As David says in his blog about our Indicators project, we have been taking a what, why and how approach. The ‘what’ is the identification of a particular visualization or correlation of what has happened in learning and teaching. The ‘why’ is an in-depth analysis into why that correlation exists. The ‘how’ is figuring out how this correlation can be harnessed or enacted upon for the betterment of learning and teaching. David’s blog post has some excellent suggestions for how academic analytics can be used for this.
So far the Indicators project has been looking for interesting correlations within the data captured by learning management systems (LMS). This has been achieved by correlating LMS data with student grades across very large sample sizes to see what patterns emerge between passing and failing students. While this aspect of the research is still in its infancy, there are another couple of areas related to academic analytics that require some further research. These areas relate to a holistic approach to data analysis by universities and the ethical considerations of collecting and using this data.
Universities typically utilize a range of IT systems that contain information that is potentially relevant to learning and teaching and CQUniversity is no exception. Most universities have systems such as student information systems, enrolment systems, human resource systems, library systems, LMS, student portals and financial systems. While there have only been limited, ad-hoc attempts to incorporate data from these systems into the learning and teaching context, there is the potential of the various university IT systems combining to benefit learning and teaching.
The combined data captured by various systems builds a detailed picture of the activities students, instructors, service areas and the institution as a whole undertake. (Dawson, Heathcoate, & Poole, 2010)
Essentially, student interactions with IT systems can be tracked from the time they enroll right through to their graduation and even after they graduate through graduate destination surveys and alumni services. This potentially provides the institution, and possibly the students, with a wealth of data that can be used to analyse and improve learning and teaching. As Dawson et al. say:
Student ICT interactions could be used to assist the student in getting back on task by suggesting context-sensitive support resources and benchmarking performance and level of engagement with peers. (Dawson, et al., 2010)
Web analytics provide an incredible opportunity for educators to receive helpful information regarding their students’ usage and behaviour patterns – on a scale that has the potential to transform the entire industry. (Rogers, R., & Pond, 2010)
So there appears little doubt that better utilizing available data sources by universities has the potential to inform and improve learning and teaching even beyond the scale considered by the Indicators project. But what are there any ethical issues that need to be considered with what some would deem to be a very ‘big brother’ approach?
I have touched on this before in a previous blog posting and David expanded upon this in a recent post of his. Seemingly related to the ethical concerns around academic analytics is confidentiality, anonymity, disclosure and informed consent, all of which, are concepts at the core of ethical governance in the social sciences (Eynon, Fry, & Schroeder, 2008). The following paragraph essentially represents what I think is a ‘real world’ analogy of academic analytics.
Some retail establishments spend considerable time and money on tracking customer activity within their stores in much the same way as academic analytics proposes within higher education. They have used video cameras to track and monitor customer movement throughout their stores so they can make better use of their retail space and staff (Girgensohn, Shipman, & Wilcox, 2008). Customers can be individually tracked and their meanderings through the store’s physical space can be plotted, recorded and aggregated for correlations or patterns. It has been said that the use of databases and hidden surveillance such as this, has heightened the public’s sensitivity to potential research abuses (Kirkup & Carrigan, 2000).
The privacy concerns relating to the types of observation mentioned in this post are categorised by:
- Informational privacy is the expectation that certain personal information should not be divulged.
- Expressive privacy relates to freedom from coercion and discrimination when making personal decisions.
- Accessibility privacy relates to physical surveillance and whether private or public surveillance might lead to fear, distraction or inhibition.
Note that there are additional arguments around private and public spaces on the Internet. Generally, a post to a public forum is deemed to be a public performance in regards to questions about privacy (Kitchin, 2003). Because LMS are typically only available for students in a particular institution, they could probably be deemed to be private spaces. Although with tools like RSS in and out, BIM and so forth this is not as clear as it used to be.
It would appear to me at first that there are ethical concerns relating to academic analytics relating to the collection (observation) and use of data captured by universities. While these concerns are, perhaps, minor when compared to the business world, they are real concerns that need to be addressed. This is especially important as the growth of institutions seeking advantage through the use of academic analytics is accelerating and there appears to be a gap in the research relating to ethical the considerations of academic analytics.
Dawson, S., Heathcoate, L., & Poole, G. (2010). Harnessing ICT potential. The adoption and analysis of ICT systems for enhancing the student learning experience. International Journal of Educational Management, 24(2), 12.
Eynon, R., Fry, J., & Schroeder, R. (2008). The ethics of internet research. In N. Fielding, R. M. Lee & G. Blank (Eds.), The handbook of online research methods (pp. 18). London: SAGE Publications.
Girgensohn, A., Shipman, F., & Wilcox, L. (2008). Determining activity patterns in retail spaces through video analysis. Paper presented at the MM’ 08.
Kirkup, M., & Carrigan, M. (2000). Video surveillance research in retailing: Ethical issues. Internation Journal of Retail and Distribution Management, 28(11), 10.
Kitchin, H. A. (2003). The tri-council policy statement and research in cyberspace: Research ethics , the Internet and revising a ‘living document’. Journal of Academic Ethics, 1, 21.
Rogers, P. C., R., M. M., & Pond, S. (2010). The use of web analytics in the design and evaluation of distance education. In G. Veletsianos (Ed.), Emerging Technologies in Distance Education (pp. 9): Athabasca University