How Analytics can Define the Future of Education Industry

How Analytics can Define the Future of Education Industry

Global Education market - 2012 to 2017

Education continues to play an important role in any country’s overall growth. The education market has become more challenging due to the rapid growth and evolution in the modes of imparting education; schools, colleges, private tuition, online education courses, distance education, test preparations, professional trainings etc.

The most concerning factor for universities or educational institutions across the world is student dropout rate, especially in developed countries. Here are some facts based on a research about the student drop out patterns in large economies.

Challenges

  • Multiple modes of education
  • Rapidly changing education trends
  • Targeting the right population is difficult
  • Selecting the right students and retaining them (curbing the drop-out rate)
  • Planning and budgeting for sustainable expansion
  • Instructor and Curriculum development

Consequences

  • Lose business gradually due to unawareness about the market and the trend
  • High drop-out rate
  • Higher debt pressure on dropouts
  • Increasing loan defaults
  • Failure of the education system
  • Universities lose revenues

How can Analytics help?

Analytics can play a vital role in the education industry by helping universities and institutes make data oriented informed decisions.

Analytics in Education

What-if scenarios, plans, budgets, forecasts

Analyzing academic, financial and operational data helps identify specific patterns and trends. This insight helps better decision making around planning budgeting and forecasting.

Reports, Dashboards, Scorecards

  • Tracking students’ performance across cohort, departments and courses and creating clusters based on different characteristics enables targeted strategies for specific segments of students. such as Students pursuing a particular course and performing exceptionally well or average or below average students finding the course very tough. For the below average cluster, the university administration can initiate structures intervention and provide them some special training to ensure retention and improved performance.
  • Analyzing the attendance data and focusing on students who missing the assigned course credit can help identify likely dropouts. Specific actions or retention programs for such students can have a significant impact on dropout rates.

Analyzing the trend

  • Analyze the curriculum and instructor development effectively on a regular basis to keep up with latest trends
  • Getting insights on how to  stay competitive and maximize profits
  • Measure spend effectiveness against the results
  • Manage fundraising , advancements and alumni relations
  • Linking student information with administrative data enables better capacity planning 

Survey Analytics for understanding student sentiment

Conducting satisfaction surveys and analyzing survey data is crucial for understanding the students’ sentiments. Surveys help identify key areas of focus needed thereby enabling focused investments and action plans. Periodic surveys help track improvement in key areas of concern and thereby the effectiveness of the actions taken.

Predictive analytics

  • Predictive analytics can help predict the future based on the past. It involves digging into historical data, finding key patterns and predicting future trends on the basis of those patterns. Predictive analytics involves various techniques viz. statistics, modelling, machine learning and data mining.
  • Data mining can help understand the reasons behind a student’s decision to leave the course midway. E.g. Insufficient or no financial-aid, high cost of education, poor grades, choice of subjects, distance from home, good job opportunity or better choice of college etc.

Case Study

BRIDGEi2i provided a predictive analytics solution to one of the premier Universities in US for scoring applicants / students who drop out either after one semester or after one year of the 4-year course. These dropouts were causing a huge cost impact since these seats remained vacant for the remaining years.

BRIDGEi2i‘s predictive analytics solution helped the university predict a particular student’s probability of drop-out, right at the time of application, based on certain characteristics they possess. As a result, the university management took several measures to to fine tune their shortlisting process and make necessary interventions to ensure the student stays back and completes the course.

BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. Our analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems and make data driven decisions across pan-enterprise processes to create sustainable business impact. To know more visit www.bridgei2i.com

This blog is authored by Nishant Jain, Analytics  Consultant at BRIDGEi2i 

About BRIDGEi2i: BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. Our analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems and make data driven decisions across pan-enterprise processes to create sustainable business impact. To know more visit www.bridgei2i.com

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The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position or viewpoint of BRIDGEi2i.

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Comments (3)

Analytics needs to be an integral resource for reviewing, collaborating and developing the methods of how we teach our students and our means for assessment.

Very true! Thanks for your comment

Agree! Thanks for your comment

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