Using Data Mining for the Early Identification of Struggling Learners in Physician Assistant Education
| Autor: | Erik W. Black, Breann Garbas, Shalon R. Buchs |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Bootstrap aggregating
Small sample Logistic regression computer.software_genre Risk Assessment Education Random forest Machine Learning Identification (information) Logistic Models Physician Assistants Outcome variable Intervention (counseling) Data Mining Humans Data mining Set (psychology) Psychology computer Medical Assisting and Transcription |
| Zdroj: | Journal of Physician Assistant Education. 32:38-42 |
| ISSN: | 1941-9430 |
| Popis: | PURPOSE Despite the importance of early intervention and remediation, the relatively short duration of physician assistant education programs necessitates the importance of early identification of at-risk learners. This study sought to ascertain whether machine learning was more effective than logistic regression in predicting remediation status among students, using the limited set of data available before or immediately following the first semester of study as predictor variables and academic remediation as an outcome variable. METHODS The analysis included one institution and student data from 177 graduates between 2017 and 2019. We employed one data mining model, random forest trees, and compared it to a traditional predictive analysis method, logistic regression. Due to the small sample size, we employed leave-one-out cross-validation and bootstrap aggregation. RESULTS Data provided evidence that the random forest algorithm correctly identified individuals who would later experience academic intervention with a 63.3% positive predictive value, whereas logistic regression exhibited a positive predictive value of 16.6%. CONCLUSIONS This single-institution study indicates that predictive modeling, employing machine learning, may be a more effective means than traditional statistical methods of identifying and providing assistance to learners who may experience academic challenges. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|