{"id":18177,"date":"2016-11-18T14:47:15","date_gmt":"2016-11-18T19:47:15","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/microsoft-helps-tacoma-public-schools-use-data-analytics-to-predict-at-risk-students\/"},"modified":"2016-11-18T14:47:15","modified_gmt":"2016-11-18T19:47:15","slug":"microsoft-helps-tacoma-public-schools-use-data-analytics-to-predict-at-risk-students","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/microsoft-helps-tacoma-public-schools-use-data-analytics-to-predict-at-risk-students\/","title":{"rendered":"Microsoft Helps Tacoma Public Schools Use Data Analytics to Predict At-Risk Students"},"content":{"rendered":"

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\"tacome-public-school-logo\"<\/a><\/strong>The Tacoma Public School System<\/strong><\/p>\n

Just a few years ago, the reputation of public education in Tacoma was anything but inspiring. A 2007 national study declared the district\u2019s five high schools, which educate nearly 30,000 students, \u201cdropout factories.\u201d The district\u2019s 55 percent on-time graduation rate was well below the national average of 81 percent [2]. Needless to say, TPS was not delivering on its promise to educate \u201cevery student, everyday\u201d [3]. Azure machine learning was just the tool to get TPS out of this rut.<\/p>\n

What can predictive analytics tell educators?<\/strong><\/p>\n

Initially, TPS questioned if predictive analytics were attainable in an educational setting. To prove their concept, the district used 5 years worth of data for students from grades 6 through 12, and focused on pulling a variety of information: demographics, health records, and student performance information such as grades and attendance. This data was then compared against the district\u2019s key benchmarks, which include math and reading standards, graduation rates, school environment, and readiness for life after high school. Although the district had concerns about what could be gleaned from mapping this data, they ultimately felt confident in the strength of the numbers. \u201cAs we progressed and used more historical data, the model proved to be almost 90 percent accurate,\u201d said Baidoo-Essien, a TPS Intelligence Analyst [2].<\/p>\n

Educators now have 72 different formats to view the data, and can even see metrics at the classroom level. \u201cThis tool was very good at helping us build some analytics to see student performance from a near-time historical perspective,\u201d says Shaun Taylor, the district\u2019s CIO [2].<\/p>\n

What has TPS done with this new information?\"students-and-teacher\"<\/a> <\/strong><\/p>\n

\u00a0<\/strong>Because teachers and school administrators now have up-to-date information on how students are performing across a spectrum of standards, educators can more effectively think about how to help students advance. Almost instantaneously, teachers can identify if a student is falling off track and implement a multitude of targeted intervention strategies. A personalized message, a one-on-one or group tutoring session, peer-to-peer mentoring, and meetings with a teacher or adviser are just a few action steps teachers can take [4]. In sum, these points of intervention are what push at-risk students across the graduation finish line prepared for success in their post-high school educational path and beyond.<\/p>\n

To share the success of the model within the district benchmarks and actions plans are discussed at school board meetings. Furthermore, takeaways from these meetings are shared with community members as well as broadcasted across the district [2].<\/p>\n

https:\/\/www.youtube.com\/watch?v=rfAoKs8XxzY&feature=youtu.be<\/p>\n

The Risks of Using Predictive Analytics in Schools <\/strong><\/p>\n

Despite the clear benefits of using predictive analytics in TPS, educators should not be unconscious consumers of the data. Why? Because we have seen computer-generated predictions go very wrong. For example, a risk assessment in Broward County, Florida wrongly labeled black people as future criminals nearly twice as often as it wrongly labeled whites, according to ProPublica, an investigative journalism organization [5].<\/p>\n

These errors lead to real problems for students and citizens and work against the very goal of using machine learning. As such, educators should remember that although data can provide a snapshot of information about student outcomes, each student is different and he or she should therefore not be blindly bucketed into categories.<\/p>\n

Going Forward<\/strong><\/p>\n

The success of Microsoft Azure in TPS has encouraged school districts in Virginia and Wisconsin to adopt this cloud technology [1]. But there is still more to do and more question to answer:<\/p>\n