Name
Abstract | ||
|---|---|---|
Colleges and universities are increasingly interested in tracking student progress as they monitor and work to improve their retention and graduation rates. Ideally, early indicators of student progress, or lack thereof, can be used to provide appropriate interventions that increase the likelihood of student success. In this paper we present a framework that uses data mining and machine learning techniques, and in particular, linear regression and a Markov network (MN), to predict the performance of students early in their academic careers. The results obtained show that the proposed framework can predict student progress, specifically student grade point average (GPA) within the intended major, with minimal error after observing a single semester of performance. Furthermore, as additional performance is observed, the predicted GPA in subsequent semesters becomes increasingly accurate, providing the ability to advise students regarding likely success outcomes early in their academic careers. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICMLA.2014.74 | Machine Learning and Applications |
Keywords | Field | DocType |
Markov processes,data mining,educational computing,educational institutions,graph theory,learning (artificial intelligence),regression analysis,GPA,MN,Markov networks,academic careers,colleges,curriculum graphs,data mining,grade point average,graduation rate improvement,linear regression,machine learning techniques,retention improvement,student performance prediction,student progress prediction,student progress tracking,universities,Markov Network,educational analytics,linear regression,student success | Data science,Graph,Psychological intervention,Grading (education),Computer science,Markov chain,Curriculum,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
3 | 0.38 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmad Slim | 1 | 53 | 5.89 |
| Heileman, G.L. | 2 | 26 | 4.69 |
| Jarred Kozlick | 3 | 8 | 1.27 |
| Chaouki T. Abdallah | 4 | 209 | 34.98 |