Month: July 2017

5 Lessons Universities Can Learn from Big Data Analytics

There has been a keen interest among universities to take advantage of big data analytics to raise the teacher’s effectiveness and improve students’ performance, while at the same time reducing the administrative workload. The student’s performance data, especially the big data courses, can be progressively captured as part of the online classroom and software-based online classroom exercise. Such data is combined with behavioral data which is taken from sources such as student’s surveys, blogs, meeting notes, student professor, and social media.

The higher education analytics

The higher institution comes with numerous students’ engagement points that can significantly benefit from big data analytics right from the alumni giving to the initial profiling.

1) Student acquisition

By taking into account the historical demographics and performance data of former and current students, it’s easier to come up with profiles of students that are highly likely to enroll. It’s also easier to employ graphic analysis that factors in prospective and current students social networks to identify the first-level friends that are highly likely to join the institution.

2) Student’s performance effectiveness

Through monitoring of the current students test performance and comparing the pre-requisite test results, big data university can integrate the teacher’s notes and social media data so as to create a detailed profile of the propensities and students behavior.

3) Student retention

The combination of Prerequisites score and analytics such as the student’s workgroups and performance effectiveness coupled with financial and social data as well as individual demographic can deliver recommendations that enable institutions to decide on whether they should retain the students. Higher education Big data certification plays a role in the delivering the effectiveness of specific recommendations that are based on the successes of previous intervention. Teachers are also in a position to make their recommendations that can be monitored for result purposes and applied to future interventions.

4) Teacher’s Effectiveness

While some institutions may be limited in terms of fine tuning and measuring teachers performance those that can measure teacher’s performance can greatly benefit from the analysis of the teacher’s performance. Performance can be measured in terms of student’s aspirations, student’s behavioral classifications, student’s demographics, and the number of students. Learn more information at Schulich School of Business or speak with one of their experts if you have any questions.

5) Student’s lifetime value

Making an early preparation regarding potential giving levels for students and alumni is one the core big data definition that shouldn’t be underestimated. Understanding the future and current wealth potential can be a great tool for messaging, targeting, and profiling to optimize alumni giving.