I was both encouraged and discouraged from reading about Purdue’s and Rio Salado’s programs. At my institution, we have enough issues trying to properly staff our help desk and other IT folks to meet relatively basic tech needs. In addition, few of our systems talk to each other. Vernon Smith spoke about their central mainframe into which all of their data pours, and we are many steps away from that sort of capability. On a personal note, I feel a bit crippled because I am not in a position to enact a ton of change.
At the same time, we plan to start small. It probably won’t involve nice graphics, but our LMS provider does have a decent suite of data from which we can ask them to generate reports. I’m hoping that some basic analysis through Excel and SPSS can plant seeds to devote more resources to exploring analytics.
Is anyone else a mid-level player who foresees many hurdles in trying to convince administration about the value of analytics?
In his presentation, Amazon.com’s John Rauser talks about the importance of both skepticism and curiosity in having a career as a data scientist. I found this quite refreshing because I am a novice (to be kind) in mathematics and engineering. However, as someone who’s been in school and worked in higher education for nearly my entire life, I’m far more advanced in skepticism (critical thinking?) and curiosity.
In fact, curiosity is what drove me to my interest in analytics in higher education. This may seem quite silly, but I read Michael Lewis’ Moneyball a few years back and read it again before the movie was released this summer. As a huge baseball fan, I enjoyed it for obvious reasons, but as a student of higher education and professional in faculty development, I started to dream about how data could be used to inform teaching and learning.
That’s where critical thinking came in. There’s a ton of data out there, but what’s useful? In Penetrating the Fog, Long and Siemens (2011) present the same idea: “using analytics requires that we think carefully about what we need to know and what data is most likely to tell us what we need to know” (p. 32). I may never become a data scientist with fortes in math and engineering, but I can partner with those who have the technical abilities to wisely and critically apply analytics to inform decision-making. Of course, I want to learn the basics of the technical skills as well!
In the Baker and Yacef (2009) article, they restate Educational Data Mining Community’s definition of educational data mining as such: “Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.”
The part that most resonates with me is the notion that EDM can help us better understand students. I mainly identify as a faculty developer and not as a data analyst, so the purposes of trying to deepen our understanding of students makes a lot of sense to me. In the end, I’m interested in how learning analytics and EDM can help us improve teaching and learning.
I’m part of a committee looking at increasing the efficiency and effectiveness of technology at my institution, and I took it upon myself to join a sub-committee on piloting a project on analytics in some of our online classes. Fortunately, I am joined by a more technically-equipped colleague on the sub-committee, but I can’t help but feel a little bit overwhelmed by my lack of knowledge concerning analytics and data mining. Most of the materials makes sense on a conceptual level, but I’m hoping to learn how to execute some of the basic data analysis too.
Is anyone else in the same boat?