Using Concept Maps to Facilitate Deep Learning in a Third Year Geomatics Engineering Course release_c4yrvcwqeva5zkcdhdiwwi4hr4

by Elena Rangelova

Published in Proceedings of the Canadian Engineering Education Association (CEEA) by Queen's University Library.

(2018)

Abstract

This paper examines the use of concept maps (a graphical tool used for organizing structured information) to assist students in learning abstract and difficult concepts in a third year geomatics engineering course. Students often describe the material in this course as "difficult to understand" and "it contains lots of complex concepts". Therefore, their learning cannot always be described as deep learning; rather, students tend to resort to surface learning in order to deal with the high complexity of the course material. The term deep learning originates from the fundametal work by Marton and Säljö, who investigate how students respond to academic tasks. Engaging in deep learning means that a student is able to organize new information in a structured way and link it to previous knowledge.
In application/xml+jats format

Archived Files and Locations

application/pdf  176.5 kB
file_lbsq3n7pmng57kfyawljovobxu
web.archive.org (webarchive)
ojs.library.queensu.ca (publisher)
Read Archived PDF
Archived
Type  article-journal
Stage   published
Date   2018-12-02
Proceedings Metadata
Open Access Publication
Not in DOAJ
In ISSN ROAD
Not in Keepers Registry
ISSN-L:  2371-5243
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: e6da9831-6539-4eba-bfaa-b530444190c9
API URL: JSON