Who Reaches the Top? Investigating Gender-Based Performance and Maximum Score Gaps in University Entrance Exams

Hermina Disnawati; Siti Irene Astuti Dwiningrum; Yohanis Ndapa Deda; Salman Rashid; Mariano Dos Santos

doi:10.58421/misro.v4i2.524

DOI:

https://doi.org/10.58421/misro.v4i2.524

Authors

Hermina Disnawati Universitas Timor, Kefamenanu https://orcid.org/0000-0002-2990-3401
Siti Irene Astuti Dwiningrum Universitas Negeri Yogyakarta, Yogyakarta
Yohanis Ndapa Deda Universitas Timor, Kefamenanu https://orcid.org/0000-0002-8545-2932
Salman Rashid University of Turbat, Turbat https://orcid.org/0009-0000-4971-0087
Mariano Dos Santos Instituto Católico para a Formação de Professores (ICFP) Baucau

Keywords:

Academic readiness, English proficiency, Gender differences, Quantitative Knowledge, University Entrance Exam

Abstract

Disparities in the academic readiness of first-year university students remain evident, continuing to challenge higher education systems, particularly when entrance assessments fail to reflect foundational competencies accurately. This study investigates whether gender-based differences exist in entrance exam performance and analyzes students' maximum scores to assess peak academic achievement in two core domains: English proficiency and Quantitative Knowledge. The research was conducted at a public university in East Nusa Tenggara, Indonesia, involving 1,186 students admitted through the independent selection pathway. A comparative quantitative approach was applied using descriptive statistics, Welch’s t-test, and boxplot visualizations in JASP 0.18.3 software. The results showed no statistically significant gender differences in either domain (English: p = 0.504, d = 0.040; Quantitative: p = 0.543, d = –0.036), with nearly identical mean scores and score variability. However, the maximum scores of 17/20 in English and 13/20 in Quantitative Knowledge were far below the full mark, indicating systemic gaps in academic preparedness and possible ceiling effects in test design. These findings support the need to redesign entrance assessments using Item Response Theory (IRT) and to implement bridging or matriculation programs. These interventions can help universities and policymakers ensure fairer and inclusive pathways to promote equitable access and success in higher education.

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References

O. D. M. Tuegeh, M. Harangi-Rákos, and A. S. Nagy, “Industry 4.0 and human resource in Indonesia: A systematic literature review,” Economic Annals-XXI, vol. 190, no. 5, pp. 171–180, 2021, doi: 10.21003/EA.V190-16.

E. Hermawan and A. L. Arifin, “What Expert Say about Empowering Human Resources in Supporting Leadership Function in Higher Education in the 21st Century,” Jurnal Iqra’ : Kajian Ilmu Pendidikan, vol. 6, no. 2, pp. 27–38, 2021, doi: 10.25217/ji.v6i2.1493.

M. Nagy and R. Molontay, “Comprehensive analysis of the predictive validity of the university entrance score in Hungary,” Assessment and Evaluation in Higher Education, vol. 46, no. 8, pp. 1235–1253, 2021, doi: 10.1080/02602938.2021.1871725.

A. Aditomo and N. F. Faridz, “Ketimpangan Mutu dan Akses Pendidikan di Indonesia: Potret Berdasarkan Survei PISA 2015,” Jan. 02, 2019. doi: 10.31227/osf.io/k76g3.

E. James-Brabham, E; Loveridge, T; Sella, F; Wakeling, P.; Carroll, D. J.; & Blakey, “How do socioeconomic attainment gaps in early mathematical ability arise.pdf,” pp. 1550–1565, 2023.

R. I. Saptatiningsih and P. Jana, “Gender Perspective Concerning the Basic Mathematical Abilities,” PONTE International Scientific Researchs Journal, vol. 75, no. 12, pp. 182–187, 2019, doi: 10.21506/j.ponte.2019.12.12.

Adaeze Regis-Onuoha and K. U. Chukwu, “Gender Differences and the Writing Achievement of University Fresh Students: A study of Federal University of Technology Owerri,” Journal of Gender, Culture and Society, vol. 2, no. 1, pp. 11–16, 2022, doi: 10.32996/jgcs.2022.2.1.2.

T. Pekkarinen, “Gender differences in behaviour under competitive pressure: Evidence on omission patterns in university entrance examinations,” Journal of Economic Behavior and Organization, vol. 115, pp. 94–110, 2015, doi: 10.1016/j.jebo.2014.08.007.

A. Kan and O. Bulut, “Examining the Relationship Between Gender DIF and Language Complexity in Mathematics Assessments,” International Journal of Testing, vol. 14, no. 3, 2014, doi: 10.1080/15305058.2013.877911.

P. Akyol, J. Key, and K. Krishna, “HIT OR MISS ? TEST TAKING BEHAVIOR IN MULTIPLE CHOICE EXAMS,” Annals of Economics and Statistics, vol. 147, no. 147, 2022.

F. Reinhold et al., “The role of spatial, verbal, numerical, and general reasoning abilities in complex word problem solving for young female and male adults,” Mathematics Education Research Journal, vol. 32, no. 2, pp. 189–211, 2020, doi: 10.1007/s13394-020-00331-0.

S. L. Tsai, M. L. Smith, and R. M. Hauser, Gender gaps in student academic achievement and inequality, vol. 20. 2018. doi: 10.1108/S1479-353920180000020008.

D. Montolio and P. A. Taberner, “Gender differences under test pressure and their impact on academic performance: A quasi-experimental design,” Journal of Economic Behavior and Organization, vol. 191, pp. 1065–1090, 2021, doi: 10.1016/j.jebo.2021.09.021.

H. Wen, Y. Liu, and N. Zhao, “Longitudinal Cognitive Diagnostic Assessment Based on the HMM/ANN Model,” Frontiers in Psychology, vol. 11, 2020, doi: 10.3389/fpsyg.2020.02145.

N. Fang, “The gender effect on academic success among first-generation engineering undergraduate (FGEU) students,” Global Journal of Engineering Education, vol. 27, no. 1, pp. 16–21, 2025.

A. Ocvitasari, D. E. Widiasi, and M. Firmansyah, “Analisis Faktor Kesiapan Akademik Terkait Orientasi Motivasi Berprestasi, M-SCORE dan Kegiatan Non-Akademik Terhadap Prestasi Akademik Mahasiswa,” Jurnal Kedokteran Komunitas, vol. 8, no. 2, pp. 74–82, 2020.

S. Sriyati, “Analisis Hasil Tes Potensi Skolastik sebagai Indikator Kesiapan Siswa Menghadapi Tes UTBK 2022,” Tarbiyah Wa Ta’lim: Jurnal Penelitian Pendidikan dan Pembelajaran, vol. 9, no. 2, pp. 74–83, 2022, doi: 10.21093/twt.v9i1.4210.

T. Anjar, “Student Readiness High School Entrance Exams Higher Education and the Role of School Counselors,” GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling, vol. 5, no. 1, p. 50, 2015, doi: 10.24127/gdn.v5i1.62.

B. R. Nainggolan, P. Purnawarman, and D. Sukyadi, “Comprehension Processes and Critical Thinking Skills in Two University Entrance Exams in Indonesia: a Content Analysis of the Sbmptn and the Simak Ui 2008-2019,” Polyglot: Jurnal Ilmiah, vol. 18, no. 1, p. 1, 2022, doi: 10.19166/pji.v18i1.4908.

R. Naylor and N. Mifsud, “Structural inequality in higher education: Creating institutional cultures that enable all students,” 2019.

M. S. Nitko, Anthony J..,Brookhart, Educational Assessment of Students: Pearson New International Edition. Always learning. Pearson custom library. PEARSON, 1987.

A. Sudheesh and R. M. Golden, “Exploring Temporal Functional Dependencies Between Latent Skills in Cognitive Diagnostic Models,” in Springer Proceedings in Mathematics and Statistics, 2021, pp. 253–262. doi: 10.1007/978-3-030-74772-5_23.

J. Cohen, Statistical Power Analysis for the Behavioral Sciences, Second Edi. USA: Lawrence Erlbaum Associates, 1988.

G. M. Sullivan and R. Feinn, “ Using Effect Size—or Why the P Value Is Not Enough ,” Journal of Graduate Medical Education, vol. 4, no. 3, pp. 279–282, 2012, doi: 10.4300/jgme-d-12-00156.1.

E. Ghasemi and H. Burley, “Gender, affect, and math: a cross-national meta-analysis of Trends in International Mathematics and Science Study 2015 outcomes,” Large-Scale Assessments in Education, vol. 7, no. 1, pp. 1–25, 2019, doi: 10.1186/s40536-019-0078-1.

D. Giofrè, C. Cornoldi, A. Martini, and E. Toffalini, “A population level analysis of the gender gap in mathematics: Results on over 13 million children using the INVALSI dataset,” Intelligence, vol. 81, no. June, p. 101467, Jul. 2020, doi: 10.1016/j.intell.2020.101467.

N. M. Else-Quest, J. S. Hyde, and M. C. Linn, “Cross-National Patterns of Gender Differences in Mathematics: A Meta-Analysis,” Psychological Bulletin, vol. 136, no. 1, pp. 103–127, 2010, doi: 10.1037/a0018053.

D. T. Conley, “College Readines,” pp. 1–11, 2007.

R. J. Shavelson et al., “Gap analysis and data visualization: Creating a more equitable assessment culture in higher education,” Thinking Skills and Creativity, vol. 47, no. 8, pp. 1274–1288, 2022, doi: 10.1080/02602938.2022.2067265.

M. Fine and K. Pryiomka, “Assessing College Readiness through Authentic Student Work: How the City University of New York and the New York Performance Standards Consortium Are Collaborating toward Equity,” Learning Policy Institute, no. July, 2020.

R. Liu and K. D. Bradley, “Differential Item Functioning Among English Language Learners on a Large-Scale Mathematics Assessment,” Frontiers in Psychology, vol. 12, no. July, pp. 1–12, 2021, doi: 10.3389/fpsyg.2021.657335.

L. Stoffelsma and W. Spooren, “The Relationship Between English Reading Proficiency and Academic Achievement of First-Year Science and Mathematics Students in a Multilingual Context,” International Journal of Science and Mathematics Education, vol. 17, no. 5, pp. 905–922, 2019, doi: 10.1007/s10763-018-9905-z.

A. Tunde, “Educational Resources Availability and Utilization As Determinant of Students Academic Performance in South West Nigeria,” African Journal of Education and Practice, vol. 8, no. 5, pp. 45–63, 2022, doi: 10.47604/ajep.1636.

H. Disnawati and Y. N. Deda, “Miskonsepsi Mahasiswa Pada Materi Himpunan: Analisis Menggunakan Kriteria Certainty of Response Index,” Jurnal Pendidikan Matematika (Jupitek), vol. 4, no. 2, pp. 95–102, 2022, doi: 10.30598/jupitekvol4iss2pp95-102.

H. Disnawati, C. N. Salsinha, N. Deda, and N. L. Bees, “Student s ’ Mathematical Reasoning Errors in Solving Computer Based Written Exam Model Questions Based on Newman ’ s Theory,” vol. 7, no. 2, pp. 490–501, 2023.

Nisa Triyatul Fitri, Arif Muchyidin, and Reza Oktiana Akbar, “Mastery of Trigonometry Material and Its Effect on Students’ Ability to Solving Three Dimensional Problems,” Journal of Mathematics Instruction, Social Research and Opinion, vol. 1, no. 2, pp. 97–106, 2022, doi: 10.58421/misro.v1i2.23.

S. M. Yoong and Fu Sai Hoe, “Teachers’ Efficacy to Teach Students with Mathematics Learning Difficulties,” Journal of Mathematics Instruction, Social Research and Opinion, vol. 1, no. 3, pp. 117–130, 2022, doi: 10.58421/misro.v1i3.29.

E. Istiyono, W. S. B. Dwandaru, Y. A. Lede, F. Rahayu, and A. Nadapdap, “Developing IRT-based physics critical thinking skill test: A CAT to answer 21st century challenge,” International Journal of Instruction, vol. 12, no. 4, pp. 267–280, 2019, doi: 10.29333/iji.2019.12417a.

H. Ravand, “Assessing Measurement Invariance in a University Entrance Exam: A Comparison of Multigroup Confirmatory Factor Analysis Alignment Method vs. Multigroup Item Response Theory,” Educational Methods and Psychometrics, vol. 2, no. 1, pp. 21–30, 2024, doi: 10.61186/emp.2024.4.

E. Apino, H. Retnawati, W. Purbani, and K. Hidayati, “The Statistical Literacy of Mathematics Education Students: An Investigation on Understanding the Margin of Error,” TEM Journal, vol. 13, no. 1, pp. 293–302, 2024, doi: 10.18421/TEM131-31.

J. Arlinwibowo, H. Retnawati, and B. Kartowagiran, “Item Response Theory Utilization for Developing the Student Collaboration Ability Assessment Scale in STEM Classes,” Ingenierie des Systemes d’Information, vol. 26, no. 4, pp. 409–415, 2021, doi: 10.18280/ISI.260409.

S. Ketabi, Dr. S. M. Alavi, and Dr. H. Ravand, “Diagnostic Test Construction: Insights from Cognitive Diagnostic Modeling,” International Journal of Language Testing, vol. 11, no. 1, 2021, [Online]. Available: https://api.semanticscholar.org/CorpusID:276481758

S. M. Alavi, H. Karami, and M. H. Kouhpaeenejad, “Examining the Fairness of the University Entrance Exam: A Latent Class Analysis Approach to Differential Item Functioning,” Atu Press Issues in Language Teaching (Ilt), vol. 10, no. 1, pp. 147–170, 2021.

A. M. Black, “The role of bridging programmes in supporting student persistence and prevention of attrition: a UK case study,” Studies in Higher Education, vol. 49, no. 9, pp. 1519–1531, 2024, doi: 10.1080/03075079.2023.2269246.

M. Maxwell and J. Gleason, “Item efficiency: an item response theory parameter with applications for improving the reliability of mathematics assessment,” International Journal of Mathematical Education in Science and Technology, vol. 50, no. 2, pp. 216–243, 2019, doi: 10.1080/0020739X.2018.1492038.

Learn & Work Ecosystem Library, “Bridging the Gap: A State Leader’s Guide to Implementing Summer Bridge Programs (Education Strategy Group).” Accessed: Jun. 25, 2025. [Online]. Available: https://learnworkecosystemlibrary.com/initiatives/bridging-the-gap-a-state-leaders-guide-to-implementing-summer-bridge-programs-education-strategy-group/