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Digital learning approaches in an intermediate-level computer science course

Digital learning approaches in an intermediate-level computer science course Digital learning has become a global trend. Partly or fully automatic learning systems are integrated into education in schools and universities on a previously unseen scale. Learning systems have a lot of potential for re-education, life-long learning and for increasing access to educational resources. Learning systems create massive amounts of data about learning behaviour. Analysing that data for educational decision making has become an important track of research. The purpose of this paper is to analyse data from an intermediate-level computer science course, which was taught to 141 students in spring 2018 at University of Turku, Department of Future Technologies, Finland.Design/methodology/approachThe available variables included number of submissions, submission times, variables of groupwork and final grades. Associations between these variables were looked at to reveal patterns in students’ learning behaviour.FindingsIt was found that time usage differs per different grades so that students with grade 4 out of 5 used most time. Also, it was found that studying at night is connected to weaker learning outcomes than studying during daytime. Several issues in relation to groupwork were revealed. For example, associations were found between prior skills, preference for individual vs groupwork, and course learning outcomes.Research limitations/implicationsThe research was limited by the domain of available variables, which is a common limitation in learning analytics research.Practical implicationsThe practical implications include important ideas for future research and interventions in digital learning.Social implicationsThe importance of research on soft skills, social skills and collaboration is highlighted.Originality/valueThe paper points a number of important ideas for future research. One important observation is that some research questions in learning analytics need qualitative approaches, which need to be added to the toolbox of learning analytics research. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Information and Learning Technology Emerald Publishing

Digital learning approaches in an intermediate-level computer science course

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2056-4880
DOI
10.1108/ijilt-06-2018-0079
Publisher site
See Article on Publisher Site

Abstract

Digital learning has become a global trend. Partly or fully automatic learning systems are integrated into education in schools and universities on a previously unseen scale. Learning systems have a lot of potential for re-education, life-long learning and for increasing access to educational resources. Learning systems create massive amounts of data about learning behaviour. Analysing that data for educational decision making has become an important track of research. The purpose of this paper is to analyse data from an intermediate-level computer science course, which was taught to 141 students in spring 2018 at University of Turku, Department of Future Technologies, Finland.Design/methodology/approachThe available variables included number of submissions, submission times, variables of groupwork and final grades. Associations between these variables were looked at to reveal patterns in students’ learning behaviour.FindingsIt was found that time usage differs per different grades so that students with grade 4 out of 5 used most time. Also, it was found that studying at night is connected to weaker learning outcomes than studying during daytime. Several issues in relation to groupwork were revealed. For example, associations were found between prior skills, preference for individual vs groupwork, and course learning outcomes.Research limitations/implicationsThe research was limited by the domain of available variables, which is a common limitation in learning analytics research.Practical implicationsThe practical implications include important ideas for future research and interventions in digital learning.Social implicationsThe importance of research on soft skills, social skills and collaboration is highlighted.Originality/valueThe paper points a number of important ideas for future research. One important observation is that some research questions in learning analytics need qualitative approaches, which need to be added to the toolbox of learning analytics research.

Journal

The International Journal of Information and Learning TechnologyEmerald Publishing

Published: Sep 17, 2019

Keywords: Learning analytics; Digital learning; Computer science education

References