Context (archaeology)

Mathematics education

Engineering

Educational data mining

Analytics

Biology

Statistics

Systems engineering

Economics

Student engagement

Computer science

Cognitive science

Paleontology

Statistic

Psychology

Task (project management)

Mathematics

Learning analytics

Attendance

Data science

Artificial intelligence

Economic growth

Machine learning

Intuition

Computers & Education

Finding a solution to the problem of student retention is an often-required task across Higher Education. Most often managers and academics alike rely on intuition and experience to identify the potential risk students and factors. This paper examines the literature surrounding current methods and measures in use in Learning Analytics. We find that while tools are available, they do not focus on earliest possible identification of struggling students. Our work defines a new descriptive statistic for student attendance and applies modern machine learning tools and techniques to create a predictive model. We demonstrate how students can be identified as early as week 3 (of the Fall semester) with approximately 97% accuracy. We, furthermore, situate this result within an appropriate pedagogical context to support its use as part of a more comprehensive student support mechanism.

Utilizing early engagement and machine learning to predict student outcomes