Catalyst has innovated with the University of Canterbury (UC) using Moodle and machine learning technology TensorFlow to help both staff and students identify engagement levels versus their cohort. This early intervention enables students to be connected to additional support, wider university services and ultimately improve engagement and performance levels.
UC, like the rest of universities in New Zealand, experiences first-year student drop-out rates of around 20%, and even higher drop-out rates amongst Maori and Pacific students. Through taking action from these analytics UC plans to significantly increase retention rates. By extending their Moodle platform UC can proactively monitor and connect with at-risk students, specifically among Maori and Pacifica populations, with appropriate support to assist students before they get to the point of dropping out.
Catherine Moran, Vice-Chancellor Academic at the University of Canterbury, says this machine learning helps to monitor what students are doing in their classes and their behaviours with the learning platform. During COVID-19 with all learning online the tool has been extensively utilised and already has made a significant difference. A number of students were identified with dropping engagement levels which have then been guided and brought back on-track in the vast majority of cases. From a simple engagement of a text message to check in, to helping with computer equipment and others receiving more personal support the end result has been very positive.
This is a fantastic example of using machine learning technology for good, Catalyst is proud to help and will continue to support and collaborate with the University of Canterbury.
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