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Sequence generation for learning: a transformation from past to future

Sequence generation for learning: a transformation from past to future The purpose of this paper is to identify the different sequence generation techniques for learning, which are applied to a broad category of personalized learning experiences. The papers have been classified using different attributes, such as the techniques used for sequence generation, attributes used for sequence generation; whether the learner is profiled automatically or manually; and whether the path generated is dynamic or static.Design/methodology/approachThe search for terms learning sequence generation and E-learning produced thousands of results. The results were filtered, and a few questions were answered before including them in the review. Papers published only after 2005 were included in the review.FindingsThe findings of the paper were: most of the systems generated non-adaptive paths. Systems asked the learners to manually enter their attributes. The systems used one or a maximum of two learner attributes for path generation.Originality/valueThe review pointed out the importance and benefits of learning sequence generation systems. The problems in existing systems and future areas of research were identified which will help future researchers to pursue research in this area. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Information and Learning Technology Emerald Publishing

Sequence generation for learning: a transformation from past to future

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2056-4880
DOI
10.1108/ijilt-01-2019-0014
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to identify the different sequence generation techniques for learning, which are applied to a broad category of personalized learning experiences. The papers have been classified using different attributes, such as the techniques used for sequence generation, attributes used for sequence generation; whether the learner is profiled automatically or manually; and whether the path generated is dynamic or static.Design/methodology/approachThe search for terms learning sequence generation and E-learning produced thousands of results. The results were filtered, and a few questions were answered before including them in the review. Papers published only after 2005 were included in the review.FindingsThe findings of the paper were: most of the systems generated non-adaptive paths. Systems asked the learners to manually enter their attributes. The systems used one or a maximum of two learner attributes for path generation.Originality/valueThe review pointed out the importance and benefits of learning sequence generation systems. The problems in existing systems and future areas of research were identified which will help future researchers to pursue research in this area.

Journal

The International Journal of Information and Learning TechnologyEmerald Publishing

Published: Sep 17, 2019

Keywords: Learning objects; Concept maps; Learning path; Automatic sequencing of objects; Sequence generation; Soft computing approaches; Bio-inspired computation

References