Journal
| Title | Mining student at risk in higher education using predictive models Posted by January Naga |
| Authors | Febro, January; Barbosa, Jocelyn |
| Publication date | 2017 |
| Journal | Journal of Advances in Technology and Engineering Research |
| Volume | 3 |
| Issue | 4 |
| Pages | 117-132 |
| Abstract | This research uses an approach in Data Mining techniques to analyze historical data of students. The goal was to predict and investigate factors affecting student leavers using university data on at-risk freshmen students in higher institution. Feature selection as preprocessing methods were utilized for 30 potential predictors to identify the most relevant factors. The models were implemented using various data mining algorithms and it is found that Adaptive Boosting using Decision Tree gave 92.20% accuracy. The models were trained using records from student dataset collected from the Mindanao State UniversityâÃÂàMarawi, academic year 2010-2015. In addition, this research also verified the precision of the models through 10-fold cross validation, which can give veracity about what kind of data mining models works best in HEI data mining analysis. Benefits of the prediction model includes improving student retention and graduation rates. |
| Index terms / Keywords | Educational Data Mining, Knowledge Discovery in Databases, Student Prediction |
| DOI | I |