Machine Learning for Survival Analysis: A Survey <s> Student Retention <s> Survival analysis was used to model theretention of 8,867 undergraduate students at OregonState University between 1991 and 1996. Attrition wasfound to increase with age, and decrease with increasing high school GPA and first-quarter GPA.Non-residents had higher attrition rates than didresident and international students, and students takingthe Freshman Orientation Course appeared to be atreduced risk of dropping out. Statistically significantassociations of retention with ethnicity/race andcollege at first enrollment were also noted. Aproportional hazards regression model was developed topredict a student's probability of leaving school basedon these demographic and academic variables. Theseanalyses are helping to guide the university's effortsto improve retention through marketing, recruitment, and the development of orientation and otherprograms. <s> BIB001 </s> Machine Learning for Survival Analysis: A Survey <s> Student Retention <s> Retention of students at colleges and universities has been a concern among educators for many decades. The consequences of student attrition are significant for students, academic staffs and the universities. Thus, increasing student retention is a long term goal of any academic institution. The most vulnerable students are the freshman, who are at the highest risk of dropping out at the beginning of their study. Therefore, the early identification of {\emph{``at-risk''}} students is a crucial task that needs to be effectively addressed. In this paper, we develop a survival analysis framework for early prediction of student dropout using Cox proportional hazards regression model (Cox). We also applied time-dependent Cox (TD-Cox), which captures time-varying factors and can leverage those information to provide more accurate prediction of student dropout. For this prediction task, our model utilizes different groups of variables such as demographic, family background, financial, high school information, college enrollment and semester-wise credits. The proposed framework has the ability to address the challenge of predicting dropout students as well as the semester that the dropout will occur. This study enables us to perform proactive interventions in a prioritized manner where limited academic resources are available. This is critical in the student retention problem because not only correctly classifying whether a student is going to dropout is important but also when this is going to happen is crucial for a focused intervention. We evaluate our method on real student data collected at Wayne State University. Results show that the proposed Cox-based framework can predict the student dropouts and semester of dropout with high accuracy and precision compared to the other state-of-the-art methods. <s> BIB002
In higher education, student retention rates can be evaluated by the percentage of students who return to the same university for the following semester after completing a semester of study. In the United States and around the world, one of the long-term goals of a university is to improve student retention. Higher student retention rates make it more probable that the university will be ranked higher, secure more government funding, and have an easier path to program accreditation. For all of these reasons, directors and administrators in higher education constantly try to implement new strategies to increase student retention. Survival analysis has shown some success in improving student retention BIB001 BIB002 . The goal of survival analysis is to estimate the time of event occurrence, which is critical for addressing student retention problems because correctly identifying whether a student will drop out and estimating when that drop out will happen are both important. In such cases, the ability to reliably estimate the drop out risk at an early stage of student education using both pre-enrollment and post-enrollment information would be immensely helpful.