Predicting adult students’ online learning persistence: a case study in South Korea using random forest analysis
Author:
Affiliation:
1. Institute of Future Distance Education, Korea National Open University, Seoul, South Korea
2. College of Liberal Arts, CHA University, Pocheon-si, Gyeonggi-do, South Korea
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/14703297.2024.2305939
Reference62 articles.
1. Process Variable Importance Analysis by Use of Random Forests in a Shapley Regression Framework
2. Shape Quantization and Recognition with Randomized Trees
3. College Influence: A Comprehensive View
4. Astin, A. W. (1975). Preventing students from dropping out. Jossey-Bass.
5. Student involvement: A developmental theory for higher education;Astin A. W.;Journal of College Student Development,1999
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