The Shape of Things to Come: A Brave New World?

Session Time and Location

Date:
Thu, 7 Jun 2018
Time:
Time:
2:00pm to 3:00pm
Room: Room:
Maplewood B
Session Track
Session Format

Speaker(s)

Learning analytics promise rich sources of evidence to inform educational decision-making (e.g., HEC, 2016).  New access to student data trails, combined with demographic information, help us to identify “at risk” students, “engage” them with customized learning experiences, fostering their “success”.

This promise, however, comes with risks (Lawson et al, 2016; Wise & Shaffer, 2015).  The pathway from statistical significance in learning analytics to significant learning (Fink, 2003) is not straightforward. 

This workshop will explore how big data intersects with significant learning.    We will interrogate constructs such as “engagement”, “at risk”, and “success”,  and unpack  epistemological assumptions underlying learning analytics.

 

  • Session References +

    Anderson, C. (2008, 23 June). The end of theory: The data deluge makes the scientific method obsolete.  Wired Magazine. http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory

    Biggs, J.B. and Tang, C. 2007. Teaching for quality learning at university, 3rd ed, Berkshire: Open University Press.

    Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007, July/August) Academic analytics: A new tool, a new era. EDUCAUSE Review . http://net.educause.edu/ir/library/pdf/erm0742.pdf

    Drachsler, H., & Greller, W. (2012). The pulse of learning analytics understandings and expectations from the stakeholders. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ’12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 120–129). New York: ACM.

    Gašević, D.; Dawson, S.; Siemens, G. (2015). "Let's not forget: Learning analytics are about learning". TechTrends. 59 (1): 64–71. doi:10.1007/s11528-014-0822-x

    Fink, D.L. (2003). Creating Significant Learning Experiences. San Francisco: Jossey Bass.

    Greller, W., and Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society. 15 (3), 42–57.

    Higher Education Commission (HEC), UK, (2016). From Bricks to Clicks: The Potential of Data and Analytics in Higher Education, London UK: http://www.policyconnect.org.uk/hec/sites/site_hec/files/report/419/fieldreportdownload/frombrickstoclicks-hecreportforweb.pdf

    Lawson, C., Beer, C., Rossi, D. et al. (2016).  Identification of ‘at risk’ students using learning analytics: the ethical dilemmas of intervention strategies in a higher education institution. Education Tech Research Dev 64: 957. doi:10.1007/s11423-016-9459-0

    Long, P.D., and Siemens, G. (2011) Penetrating the Fog: Analytics in learning and education. http://er.educause.edu/articles/2011/9/penetrating-the- fog-analytics- in-learning- and-education.

    Munoz, C., Smith, M., & Patil, D. J. (2016). Big data: A report on algorithmic systems, opportunity, and civil rights. Executive Office of the President, USA. https://www.hsdl.org/?abstract&did=792977

    Knight, S., Buckingham Shum, S., & Littleton, K. (2014). Epistemology, assessment, pedagogy: Where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2). http://epress.lib.uts.edu.au/journals/index.php/JLA/article/view/3538

    Macfadyen, L. P., Dawson, S., Pardo, A., & Gašević, D. (2014). Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge.Research & Practice in Assessment, 9, 17–28

    van Trigt, M. (2016). How Data Can Improve the Quality of Higher Education (white paper). SURFnet: Utrecht.  Available at https://www.surf.nl/en/knowledge-base/2016/whitepaper-how-data-can-improve-the-quality-of-higher-education.html

    Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2 (2), 5–13.