M.A.C. A Hypermedia System for Learning Electronics

2005 ◽  
pp. 287-296
Author(s):  
A. Salaverría ◽  
M. J. Moure ◽  
M. D. Valdés ◽  
E. Mandado ◽  
M. Pérez Cota
Keyword(s):  
Hypermedia System

A Schematic Entry Drawing Capability in a Linearised Hypermedia System

1994 ◽  
Vol 13 (5) ◽  
pp. 293-303 ◽  
Author(s):  
I. D. Benest
Keyword(s):  
Hypermedia System

Enhancing Mechanism Simulation in Engineering Education by a Hypermedia System

1996 ◽  
Author(s):  
E. Giannotti ◽  
C. Galletti
Keyword(s):  
Engineering Education ◽  
Engineering Students ◽  
Mechanics Of Machines ◽  
Educational Tools ◽  
Laboratory Activities ◽  
Simulation Based ◽  
Hypermedia System

Abstract An attempt to focus problems encountered in teaching mechanics of machines to engineering students has stimulated the authors of this paper to develop specific software, lectures and laboratory activities with different emphases and relations between the teacher’s and students’ tasks. A multi-year study has been made to test if and how simulation, based on a hypermedia system, can be used in developing educational tools for this new courseware. As a result, new tools for learning mechanics of machines are proposed.

Personalized Guided Routes in an Adaptive Evolutionary Hypermedia System

2003 ◽  
pp. 196-207 ◽  
Author(s):  
Nuria Medina-Medina ◽  
Fernando Molina-Ortiz ◽  
Lina García-Cabrera ◽  
José Parets-Llorca
Keyword(s):  
Hypermedia System

A Learner Model Based on Bayesian Networks in Adaptive Educational Hypermedia Systems

2018 ◽  
pp. 65-94
Keyword(s):  
Computational Modeling ◽  
Bayesian Networks ◽  
Educational System ◽  
Probabilistic Framework ◽  
Adaptive Hypermedia ◽  
Learner Modeling ◽  
Learner Model ◽  
Learning Path ◽  
Hypermedia System ◽  
Probabilistic Context

The work presented in this chapter lies within learner modeling in an adaptive educational system construed as a computational modeling of the learner. All actions of the learner in a learning situation on an adaptive hypermedia system are not limited to valid or invalid actions (true and false), but they are a set of actions that characterize the learning path of formation. Thus, one cannot represent the information from the system of each learner using relative data. It requires putting the work in a probabilistic context due to the changes in the learner model information during formation. In this chapter, the authors propose to use Bayesian networks as a probabilistic framework to resolve the issue of dynamic management and update of the learner model. The experiments and results presented in this work are arguments in favor of the hypothesis and can also promote reusing the modeling obtained through different systems and similar modeling situations.

An Adaptive System for Retrieval and Composition of Learning Objects

2011 ◽  
Vol 2 (4) ◽  
pp. 42-59
Author(s):  
Burasakorn Yoosooka ◽  
Vilas Wuwongse
Keyword(s):  
Adaptive System ◽  
Reference Model ◽  
Learning Objects ◽  
Preliminary Evaluation ◽  
Learner Characteristics ◽  
New Approach ◽  
Automatic Retrieval ◽  
Hypermedia System ◽  
Content Object ◽  
Object Reference

This paper proposes a new approach to automatic retrieval and composition of Learning Objects (LOs) in an Adaptive Educational Hypermedia System (AEHS) using multidimensional learner characteristics to enhance learning effectiveness. The approach focuses on adaptive techniques in four components of AEHS: Learning Paths, LO Retrieval, LO Sequencing, and Examination Difficulty Levels. This approach has been designed to enable the adaptation of rules to become generic. Hence, the application to various domains is possible. The approach dynamically selects, sequences, and composes LOs into an individual learning package based on the use of domain ontology, learner profiles, and LO metadata. The Sharable Content Object Reference Model is employed to represent LO metadata and learning packages in order to support LO sharing. The IMS Learner Information Package Specification is used to represent learner profiles. A preliminary evaluation of the developed system indicates the system’s effectiveness in terms of learners’ satisfaction.

Web Site Usability

2011 ◽  
pp. 1078-1083
Author(s):  
Louis K. Falk ◽  
Hy Sockel
Keyword(s):  
Task Performance ◽  
Error Rate ◽  
Web Sites ◽  
Web Site ◽  
Model Development ◽  
Web Usability ◽  
Self Image ◽  
Subjective Satisfaction ◽  
Hypermedia System

Strictly speaking, the term usability has evolved from one of use to also include design and presentation aspects. A large amount of research has been conducted using this wider definition. These studies include everything from model development (Cunliffe, 2000) to personal self image on Web sites (Dominick, 1999) to the purpose of a Web site (Falk, 2000), and to Web site effectiveness (Briggs & Hollis, 1997). Ultimately, these topics are related to usability and the success a Web site enjoys. The construct of usability covers a range of topics. This paper specifically addresses Web usability from the perspective of how easy a system is to learn, remember and use (Rosen, Purinton & Lloyd, 2004). The system features should emphasize subjective satisfaction, low error rate and high task performance (Calongne, 2001). In this regard, usability is a combination of the underlying (hypermedia) system engine and the contents and structure of the document, and how these two elements fit together (Lu & Yeung, 1998).

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