Learning analytics and beyond

Tonight I heard an interview on RN Drive by Patricia Karvelas about how universities are tracking the online activity of students. Universities have done this for a while under the guise of what they call ‘learning analytics’ but have not really told students about it. Supposedly, the analytics that they gather on students online activity in the learning management system or via network usage will enable the universities to make better decisions in how to improve learning.

I have so many concerns about this least of all is the privacy concern. In the interview, the president of the University of Melbourne Student Union, Tyson Holloway-Clarke, points out that many students share the sentiment that:

If you’ve got nothing to hide, you’ve got nothing to be afraid of.

I find this deeply concerning as students do not even question what the information will be used for nor do they wonder whether the data collected is actually an accurate representation of their activity. If you are wondering why privacy matters yourself (perhaps you share the sentiment of having nothing to hide) you might like to view this excellent TED talk on Why Privacy Matters by Glenn Greenwald.

Not only does privacy matter, the data that is captured and the way that it is analysed for supposed learning analytics is also an inaccurate representation of student learning.

Looking across the history of our species, one sees much more experience of learning from networks of family, friends and acquaintances than of learning in formally constituted educational institutions, such as schools and universities. (Carvalho & Goodyear, 2014: p.3)

I created the following online interactive presentation, Creating the best learning environment where I discuss that in the future, we need to take back control of our identities and not let corporations, government or educational institutions control our data. Akkerman and van Eijck (in Carvalho & Goodyear, 2014: p.7) indicate that the student or learner label can even have adverse effects on participants in a learning community because the label can:

(i) obscure those aspects of their activity that are not seen as directly supportive of learning, and

(ii) render less visible the connections between their current activity and their activities and experiences outside the immediate ‘learning’ context.

One way that we could approach this is by getting rid of learning management systems and costly learning analytics that collect the wrong data anyway and give control back to the student. In this way we could be empowering students to create their own personal digital identities. David Jones kindly pointed me to an article that clearly outlines the benefits for students to create their own personal cyberinfrastructure:

[Students] would play with wikis and blogs; they would tinker and begin to assemble a platform to support their publishing, their archiving, their importing and exporting, their internal and external information connections. They would become, in myriad small but important ways, system administrators for their own digital lives. In short, students would build a personal cyberinfrastructure, one they would continue to modify and extend throughout their college career — and beyond.

In building that personal cyberinfrastructure, students not only would acquire crucial technical skills for their digital lives but also would engage in work that provides richly teachable moments ranging from multimodal writing to information science, knowledge management, bibliographic instruction, and social networking.

 A Domain of One’s Own in Wired (2012)

Having started out creating a blog reluctantly through this course, as I did not believe I wanted to add any more digital junk to the world, I have done a complete flip. I can see now how I am empowered by writing my own blog and creating my own digital identity. Why would we want this to be controlled by anyone but ourselves. I can also see how in the future beyond this course, I will continue to write using this blog and use it as my personal information management tool.

The next step is to extend my digital identity by creating a personal learning network. The following video by Alvin Trusty provides an easy 3 step way to do this:

  1. Follow the leaders
  2. Try the best stuff
  3. Write about it

By taking charge of your own online identity at least you will have the ability to guide how others might identify you when you are googled or as the Alvin Trusty video illustrated that you might become part of someone else’s personal learning network.

No Author. (2012)  A Domain of One’s Own.Wired.

Carvalho, L., & Goodyear, P. (2014). The architecture of productive learning networks. Routledge.

4 thoughts on “Learning analytics and beyond

  1. Hi Brigitte
    You raise very salient points. On the topic of LMSs, I think I would like to see a balance between the use of LMS and a student’s personal cyber-infrastructure, rather than an eradication of LMSs. I’m sure you didn’t mean getting rid of them all together. Both tools have advantages and disadvantages; and is there any reason why both can’t remain part of an individual learners’s toolkit?
    I enjoy using LMS and appreciate the analytic capability; although I do concede that the data from LMS alone is limited. Suthers and Rosen (2011) claim that ” Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs. As a result, the coherence of distributed interaction and emergent phenomena are analytically cloaked”. I use LMS data primarily for course design purposes – to understand what the student do not participate in and where an alternative may need to be included. I’ve mostly used the data in a general way to diagnose issues relating to a cohort as a whole, rather than issues relating to individuals. However, I must admit I have found data pertaining to individual student activity useful when combined with other data; meaning the LMS data is never enough to complete the picture on its own.

    Suthers, D. D., & Rosen, D. (2011). A unified framework for multi-level analysis of distributed learning Proceedings of the First International Conference on Learning Analytics & Knowledge, Banff, Alberta, February 27-March 1, 2011.


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