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DATA ANALYTICS
division to facilitate (and subsidize) tech projects that sup- port many CSU campuses. The same group handles sys- temwide contracts with education technology companies, including learning management system vendors.
That’s where this story really begins — with the LMS. Kathy Fernandes, senior director for Learning Design and Technolo- gies, and Jean-Pierre Bayard, director for Systemwide Learn- ing Technologies and Program Services, both part of Aca- demic Technology Services, consider the LMS a goldmine for retention work. “We both love to say that the data in a student information system is dead data, meaning it’s already passed and there’s nothing you can do to change it,” said Fernandes. “However, if faculty are using the LMS, you can peek into the ‘live data’ and see where the students are. If you’re going to try to improve student retention, that’s where you need to catch them in their learning process — not after the course is over.”
With the support of Assistant Vice Chancellor Gerry Hanley, they set out to understand the potential for using that live data to advance the system’s student success goals. Along the way, they hoped, they could help the cam- puses learn how to redesign key courses and initiate a culture of data analytics in academic offerings.
The Starting Line
Although the 23 CSU campuses use different LMSes, Blackboard Learn and Moodle are the most common. Black-
“If faculty are using the LMS, you can peek into the ‘live data’ and see where the students are. To improve student retention, that’s where you need to catch them in their learning process — not after the course is over.”
— Kathy Fernandes, CSU
board offers predictive analytics products for each: Learn has the platform-agnostic Blackboard Predict, and Moodle has X-Ray Learning Analytics.
One of the differences between the two products is that Predict pulls past data from the student information system to flesh out the predictive model, while X-Ray relies solely on the data in the LMS. Also, noted Fernandes, X-Ray uses machine learning to interpret whether a student is just going through the motions or is truly engaged in the class. “It liter- ally will show you a student who may be replying in the dis- cussion board but really isn’t engaged in the discussion,” she explained. The program identifies those individuals in the discussion who are truly acting as the leaders.
Academic Technology Services offered to subsidize the testing of the two products, and a handful of schools came forward in December 2016. “It’s not like people aren’t paying attention to the wave of learning analytics,” said Fernandes. By joining the project, the campuses knew they’d get more help than if they were going to do it on their own. “They would have had to get a lot more oomph and budget behind it to make significant progress.”
Four campuses were early buy-ins. Chico State and San Diego State would test Predict. San Francisco State and Sonoma State would try X-Ray. Each campus would set its own goals and determine how best to choose the courses and faculty that would participate.
3 Kinds of Potholes
That’s when the project began hitting potholes.
Pothole No. 1: Sonoma State, which self-hosted Moodle, ran into a problem with the version of PHP running in its Moodle instance, which meant the latest version of X-Ray wouldn’t work. When new leadership arrived, the school
decided it was time to hold an LMS review.
Solution: Make sure the car is ready for the road trip.
The university put the X-Ray pilot on hold. “You don’t change your LMS and then do learning analytics at the same time,” explained Bayard.
Pothole No. 2: The X-Ray pilot at San Francisco State was
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