Block 4 Activity 5: Overview of Learning Analytics

Following reading Ferguson (2012), Learning analytics: drivers, developments and challenges I will share the main points raised about learning  analytics:

  • Learning analytics is a fast-growing area of Technology-Enhanced Learning (TEL) analytics elearningresearch.
  • Learning analytics has strong roots in business intelligence, web analytics, educational data mining (EDM) and recommender systems.
  • Learning analytics can be used to handle ‘big data’ that can’t be dealt with manually.
  • Web 2.0 opened up new possibilities for collecting web content from a wide range of different sources.  EDM could be used to enhance web-based learning environments both for the educator’s evaluation of the learning process and for the learners as they progress along their journey (Zaïane, 2001).
  • Social Network Analysis (SNA) was integrated into learning analysis and could be used to investigate and promote collaborative and cooperative connections between learners, tutors and resources (De Laat et al., 2007; Haythornthwaite, 2006).  This sits within a constructivist belief that considers knowledge to be constructed through a community of practice (Lave and Wenger, 1991; Wenger 1998).
  • Political concerns emerged which increased demand on educational institutions to measure, demonstrate and improve performances. Competition grew to become one of the best in the world.
  • Learning analytics need to provide value to learners in formal, informal or blended environments.  They are used to understand and optimise learning and the learning environment.
  • There are still challenges ahead as we look towards integrating experiences from the learning sciences, working with larger datasets, engaging with learner perspectives and creating a set of ethical guidelines.
  • Learning analytics (educational) and Academic analytics (political/economic) need to communicate with each other.

Learning analytics, when used effectively and efficiently, can benefit governments, educational institutions and teachers/learners.

References:

De Laat, M., Lally, V, Lipponen, L. and Simons, R-J.(2007) ‘Investigating patterns of interaction in networked learning and computer-supported collaborative learning: a role for social network analysis’, International Journal of Computer Supported Collaborative Learning, Vol. 2, pp. 87-103.

Ferguson, R. (2012). Learning analytics: drivers, developments and challenges.  International Journal of Technology Enhanced learning, 4(5/6) pp. 304-317). Available online at http://oro.open.ac.uk/36374/1/IJTEL40501_Ferguson%20Jan%202013.pdf  (accessed on 06 July 2016).

Hawthornthwaite, C. (2006) ‘Facilitating collaboration in online learning’, Journal of Asynchronous Learning Networks, Vol. 10, No. 1.

Lave, J. and Wenger, E. (1991) Situated Learning: Legitimate Peripheral Participation, Cambridge University Press, Cambridge.

Wenger, E. (1998) Communities of Practice: Learning, Meaning, and Identity, Cambridge University Press, Cambridge.

Zaïane, O. R.(2001) ‘Web usage mining for a better web-based learning environment’, paper presented at The 4th IASTED International Conference on Advanced Technology for Education (CATE’01), 27-28 June, Banff, Canada.

 

Image available at:

http://www.elearninglearning.com/create/?open-article-id=4646301

 

 

 

 

 

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