Predictive Validity of Entrance Selection Tests for Academic Performance and Study Completion in Postgraduate Education

Sendi Ramdhani; Amalia Sapriati; Kristanti Ambar Puspitasari

doi:10.57092/ijetz.v5i3.843

Authors

Sendi Ramdhani Universitas Terbuka
Amalia Sapriati Universitas Terbuka
Kristanti Ambar Puspitasari Universitas Terbuka

DOI:

https://doi.org/10.57092/ijetz.v5i3.843

Keywords:

Academic performance, Distance education, Postgraduate admissions, Predictive validity, Study duration

Abstract

This study examined the predictive validity of postgraduate entrance-test scores for cumulative grade point average (GPA) and study duration at the Postgraduate School of Universitas Terbuka, Indonesia. Using retrospective institutional records, the study included 112 alumni admitted under comparable testing procedures implemented from 2021 onward; analyses involving English proficiency were based on 111 cases because one score was recorded on a non-comparable scale. Pearson correlations and multiple linear regression were used to assess predictive relationships. Numerical reasoning showed the strongest bivariate association with GPA (r = .227, p = .016), followed by verbal reasoning (r = .195, p = .040). No entrance-test component was significantly associated with study duration. In the multivariable model, English proficiency (β = −.233, p = .011), numerical reasoning (β = .271, p = .017), and figural reasoning (β = −.286, p = .004) were significant predictors of GPA, with the model explaining 17.9% of its variance (adjusted R² = .139). However, after adjustment for study program, only numerical reasoning remained statistically significant (β = .230, p = .044). Entrance-test scores provided little evidence of predictive utility for study duration after program adjustment. Overall, the findings indicate limited and component-specific predictive validity, with program-level heterogeneity influencing the observed relationships. These results highlight the need to evaluate entrance-test components in relation to specific academic outcomes and institutional contexts rather than treating aggregate test performance as a uniformly reliable predictor of postgraduate success.

Downloads

Download data is not yet available.

References

Adam, N. S., Kartowagiran, B., Yansa, H., Marlina, & Amiruddin, M. (2022). Self-regulated learning strategy in conducting remote lecturing during COVID-19 pandemic. Jurnal Pendidikan Indonesia, 11(2), 274–282. https://doi.org/10.23887/jpiundiksha.v11i2.34801

Al-Qahtani, M. F., & Alanzi, T. M. (2019). Comparisons of the predictive values of admission criteria for academic achievement among undergraduate students of health and non-health science professions: A longitudinal cohort study. Psychology Research and Behavior Management, 12, 1–6. https://doi.org/10.2147/PRBM.S183651

Almalki, S. A., AlJameel, A. H., Alghomlas, Z., Alothman, T., & Alhajri, F. (2024). Assessing the predictive validity of pre-admission criteria on dental students’ academic performance: A cross-sectional study. BMC Oral Health, 24, Article 90. https://doi.org/10.1186/s12903-023-03839-6

Andrade, M. S. (2006). International students in English-speaking universities: Adjustment factors. Journal of Research in International Education, 5(2), 131–154. https://doi.org/10.1177/1475240906065589

Althewini, A. (2026). How well do admission criteria predict student performance in one medical course? BMC Medical Education, 26, Article 312. https://doi.org/10.1186/s12909-025-08378-y

Bo, W. V., Fu, M., & Lim, W. Y. (2023). Revisiting English language proficiency and its impact on the academic performance of domestic university students in Singapore. Language Testing, 40(1), 133–152. https://doi.org/10.1177/02655322211064629

Botha, L., Viviers, H. A., Fouché, J. P., & Kruger, P. (2025). Success factors for postgraduate accountancy and related business students in higher education: A systematic review. Journal of Further and Higher Education, 49(6), 749–765. https://doi.org/10.1080/0309877X.2025.2495957

Bridgeman, B., Burton, N., & Cline, F. (2009). A note on presenting what predictive validity numbers mean. Applied Measurement in Education, 22(2), 109–119. https://doi.org/10.1080/08957340902754577

Broadbent, J. (2017). Comparing online and blended learners’ self-regulated learning strategies and academic performance. The Internet and Higher Education, 33, 24–32. https://doi.org/10.1016/j.iheduc.2017.01.004

Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007

Chakiso, Z. T., Bushisso, E. W., & Wanna, W. (2025). Unveiling predictive validity of English language exam on student achievement: Mediated by self-rated English proficiency. Language Testing in Asia, 15, Article 26. https://doi.org/10.1186/s40468-025-00356-x

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates.

de Boer, T., & van Rijnsoever, F. (2022). In search of valid non-cognitive student selection criteria. Assessment & Evaluation in Higher Education, 47(5), 783–800. https://doi.org/10.1080/02602938.2021.1958142

de Boer, T., & van Rijnsoever, F. J. (2023). One field too far? Higher cognitive relatedness between bachelor and master leads to better predictive validity of bachelor grades during admission. Assessment & Evaluation in Higher Education, 48(7), 966–979. https://doi.org/10.1080/02602938.2022.2158453

Dora, Y. M., Widokarti, J. R., Ismail, A., & Buana, D. M. A. (2023). Online course: Factors influencing students’ decision. Jurnal Organisasi dan Manajemen, 19(2), 316–331. https://doi.org/10.33830/jom.v19i2.5103.2023

Feldon, D. F., Litson, K., Cahoon, B., Feng, Z., Walker, A., & Tofel-Grehl, C. (2024). The predictive validity of the GRE across graduate outcomes: A meta-analysis of trends over time. The Journal of Higher Education, 95(1), 120–148. https://doi.org/10.1080/00221546.2023.2187177

Ferrão, M., & Almeida, L. (2019). Differential effect of university entrance score on first-year students’ academic performance in Portugal. Assessment & Evaluation in Higher Education, 44(4), 610–622. https://doi.org/10.1080/02602938.2018.1525602

Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84. https://doi.org/10.1016/j.iheduc.2015.10.002

Guntur, M., & Purnomo, Y. W. (2024). A meta-analysis of self-regulated learning interventions studies on learning outcomes in online and blended environments. Online Learning, 28(3). https://doi.org/10.24059/olj.v28i3.4025

Ihlenfeldt, S. D., & Rios, J. A. (2023). A meta-analysis on the predictive validity of English language proficiency assessments for college admissions. Language Testing, 40(2), 276–299. https://doi.org/10.1177/02655322221112364

Isaacs, T., Hu, R., Trenkic, D., & Varga, J. (2023). Examining the predictive validity of the Duolingo English Test: Evidence from a major UK university. Language Testing, 40(3), 748–770. https://doi.org/10.1177/02655322231158550

Jaehn, M., Hissbach, J., Frickhoeffer, M., Weppert, D., Zimmerhofer, A., Hampe, W., Kadmon, M., & Becker, N. (2025). Predictive validity of admission tests and educational attainment on preclinical academic performance: A multisite study. BMC Medical Education, 25, Article 1255. https://doi.org/10.1186/s12909-025-07974-2

Jansen, R. S., van Leeuwen, A., Janssen, J., Kester, L., & Kalz, M. (2017). Validation of the self-regulated online learning questionnaire. Journal of Computing in Higher Education, 29(1), 6–27. https://doi.org/10.1007/s12528-016-9125-x

Kintu, M. J., Zhu, C., & Kagambe, E. (2017). Blended learning effectiveness: The relationship between student characteristics, design features and outcomes. International Journal of Educational Technology in Higher Education, 14, Article 7. https://doi.org/10.1186/s41239-017-0043-4

Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2001). A comprehensive meta-analysis of the predictive validity of the Graduate Record Examinations: Implications for graduate student selection and performance. Psychological Bulletin, 127(1), 162–181. https://doi.org/10.1037/0033-2909.127.1.162

Kurysheva, A., Koning, N., Fox, C. M., van Rijen, H. V. M., & Dilaver, G. (2022). Once the best student always the best student? Predicting graduate study success using undergraduate academic indicators: Evidence from research masters’ programs in the Netherlands. International Journal of Selection and Assessment, 30(4), 579–595. https://doi.org/10.1111/ijsa.12397

Kurysheva, A., van Rijen, H. V. M., Stolte, C., & Dilaver, G. (2023). Validity, acceptability, and procedural issues of selection methods for graduate study admissions in the fields of science, technology, engineering, and mathematics: A mapping review. International Journal of STEM Education, 10, Article 55. https://doi.org/10.1186/s40594-023-00445-4

Locke, L. F., Silverman, S. J., & Spirduso, W. W. (2009). Reading and understanding research. SAGE.

Lohman, D. F. (2005). The role of nonverbal ability tests in identifying academically gifted students: An aptitude perspective. Gifted Child Quarterly, 49(2), 111–138. https://doi.org/10.1177/001698620504900203

Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1), 205–222. https://doi.org/10.24059/olj.v22i1.1092

Martirosyan, N. M., Hwang, E., & Wanjohi, R. (2015). Impact of English proficiency on academic performance of international students. Journal of International Students, 5(1), 60–71. https://doi.org/10.32674/jis.v5i1.443

Molontay, R., & Nagy, M. (2023). How to improve the predictive validity of a composite admission score? A case study from Hungary. Assessment & Evaluation in Higher Education, 48(4), 419–437. https://doi.org/10.1080/02602938.2022.2093835

Neumann, H., Padden, N., & McDonough, K. (2019). Beyond English language proficiency scores: Understanding the academic performance of international undergraduate students during the first year of study. Higher Education Research & Development, 38(2), 324–338. https://doi.org/10.1080/07294360.2018.1522621

Paris, J., & Heiser, R. (2022). An examination of the predictive validity of a measure of college admissions applicants’ attributions of success and failure. Journal of Postsecondary Student Success, 1(4), 112–140. https://doi.org/10.33009/fsop_jpss130205

Paris, J. H., Beckowski, C. P., & Fiorot, S. (2023). Predicting success: An examination of the predictive validity of a measure of motivational-developmental dimensions in college admissions. Research in Higher Education, 64(8), 1191–1216. https://doi.org/10.1007/s11162-023-09743-w

Patac, A., Jr., Patac, L. P., & Caadan, J. G. (2024). Graduate school’s impact on careers: Insights and implications from master of arts in education graduates. Contemporary Educational Researches Journal, 14(3), 154–167. https://doi.org/10.18844/cerj.v14i3.9526

Pusat Layanan Tes Indonesia. (n.d.). Tes Potensi Dasar Akademik (TPDA). https://plti.co.id/tpda

Punongbayan, J. C. B. (2025). Balancing excellence and equity in predictive college admissions: Insights from the University of the Philippines. Education Economics, 33(5), 794–817. https://doi.org/10.1080/09645292.2024.2421165

Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838

Rosita, T., Ramdhani, S., & Sapriati, A. (2023). Mapping student engagement: Analyzing the nexus of course engagement, resource utilization, and online tutorial participation. Al-Ishlah: Jurnal Pendidikan, 15(4), 5639–5649. https://doi.org/10.35445/alishlah.v15i4.4475

Seyfried, M., & Hollenberg, S. (2024). Student selection in higher education: The organisational performance dilemma. Journal of Higher Education Policy and Management, 46(6), 671–686. https://doi.org/10.1080/1360080X.2024.2365514

Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310. https://doi.org/10.1214/10-STS330

Sorgente, A., Pietrabissa, G., Antonietti, A., Bonanomi, A., Castelnuovo, G., Lanz, M., Tagliabue, S., & Traficante, D. (2025). How to select candidates for an undergraduate degree in psychology? Combining high-school GPA and admission test score. Asia Pacific Education Review, 26, 459–471. https://doi.org/10.1007/s12564-024-09978-x

Stemler, S. E. (2012). What should university admissions tests predict? Educational Psychologist, 47(1), 5–17. https://doi.org/10.1080/00461520.2011.611444

Theobald, M. (2021). Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis. Contemporary Educational Psychology, 66, Article 101976. https://doi.org/10.1016/j.cedpsych.2021.101976

Tinto, V. (1997). Classrooms as communities: Exploring the educational character of student persistence. The Journal of Higher Education, 68(6), 599–623. https://doi.org/10.1080/00221546.1997.11779003

Universitas Terbuka. (2025). Katalog program magister Sekolah Pascasarjana Universitas Terbuka 2025/2026.https://www.ut.ac.id/wp-content/uploads/2025/06/04-Katalog-Program-Magister-Sekolah-Pascasarjana-Universitas-Terbuka-2025-2026-05062025.pdf

van der Zanden, P. J. A. C., Denessen, E., Cillessen, A. H. N., & Meijer, P. C. (2019). Patterns of success: First-year student success in multiple domains. Studies in Higher Education, 44(11), 2081–2095. https://doi.org/10.1080/03075079.2018.1493097

Vos, S. G., Wijnia, L., Martens, R. L., & de Groot, R. H. M. (2025). How do distance learners self-regulate learning? A latent profile analysis of self-regulated learning and its relationship with self-regulatory efficacy, motivation, need satisfaction, need frustration, academic achievement, and well-being. Learning and Individual Differences, 120, Article 102678. https://doi.org/10.1016/j.lindif.2025.102678

Vulperhorst, J., Lutz, C., de Kleijn, R., & van Tartwijk, J. (2018). Disentangling the predictive validity of high school grades for academic success in university. Assessment & Evaluation in Higher Education, 43(3), 399–414. https://doi.org/10.1080/02602938.2017.1353586

Walters, T., Abeyta, A., Bean, A. J., & Wilson, M. A. (2022). The impact of holistic review on correlations between doctoral student outcomes, and GPA and GRE scores in the biomedical sciences. PLOS ONE, 17(12), Article e0279258. https://doi.org/10.1371/journal.pone.0279258

Wilson, M. A., Odem, M. A., Walters, T., DePass, A. L., & Bean, A. J. (2019). A model for holistic review in graduate admissions that decouples the GRE from race, ethnicity, and gender. CBE—Life Sciences Education, 18(1), Article ar7. https://doi.org/10.1187/cbe.18-06-0103

Xu, Z., Zhao, Y., Zhang, B., Liew, J., & Kogut, A. (2023). A meta-analysis of the efficacy of self-regulated learning interventions on academic achievement in online and blended environments in K–12 and higher education. Behaviour & Information Technology, 42(16), 2911–2931. https://doi.org/10.1080/0144929X.2022.2151935

Zhang, Z. (2024). The impact of graduate education scale on the innovation capability of the tertiary industry. International Journal of Education and Humanities, 13(2), 7–10. https://doi.org/10.54097/g5yb5d05