AcAdv, AdvTech, Higher Education

Today’s #AcAdv Chat Topic: Data Analytics in Academic Advising #highered

A couple of week’s ago, I was fortunate to join the Open SUNY COTE Summit 2017. I will be sure to share more about the #COTEsummit learning in the coming weeks; however, the last session helped me think about framing TODAY’s (3/21) #AcAdv Chat I’ll be moderating from 12-1 pm CT: Data Analytics in #AcAdv 

During the #COTEsummit Learning Analytics panel hosted by OLC, we dug into what information we know and how we use it to understand more about our learners.  Many academic advising units/divisions, often jump to the platform or process for how we analyze students to predict learner behavior:

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But before advising leaders in higher ed jump on the big data bandwagon or decide to implement technology platform to collect data, I think our support units need to identify what information and data we need to know to effectively support our learners. Let’s make decisions on the data that is most helpful, instead of letting predictive analytics make decisions for us at our institutions. What often gets lost in this conversation and planning is this: learning or learning analytics.

Learning analytics are about learning (Gašević, Dawson, & Siemens, 2015). Sometimes we forget this about learning analytics when the phrase data is tossed out at the  “strategic-planning-task-force-retention-student-success-operation” meeting occurs at our universities and colleges. Sure, learning analytics might be most relevant for instructors and faculty; however, learning data is also critical for those who support the instructional design, scaffold student success, and provide academic advising/support in higher education.

Image c/o Giulia Forsythe

In thinking about academic advising and learner support, I have SO many questions about data and data analytics for this #AcAdv Chat topic… here are just a few:

  • How does your institution collect, store, and share data campus-wide?
  • What do you do as a staff or faculty member to interpret the data?
  • Are you able to interpret, read, and translate the information provided about your learners?
  • Are there real-time notifications where students, staff, and faculty can interpret academic progress? What does this look like at your campus?
  • Do your data sets on campus talk to one another? Is there much interaction between your student information system, learning management system, institutional portal, or institutional research data? Why or why not?
  • What challenges and/or issues have you thought about for how data is collected and/or reviewed for learner support?
  • Who or what office can you reach out to on campus for “data analysis” or digging into your learner data to interpret further to support the work you do?

What thoughts or questions do you have about this issue, higher ed? Won’t you join us for today’s #acadv chat conversation? Here’s how:

TWEETS from the #AcAdv Chat conversation on 03.21.17

Reference:

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.

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