Identifying the High Risk Duration to the Semester to Drop Out of College Using Dropout Probability
DOI:
https://doi.org/10.52731/lir.v004.287Keywords:
Dropout Probability, Dropout Prediction, Droput RiskAbstract
This study investigates dropout probability, which quantifies the risk of a student discontinuing their studies, to determine the length of the high-risk phase prior to dropout. The departure of a student from college poses significant adverse impacts on both the individual and the institution. Universities have implemented various dropout prevention measures; however, their effective-ness hinges on timely execution. Through an analysis of dropout probabilities by semester for 173 students who eventually dropped out, it was found that these students were at an increased risk of dropping out an average of 2.97 semesters, or approximately 18 months, before actually leaving the university.
References
Ministry of Education, Culture, Sports, Science and Technology. [Survey on the status of student enrollment (drop-outs and leaves of absence) (as of the end of the 2022 academic year)] “Gakusei no shugakujokyo (Chutai, Kyugaku) tou ni kansuru Chosa no Kekka ni Tsuite (in Japanese)”. 2023.
Yomiuri Shimbun Kyoiku Network, [University Competencies 2019] ”Daigaku no Jitsury-oku 2019 (in Japanese)”,Chuo Koron Shinsho, 2018..
Ministry of Education, Culture, Sports, Science and Technology, [Withdrawal Status of Six-Year Programs of School of Pharmacy - Results of the 2023 Survey] “Yakugakubu no 6nensei Katai ni okeru Taigaku Jokyo tou – 2023nen (Reiwa 5nen) do Chosa Kekka”, 2024; https://www.mext.go.jp/content/202309011-mxt _igaku-000027071_01.pdf
A. Seidman, “Taking Action: A Retention Formula and Model for Student Success,” College student retention: Formula for student success, pp. 267–284, Jan. 2012.
M. A. Bingham and N. W. Solverson, “Using Enrollment Data to Predict Retention Rate,” J. Stud. Aff. Res. Pract., vol. 53, no. 1, pp. 51–64, Feb. 2016.
C. E. Lopez Guarin, E. L. Guzman, and F. A. Gonzalez, “A Model to Predict Low Academic Performance at a Specific Enrollment Using Data Mining,” IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 10, no. 3, pp. 119–125, Oct. 2015.
Nobuhiko Kondo and Toshiharu Hatanaka., [Modeling of Learning Process based on Bayesian Network] “Bayesian Network niyoru Shugakujoutai Suitei Model no Koutiku (in Japanese)”. Nihon Kyoikukougakkai Ronbunshi (Japan journal of educational technology), vol. 41, no. 3, pp. 271–81, 2018,
Naruhiko Shiratori, Tetsuya Oishi, Shintaro Tajiri, Masao Mori, Masao Murota, [Making Dropout Patterns Using Transition of Dropout Probability] ”Chutaikakuritsu no seni wo motiita chutaigakusei no ruikeika (in Japanese)”. Nihon Kyoikukougakkai Ronbunshi (Japan journal of educational technology), vol 44, no.1, pp. 11-22, 2020.
Naruhiko Shiratori, “Typology of students graduating from college using dropout probabili-ties,” IIAI Letters on Institutional Research, vol. 3, p. 1, 2023.
