Publish at March 14 2016 Updated July 21 2026
Big data is set to profoundly change the way we assess learning and our understanding of learning processes.
The promise of big data is to continuously capture individual data, contextual data, and relational data among learners.
This will mark the end of the distinction between learning time and assessment time. Learning and assessment will occur continuously, with personalized feedback based on the learner’s progress and level of autonomy.
But what lies beyond these promises? A team of researchers at Stanford University has examined the added value of this big data in assessment and presents a selection of practical use cases within three distinct online learning environments.
Through this series of examples, the researchers demonstrate how the use of big data for evaluation purposes can help us better understand the many facets of learning. The ambition is lofty, but there is a caveat: there is a significant difference (and gap) between measuring and using.
As the authors point out, a 2011 study by the National Research Council revealed the ineffectiveness of ten years of education policies based on assessment results in the United States.
Having complex, large-scale data does not necessarily mean interpreting it correctly, much less successfully using it. This remains a challenge for the years ahead.
Illustration: Laborant, Shutterstock
1. Stanford University Lytics Lab. “The Future of Data–Enriched Assessment” in *Research and Practice in Assessment*, vol. 9, 2014. http://www.rpajournal.com/dev/wp-content/uploads/2014/10/A1.pdf
2. National Research Council. Incentives and test-based accountability in education (2011). http://www.nap.edu/read/12521/chapter/1
(Links accessed on March 14, 2016)
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