Introduction
The use of big data in institutions of higher education has become more popular. As more and more educational institutions search for solutions to the educational crisis in the United States, the popularity of student data mining practices is becoming increasingly apparent. Around the country, hundreds of colleges are tracking students’ digital footprints whether it is their attendance, studying habits, school outings, and even monitoring ID cards. New data analytic practices are being adopted by educational institutions who claim that these will increase graduation rates and lower the cost of college, particularly among less financially secure students. Despite the rewards associated with the use of data analytics in higher education, it still presents some undeniable risks. For some the reward is worth it but for many, I included, the risks are too great.
How can Data Collection in Higher Education Affect the College Experience?
Lower costs and increase graduation rates
In higher institutions of learning, the costs are higher, and the graduation rates are less than optimum for certain institutions and some students. Therefore, academic success is even more vital. The use of technology in learning and teaching has created more data about students’ engagement and comprehension of course content. Furthermore, data analytic tools have conferred the ability to analyze, assess and predict students’ success based on this data. Data collected from learning analytics is used to find predictors of academic success, measure learning outcomes and identify at-risk students. Identifying at-risk students serve as the first step in coming up with mitigating plans and intervening with extra support services to aid the student to succeed. Additionally, with enough data collected, the learning experience for students can be personalized to meet individual needs (Clemmons 1).
Privacy
Colleges and universities use big data to predict performance at the cost of privacy. Digitization of education has placed intensive surveillance on the rise (Warrell 1). Analytical tools have been developed and implemented to track how long students spend learning, sleeping, exercising and socializing. Large volumes of data are collected with the justification that they will aid students to obtain more from the investment they are making in their education. Big data is used to monetize human activities like education as safeguarding the privacy of students grows into an enormous concern. Access to personal data denotes a degree of intrusion which is justified by notions of seeking the greater good and education for all. Students in higher education are concerned about how their data is being mined and used by their institutions. Additionally, monitoring students more closely serves only to increase assessment anxiety and make them feel more self-conscious (Warrell 1).
Quality of education
The imminent data explosion has generated a lot of enthusiasm due to the use of machine learning to foster student learning and deployment of artificial intelligence to service human intelligence (Carr 1). However, this enthusiasm should be tempered with skepticism. The impact of technology on education needs a reality check (Warrell 1). As users became more vulnerable and attracted to consuming free online university content, participation is promoted at the expense of educational rigor (Carr 1). Machine learning (such as Massive Open Online Courses, MOOCs) in education are mostly theoretical and have limited application.
Furthermore, it is very difficult for a computer to replicate the intricate and ineffable experience of learning and teaching that takes place on a campus or college. Cloud computing does allows vast amounts of data to be transmitted and stored at minimum costs but at what other costs? There have been instances where hackers have accessed big data banks and exploited the information to their advantage. Furthermore, higher education institutions have exploited the assumed anonymous data to offer particular students additional courses and tutorials at an extra fee. Therefore, data mining works more to keep colleges and universities from losing hefty annual tuitions due to dropouts than promoting the quality of education.
Conclusion
Institutions of higher education are becoming more involved in student data mining practices in the pretext of improving performance, increasing graduation rates and lowering the cost of higher education. However, as they collect big data, they access personal information about students which represents an infringement of their privacy. Mining for this data may cause self-consciousness, assessment anxiety in students or even the information may land in the wrong hands. Furthermore, technology, online learning, that assists universities and colleges collect some of these data, puts more emphasis on participation rather than educational rigor and serves only to isolate socially students.
Works Cited
Carr, Nicholas. The Crisis in Higher Education. MIT Technology Review. 2012, September 27. Internet source. <https://www.technologyreview.com/s/429376/the-crisis-in-higher-education/>.
Warrell, Helen. Students under surveillance. The Financial Times Limited, 2015, July 24. Internet source. 24 February 2016.http://www.ft.com/intl/cms/s/2/634624c6-312b-11e5-91ac-a5e17d9b4cff.html#slide0
Clemmons, Raechelle. Technology’s impact on higher education. Green baypressgazette. 2014, October 21. Internet source. 24 February 2016.<http://www.greenbaypressgazette.com/story/money/2014/10/21/technologys-impact-higher-education/17693719/>.
Leskes, Andrea and Miller, Ross. Purposeful pathways: Helping students achieve key learning outcomes. Washington, DC: Association of American Colleges & Universities.2006, September 1. Print.
