Can Big Data Decrease the Number of College Dropouts?

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For this week I chose an article named Will You Graduate? Ask Big Data from The New York Times. The article does a great job presenting some of the positive and negative outcomes of the use of big data in colleges and even elaborates on the idea of developing ethical guidelines for its use. I picked this article because last class we mentioned that positive consequences for the use of big data exist and, even though there is room for improvement, the use of big data in academics presents a promising future.

The article has the central idea of using predictive analysis to foretell when some students will be in danger of dropping out. The process consists of tracking down the academic path of successful students, such as higher scores on introductory classes, to predict which students will need to use more university resources to graduate. Dr. Richard Sluder from Middle Tennessee State University says that before predictive analytics, many of the D grades went unnoticed because advisers were mainly monitoring GPA and not grades by course. Dr. Sluder then explains that big data is allowing advisers to understand that a lower grade in certain courses, especially those that involve practicing reading comprehension or basic mathematics, can be an indicator of which students need help. The use of resources, like writing coaching and tutoring, is an important factor in determining if students will drop out or not. In this case, big data allows institutions to target students who need extra-help, which I think is something that will both decrease the number of college dropouts and create a positive feedback loop in which more people learn about campus resources.

Other areas of improvement come from the advising department. Even though the use of big data in academic analysis is fairly new when compared to its use in other areas, many institutions are already seeing positive outcomes and encouraging them. For example, Georgia State has significantly invested in advising because of analytics results; now instead of having a 1:750 adviser-to-student ratio, they have a 1:300 ratio. Another example is Stanford University, which developed a digital tool based on 15 years of data that helps students with the task of choosing among 5,000 undergraduate classes. To this, Michell L. Stevens, an associate professor who led the development of this tool, says: “No singles advisor, however wise and alert, can possibly be aware of all the instructional opportunities.” What I really like about this overall improvement is the combination of both data analytics and human expertise. I think that because academics is such a personal topic, many institutions are taking the good decision of not letting data guide every decision. The previous examples show that academic big data, when mixed with advising, can be successful.

This last “positive” outcome of data analytics according to the article is a little more controversial. Personally, I feel more comfortable with the idea of using previous, anonymous data to lay down the best academic path for the student. However, what Sudha Ram, the director of the Center for Business Intelligence and Analytics at the University of Arizona, is doing is quite different. According to his research, if students are not socially integrated into college they tend to drop out. Because of this, he is now observing freshmen conduct by tracking the information on their identification cards when they access the library, gym, cafeteria, etc. As we discussed in class, the fact that data is available does not mean that its use is ethical; there is a risk that students details can become public, and tracking down the actions of students compromises their right to privacy.


There are other problems associated with the use of big data to predict and prevent college dropouts. Students whose initial academic performance is low can be discouraged from trying harder in their chosen field because data patterns can produce a feeling of predestination. Martin Kurzweil, a program director at an education research organization (Ithaka S+R), also expresses his concern over the fact that predictions could present a temptation to “weed out at-risk students to improve a school’s ranking.” (For more drama about Maryland university ex-president Simon Newman’s and his administration’s plan to “cull struggling freshmen as a part of an effort to improve retention numbers” click here.) However, I think the use of big data in academics is quite promising due to the following reason:

“In June, Ithaka S+R and a team from Stanford brought together 73 specialists from universities, analytics companies, foundations and the Department of Education for three days of discussion on developing standards and ethical guidelines for big data on college campuses.”

This discussion represents an effort to regulate the power that big data can give institutions. While I believe that we are still far away from developing guidelines for the use of big data in general, it is important to consider that standardizing the use of big data for smaller areas can be a way to tackle this huge task. Big data, being used correctly, is decreasing the number of college dropouts, demonstrating that big data can have positive outcomes… shocking, right?


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