Name
Title | ||
|---|---|---|
Improving Predictive Modeling for At-Risk Student Identification: A Multistage Approach |
Abstract | ||
|---|---|---|
Performance prediction is a leading topic in learning analytics research due to its potential to impact all tiers of education. This study proposes a novel predictive modeling method to address the research gaps in existing performance prediction research. The gaps addressed include: the lack of existing research focus on performance prediction rather than identifying key performance factors; the lack of common predictors identified for both K-12 and higher education environments; and the misplaced focus on absolute engagement levels rather than relative engagement levels. Two datasets, one from higher education and the other from a K-12 online school with 13 368 students in more than 300 courses, were applied using the predictive modeling technique. The results showed the newly suggested approach had higher overall accuracy and sensitivity rates than the traditional approach. In addition, two generalizable predictors were identified from instruction-intensive and discussion-intensive courses. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TLT.2019.2911072 | IEEE Transactions on Learning Technologies |
Keywords | Field | DocType |
Predictive models,Input variables,Brain modeling,Analytical models,Educational technology,Bibliographies | Educational technology,Learning analytics,Computer science,Knowledge management,At-risk students,Learner engagement,Performance prediction,Higher education | Journal |
Volume | Issue | ISSN |
12 | 2 | 1939-1382 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jui-Long Hung | 1 | 84 | 8.76 |
| Brett E. Shelton | 2 | 2 | 1.04 |
| Juan Yang | 3 | 52 | 10.70 |
| Xu Du | 4 | 37 | 15.92 |