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Identify Predictors of Student Disengagement - Apply Machine Learning Tools - Arts and Humanities Assessment Answer
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Internal Code: E_AI_DHEI_AHA
Arts and Humanities Assessment Answer
TASK: Hypothetical Scenario In the year 2022 All-Sydney University, Cabramatta, is having a crisis in student retention rates. The University Trustees have told senior management they must fix whatever is causing this, because if they don’t the State and Federal funding on which the university relies will be stopped, and that will compromise their capacity to offer courses and to fund student facilities. Senior staff in the University are in a panic but are unsure what to do. Student disengagement is a long-standing problem. And the teaching staff are at a loss about what to do; they’re having a hard enough time dealing with sudden curriculum changes and the introduction of digital innovations neither they nor the students fully understand. So, everyone is wondering: what do we do? Where do we even start? Then the Deputy Vice Chancellor for Infrastructure has an idea. What if the new digital administrative and teaching systems aren’t the problems, but the key to a solution? Why not look at the rich range of data already collected by various automated and networked systems to figure out why students are dropping out of their degrees? There’s the data on GPA, grades, and attendance, and the data on misconduct cases and outcomes. More importantly though, in the two years since the University introduced a scannable student ID card system, they’ve got a record number of individual data points. Those scannable cards register when specific parts of the campus facilities are used (gym; library; computer labs), digitally signals attendance in tutorials and lectures, logs purchases in the co-op and food court vendors (as the ID cards work for cashless buying), and record a full record of access to online learning resources. In addition to the data logged by the use of ID cards round campus, parking is monitored by license plate number recognition cameras at all the university entrances. The campus Wi-Fi network also tracks students’ internet use and can monitor individual student movement throughout campus grounds, using their mobile phone as proxies. All these new measures have over the last couple of years produced large quantities of ‘small data’ that—if machine learning and data analysis was used on that data—could identify the causes for the crisis in student retention: and with a high degree of accuracy about the specific individual factors that might make a student at-risk of dropping out. And once they know that, they can design intervention strategies to turn the situation around—promoting positive educational outcomes for all. So, a local data science company, NoSeen, is contracted by the university to:- identify predictors of student disengagement as an indicator for dropping out, and apply machine learning tools (text and data mining) to these predictors in order to flag at-risk students
- equip teachers with fine-grained information to allocate resources and assist at-risk students by suggesting specific interventions, such as talking to the student directly, adjusting their workload and schedule, contacting parents or guardians, or automatically triggering contact by the student counselling service or academic course advisers;
- ensure transparency in the way the system is used.
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