Consider the reasons for the use of learning analytics that are given in these papers, and reflect on them in relation to the recommendations you and others made in Activity 3 and the problems that you thought learning analytics might be able to address.

Norris et al. (2009) describes new ways of combining data using Web 2.0. Although this terminology is now outdated, the concept of pulling many data sources together through the web has come to pass. Campbell et.al (2007) discusses the importance of improving educational attainment to prevent the US from falling behind other nations. Predictive modelling can now economically be applied to education settings with the need for teams of statisticians or super computers. This could include geospatial data as I recommended in the last activity. This would not have been possible around 2009 without large computing facilities.

This type of described visualisation is becoming more common. There is great interest in dashboards of up to the minute data in our institution. As [Norris] goes on to predict, more skilled staff will be required to choose the correct data sources to gain meaningful results. Therefore the sources of data and potential actions leading from the results need to be carefully considered.

There are obviously caveats to the provided information. There may be an intervention flagged by analytics data which could be a ‘false alarm’. Extended absence or non-participation in the VLE could be suggested for students who are doing well and have made arrangements to keep up out-with the recorded data. There is also the real possibility that a change in achievement in a particular course is simply a result of the capabilities of the particular cohort. It may be that the participation is recorded on social media

I have attended several online seminars on analytics focussed on the FE sector. One such session provided by Glantis (2020) presented active dashboard information on KPIs, attendance and similar data sources. This commercial offering helps institutions create meaningful visualisations using their previous experience in the sector.

Netlytic, described by Poquet et al. (2020) analyses text and social networks to create visualisations of online conversations. This platform can be used as an instruction or research tool. There are example assignments provided to give students with experience of analysing vast amounts of social media data without the need for programming skills.

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References:

Campbell, J.P., DeBlois, P.B. and Oblinger, D.G. (2007) ‘Academic analytics: a new tool for a new era’, Educause Review, vol. 42, no. 4, pp. 40–57 [Online]. Available at http://www.educause.edu/ ero/ article/ academic-analytics-new-tool-new-era

Glantus (2020) Data at your fingertips in time for the new academic year. (Online) https://app.livestorm.co/glantus/data-at-your-fingertips-in-time-for-the-new-academic-year/live (Accessed: 3rd July 2020).

Norris, D., Baer, L. and Offerman, M. (2009) ‘A national agenda for action analytics’, paper presented at the National Symposium on Action Analytics, 21–23 September 2009, St Paul, Minnesota, USA (Online). Available at http://lindabaer.efoliomn.com/ uploads/ settinganationalagendaforactionanalytics101509.pdf (Accessed 3rd July 2020)

Poquet, S. And Chen B. (2020), Analyzing Learning and Teaching through the Lens of Networks. (Online), Available at https://www.youtube.com/watch?v=gEd95ILsBjc (Accessed 22nd April 2020)