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Simulation of an adaptive e-learning...

Artzi, Isac.

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Simulation of an adaptive e-learning environment: A research and development platform for online instructional designers.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Simulation of an adaptive e-learning environment: A research and development platform for online instructional designers./
Author:	Artzi, Isac.
Description:	233 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-09(E), Section: A.
Contained By:	Dissertation Abstracts International76-09A(E).
Subject:	Instructional design. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3700893
ISBN:	9781321711301

Simulation of an adaptive e-learning environment: A research and development platform for online instructional designers.
Artzi, Isac.

Simulation of an adaptive e-learning environment: A research and development platform for online instructional designers. - 233 p.

Source: Dissertation Abstracts International, Volume: 76-09(E), Section: A.

Thesis (Ph.D.)--Capella University, 2015.

This study investigates how the creation of personalized learning content types and presentation can accommodate individual learning styles of students engaged in web-based education. The study presents and assesses methods to match learning styles and content types. To that effect a software simulation was created, which can generate any number of learner profiles and attempts to match them with simulated learning objects from a repository. The simulation demonstrates and tests a matching algorithm between learning styles and learning object attributes. The study shows how the system can automatically present educational content in more appropriate media types if the students perform poorly with the current set. Thus the research presented here bridges Computer Science and Mathematics with Social Science and Instructional Design and uses methods from all disciplines cooperatively. The study establishes a framework for creating a customized learning experience for each individual learner, in terms of content and media types, which are matched to learner characteristics. In this new framework the study challenges the notion that knowledge of learning characteristics is required prior to the start of interaction with the learning environment. The learning system continuously adapts to measured changes in the learning characteristics. An exploratory pilot study with human subjects was conducted to help visualize abstract elements of the adaptive algorithm and assist in the presentation of its functionality. The pilot assessed the ability of the system to identify dynamic learning styles and its ability to match educational content accordingly. The pilot was used as a qualitative tool to help improve and refine the algorithm, which in turn was used to simulate a large-scale experiment, otherwise not feasible with human subjects. This study can advance our thinking about learning styles and customized learning environments. The final outcomes are: a simulation platform for instructional designers, which generates statistical information about the subject being researched; a new theory about learning styles which challenges existing ones; mathematical proofs of all algorithms; and statistical treatment of all research questions. The cross-disciplinary nature of this research - merging elements of Computer Science, Mathematics, and Instructional Design dictated that elements from all three disciplines were used, thus making this a much more robust study than one using an Instructional Design perspective only.

ISBN: 9781321711301Subjects--Topical Terms:

3172279
Instructional design.

Simulation of an adaptive e-learning environment: A research and development platform for online instructional designers.
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245 1 0 $a Simulation of an adaptive e-learning environment: A research and development platform for online instructional designers.
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500 $a Source: Dissertation Abstracts International, Volume: 76-09(E), Section: A.
500 $a Adviser: James McDermott.
502 $a Thesis (Ph.D.)--Capella University, 2015.
520 $a This study investigates how the creation of personalized learning content types and presentation can accommodate individual learning styles of students engaged in web-based education. The study presents and assesses methods to match learning styles and content types. To that effect a software simulation was created, which can generate any number of learner profiles and attempts to match them with simulated learning objects from a repository. The simulation demonstrates and tests a matching algorithm between learning styles and learning object attributes. The study shows how the system can automatically present educational content in more appropriate media types if the students perform poorly with the current set. Thus the research presented here bridges Computer Science and Mathematics with Social Science and Instructional Design and uses methods from all disciplines cooperatively. The study establishes a framework for creating a customized learning experience for each individual learner, in terms of content and media types, which are matched to learner characteristics. In this new framework the study challenges the notion that knowledge of learning characteristics is required prior to the start of interaction with the learning environment. The learning system continuously adapts to measured changes in the learning characteristics. An exploratory pilot study with human subjects was conducted to help visualize abstract elements of the adaptive algorithm and assist in the presentation of its functionality. The pilot assessed the ability of the system to identify dynamic learning styles and its ability to match educational content accordingly. The pilot was used as a qualitative tool to help improve and refine the algorithm, which in turn was used to simulate a large-scale experiment, otherwise not feasible with human subjects. This study can advance our thinking about learning styles and customized learning environments. The final outcomes are: a simulation platform for instructional designers, which generates statistical information about the subject being researched; a new theory about learning styles which challenges existing ones; mathematical proofs of all algorithms; and statistical treatment of all research questions. The cross-disciplinary nature of this research - merging elements of Computer Science, Mathematics, and Instructional Design dictated that elements from all three disciplines were used, thus making this a much more robust study than one using an Instructional Design perspective only.
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