Understanding Students’ Attrition Behavior In A Philippine Private Higher Education Institution: Basis For An Intervention Plan
Jonalyn Irish Bernardino | Anna Fernandez | Wendelyn Molera | Ma. Michelle Mensenares | Ma. Czarina Simbulan
Discipline: Education
Abstract:
The pandemic COVID-19 pandemic had an enormous impact in the educational sector
across the different countries and nations. In the Philippines, the students’ attrition had
significantly increased during the peak of the pandemic despite the efforts of Philippine
educational institutions and agencies. In this inquiry, the researchers examined the
students' attrition behavior from a Philippine private higher education institution. To
do so, a qualitative-documentary analysis approach was adapted for 2019 to 2022.
Results showed that academic, financial, and personal were the common reasons for
temporarily discontinuation of students in a private educational institution. More so, the
majority of the students in their second year of completing their course related to design
and the arts tend to discontinue their studies.
References:
- Bouchirika, I. (2023, October 31). College dropout rates: 2023 statistics by race, gender & income. Research.com. https://research.com/universities-colleges/college-dropout-rates
- Butawan, J. (2020). Project (SEB): Sagip Estudyanteng Bulihan - an intervention program to reduced dropout rate in Bulihan integrated national high school. International Journal of Innovative Science and Research Technology, 5. https://ijisrt.com/assets/upload/files/IJISRT20DEC337.pdf
- Chi, C. (2023). Pandemic doubled attrition rate of college students — CHED data. Philstar.com; Philstar.com. https://www.philstar.com/headlines/2023/08/23/2290834/pandemic-doubled-attrition-rate-college-students-ched-data
- Chung, J. & Lee, S. (2019). Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, 96, 346–353. https://doi.org/10.1016/j.childyouth.2018.11.030
- Crabtree, S, & Saransomrurtai, C. (2021). Southeast Asia sees sharp decline in education satisfaction. Gallup.com https://news.gallup.com/poll/355337/southeast-asia-sees-sharp-decline-education-satisfaction.aspx
- Cruz, R. (2023, May 18). CHED reports higher enrollment; graduation, drop out rates unchanged. ABS-CBN News; ABS-CBN News. https://news.abs-cbn.com/news/05/18/23/ched-reports-higher-enrollment-graduation-drop-out-rates-unchanged
- Dockery, D. (2012). School dropout indicators, trends, and interventions for school counselors. https://files.eric.ed.gov/fulltext/EJ978868.pdf
- Gerson, J. (2020). A QDA recipe? A ten-step approach for qualitative document analysis using MAXQDA. MAXQDA. https://www.maxqda.com/blogpost/qualitative-document-analysis
- Gubbels, J., Claudia, & Assink, M. (2019). Risk factors for school absenteeism and dropout: a meta-analytic review. Journal of Youth and Adolescence, 48(9), 1637–1667. https://doi.org/10.1007/s10964-019-01072-5
- Gutiérrez-de-Rozas, B., Molina, E. C., & López-Martín, E. (2022). Academic failure and dropout: Untangling two realities. European Journal of Educational Research, 11(4), 2275-2289. https://doi.org/10.12973/eu-jer.11.4.2275
- Hanson, M. (2023, October 29). College dropout rates. EducationData.org, https://educationdata.org/college-dropout-rates
- Kakuchi, S. (2021). Student dropout rate on the rise due to pandemic impact. University World News. https://www.universityworldnews.com/post.php?story=2021031006383627
- Kerby, M. (2015). Toward a new predictive model of student retention in higher toward a new predictive model of student retention in higher education: an application of classical sociological theory education: an application of classical sociological theory. Retrieved December 13, 2023, from https://digitalcommons.wku.edu/cgi/viewcontent.cgi?article=1268&context=fac_staff_papers
- Lee, J., Solomon, M., Stead, T., Kwon, B., & Ganti, L. (2021). Impact of COVID-19 on the mental health of US college students. BMC Psychology, 9(1). https://doi.org/10.1186/s40359-021-00598-3
- More School Drop-Outs in Asian Countries: UNESCO. (2020). Radio Free Asia. https://www.rfa.org/english/news/social/127898-20040210.html
- Morgan, H. (2022). Conducting a qualitative document analysis. The Qualitative Report, 27(1), 64-77. https://doi.org/10.46743/2160-3715/2022.5044
- Nicoletti, M. do C. (2019). Revisiting the Tinto’s theoretical dropout model. Higher Education Studies, 9(3), 52. https://doi.org/10.5539/hes.v9n3p52
- Orion, H., Erikka, J., Forosuelo, & Cavalida, J. (2014). Factors affecting students’ decision to drop out of school. 2(1). https://www.cjc.edu.ph/wp-content/uploads/2017/02/slongan-v2-01.pdf
- Orong, M., Caroro, R., Durias, G., Cabrera, J., Lonzon, H., & Ricalde, G. (2020). A predictive analytics approach in determining the predictors of student attrition in the higher education institutions in the Philippines. Proceedings of the 3rd International Conference on Software Engineering and Information Management.
- Palinkas, L., Horwitz, S., Green, C., Wisdom, J., Duan, N., & Hoagwood, K. (2013). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544. https://doi.org/10.1007/s10488-013-0528-y
- Parreño, S. (2023). School dropouts in the Philippines: causes, changes and statistics. Sapienza: International Journal of Interdisciplinary Studies, 4(1). http://portal.amelica.org/ameli/journal/725/7253717004/html/
- Putra, P, Liriwati, F., Tahrim, T., Syafrudin, S., & Aslan, A. (2020). The students learning from home experience during COVID-19 school closures policy in Indonesia. Jurnal IQRA, 5 (2). ISSN 2527-4449
- Rastrollo-Guerrero, J., GómezāPulido, J. , & Durán-Domínguez, A. (2020). Analyzing and predicting students’ performance by means of machine learning: a review. Applied Sciences, 10(3), 1042–1042. https://doi.org/10.3390/app10031042.
- Villegas-Ch, W., Govea, J., & Revelo-Tapia, S. (2023). Improving student retention in institutions of higher education through machine learning: A sustainable approach. Sustainability, 15(19), 14512–14512. https://doi.org/10.3390/su151914512
ISSN 3028-0923 (Online)
ISSN 3027-9615 (Print)