Using Student Data as a Map, Not a Target

Concurrent Session 9

Session Materials

Brief Abstract

Schools often use demographic, behavioral and non-cognitive data about students to “target” at-risk learners. This is good, but it is somewhat parallel to Google Maps using the real time location data from your phone (behavioral data) coupled with factors from your profile (demographic data) added to your driving tendencies (non-cognitive data) and then calling your phone to tell you that you are lost. What is most practical is for Google Maps to aggregate your location, speed and other data to tell you which way to turn just at the right moment to find the best restaurant. How can schools similarly use student data to guide them along the path of persistence?

Presenters

Lead Presenter: Wendi Prater, Lone Star College

Wendi has many years of executive leadership experience and has led business partnerships of Fortune 500 clients; reporting directly to CEO’s, CIO’s and state Governors. Specialties in: SPHR - Senior Professional in Human Resource Certification, Human Resource Certification Institute (HRCI), Alexandria, VA, 2002-2017; and SCP-SHRM - SHRM Senior Certified Professional 2015 - 2017. Industrial arts and manufacturing knowledge: TIG/MIG welding, CNC (ShopBot, Waterjet, Tormach), Illustrator, CorelDraw, AutoDesk, VCarve Pro for CNC, textiles fabrication, rapid prototyping (3D MakerBot), laser design fabrication.

Additional Authors

Zach Bolian, Civitas Learning

Zach's career focus has been producing insights from disparate, dirty and often unorganized data by using a combination of data mining techniques, statistical methods as well as financial modeling and forecasting.

Extended Abstract

Schools collect lots of data about their students – standardized test scores, prior grades, engagement metrics from the LMS, interest inventories and included in this list would be the non-cognitive data (motivation, procrastination, availability of time, technology skills, etc.) that is sometimes collected about students.  Having all of this data about a student is good, but what would be great is making the right data available at the right time to the right person to help the student succeed.  So how do we structure the data that we have about students to be relevant and accessible across the student’s experience with the school?  Technically how can this be done?  Legally with regard to FERPA how can this be done?  Just like Google Maps is an example of big data, most people are not interested in all of the data in Google Maps, they just want their phone to tell them to turn right or left at that moment.  Similarly, how can we equip our faculty and student success coaches with the right data about students at the right time?