scholarly journals Big Data in Education: Balancing the Benefits of Educational Research and Student Privacy: Workshop Summary

2017 ◽  
2018 ◽  
Vol 16 (3) ◽  
pp. 263-279 ◽  
Author(s):  
Joel R. Reidenberg ◽  
Florian Schaub

Education, Big Data, and student privacy are a combustible mix. The improvement of education and the protection of student privacy are key societal values. Big Data and Learning Analytics offer the promise of unlocking insights to improving education through large-scale empirical analysis of data generated from student information and student interactions with educational technology tools. This article explores how learning technologies also create ethical tensions between privacy and the use of Big Data for educational improvement. We argue for the need to demonstrate the efficacy of learning systems while respecting privacy and how to build accountability and oversight into learning technologies. We conclude with policy recommendations to achieve these goals.


Chapter 3 builds on the previous chapters and provides a summary of big data-style research within the Community of Inquiry scholarly literature, as well as examples from educational research broadly. This chapter also connects to the broader topics of machine learning, data analytics, learning analytics, and educational data mining. Constructs from the Community of Inquiry are integrated into this synthesis and overview. Unfortunately, only a fraction of the studies in educational research broadly today exhibit the tell-tale signs of big data: data volume and variety, new environments or instrumented sources of larger data, often with emerging tools and platforms critical to the analysis of the resulting datasets. A list of additional readings is provided.


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