{"id":31965,"date":"2018-11-13T14:11:47","date_gmt":"2018-11-13T19:11:47","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-reduces-dropouts-at-the-university-of-arizona\/"},"modified":"2018-11-13T14:11:47","modified_gmt":"2018-11-13T19:11:47","slug":"machine-learning-reduces-dropouts-at-the-university-of-arizona","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-reduces-dropouts-at-the-university-of-arizona\/","title":{"rendered":"Machine Learning Reduces Dropouts at the University of Arizona"},"content":{"rendered":"

Student retention is one of the most significant issues facing higher education institutions today – in the U.S. only 60% of students graduate within six years. [1]<\/sup> Machine learning is a valuable tool that universities can use to improve their student support and retention processes.\u00a0 The value of machine learning for the University of Arizona (\u201cUA\u201d) lies in its predictive power, as it will empower them to extract new features that will consequently enable faster, more accurate identification of students who are at-risk of dropping out.<\/p>\n

New Feature Extraction<\/strong><\/p>\n

Machine learning helps UA identify previously unmeasured variables that affect student retention.\u00a0 Current approaches to data analysis focus only on highly measurable information such as students\u2019 course grades or demographic information. Machine learning provides the opportunity to move beyond these. For example, UA is leveraging location and purchase data from student swipe cards that give insight into students\u2019 non-academic lives (see below). Machine learning will help identify which of these factors correlate to student retention and improve UA\u2019s ability to accurately identify at-risk students.<\/p>\n

Faster Response<\/strong><\/p>\n

The second reason machine learning is critical for UA is that it allows them to intervene much sooner to support struggling students. According to UA researcher Sudha Ram, \u201cfreshmen who ultimately leave the university make the decision to do so in the first 12 weeks\u2026long before final grades are posted.\u201d [2] <\/sup>By expanding the range of factors to include data beyond grades, UA will be able to identify students earlier, increasing the likelihood that an intervention will be effective.<\/p>\n

Current Strategies<\/strong><\/p>\n

UA is already leveraging machine learning to great effect, as the 2017-18 one-year retention rate of 86.5% was the highest in the history of the university and exceeded the targets set by the Arizona Board of Regents. [3]<\/sup> In the short term they are expanding the types of measurable data that they analyze. Their analysis now includes nearly 800 factors in addition to academic performance. [4] <\/sup>For example, Assistant Provost Angela Baldasare confirms that \u201cin the financial aid world, we found some early indicators.\u201d [5]<\/sup> This expansion into new features beyond academic performance makes their predictive models more accurate.<\/p>\n

In the medium term, UA is exploring ways to incorporate some less observable data by collecting location and purchasing information from students\u2019 ID cards. Researchers led by Dr. Ram use the transactional data to infer how well a student is socially integrated into campus life. For example, by analyzing how regularly students use campus facilities, such as the gym or library, researchers can determine if students have developed strong routines. By mapping card transactions that occur very near in time and at the same location, researchers can make inferences about students\u2019 implicit friend groups and social networks. [6]<\/sup> \u00a0Both of these factors have shown to greatly improve the accuracy of the model\u2019s predictions, as According to Dr. Ram, the current model is \u201cable to do a prediction at the end of the first 12 weeks of the semester with 85 to 90 percent recall.\u201d [7]<\/sup> These efforts are currently only research-based using de-identified data, but in the next two to ten years these techniques could be put into practice to the great benefit of students.<\/p>\n

More Data vs. Privacy<\/strong><\/p>\n

The challenge with expanding the number of factors and types of data used in the predictive models is that at a certain point you risk infringing on students\u2019 privacy. Several critics of Dr. Ram\u2019s approach believe that she has already gone too far. Fortune<\/em> writer David Meyer, for example, believes that these \u201canalytics techniques may hold a lot of promise, but they also clash with privacy rights.\u201d [8]<\/sup><\/p>\n

I strongly believe that UA should continue to expand the amount and types of data it uses to identify at-risk students.\u00a0 The more data they are able to collect the better they will be at preventing students from dropping out. In order to do this however, they must develop a proactive and robust data privacy policy. Students must be informed that this data is being collected and must be given the opportunity to opt out if they so choose. While there could be a risk that a large number of students will choose not to participate, I believe that If UA is able to clearly articulate its value proposition to students then it should have few problems with students opting out.<\/p>\n

Questions Remain<\/strong><\/p>\n

I am somewhat unsure how students would respond if told about Dr. Ram\u2019s data collection. I believe that students today are much more lenient with their personal data than students were a generation ago and most would not choose to opt out. I am curious as to my classmates\u2019 perspectives on this. Would you allow the university to track your data if it meant they would be more responsive in providing student support?<\/p>\n

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Word Count: 797<\/p>\n

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Endnotes: <\/strong><\/p>\n

[1] <\/sup>Joseph B. Treaster, \u201cWill You Graduate? Ask Big Data,\u201d New York Times<\/em>, February 2, 2017, [https:\/\/www.nytimes.com\/2017\/02\/02\/education\/edlife\/will-you-graduate-ask-big-data.html], accessed November 2018.\u00a0<\/sup><\/p>\n

[2]<\/sup> Alexis Blue, \u201cResearcher Looks at \u2018Digital Traces\u2019 to Help Students,\u201d University of Arizona News<\/em>, March 7, 2018, [https:\/\/uanews.arizona.edu\/story\/researcher-looks-digital-traces-help-students<\/a>], accessed November 2018.<\/p>\n

[3]<\/sup> \u201cUA Retention at an All-Time High,\u201d University of Arizona News<\/em>, October 4, 2017, [https:\/\/uanews.arizona.edu\/story\/ua-retention-rates-exceed-abor-goals<\/a>], accessed November 2018.<\/p>\n

[4] <\/sup>\u201cResearcher Looks at \u2018Digital Traces\u2019 to Help Students,\u201d University of Arizona News<\/em><\/p>\n

[5]<\/sup> \u201cUA Retention at All-Time High,\u201d University of Arizona News<\/em><\/p>\n

[6] <\/sup>Ram, S, Wang, Y, Currim, F & Currim, S 2015,\u00a0Using big data for predicting freshmen retention. in\u00a02015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015.<\/em>\u00a0Association for Information Systems, 2015 International Conference on Information Systems: Exploring the Information Frontier, ICIS 2015, Fort Worth, United States,\u00a012\/13\/15.<\/p>\n

[7] <\/sup>\u201cResearcher Looks at \u2018Digital Traces\u2019 to Help Students,\u201d University of Arizona News<\/em><\/p>\n

[8] <\/sup>David Meyer, \u201cAn American University Is Spying on Students to Predict Dropouts. Here’s What That Says About Big Data in the U.S.,\u201d Fortune<\/em>, March 13, 2018, [http:\/\/fortune.com\/2018\/03\/13\/university-arizona-catcard-big-data-dropouts\/], accessed November 2018.<\/p>\n

\u00a0<\/sup><\/p>\n

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 <\/p>\n","protected":false},"excerpt":{"rendered":"

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