Avoiding Failure with Learning Analytics

Mohammad Khalil

Learning analytics depend strongly on data to improve the quality and value of learning experience. However, these gains come at a cost! The investment in data processing and collection are vital but do not guarantee success. Rebecca Ferguson and Doug Clow (2017) met learning analytics experts and asked how to avoid failures with learning analytics?

The general outcome was summarized by which learning analytics should be implemented successfully having in mind clear and understood purposes of using learning analytics. deploying learning analytics in hope to make an improvement is a wrong approach; everyone involved should know why analytics are introduced and why they are needed. 

The authors categorized three main aspects of avoiding failure:

  • Strategic Development: Institutions should develop system models and strategic plans of how learning analytics will be developed and deployed. the model needs to consider design, implementation, structure, behavior, evaluation, planning, requirements, and configuration.  
  • Capacity Building: A system model of learning analytics needs to have skilled data analysts. The analysts need to realize that confidentiality is a must. Analysts should deal with the data professionally and refine good algorithms for interpreting the data.
  • Ethics: Institutions need to be clearly aware of why student data are collected and analyzed. In addition, they have to know who benefits from storing and analyzing the data! Students voice need to be heard according to Ferguson and Clow (2017).

In the end, Ferguson and Clow (2017) summarized that in order not to fail, it is important to have a clear vision of what institutions want to achieve using learning analytics. This vision should be close to the institutions' priorities, of course! 

Ferguson, R., & Clow, D. (2017). Learning Analytics: Avoiding Failure. Educause Review Online31.

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