25,178 research outputs found
Predicting time to graduation at a large enrollment American university
The time it takes a student to graduate with a university degree is mitigated
by a variety of factors such as their background, the academic performance at
university, and their integration into the social communities of the university
they attend. Different universities have different populations, student
services, instruction styles, and degree programs, however, they all collect
institutional data. This study presents data for 160,933 students attending a
large American research university. The data includes performance, enrollment,
demographics, and preparation features. Discrete time hazard models for the
time-to-graduation are presented in the context of Tinto's Theory of Drop Out.
Additionally, a novel machine learning method: gradient boosted trees, is
applied and compared to the typical maximum likelihood method. We demonstrate
that enrollment factors (such as changing a major) lead to greater increases in
model predictive performance of when a student graduates than performance
factors (such as grades) or preparation (such as high school GPA).Comment: 28 pages, 11 figure
Finding Influential Users in Social Media Using Association Rule Learning
Influential users play an important role in online social networks since
users tend to have an impact on one other. Therefore, the proposed work
analyzes users and their behavior in order to identify influential users and
predict user participation. Normally, the success of a social media site is
dependent on the activity level of the participating users. For both online
social networking sites and individual users, it is of interest to find out if
a topic will be interesting or not. In this article, we propose association
learning to detect relationships between users. In order to verify the
findings, several experiments were executed based on social network analysis,
in which the most influential users identified from association rule learning
were compared to the results from Degree Centrality and Page Rank Centrality.
The results clearly indicate that it is possible to identify the most
influential users using association rule learning. In addition, the results
also indicate a lower execution time compared to state-of-the-art methods
- …