Content-type: text/html
[] []
Applications of Analytics

Identify Students At Risk of Failing

Category: Predictive Analytics

Working with a learning management system and different types of data, analytics tools can identify factors statistically correlated with students at risk of failing failing or dropping out.” (Scholes, 2016). For example, a Jisc report describes several such projects, including one at New York Institute of Technology (NYIT) that used four data sources: “admission application data, registration / placement test data, a survey completed by all students, and financial data.” (Sclater, Peasgood and Mullan, 2016) And for example, “using the trace data collected by the Blackboard learning management system (LMS) and data from the institutional Student Information System (SIS), Course Signals uses a data-mining algorithm to identify students at risk of academic failure in a course” (Gasevic, Dawson & Siemens, 2015).

The purpose of identifying at-risk students is to provide the institution with the opportunity to prevent students from failing or dropping out before it happens. Analytics have been effective in this regard; for example, “Georgia State University has a proven record of using predictive analytics to improve student retention and graduation rates,” according to university staff. (Neelakantan, 2019; Banan, 2019) This institution benefits as well; an RPK report recently suggested that “expected increases in student retention rates could generate net revenue averaging $1 million annually per institution” (Desrochers & Staisloff, 2017).

Examples and Articles

5 applications of learning analytics

"It is possible to predict some course variables: how many students will pass and how many will fail, which will be the churn rate, which will be the group average, etc." Also: "Data visualization is the main application of learning analytics. It aims to help instructors to visualize and analyze the ongoing activities of the students. Once the data is stored, it is possible to create dashboards where all the data can be easily visualized." Offers an example of a dashboard.

Direct Link

,

The Student Net- Using Machine Learning Algorithms to Address our Failing Guidance System
"We developed The Student Net to correctly identify at-risk students and help direct and organize guidance resources so that these at-risk students get the support they need before serious consequences occur." Direct Link

Do you have another example of Identify Students At Risk of Failing? Suggest it here

Force:yes