Potential for big data in higher education

Big data has been widely discussed as a way to understand data beyond general insights and into more advanced.

Colleges are a crucial place for student learning and development and with the amount of press devoted to student debt, access to college and attrition, the question is what are we doing with our data? We hear words like assessment and accountability and the understanding of the data is mostly descriptive – let’s take this opportunity to challenge ourselves further with the data.

Predictive analytics and machine learning has been widely successful in cracking down on crime, credit fraud as well as predicting the movies we’d like to watch on Netflix.

Here are some ideas higher education can use data science to support the initiatives and shape the student experience:

  1. Using admissions data of enrolled students and their academic record to predict attrition and retention
    1. Much talk has been about grades, test scores that do not validate a student’s success but can we use the data to track students in academic difficulty? Maybe this might allow us to offer additional services and tutoring.
  2. Using predictive analytics in improving student learning and classes students might be interested in teaching
    1. Using the existing data in a curriculum about the method and style of teaching, faculty teaching experience, class size, grades on different assessments to determine whether the class is going to be successful in their current form or
  3. Amount of debt: income ratio upon graduation
    1. Using student’s financial aid profile to examine patterns between income and amount of student loans
      1. The data might help us identify potential students at risk for significant debt and help students understand how much loans they really need for more financial responsibility.

Here are some additional resources on ways to use data science for higher education:

EdSurge

 

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