Predictive Analytics for Student Sucess

The research interests of our group is in finding patterns in data. These patterns may be subtly hidden or blatantly obvious. The tools we use include but are are not limited to:

  • Descriptive Statistics
  • Quantitative Data Analysis  Live-Oak-Sepia
  • Qualitative Data Analysis
  • Machine Learning
  • Predictive Analytics
  • Mathematical Modeling

Research is focused in specific domain specific areas, particularly student success in college. In education, some typical research questions are:

Does the choice of instrutor affect the distribution of grades in a class?
Does the choice of instructor affect the distribution of grades in a class?
  • What are the early predictors of student success (failure) (e.g, GPA, specific course grades, GPA, study skills, work/study, etc)?
  • Can we identify students early enough in their academic careers to make a difference by developing models using predictive analytics?
  • As an instructor, what did my students learn (not learn) in my class last week, and what do I have to do to fix things next week?


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