CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Measuring self-regulated learning and online learning events to predict student academic performance
Authors
Jitka Vaculíková
Publication date
31 January 2018
Publisher
Masarykova univerzita, Filozofická fakulta
Abstract
The aim of this study is to identify whether the combination of self-reported data that measure self-regulated learning (SRL) and computer-Assisted data that capture student engagement with an online learning environment could be used to predict student academic achievement. Personally engaged study strategies focused on deep-level learning, the process of taking control, and the evaluation of students' own learning characterize SRL. Diverse theories on how students benefit from SRL underline its positive impact on student academic outcomes. Similarly, there is no doubt that the future trend in education leans towards the integration of technolog y into teaching in order to exploit its full potential. To benefit from both approaches, a combination of self-reported data and detailed online learning events obtained from an online learning environment were investigated in relation to their ability to predict student academic achievement. A case study of 54 university students enrolled in a blended-learning course showed that of the tested SRL variables and observed learning activities, student interaction with auxiliary materials that were part of the course helped to predict academic outcomes. Despite the relatively low ability of the model to explain why some students were able to become successful learners, the presented results highlight the importance of analysing online learning events in computer-Assisted teaching and learning. © 2018 Masaryk University, Faculty of Arts. All rights reserved
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Institutional repository of Tomas Bata University Library
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:publikace.k.utb.cz:10563/1...
Last time updated on 15/08/2019
Masaryk University Journals / Časopisy Masarykovy univerzity
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:ojs.journals.muni.cz:artic...
Last time updated on 20/07/2022