Building Conceptual Knowledge for Managing Learning Paths in e-Learning

2006 ◽  
pp. 66-77
Author(s):  
Yu-Liang Chi ◽  
Hsun-Ming Lee
Keyword(s):  
Conceptual Knowledge ◽  
E Learning ◽  
Learning Paths

A Strategy to Join Adaptive and Reputation-Based Social-Collaborative E-Learning, Through the Zone of Proximal Development

2013 ◽  
Vol 11 (3) ◽  
pp. 12-31 ◽  
Author(s):  
Maria De Marsico ◽  
Andrea Sterbini ◽  
Marco Temperini
Keyword(s):  
Collaborative Learning ◽  
Adaptive Learning ◽  
Zone Of Proximal Development ◽  
Learning Activities ◽  
Proximal Development ◽  
Student Model ◽  
Web Based ◽  
E Learning ◽  
Learning Paths ◽  
The One

The educational concept of “Zone of Proximal Development”, introduced by Vygotskij, stems from the identification of a strong need for adaptation of the learning activities, both traditional classroom and modern e-learning ones, to the present state of learner’s knowledge and abilities. Furthermore, Vygotskij’s educational model includes a strong bent towards social and collaborative learning. The joint answer to these two trends can be concretely implemented through a tight integration between personalized learning paths and collaborative learning activities. Along this line, the authors designed the combination of the functions of two pre-existing prototypes of web-based systems, to investigate how the above integration can merge adaptive and social e-learning. LECOMPS is a web-based e-learning environment for the automated construction of adaptive learning paths. SOCIALX is a web-based system for shared e-learning activities, which implements a reputation system to provide feedback to its participants. The authors propose a two-way tunneling strategy to integrate the above prototypes. The result is twofold: on the one hand the use of the student model supported by LECOMPS in an adaptive e-learning course is extended to support choosing exercise activities delivered through SOCIALX; on the other hand the reputation and the skills gained during social-collaborative activities are used to update the student model. Under the social perspective induced by the integration, the authors present a mapping between the student model and the definition of Vygotskij’s Autonomous Problem Solving and Proximal Development regions, with the aim to provide the learner with better guidance, especially in the selection of available social learning activities.

Learning Assistant - Personalizing Learning Paths in e-Learning Environments

2008 ◽  
Author(s):  
Halina Kwasnicka ◽  
Dorota Szul ◽  
Urszula Markowska-Kaczmar ◽  
Pawel B. Myszkowski
Keyword(s):  
Learning Environments ◽  
E Learning ◽  
Learning Paths

Cognitive Load Theory in E-Learning

2012 ◽  
pp. 1178-1211 ◽  
Author(s):  
Christa M. van Mierlo ◽  
Halszka Jarodzka ◽  
Femke Kirschner ◽  
Paul A. Kirschner
Keyword(s):  
Heart Rate ◽  
Cognitive Load ◽  
Skin Conductance ◽  
Conceptual Knowledge ◽  
Cyclic Process ◽  
Dual Tasks ◽  
Future Directions ◽  
Self Reports ◽  
E Learning ◽  
Extraneous Load

Cognitive load can be assessed and monitored using a multitude of subjective (self-reports, i.e. Hart & Staveland, 1988; Paas, 1992) and more objective methods (dual tasks, eye-tracking, heart-rate measurements, skin conductance measurements, cf. Brünken, Plass, & Leutner, 2003; Beatty, 1982, Paas, van Merriënboer, & Adam, 1994), either during the learning or afterwards, so that instruction can be optimized based on mental effort data using iterative design (a cyclic process of prototyping, testing, analyzing, and refining a product or process, ultimately improving the quality and functionality of the design). Computer simulations provide an excellent environment to apply CLT principles. However, such e-environments are technically complex and therefore add to extraneous load. Separating the technical knowledge of how to use the computer interface from the actual conceptual knowledge using sequencing should reduce this load to a reasonable extent (cf. Clarke, Ayres & Sweller, 2006). The authors provide guidelines on how to use CLT in the design of e-environments and discuss what future directions can be taken to further optimize the design of such environments.

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.

Ontology-based Adaptive Dynamic e-Learning Map Planning Method for Conceptual Knowledge Learning

2016 ◽  
Vol 11 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Tsung-Yi Chen ◽  
Hui-Chuan Chu ◽  
Yuh-Min Chen ◽  
Kuan-Chun Su
Keyword(s):  
Adaptive Learning ◽  
Conceptual Knowledge ◽  
Pso Algorithm ◽  
Asynchronous Learning ◽  
Knowledge Map ◽  
Learning Needs ◽  
Adaptive Dynamic ◽  
Learning Content ◽  
E Learning ◽  
The Individual

E-learning improves the shareability and reusability of knowledge, and surpasses the constraints of time and space to achieve remote asynchronous learning. Since the depth of learning content often varies, it is thus often difficult to adjust materials based on the individual levels of learners. Therefore, this study develops an ontology-based adaptive dynamic knowledge concept e-learning mechanism that generates learning maps based on learner characteristics and guides learners effectively. To achieve this goal, this study proposes an adaptive dynamic concept e-learning navigation procedure, designs learning models based on the adaptive learning needs of learners, and develops knowledge map model and learning map model. Finally, this study designs adaptive dynamic concept learning map-planning algorithms based on the particle swarm optimization (PSO) algorithm. The learning maps generated by these algorithms meet the dynamic needs of learners by continually adjusting and modifying the learning map throughout the learning process. Adapting the adaptive learning content according to the dynamic needs of learners allows learners to receive more instruction in a limited period.

Ontology-Based Adaptive Dynamic E-Learning Map Planning Method for Conceptual Knowledge Learning

2016 ◽  
pp. 619-640
Author(s):  
Tsung-Yi Chen ◽  
Hui-Chuan Chu ◽  
Yuh-Min Chen ◽  
Kuan-Chun Su
Keyword(s):  
Adaptive Learning ◽  
Conceptual Knowledge ◽  
Pso Algorithm ◽  
Asynchronous Learning ◽  
Knowledge Map ◽  
Learning Needs ◽  
Adaptive Dynamic ◽  
Learning Content ◽  
E Learning ◽  
The Individual

E-learning improves the shareability and reusability of knowledge, and surpasses the constraints of time and space to achieve remote asynchronous learning. Since the depth of learning content often varies, it is thus often difficult to adjust materials based on the individual levels of learners. Therefore, this study develops an ontology-based adaptive dynamic knowledge concept e-learning mechanism that generates learning maps based on learner characteristics and guides learners effectively. To achieve this goal, this study proposes an adaptive dynamic concept e-learning navigation procedure, designs learning models based on the adaptive learning needs of learners, and develops knowledge map model and learning map model. Finally, this study designs adaptive dynamic concept learning map-planning algorithms based on the particle swarm optimization (PSO) algorithm. The learning maps generated by these algorithms meet the dynamic needs of learners by continually adjusting and modifying the learning map throughout the learning process. Adapting the adaptive learning content according to the dynamic needs of learners allows learners to receive more instruction in a limited period.

Exploiting trust into e-learning: adding reliability to learning paths

2009 ◽  
Vol 1 (4) ◽  
pp. 253 ◽  
Author(s):  
V. Carchiolo ◽  
D. Correnti ◽  
A. Longheu ◽  
M. Malgeri ◽  
G. Mangioni
Keyword(s):  
E Learning ◽  
Learning Paths
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