CITE Open Conference Systems, Engaging Learning & Empowering Change

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Enhancing teaching through learning analytics
paula hodgson, sonia cheung

Last modified: 2018-04-18

Abstract


Learning analytics focuses on learners and their learning behaviour, and data from course management and student information systems can be collected to identify factors that may contribute student success. Research has shown that student data sets can be used to develop models for analysis and visualization to reveal patterns in teaching and learning processes in programs. It is believed that providing students tools to check their activities and grades against an anonymous summary of their peers may raise underperforming students’ awareness and motivation for help seeking. Such use of analytics for intervention is based on a belief that student’s self-efficacy and self-regulated learning is important to make a difference in student performance. Alternatively, learning analytics can provide teachers an opportunity to describe student learning behaviour within a learning management system (LMS) and enable them to change how they design and teach their courses. This implies that learning analytics may affect how students learn and teacher’s responsive actions.

Learning analytics is data driven and three types of data gathering can provide teachers information for learning design in an online learning environment. The first type of data is user tracking where the learning management system captures what students do over time. Measures from the LMS data include access to course materials, number of logins, and the messages created. Another type is social network analysis where student interaction in an online activity can be tracked to show who are central to the connection or isolated. The third is text analytics that students’ created content are subject to content analysis to find out their use of key concepts. It was pointed out that it is necessary for identifying meaning learning behaviours contextually relevant to the course design and learning environment. More recently studies have been conducted to investigate students’ expectations towards features of learning analytics system.

In the context of the Chinese University of Hong Kong, the 4-year curriculum review presents an opportunity for a data driven review of the new curriculum. The project aims to identify factors that impact on student success in the 4-year curriculum and to use this information to develop measures and reporting systems for student support. Data from the LMS will be discussed with teachers, using Blackboard Analytics or tools developed for learning analytics. Apart from quantitative analysis of data sets, the project will interview teachers, academic advisors, program leaders and alumni to gather recommendations for improving the curriculum and feedback on what support would have been useful for student learning.