  
Devadatta Sinha
Keyword(s):  
Collaborative Learning ◽  
Concept Maps ◽  
Concept Map ◽  
Learning Systems ◽  
Learning System ◽  
Short Message ◽  
Web Based ◽  
Before And After ◽  
Collaboration Process ◽  
Tools And Techniques

The growth of communication technologies in the last two decades has led to development of web based learning systems for a variety of applications. The efficiency of such learning systems has often been enhanced by the use of collaboration tools and techniques. This study proposes a method of collaboration for construction of Concept Map of learning among a set of ‘n' learners divided into ‘k' groups using Short Message Services (SMS). This concept map is used as a sequence for construction of learning system. The functional modules of the architecture of this system are derived from the ‘Extended' theory of Meaningful learning proposed by David Ausubel. Two approaches have been used for evaluating this collaborative learning system. Firstly, paired t-test was conducted on student marks before and after collaboration to find the degree of significance between these. Secondly, efficiency of the collaboration process is computed using a set of Collaborative Efficiency Indexes (CEI) derived from a set of proposed metrics.

A “Mixed” Strategy for Collaborative Group Formation and Its Learning Outcomes

2018 ◽  
Vol 46 (4) ◽  
pp. 440-462
Author(s):  
Anal Acharya ◽  
Devadatta Sinha
Keyword(s):  
Collaborative Learning ◽  
Learning Styles ◽  
Group Formation ◽  
Learning System ◽  
Regression Coefficients ◽  
Collaborative Group ◽  
Subject Knowledge ◽  
Personal Learning ◽  
Learning Group ◽  
Prototype Learning

This study uses homogeneity in personal learning styles and heterogeneity in subject knowledge for collaborative learning group decomposition indicating that groups are “mixed” in nature. Homogeneity within groups was formed using K-means clustering and greedy search, whereas heterogeneity imbibed using agenda-driven search. For checking learning effectiveness, a simple schema of collaborative learning was proposed and prototype learning system developed using Android Emulator. Multiple regression analysis was applied on their learning results to derive regression coefficients for determining learning efficiency. The derived set of regression coefficients suggests more the time taken to form groups, better the student learning quality.

Assessment of Cognitive Load in Multimedia Learning Using Dual-Task Methodology

2002 ◽  
Vol 49 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Roland Brünken ◽  
Susan Steinbacher ◽  
Jan L. Plass ◽  
Detlev Leutner
Keyword(s):  
Cognitive Load ◽  
Dual Task ◽  
Cognitive Load Theory ◽  
Multimedia Learning ◽  
Learning Systems ◽  
Learning Materials ◽  
New Approach ◽  
Direct Assessment ◽  
Load Theory ◽  
Audiovisual Presentation

Abstract. In two pilot experiments, a new approach for the direct assessment of cognitive load during multimedia learning was tested that uses dual-task methodology. Using this approach, we obtained the same pattern of cognitive load as predicted by cognitive load theory when applied to multimedia learning: The audiovisual presentation of text-based and picture-based learning materials induced less cognitive load than the visual-only presentation of the same material. The findings confirm the utility of dual-task methodology as a promising approach for the assessment of cognitive load induced by complex multimedia learning systems.

scholarly journals Learning log-based automatic group formation: system design and classroom implementation study

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Changhao Liang ◽  
Rwitajit Majumdar ◽  
Hiroaki Ogata
Keyword(s):  
Collaborative Learning ◽  
Group Work ◽  
Intelligent System ◽  
Group Formation ◽  
Modern Society ◽  
Computer Supported Collaborative Learning ◽  
Classroom Implementation ◽  
Actual Learning ◽  
Group Member ◽  
Learning Log

AbstractCollaborative learning in the form of group work is becoming increasingly significant in education since interpersonal skills count in modern society. However, teachers often get overwhelmed by the logistics involved in conducting any group work. Valid support for executing and managing such activities in a timely and informed manner becomes imperative. This research introduces an intelligent system focusing on group formation which consists of a parameter setting module and the group member visualization panel where the results of the created group are shown to the user and can be graded. The system supports teachers by applying algorithms to actual learning log data thereby simplifying the group formation process and saving time for them. A pilot study in a primary school mathematics class proved to have a positive effect on students’ engagement and affections while participating in group activities based on the system-generated groups, thus providing empirical evidence to the practice of Computer-Supported Collaborative Learning (CSCL) systems.

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