Big Data and Education: Prospects and Problems

The Data & Society Research Institute, The Leadership Conference on Civil and Human Rights, and New America’s Open Technology Institute recently organized a conference on “Data & Civil Rights”. The organizers released a series of research briefs that summarize current research literature, practical challenges and emerging questions surrounding ‘big data’ in different areas of society – including education:

“Many education reformers see the merging of student data, predictive analytics, processing tools, and technology-based instruction as the key to the future of education and a means to further opportunity and equity in education.”

Big Data: Discovery of Patterns and Relationships: For educators the ‘Data & Civil Rights: Education Primer’ offers a balanced view of both the opportunities and risks associated with learning analytics and other uses of data mining in education.


On the plus side, leveraging large datasets has the potential to improve efficiency, effectiveness of education providers and the learning experience of individual students.

“Data mining can support a variety of education-related functions, including building student models to individualize instruction, map learning domains, evaluate pedagogical support, and contribute to learning science. Analytics techniques can be used to create models to predict registration, student performance, and retention. The wealth of new information about students is used to detect cheating or plagiarism, create college or course recommendation engines, and identify abnormal results. It can also be used for administrative, recruiting, and fundraising purposes.”


Though data-driven education has the potential to improve access to and the quality of teaching, it does not come without risks and potentially severe side effects:

“It may perpetuate persistent labeling, deepen rather than lessen concerns about resources, violate peoples’ expectations of privacy, and enable inappropriate or harmful repurposing of educational data in non-educational contexts. For example, students or their guardians may find it impossible to eschew or reverse flawed algorithmic assessments. The identification of students as “at risk” might not allow them to remove any harmful record of their failures if they improve later on. Students may see labels as self-fulfilling prophecies and predictive analytics may prime educators to make prior judgments about students’ capabilities and character.”

Further Information

EditLib offers free access to several interesting case studies on the use of learning analytics. Review these papers from past AACE conferences to gain an idea how educational data mining is applied in practice:

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