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quality. “At our volume, it’s tough to be able to look at every interaction throughout a day to determine where we have opportunities for improvement,” Dover explained. “With machine learning, with some of the deep data analysis, with this sentiment analysis, it can really surface those problem points faster for us with less effort, so we can focus on them.”
The results could also help feed into student success, he suggested: “Okay. They were expressing major issues with WiFi quite a few times. We notice they’re having issues getting assignments turned in and on time. Is this why?”
Don’t Ignore What’s on Social Media
Who would think that 140 characters could communicate so much? But rather than turning to Twitter as a thermometer of student sentiment, Dover advised keeping things simple. “Don’t try and eat the elephant in one bite. Figure out what the pain point is and what you want to monitor.”
Then develop a plan for response with the understanding “that the plan is going to change because you’re going to learn as you go along.” If, as Arizona State does, you come up with scripted verbiage used repeatedly in your responses, have it vetted by the communications folks.
Turning to Twitter as a lever for institutional change may seem a stretch. “It’s very easy for people to dismiss negative comments as someone just complaining,” acknowledged U Georgia’s Testement. However, she added, a continual flurry of negative or positive comments signals something else going on “that’s worth taking a deeper dive into” and learning about.
Dian Schaffhauser is a senior contributing editor for Campus Technology. CAMPUS TECHNOLOGY | July 2017 BACK TO TOC

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