Unleashing the Power of Predictive Analytics to Identify At-Risk Students in Computer Science

This publication has 41 references indexed in Scilit:

• Better Than Expected: Using Learning Analytics to Promote Student Success in Gateway Science
  Change: The Magazine of Higher Learning, 2014
• A Survey on Pre-Processing Educational Data
  2013
• Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models
  Computers & Education, 2013
• Predicting and analyzing secondary education placement-test scores: A data mining approach
  Expert Systems with Applications, 2012
• Course signals at Purdue
  2012
• Early and dynamic student achievement prediction in e‐learning courses using neural networks
  Journal of the American Society for Information Science and Technology, 2008
• Is Modeling of Freshman Engineering Success Different from Modeling of Non‐Engineering Success?
  Journal of Engineering Education, 2008
• Prediction of University Students' Academic Achievement by Linear and Logistic Models
  The Spanish Journal of Psychology, 2008
• Using Correlational and Prediction Data to Enhance Student Achievement in K-12 Schools: A Practical Application for School Counselors
  Professional School Counseling, 2006
• A Coefficient of Agreement for Nominal Scales
  Educational and Psychological Measurement, 1960