Insight and Action Analytics: Three Case Studies to Consider
The main purpose of this Journal was to analyse the data of undergraduate-graduate student, leverage insight and action analytics in their ongoing efforts to help students learn well and finish strong. de ne insight analytics as the family of activities that bring data from disparate sources together to help create a more complete view of student progression. In the most basic terms, this means
(a) federating data from an institution’s Student Information System (SIS) and Learning Management System (LMS);
(b) using sophisticated data science tools and techniques, including machine learning, data availability segmentation and clustering, to create and compete feature variables derived from the diverse sources;
(c) building an array of predictive models; and then
(d) leveraging a variety of visualization techniques we explore the resulting historic and predictive student progression/ flow models for insights that help better understand how students succeed and face challenges on their higher education journeys.
After they collected all the data, models are developed, they create a cloud– based, production–quality, predictive flow–model infrastructure for each institution that is updated at minimum on a rolling five–term cadence to keep the student–level predictions as current as possible. If they already reached this step, they will add some additional data sources in this mix, such as Census, application data, card swipe, CRM, and more, and then testing these new data streams for added predictive power to drive decisions about how or whether to add them to the production system.
They’ve created a platform application called Illume TM that brings insights from this work to our institutional partners, allowing them to view student progression dynamics filtered by chosen segments (e.g., part–time, full–time, Pell recipients, distinct campuses, members of intervention category), often testing assumptions about performance and possible historic and predictive trends. They also developed some other softwares to support this, such as Degree MapTM, InspireTM, and Hoot.MeTM family of apps.
There are three study case that ran by Civitas Learning.
Case Study One: Early Intervention for Course Success: Pilot program to test the efficacy of using predictive–analytics–based interventions on driving improvements to student course completion rates.
Case Study Two: Early Intervention by Faculty for Persistence Gains Executive Summary: Pilot program to test the efficacy of using predictive analytics based interventions to drive improvements in student persistence rates.
Case Study Three: Early Intervention by Advisors for Persistence Gains Executive Summary : Pilot program to test the efficacy of using predictive analytics based interventions on driving improvements to student persistence.
These research is very useful in terms to help the students learn well and finish strong. The application or platform that has been made are very useful in order to analyse the data and giving the predictive analysis. By having predictive analysis system, they will be on the right track and keep focus on how to finish strong, and also learn well. They will know earlier if there’s any abnormal symptom that may lead to fail. The system will predict with perhaps, high accuracy.