A Framework for Developing and Implementing u-Learning Models

2010 ◽  
pp. 310-323
Author(s):  
Des Casey ◽  
Janet Fraser
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
System Architecture ◽  
Learning Model ◽  
Learning Models ◽  
Learning Content

The advent of u-learning environments requires the development of appropriate u-learning models to inform the use of such environments. As there is no single u-learning model to suit all environments and learning situations, there is a need to develop a methodology for developing models appropriate to various environments and situations. This chapter outlines such a methodology as a useful framework on which to base the derivation of particular models for specific situations. The study then illustrates the use of this methodology to derive a particular model: a task-based u-learning model, incorporating well-bounded learning content. Following this, the study proposes a system architecture to embody this derived u-learning model, and, then describes the implementation of this architecture through the development and deployment of the Walkabout u-Learning Environment.

U-Learning

2011 ◽  
pp. 381-394 ◽  
Author(s):  
Des Casey
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Mobile Devices ◽  
System Architecture ◽  
Learning Model ◽  
Human Learning ◽  
Ubiquitous Learning ◽  
Proposed Model ◽  
E Learning ◽  
Structured Knowledge

Increasingly powerful, handheld, networked devices provide an opportunity for educators to explore and implement ubiquitous learning (u-learning) environments through the extension of e-learning environments to include applications and services delivered to mobile devices. U-learning environments should be developed with both an understanding of human learning in general, and the learning of formally structured knowledge through de-structured u-learning environments. This study proposes a learning model for u-learning environments and a system architecture based on this model. The study outlines the features of the Walkabout u-Learning Environment, which has been implemented and trialled in accordance with the proposed model and architecture.

scholarly journals Logical word learning: The case of kinship

2019 ◽  
Author(s):  
Francis Mollica ◽  
Steven T. Piantadosi
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Conceptual Development ◽  
Learning Model ◽  
Developmental Model ◽  
Learning Trajectories ◽  
Model Framework ◽  
Model Simulations ◽  
Linguistic Data ◽  
Kinship Term

In this paper, we propose a framework for conceptual development through the lens of program induction. We implement this framework to model the acquisition of kinship term concepts, resulting in the first formal developmental model for kinship acquisition. We demonstrate that our model can learn several kinship systems of varying complexity using cross-linguistic data from English, Pukapuka, Turkish and Yanomamö. More importantly, the behavioral patterns observed in children learning kinship terms, under-extension and over-generalization, fall out naturally from our learning model. We conducted interviews to simulate realistic learning environments and demonstrate that the characteristic-to-defining shift is a consequence of our learning model in naturalistic contexts containing abstract and concrete features. We use model simulations to discuss the influence of simplicity and learning environment on the order of acquisition of kinship terms, positing novel predictions for the learning trajectories of kinship terms. We conclude the paper with a discussion of how this model framework generalizes beyond kinship terms and the limitations of our model.

scholarly journals Logical word learning: The case of kinship

2021 ◽  
Author(s):  
Francis Mollica ◽  
Steven T. Piantadosi
Keyword(s):  
Learning Environment ◽  
Computational Model ◽  
Learning Environments ◽  
Conceptual Development ◽  
Learning Model ◽  
Learning Trajectories ◽  
Model Framework ◽  
Model Simulations ◽  
Linguistic Data ◽  
Kinship Term

AbstractWe examine the conceptual development of kinship through the lens of program induction. We present a computational model for the acquisition of kinship term concepts, resulting in the first computational model of kinship learning that is closely tied to developmental phenomena. We demonstrate that our model can learn several kinship systems of varying complexity using cross-linguistic data from English, Pukapuka, Turkish, and Yanomamö. More importantly, the behavioral patterns observed in children learning kinship terms, under-extension and over-generalization, fall out naturally from our learning model. We then conducted interviews to simulate realistic learning environments and demonstrate that the characteristic-to-defining shift is a consequence of our learning model in naturalistic contexts containing abstract and concrete features. We use model simulations to understand the influence of logical simplicity and children’s learning environment on the order of acquisition of kinship terms, providing novel predictions for the learning trajectories of these words. We conclude with a discussion of how this model framework generalizes beyond kinship terms, as well as a discussion of its limitations.

Applying Constructivism to Online Learning

2009 ◽  
pp. 58-73 ◽  
Author(s):  
Jennifer Lee ◽  
Lin Lin
Keyword(s):  
Online Learning ◽  
Instructional Design ◽  
Learning Environment ◽  
Learning Environments ◽  
Learning Models ◽  
Online Learning Environments ◽  
Design Models ◽  
The Past ◽  
Environment Assessment ◽  
Five Elements

Based on constructivist principles, this chapter provides a new instructional design map for online learning environments. This instructional design map includes considerations of five elements, namely, learner, knowledge, learning environment, assessment, and technology. Considerations of these elements are based on analyses of the past and existing instructional design models, online learning models, and constructive principles. Applications of the instructional design map are also discussed in the chapter.

Didactic Design Pattern

2011 ◽  
pp. 191-198
Author(s):  
Sven Wippermann
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Best Practice ◽  
Virtual Learning Environment ◽  
Social Dimension ◽  
Digital Learning ◽  
Learning Content ◽  
Continuous Feedback ◽  
Digital Learning Environments ◽  
Specific Learning

According to the theory of moderate constructivism, learning processes contain a social dimension. In this context, a continuous feedback is essential for supporting (students’) learning. Especially, digital learning environments should offer ways to give and receive feedback, because they lack the possibilities of personal interaction. The pattern presented in this chapter captures a best practice on evaluating certain aspects of the learning process in regard to the learning content and learning atmosphere. Feedback on the content is given non-anonymously in a discussion forum, whereas the learning atmosphere is rated anonymously within a virtual learning environment. The pattern captures a specific, didactic driven method within a learning environment and is therefore particularly useful for lecturers who want to give and receive feedback on specific learning topics, and lecturers who want to gain an insight in their student’s learning emotions.

SOA-Frameworks for Modular Virtual Learning Environments

2010 ◽  
pp. 277-293
Author(s):  
Fredrik Paulsson ◽  
Mikael Berglund
Keyword(s):  
Learning Environment ◽  
Web Service ◽  
Learning Environments ◽  
Virtual Learning ◽  
Learning Objects ◽  
Virtual Learning Environment ◽  
Virtual Learning Environments ◽  
Learning Object ◽  
Learning Content ◽  
Java Rmi

A general SOA framework for Virtual Learning Environments, based on the VWE Learning Object Taxonomy, is suggested in this chapter. Five basic and general services are suggested for implementation of modular Virtual Learning Environments. The design of the service framework was tested by implementation in two prototypes, using two different approaches where a Java-RMI based implementation was compared to a Web Service (SOAP) based implementation. By implementing the VWE Learning Object Taxonomy and the VWE SOA framework, the prototypes showed that a level of modularity, similar to the level of modularity of Learning Objects, could be achieved for the Virtual Learning Environment as well. Using the VWE Learning Object Taxonomy, this was accomplished by including the learning content and the Virtual Learning Environment into the same conceptual space. The comparison of the prototypes showed that the Web Service approach was preferred in favor of the Java-RMI approach. This was mainly due to platform neutrality and the use of the http-protocol. The study was supplemented by an analysis of the two approaches in relation to a third, REST-based approach.

Didactic Design Pattern

2012 ◽  
pp. 215-222
Author(s):  
Sven Wippermann
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Best Practice ◽  
Virtual Learning Environment ◽  
Social Dimension ◽  
Digital Learning ◽  
Learning Content ◽  
Continuous Feedback ◽  
Digital Learning Environments ◽  
Specific Learning

According to the theory of moderate constructivism, learning processes contain a social dimension. In this context, a continuous feedback is essential for supporting (students’) learning. Especially, digital learning environments should offer ways to give and receive feedback, because they lack the possibilities of personal interaction. The pattern presented in this chapter captures a best practice on evaluating certain aspects of the learning process in regard to the learning content and learning atmosphere. Feedback on the content is given non-anonymously in a discussion forum, whereas the learning atmosphere is rated anonymously within a virtual learning environment. The pattern captures a specific, didactic driven method within a learning environment and is therefore particularly useful for lecturers who want to give and receive feedback on specific learning topics, and lecturers who want to gain an insight in their student’s learning emotions.

Engaging Learning Models with Information and Communication Technologies in Advancing Electronic Learning

2010 ◽  
pp. 205-221
Author(s):  
Jen-Her Wu ◽  
Robert D. Tennyson ◽  
Tzyh-Lih Hsia
Keyword(s):  
Learning Environments ◽  
Information And Communication Technologies ◽  
Communication Technologies ◽  
Learning Model ◽  
Learning Models ◽  
Classroom Learning ◽  
Electronic Learning ◽  
Technology And Learning ◽  
Information And Communication ◽  
E Learning

Emerging information and communication technologies and learning models have triggered a new wave of educational innovation: electronic learning (E-learning). This study employs a hypercube innovation model to analyze the differences in technology and learning models in conventional (face-to-face) classroom learning and E-learning environments. The results of the analyses indicate that the innovation from traditional classroom learning to E-learning is radical for both the learner and instructor, leading to drastic changes in the technology and learning model. For education institutions, the technology is a fundamental change, while the learning model is reinforced. From the dynamic capability perspectives, a set of core capabilities needed for successfully exploiting E-learning is identified. These results provide insight for learners, instructors, and education institutions for enhancing their understanding of E-learning innovation and provide guidelines to help E-learning stakeholders adapt from conventional classrooms to E-learning environments.

Development of a Constructivist and Connectivist Learning Model for Undergraduates Involving Cloud Technology in Order to Promote the Creation of Innovative Education

2021 ◽  
Vol 12 (4) ◽  
pp. 63-67
Author(s):  
Kanokrat Jirasatjanukul ◽  
◽  
Nuttakan Pakprod ◽  
Julaporn Khammungkul
Keyword(s):  
Information Technology ◽  
Standard Deviation ◽  
Learning Environment ◽  
Educational Innovation ◽  
Learning Model ◽  
Learning Models ◽  
Cloud Technology ◽  
Purposive Sampling ◽  
The Creation ◽  
Innovative Education

The objectives of this research are as follows: 1) to analyze the constructivist and connectivist framework involving cloud technology; 2) to develop a constructivist and connectivist learning model involving cloud technology; and 3) to evaluate the constructivist and connectivist learning model involving cloud technology for undergraduates as a means of promoting innovative education. The samples consisted of 7 experts in the design of learning models and in the use of information technology and communications who were chosen by purposive sampling and who had at least 5 years’ relevant experience. The data were analyzed by mean and standard deviation. The evaluation results revealed that the constructivist and connectivist learning model involving cloud technology for undergraduates as a means of promoting innovative education was at the highest level of overall feasibility (x = 4.68, S.D. = 0.47). The implication of this study points out the feasibility of learning model to allow promotion of learning environment to encourage educational innovation and the development of innovation skills on the part of the students.

SOA-Frameworks for Modular Virtual Learning Environments

2012 ◽  
pp. 419-435
Author(s):  
Fredrik Paulsson ◽  
Mikael Berglund
Keyword(s):  
Learning Environment ◽  
Web Service ◽  
Learning Environments ◽  
Virtual Learning ◽  
Learning Objects ◽  
Virtual Learning Environment ◽  
Virtual Learning Environments ◽  
Learning Object ◽  
Learning Content ◽  
Java Rmi

A general SOA framework for Virtual Learning Environments, based on the VWE Learning Object Taxonomy, is suggested in this chapter. Five basic and general services are suggested for implementation of modular Virtual Learning Environments. The design of the service framework was tested by implementation in two prototypes, using two different approaches where a Java-RMI based implementation was compared to a Web Service (SOAP) based implementation. By implementing the VWE Learning Object Taxonomy and the VWE SOA framework, the prototypes showed that a level of modularity, similar to the level of modularity of Learning Objects, could be achieved for the Virtual Learning Environment as well. Using the VWE Learning Object Taxonomy, this was accomplished by including the learning content and the Virtual Learning Environment into the same conceptual space. The comparison of the prototypes showed that the Web Service approach was preferred in favor of the Java-RMI approach. This was mainly due to platform neutrality and the use of the http-protocol. The study was supplemented by an analysis of the two approaches in relation to a third, REST-based approach.

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