Publisher
Springer Nature Switzerland
Reference19 articles.
1. Adnan, M., et al.: Predicting at-risk students at different percentages of course length for early intervention using machine learning models. IEEE Access 9, 7519–7539 (2021)
2. Alqahtani, T., et al.: The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Res. Soc. Adm. Pharm. 19(8), 1236–1242 (2023)
3. Beeching, E., et al.: Open LLM Leaderboard (2023)
4. Berka, P., Marek, L.: Bachelor’s degree student dropouts: who tend to stay and who tend to leave? Stud. Educ. Eval. 70, 100999 (2021)
5. Coleman, C., Baker, R., Stephenson, S.: Brightbytes: a better cold-start for early prediction of student at-risk status in new school districts (2019)