Deep learning-based AI model for predicting academic success and engagement among physical higher education students
Chenyang Li, Zhiying Cao · 2025
Read Deep learning-based AI model for predicting academic success and engagement among physical higher education students by Chenyang Li, Zhiying Cao (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12749542.txt.
Description
A peer-reviewed research article from Europe PMC (2025). Academic achievement and student engagement are essential components of educational success, particularly in physical education (PE) programs, where cognitive and physical competencies intersect. This study introduces HybridStackNet, a stacked ensemble model integrating Random Forest and Support Vector Machine (SVM) as base learners and Logistic Regression as a meta-learner, aimed at jointly predicting academic success and engagement among higher education PE students. The model was trained on a publicly available dataset from Kaggle comprising 500 instances with academic, behavioral, and physical attributes.
Catalog record
| CREATOR | Chenyang Li, Zhiying Cao |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 127 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |