RMIT University
Browse

Improving quality of feedback using a technology-supported learning system

conference contribution
posted on 2024-10-31, 20:02 authored by Tass Grigoriou, Christopher Cheong, France Cheong
Feedback is a crucial element in the learning process and it can be quite challenging to provide the right feedback upon which students can act upon to improve their learning. Providing proper feedback is even more challenging in contemporary educational environments due the presence of a number of factors such as: changing expectations of students, variety of teaching modes, etc. Our objective in this study is to develop a scalable technology-enhanced learning approach that provides quality and actionable feedback to students which they can use it to improve their learning. We provide a detailed explanation of the pedagogical foundations of our approach as well as the design and implementation of the technological supporting system. 142 students across three undergraduate IS- related courses were exposed to the new learning approach over a period of 12 weeks. The outcome of this was then evaluated using a retrospective pre-test on several dimensions of feedback. In this paper, we only report the findings related to the quality of feedback. The data is then analyzed using descriptive statistics and the Wilcoxon Signed Rank Test. Results showed that the approach was promising as students found the feedback to be relevant, adequate, timely and generally better than in other courses.

History

Start page

1

End page

15

Total pages

15

Outlet

Pacific Asia Conference on Information Systems (PACIS 2015)

Name of conference

PACIS 2015

Publisher

Association for Information Systems

Place published

Singapore

Start date

2015-07-05

End date

2015-07-09

Language

English

Copyright

© 2015 the authors

Former Identifier

2006062638

Esploro creation date

2020-06-22

Fedora creation date

2016-06-16

Usage metrics

    Scholarly Works

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC