Learning analytics – using data for student success

Dec 11, 2018

Digitalization of  educational services is an ongoing process where different services are on their way of being digitized in the near future. As more learning is happening online, data traces left by the learners are drawing the interest of educational and data scientists, developers, educational institutions and governments.

Activities of measurement, collection, analysis and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs are usually called learning analytics.

Gathering information

Data gathering and utilisation of learners’ activities and performances is still emerging, but it is expected that learning analytics data could be used to support learners’ decision-making, make predictions, for instance to identify students at risk, and provide teachers and administration with educational data on the course or institution level. However, self-reflection of the students is one thing that is named as being the most valuable in learning analytics applications.

With Compleap we hope to stimulate users self-reflection by providing competence visualisations and educational recommendations.

Information safety

At the same time, legal and ethical aspects of the data collection and manipulation must be carefully considered in developing learning analytics services in Compleap and in any other educational services.  Transparency and consent are crucial parts of learning analytics. Parents, students, guidance counsellors and other stakeholders should be able understand data sources, purpose, access usage boundaries and interpretation possibilities to feel safe, enjoy and truly benefit from digitalised educational services.

References:

  1. Siemens, G. (2013). Learning Analytics: The emergence of a Discipline. American Behavioural Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851
  2. Sclater, N., & Bailey, P. (2015). Code of practice for learning analytics. Jisc. Retrieved from https://www.jisc.ac.uk/guides/code-of-practice-for-learning-analytics
  3. Drachsler, H., & Greller, W. (2016). Privacy and Analytics – it’s a DELICATE Issue. A Checklist for Trusted Learning Analytics. In Proceedings of the 6th Learning Analytics and Knowledge Conference (pp. 89-98). New York, NY: ACM. https://doi.org/10.1145/2883851.2883893