Dr Jingyun Wang jingyun.wang@durham.ac.uk
Assistant Professor
Ontology Technique and Meaningful Learning Support Environments
Wang, Jingyun
Authors
Contributors
Demetrios Sampson
Editor
J. Michael Spector
Editor
Dirk Ifenthaler
Editor
Pedro Isaías
Editor
Stylianos Sergis
Editor
Abstract
In this chapter, we present two ontology-driven learning support systems, which intend to provide meaningful learning environment: a customizable language learning support system (CLLSS) and a visualization learning support system for e-book users (VSSE). CLLSS was built to provide an interface for the learning objects arrangement which displays the visual representation of knowledge points and their relations. The intention underlying the development of CLLSS is to encourage instructors to orient their teaching materials to specific knowledge points and even directly to relations between knowledge points. With these orientations, CLLSS is able to provide an environment in which learners can readily distinguish between related knowledge points. In the other hand, VSSE is designed and developed to help e-book learners to effectively construct their knowledge frameworks. Making use of e-book logs, VSSE supports not only meaningful receptive learning but also meaningful discovery learning. In other words, two learning modes are provided in VSSE: (a) reception comparison mode, in which learners are provided directly with complete versions of relation maps; and (b) cache-cache comparison mode, where all information concerning relations is hidden at the first stage of learning, and in the second stage learners are encouraged to actively create them.
Citation
Wang, J. (2019). Ontology Technique and Meaningful Learning Support Environments. In D. Sampson, J. M. Spector, D. Ifenthaler, P. Isaías, & S. Sergis (Eds.), Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment (215-229). Springer Verlag. https://doi.org/10.1007/978-3-030-15130-0_11
Online Publication Date | May 25, 2019 |
---|---|
Publication Date | 2019 |
Deposit Date | Sep 21, 2020 |
Publicly Available Date | Nov 8, 2021 |
Publisher | Springer Verlag |
Pages | 215-229 |
Book Title | Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment |
ISBN | 9783030151294 |
DOI | https://doi.org/10.1007/978-3-030-15130-0_11 |
Public URL | https://durham-repository.worktribe.com/output/1627427 |
Files
Accepted Book Chapter
(964 Kb)
PDF
Copyright Statement
This a post-peer-review, pre-copyedit version of a chapter published in Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-15130-0_11
You might also like
Simplifying Multimedia Programming for Novice Programmers: MediaLib and Its Learning Materials
(2024)
Presentation / Conference Contribution
Analysing Learner Behaviour in an Ontology-Based E-learning System: A Graph Neural Network Approach
(2024)
Presentation / Conference Contribution
An Adaptive Learning Support System based on Ontology of Multiple Programming Languages
(2023)
Presentation / Conference Contribution
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search