I’m headed to my first BbWorld conference in July and as luck would have it, it takes place in Las Vegas. Now I have a very special relationship with Vegas. I made my first trip there as part of a cross-country family vacation when I was fourteen. I spent all of my “Vegas money” at the Circus Circus carnival, but I did get to witness my 16-year old brother dropping $100 on a single hand of blackjack. Both were equally exciting to me at the time. One year, I made 7 trips to Vegas since it was a scant 3.5 hour drive from where I was living in Los Angeles. The Star Trek themed casino was especially enticing. Most significantly, though, I’ve visited Vegas for 20 of the last 21 years coinciding with the NCAA March Madness tournament (I had a lousy reason for missing that one year…my wife gave birth to twins). That’s been such an impactful recurring event for me and my father and brother, that my father had shirts made up for the 20th year. There’s nothing like walking around Vegas wearing a shirt with your own name on it. Good times!

Leaving the Sharkey gambling nostalgia aside, there’s a certain fatality and symmetry that Vegas represents to my professional career. As a math major, COBOL programmer, data analyst, analytics entrepreneur, and somewhat of a thought leader in learning analytics…what happens in Vegas doesn’t necessarily stay in Vegas. It can relate and impact how I think about analytics in higher education. No, I’m not planning on embezzling money from the company and betting it all on black (least of all because that’s a bland bet with a 46.37% probability of winning). But there are axioms and rules I follow at the tables that relate directly to analytics:

#1: Math above all

They don’t call ’em “Laws of Probability” for nothing. Statistics, normal distributions, and expected values are hard mathematical principles that are the underpinnings for Vegas gaming. With analytics and predictive modeling, it’s the same thing. If the institution develops a predictive model (or partners with a provider), there are algorithms, rules, and definitions that determine how the predictions are generated. They aren’t always right, but they should be helpful (the old ‘better than a coin flip’). Also models shouldn’t be a mystery. There should be no closed/proprietary formulae. If working with another provider, it should be a partnership (for more, see this blog post). As I’m fond of saying, your secret sauce isn’t so secret.

#2: Have a plan

I have a betting methodology I use for Blackjack. I’m not writing any books about it because it’s not a “guaranteed winning strategy” and I didn’t invent it. It’s a consistent methodology…I have a strategy for increasing/decreasing my bets from hand to hand and for how I play each hand. While this doesn’t guarantee me wins, I believe that it allows me to minimize my losses given the rules of the game. Press my bets when I win, never take insurance, always double down on a soft hand when the dealer shows a 5 or 6. The point is, I have a plan for how I play most hands so I’m not “winging it” and making irrational moves. If an institution is trying to leverage data in order to improve student success, they should know what they are going to do with the information before even starting the project. I use the following thought experiment with potential partners all of the time. Let’s say I give you a list of 100 student names and I guarantee you that all will drop from their classes next week. Forget how I know. The point is, what would you do with that list? This experiment takes the focus off of the data and analysis, and instead makes you think about the process and intervention.

#3: Know your goal

This is perhaps my most important Vegas rule. When I tell a family member or friend that I’m going to Vegas, someone will inevitably say “Good luck…I hope you win some money!” I will actually pause and tell them, “I don’t go there to win money…I go there to have fun.” That’s my goal. It’s entertainment. Sometimes the entertainment will cost me a few bucks…sometimes I may be entertained and come home with a few extra dollars in my pocket. The goal, though, is to enjoy my time with the folks I’m with and to have fun people-watching. When I talk about analytics, I ask a similar question. That is, what is your goal with using analytics? Is it to improve the course pass rates? Graduate more students in 4 years? Ensure that more students know how to solve two equations with two variables? These are all different goals and require different approaches to data. Saying that “we’re doing analytics” isn’t the right way to focus. I explained this in a blog post a couple of years ago where I talked about the three dimensions of student success (learning, progression, and engagement).

I hope you realize that I’m not trying to convince everyone to gamble or to follow my Vegas lead. To each his/her own. I just like it when worlds collide. I like drawing parallels between different situations and I like the little anecdotes that exemplify my philosophies. I’m sure there will be many more stories to come…you can bet on it.


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