Prediction of College Major Persistence Based on Vocational Interests, Academic Preparation, and First-Year Academic Performance

208 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Dropout prediction in e-learning courses through the combination of machine learning techniques (2009, methodology): “Another study which focuses on the prior academic performance of the students in order to identify dropout predictors is the study of Allen & Robbins (2008) who use hierarchical logistic regression to test a theoretical model to predict college major persistence over a large data set of 50,000…”
Trajectories of change in students’ self-concepts of ability and values in math and college major choice (2015, background): “Allen and Robbins (2008) found that interest in a subject in high school as reported on the interest inventory of the US college readiness ACT assessment was predictive of major choice in college.”
Patterns of Persistence in Intended College Major with a Focus on STEM Majors. (2010, methodology): “Based on previous research (Allen & Robbins, 2008) and an analysis of highly related domains, a number of CIP codes were grouped together by first two digits to represent a broad major-field category.”