What can we learn from learning analytics? A case study based on an analysis of student use of video recordings
Abstract
Over recent years the use of lecture capture technology has become widespread in higher education. However, clear evidence of the learning benefits of this technology is limited, with contradictory findings reported in the literature. The reasons for this lack of consistent evidence may include methodological issues and differences in the context of previous studies. This paper describes a study using server log data to explore student use of video recordings quantitatively in the context of science courses at Imperial College London. The study had two aims: to understand more about the general principles that underpin a learning analytics study and to seek answers to the following specific research questions: (1) How much use is made of video recordings? (2) How does the use of recordings in a module vary over time? (3) Is the use of recordings different for different modules or subjects? (4) Is the use of recordings different for subgroups of students, for example, students with specific learning differences or English as a second language, students attaining different grades? (5) Is the use of recordings different for different types of content? Using learning analytics enabled the discovery of context-specific actionable insights: recommendations for both staff and students and ideas for further research. General conclusions were also drawn on how best to undertake learning analytics studies in order to deliver evidence and insights to improve learning and teaching.
Published: 28 December 2018
Citation: Research in Learning Technology 2018, 26: 2087 - http://dx.doi.org/10.25304/rlt.v26.2087
Downloads

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to Research in Learning Technology retain the copyright of their article and at the same time agree to publish their articles under the terms of the Creative Commons CC-BY 4.0 License (http://creativecommons.org/licenses/by/4.0/) allowing third parties to copy and redistribute the material in any medium or format, and to remix, transform, and build upon the material, for any purpose, even commercially, under the condition that appropriate credit is given, that a link to the license is provided, and that you indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.