An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout
Identifying and monitoring students who are likely to dropout is a vital issue for universities. Early detection allows institutions to intervene, addressing problems and retaining students. Prior research into the early detection of at-risk students has opted for the use of predictive models, but a comprehensive assessment of the suitability of different algorithms and approaches is complicated by the large number of variable features that constitute a student's educational experience....[Show more]
|Collections||ANU Research Publications|
|Source:||ACM International Conference Proceeding Series|
|01_Manrique_An_Analysis_of_Student_2019.pdf||947.65 kB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.