="http://www.w3.org/2000/svg" viewBox="0 0 512 512">

1 Data and analytics

Abbildung 1.1 – Learning Analytics word cloud BY ELI-LA-Word-Cloud-2, CC BY-SA

1.1 – Leaning analytics in higher education

The following infographic gives an overview of the current deployment of learning analytics in education.

An Infographic by Open Colleges

You might have noticed that student data is gathered from various systems and combined to make predictions, interventions and adaptions to the learning process. However, paradoxically, students are not the main stakeholders of learning analytics. Learning analytics is often seen as something that is “done” to the student, rather than something controlled and used by the student.

1.1.1 – Definition of Learning Analytics

The Society for Learning, Analytics and Research (SOLAR) defined learning analytics in 2011 as:

“Measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.” (LAK ’11)

However, the current deployment in higher education is probably more accurately described by Campbell, DeBlois and Oblinger’s (2007) definition of academic analytics as an engine that supports decision-making or guides actions by capturing, reporting, predicting, acting upon and redefining processes in education. A literature review by Leitner, Khalil and Ebner (2017) revealed that learning analytics is currently mostly used to monitor performance, predict retention and identify or alert students who are at risk to fail. In the report of the Higher Education Commission (UK), Shacklock (2016) defines predictive analysis as a future trend in higher education. The interventions that have been triggered by such predictive analysis have been documented to have positive effects on student retention (e.g. Arnold and Pistilli, 2012; Siemens, Dawson and Lynch, 2013).

1.2 – Current issues and challenges

“While it is oſten perceived that education is rife with data, very little is related to capturing the conditions for learning (internal and external)” (Gašević, Dawson and Siemens, 2015, p. 68).

Following you can find a summary of current issues and challenges of learning analytics that have implications for personalised learning in mass higher education:

  • Even though it is highly recommended, students are often not seen as the main stakeholders of learning analytics (Slade and Prinsloo, 2013; Khalil and Ebner, 2015; Roberts et al., 2016).
  • Learning analytics often relies on data captured in virtual learning environments, combined with other sources of student data and offers little insight into learner-generated data and content metadata (Siemens, Dawson and Lynch, 2013). Once a student downloads a resource or clicks on a link outside the system, the learning process can no longer be monitored.
  • The use of learning analytics raises many privacy and ethical issues. One of the major concerns is the risk of profiling students based on limited sets of parameters, which can result in bias or limit the student’s potential and self-efficacy (Greller and Drachsler, 2012; Khalil and Ebner, 2015).
  • Another known problem is the tendency to optimise the learning process “to a metric that does not reflect what is more fundamental desired as an outcome” (Clow, 2012, p. 137). Consequently, Gašević et al. (2016) argue that learning analytics should consider course-specific instructional conditions to specify what is valued in a course.

1.3 – ACTIVITY

Now it’s your turn to contribute to the OER. Use the comment section below or the hypothes.is feature on the right side of the page to share your insights:

  1. Does the infografic above reflect the use of learning analytics in your institution in terms of data sources, main stakeholders and purpose?
  2. What potential benefits, issues and challenges of learning analytics can you identify for your own professional practice?

1.4 – Useful resources

License

}