RMIT’s early warning sign model
Using predictive analytics, the Early Warning Signs (EWS) model pulls timely, in-depth data from over 70 data sources, allowing RMIT to identify students who may be at risk, and who may require support.
AUDIO: Ambient music track (happy and bubbly) starts and plays throughout.
VISUAL ON SCREEN: RMIT logo
Professor Belinda Tynan [00:00:03] Over the past 18 months we've been developing a new model. It's called the Early Warning Signs model. Now what is it? What, what does EWS mean? Because you probably heard it being mentioned across the university.
David McLay [00:00:15] There's already a lot of work done at RMIT around supporting students and student retention. And so what E.W.S. does is provide more data that helps to inform the decision making around the activities themselves both in the classroom and around the student experience.
Nikhil Sobti [00:00:33] Even though we know that RMIT is already ahead of the curve when it comes to student attrition. So we don't suffer severe attrition problem but we still want to support our students all the way through to completion because that's the outcome they signed up for.
Theo Dufort [00:00:46] So the first steps were to define what attrition is and how to spot it. And the next one was to establish a list of hypotheses around attrition; what drives it and what doesn't.
Julie Dang [00:00:58] The model would develop based on three hundred and sixty degree view on the student life.
Professor Belinda Tynan [00:01:05] Part of the E.W.S modelling and bringing all that data together enables us to be able to predict the behaviours of our students and then actually put in some actions to help support our students even more.
Raian Isaac [00:01:16] Last year we piloted the Early Warning Signs model in Semester Two 2018. We engaged program teams, RMIT Connect and SLAMs Connect to find better ways to support our students. The program teams were given specific insights about their students so they could actually prioritize and action them. With RMIT Connect and SLAMs Connect, we gave them information about 500 students who they reached out to with phone calls. The majority of them, 86 percent found them very helpful and almost one in three said that they actually needed some additional help and put their hand up for it.
Mairead McMillan [00:01:48] A lot of the students that did say that their services weren't relevant did identify that they, if they were struggling at that point in time that the call would have been incredibly helpful. And that to other students it would be useful.
Theo Dufort [00:02:04] The big idea is to try and understand the factors which lead students to leave RMIT and try and see if we can help them in any way before that happens.
VISUAL ON SCREEN [00:02:15]: RMIT data scientists walking on campus.
David McLay [00:02:15] So in semester two we're actually going to be adding more programs and we'll have almost 80 programs involved for EWS. The idea being that when we move into 2020, we will have all programs at the University getting E.W.S data.
Mairead McMillan [00:02:31] So as a student I know myself that sometimes I'm not always willing to reach out for help. So RMIT reaching out to students, I think is quite beneficial because we don't always have time to look into these services, and being told exactly what services might suit a student I think is really helpful and I think it benefits a lot of students, it would definitely benefit me.
VISUAL ON SCREEN [00:02:37]: Mairead McMillan walking on campus
TEXT ON SCREEN [00:02:56]: Thanks to all the RMIT teams involved in EWS: Analytics & Insights, Strategy Office, Learning ANALYTICS, RMIT Connect & Student Life.
VISUAL ON SCREEN: RMIT logo
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