The key theme for this research is “Participation through Learning Technology”, specifically using technology to work in partnership with learners, and helping to foster student engagement with new technologies.
In particular we explore the use of technology enhanced learning and teaching (TELT) resources as part of the module ‘Communications Technology’ (TM355). This is a print-based third level distance learning module that students tend to study towards the end of their degree. The module covers subjects such as digital data coding and access networks. The TELT resources were specifically designed to help students work though some of the difficult concepts covered within the module, and are designed to supplement the text.
During the presentation there will be a short demonstration detailing the different kinds of usage patterns of technology enhanced learning and teaching (TELT) resources that have been reviewed via learning analytics. Analytics were used to monitor and explore students’ use of the TELT resources throughout the duration of the module.
There are two aspects to consider; the students themselves are using TELT resources, and the researchers are engaging with learning analytics. It is hoped that analytics can used to best effect, helping students achieve their maximum potential. To supplement the quantitative data gathered via the analytics platform, further insight into student behaviour has been sought via student interviews.
Session content: evaluation and reflection
Although there has been much research in the area of data analytics in recent years (e.g. Shum and Ferguson 2012), there are questions regarding which analytic methodologies can be most effective in informing higher education teaching and learning practices (Gibson and de Freitas, 2016).
This study explores the use of specific computer aided learning and teaching (TELT) resources on the module ‘Communications Technology’ (TM355), using a specific analytics tool Analytics for Action (A4A). A4A can provide detail of how students are engaging with specific online materials, with the aim to highlight the kind of interventions that module teams can make to support students.
The prompt for this particular study was students’ relatively poor performance on a particular exam question. Using A4A it could be seen that the associated TELT resource had not been extensively used, either during the module or for revision. A key hypothesis is that those students who engaged with the TELT resources should have performed well on associated assessment questions.
The research questions cover two key areas; the effectiveness of the analytics tools and students’ perception of the TELT resources.
Via data analytics we can review:
• When the students engage with the TELT resources and whether this is at predicted times during the module.
• Whether students revisit the TELT resources.
Via individual student feedback we can explore:
• What motivates students to engage with TELT resources.
• Whether students understand topic more deeply as a result of using TELT resources.
• If students are deterred if the resources are too complicated/time consuming.
This project focuses on one module within the School of Computing and Communications in the STEM faculty to gain a clearer understanding on why students might, or might not, engage with TELT resources. Preliminary finding show variable usage patterns, so possible reasons for this are being investigated.
The findings should be of interest to module teams across many universities. This project will build on previous work undertaken in this area , e.g. Herodotou et al (2017) and Tempelaar et al (2017), and contribute to the wider body of knowledge in the area of data analytics.
Gibson, D. and de Freitas, S. (2016) ‘Exploratory analysis in learning analytics’ in Technology, Knowledge and Learning, 21 (1). pp. 5-19.
Herodotou, C., Gilmour, A., Boroowa, A., Rienties, B., Zdrahal, Z. and Hlosta, M. (2017) ‘Predictive modelling for addressing students’ attrition in Higher Education: The case of OU Analyse’ in CALRG Annual Conference 2017, 14-16 Jun 2017, The Open University, Milton Keynes, UK.
Shum, S. B., and Ferguson, R. (2012) ‘ Social learning analytics’ in Educational Technology and Society,15, 3–26
Tempelaar, D., Rienties, B. and Nguyen, Q. (2017) ‘ Towards actionable learning analytics using dispositions’ in IEEE Transactions on Learning Technologies, 10(1) pp. 6–16.
Resources for participants
Rich Goodman joined the session Analytics for tracking student engagement [18-174] 1 year, 10 months ago
Marieke Guy joined the session Analytics for tracking student engagement [18-174] 1 year, 10 months ago
Sue Watling joined the session Analytics for tracking student engagement [18-174] 1 year, 11 months ago