Session Information
09 SES 02 A, Innovations in Higher Education Admission and Student Support Programs: Enhancing Access and Success
Paper Session
Contribution
The International Standard Classification of Education (ISCED-11) has labeled post-secondary technical education as a short-cycle tertiary education program (5B). Chile, following OECD standards, has defined Tertiary Technical Education (from now on TTE) as oriented to give the necessary capacities and knowledge to perform as a professional in different areas of the labor market (Ley 21.091, 2018). Additionally, it has emphasized the opportunity to enhance successful trajectories, especially for the population that has been historically excluded from higher education and skilled jobs. In this sense, access to TTE is seen as an instrument of social mobility that seeks to reduce inequality (Brunner et al., 2022).
In turn, retention and dropouts, particularly for low-income students, have been a policy concern and a challenge to the technical educational system (Hällsten, 2017; Sarra et al., 2018; Brunner et al., 2022). The adverse effect of dropping out is dramatic and affects students and families in many ways, including greater marginalization and future lower labor market outcomes (Sosu & Pheunpha, 2019; Voelkle & Sander, 2008; O'Neill et al., 2011). Therefore, retention and dropout affect the goal of inclusion and equity that Chilean policymakers have tried to insert at the core of the TTE Chilean regulation since 2010 (Brunner et al., 2022).
As such, TTE has a disproportionate share of low-income students (Mountjoy, 2022; Sotomayor, 2018). In Chile, these institutions have more extensive participation of students from quintiles 1-3 of income. Namely, close to 50% of the enrollment of these institutions comes from the poorest 60% of the Chilean population (SIES, 2022). In this regard, a 2022 study of the Higher Education Information Service of Chile (SIES) indicates that the retention rate for first-year students of tertiary education is higher for universities (85%) compared to TTE institutions (70%) (SIES, 2022). The statistics are consistent with the study of the determinants of retention and dropout in TTE. When examining which factors influence the probability of student retention, evidence from across the globe indicates four main groups of variables: i) the sociodemographic background of the family, ii) the student's previous academic results, iii) accessibility or financing restrictions, and iv) institutional factors (Behr et al., 2020; Li & Carroll, 2017; Millea et al., 2018; among others).
The Professional Institute INACAP is one of the largest TTEs in Chile, with 15% (N=76781) of the total enrollment of the Chilean technical college system (SiES, 2022). Since 2014, INACAP has developed new and different mechanisms to support a successful trajectory of students through a program called the Progression Support System (SiAP). The program has at its core an inclusion and equity framework that has allowed a comprehensive set of initiatives articulated through a tutor that provides academic, psychosocial, and extracurricular support to students (particularly at risk) to help them successfully navigate their career pathways. The program's ultimate impact indicator is to increase first-year students' retention rates and avoid dropouts.
However, questions about the program's effectiveness have been raised, especially during the covid-19 pandemic. In this regard, we determined to measure the effect of the SiAP-program at INACAP on the first-year student retention rate for the cohorts 2017-2021. Additionally, the study sought to describe changes in the program policies and implementation during the pandemic (2020-2021) that might affect results. Finally, given the large enrollment of INACAP, findings will give insights into the institution and the Chilean tertiary education system about to what extent tutoring programming, as the core of a student system of academic progression support, facilitates retention in the educational system for vulnerable students.
Method
The INACAP SiAP-program is composed of different initiatives for all first-year students. The objective is to facilitate active and self-managed insertion into higher education. In this way, all students are assigned to an INACAP-SiAP tutor whose role is to support the development of academic skills and self-management of learning through academic monitoring and psychosocial accompaniment to identify support needs and activate internal and external networks promptly. Tutors must follow an order of priority for contact and accompaniment of students based on a student-risk predictor model. The SiAP-program has reached an average coverage of 70% (n=37.523 students) and above of the total enrollment of first-year students for cohorts 2017-2021. As a first step to evaluate its effectiveness, we decided to develop its theory of change with the SiAP team (Weiss & Connell, 1995) to establish the causal relationship between the program's multiple actions and its expected impact (retention rate of first-year students). From it, institutional data were gathered to analyze previous analyses' top results and limitations (2014-2016). Thus, selection biases generated by the multiple mechanisms through which students can be referred to each program’s components were identified. On one side, students who decide to participate in the different components of the SiAP voluntarily generate a self-selection bias. Besides, there is an endogeneity behind being referred to the program’s components, either due to having a low score in diagnostic evaluations (where academic performance would affect participation in the program) or by the tutor’s decision (where participation would be correlated with the error term). From this, the method used was the quasi-experimental propensity score matching (PSM) (Caliendo & Kopeinig, 2008), representing the best impact evaluation tool to minimize biases and thus isolate the effect of treatment on the probability of retention. The Kernel algorithm was used to take advantage of the information of all the observations located within the standard support to build a more precise counterfactual, applying a weighted average where greater weight was given to those observations that have a score closest to the treatment group and vice versa (Caliendo & Kopeinig, 2008). Additionally, policy and implementation changes in the program were tracked and analyzed to understand changes in the program better.
Expected Outcomes
The effects of the INACAP-SiAP-program on the retention rate of first-year students are positive and statistically significant. Participation in the program increases the probability of retention for the 2017 cohort by an average of 6.3 percentage points (pp) and 10 pp for the 2018 and 2019 cohorts. For the cohorts of 2020 and 2021, the magnitude of the effect increases dramatically to 40 pp and 58 pp, respectively. However, these 2020-21 estimates should be carefully analyzed due to external validity problems because of the pandemic, changes in the SiAP guidelines program during the Covid-19 pandemic, and the lower sample number of the control group due to the trend towards universal treatment. Due to the above, heterogeneous analyzes in subgroups of interest was limited to the 2017-2019 cohorts. For these cohorts, the program's impact on retention rates is substantially more significant in students enrolled in evening programs hours, particularly for the 2018 and 2019 cohorts (6.8 vs. 16.6%; 7.5 vs. 17.6%. respectively). The impact is 135% higher than that obtained in daytime students for the 2019 cohort. This magnitude difference is similar to that reported in the 2018 cohort. Accordingly, the impact on working students (vs. non-working students) is higher by approximately 50% in the magnitude of the effect between the two groups for cohorts 2017-2019, which makes this a consistent result over time. The study highlights the importance of student support systems (like the INACAP-SiAP) to help students stay on their career pathways. This effort is aligned with the equity and inclusion framework that educational policy in Chile has tried to enhance for students that see tertiary short-cycle education as an opportunity for professional jobs that allows them better opportunities in the labor market. Nonetheless, it is imperative to study the extent to which the program helps students graduate (on-time).
References
Behr, A., Giese, M., Teguim K. & Theune, K. (2020). Dropping out from Higher Education in Germany an Empirical Evaluation of Determinants for Bachelor Students. Open Education Studies, 2(1), 126-148. Brunner, J., Labrana, J., Alvarez, J. (2022). Educación superior técnico profesional en Chile: perspectivas comparadas. Santiago de Chile: Ediciones Universidad Diego Portales. https://vertebralchile.cl/wp-content/uploads/2022/07/Educacion-superior-tecnico-profesional-en-perspectiva-comparada.pdf Caliendo, M. & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31-72. https://doi.org/10.1111/j.1467-6419.2007.00527.x Faas, C., Benson, M. J., Kaestle, E. C., and Savla, J. (2018). Socioeconomic success and mental health profiles of young adults who drop out of college. J. Youth Stud. 21, 669–686. doi: 10.1080/13676261.2017.1406598 LEY 21091, 2018. Sobre educacion superior. 11 de mayo 2018 (Chile) Li, W., & Carroll, D. (2017). Factors Influencing University Student Satisfaction, Dropout and Academic Performance. National Centre for Student Equity in Higher Education. Millea, M., Wills, R., Elder, A. & Molina, D. (2018). What Matters in College Student Success? Determinants of College Retention and Graduation Rates. Education, 138(4), 309-322. Mountjoy, J. (2022). Community Colleges and Upward Mobility (Working Paper No 29254). National bureau of economic research. https://www.nber.org/papers/w29254 O'Neill, L. D., Wallstedt, B., Eika, B., and Hartvigsen, J. (2011). Factors associated with dropout in medical education: a literature review. Med. Educ. 45, 440–454. Ortiz, E. A., and Dehon, C. (2013). Roads to success in the Belgian French community's higher education system: predictors of dropout Bruxelles. Res. High. Educ. 54, 693–723. Sarra, A., Fontanella, L., and Di Zio, S. (2018). Identifying students at risk of academic failure within the educational data mining framework. Soc. Ind. Res. 1–20. Servicio de Información de Educación Superior (SIES) (2022). Ministerio de Educación. Matricula en Educación Superior en Chile. https://www.mifuturo.cl/wp-content/uploads/2022/10/Matricula_Educacion_Superior_2022_SIES_.pdf Sosu EM and Pheunpha P (2019) Trajectory of University Dropout: Investigating the Cumulative Effect of Academic Vulnerability and Proximity to Family Support. Front. Educ. Sotomayor, C.; Valenzuela, J. P. (2018). Rentabilidad de la educación superior técnica entregada por los Centros de Formación Técnica Estudios de Políticas Públicas (pp., 120-133.). Voelkle, M. C., and Sander, N. (2008). A structural equation approach to discrete-time survival analysis. J. Individ. Dif. 29, 134–147. Weiss, C.H. and Connell, J.P. (1995) Nothing as Practical as Good Theory: Exploring Theory-Based Evaluation for Comprehensive Community Initiatives for Children and Families. In: New Approaches to Evaluating Community Initiatives: Concepts, Methods, and Contexts, The Aspen Institute, 65-92.
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