Tracking the University Student Success: Statistical Quality Assessment




statistical quality assessment, students´ performance, quality assurance, learning outcomes, military and civilian students, higher education


Higher education institutions are continually striving to make education relevant to the working environment students will encounter upon graduation. One of the tools for enhancing an institution’s quality and sufficiently informing students about their outcomes and learning opportunities is a quality assessment. Quality assessment is a long process which establishes measurable student learning outcomes, then analyses and interprets them. This enables students to receive feedback on their learning and helps them to improve their performance. The authors’ objective was to gather empirical data on students´ learning in order to improve the process of learning and to refine study programmes. A longitudinal study was used to observe students’ performance and outcomes from entrance exams to state exams. Statistical analysis revealed that there is a correlation between the results of the admission tests and the study results, especially the connection between the results of the entrance test and the chance of successful completion of studies. No statistically significant correlation was found between the overall results of military students’ studies. An interesting issue is a comparison between military and civilian students, as well as civilian students´ results. As a continuous process, assessment of students’ performance was observed up until the Final State Examination.

Author Biographies

Ivana Čechová, University of Defence

Language center

Jiří Neubauer, University of Defence

Department of Quantitative Methods

Marek Sedlačík, University of Defence

Department of Quantitative Methods


  • Aboma, O. (2009) ‘Predicting First Year University Students’ Academic Success’, Electronic Journal of Research in Educational Psychology, Vol. 7, No. 3, pp. 1053–1072.

  • Act no. 111/1998 Coll., on universities § 44 (1998) [online], Available: [20 Jan 2018].

  • Barnett, R. (2011) ‘The marketised university: defending the indefensible’, in Molesworth, M., Scullion, R. and Nixon, E. (Eds), The Marketisation of Higher Education and the Student as Consumer. Routledge, Oxon, pp. 39–52.

  • Cech, P., Chromy, J. and Skupinova, S. (2015) ‘Company Training of Managers as a Part of the Human Resource Management in the Hotel Industry’, in 2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts (SGEM 2015), Psychology and Psychiatry, Sociology and Healthcare, Education, Vol. II, pp. 189–196.

  • Cechova, I., Neubauer, J. and Sedlacik, M. (2014) ‘Computer-Adaptive Testing: Item Analysis and Statistics for Effective Testing’, in Proceedings of the 13th European Conference on e-Learning ECEL-2014. Copenhagen, Denmark: Aalborg University Copenhagen, pp. 106–112.

  • Dobson, A.J. (2002) An Introduction to Generalized linear models. 2nd ed. Chapman & Hall/CRC.

  • Finney, T. Gillespie, R. and Finney, Z. (2010) ‘Are students their universities' customers? An exploratory study’, Education + Training, Vol. 52, No. 4, pp. 276–291.

  • Friedman, B. and Mandel, G.R. (2009) ‘The Prediction of College Student Academic Performance and Retention: Application of Expectancy and Goal Setting Theories’, Journal of College Student Retention Research Theory and Practice. Vol. 11, No. 2, pp. 227–246.

  • Geiger, M.A. and Cooper A.E. (2010) ‘Predicting Academic Performance: The Impact of Expectancy and Needs Theory’, The Journal of Experimental Education. Vol. 63, No. 3, pp. 251–262.

  • Gerritsen-van Leeuwenkamp, K. J., Joosten-ten Brinke D. and Kester L. (2017) ‘Assessment quality in tertiary education: An integrative literature review’, Studies in Educational Evaluation, Vol. 55, pp. 94–116.

  • Haapakorpi, A. (2011) ‘Quality assurance processes in Finnish universities: Direct and indirect outcomes and organisational conditions’, Quality in Higher Education, Vol. 17, No. 1, pp. 69–81.

  • Hazelkorn, E. (2011) Rankings and the reshaping of higher education. The battle for world-class excellence. Basingstoke: Palgrave Macmillan.

  • Hinett, K. and Knight, P. (1996) ‘Quality and assessment’, Quality Assurance in Education, Vol. 4, No. 3, pp. 3–10.

  • Houston, D. (2010) ‘Achievements and consequences of two decades of quality assurance in higher education: A personal view from the edge’, Quality in Higher Education, Vol. 16, No. 2, pp. 177–180.

  • Kappe, R. and van der Flier, H. (2012) ‘Predicting academic success in higher education: what’s more important than being smart?’, European Journal of Psychology of Education, Vol. 27, No. 4, pp. 605–619.

  • Knight, J. (2006) Higher Education Crossing Borders: A Guide to the Implications of the General Agreement on Trade in Services (GATS) for Cross-border Education. [online], Available: [12 Jan 2017]

  • Kurtus, R. (2012) Tricks for Good Grades: Strategies to Succeed in School. SfC Publishing Co. Oregon, USA.

  • Liu, S. (2016) Quality Assurance and Institutional Transformation, Higher Education in Asia: Quality, Excellence and Governance. Springer Science+Business Media Singapore.

  • Malau-Aduli, B.S., Zimitat, C. and Malau-Aduli, A.O. (2011) ‘Quality assured assessment processes: Evaluating staff response to change’, Higher Education Management and Policy, Vol. 23, No. 1, pp. 1–24.

  • Mazouch, P., Ptackova, V., Fischer, J. and Hulik, V. (2018) ‘Students Who Have Unsuccessfully Studied in the Past – Analysis of Causes’, Journal on Efficiency and Responsibility in Education and Science, Vol. 11, No. 3, pp. 66–72.

  • McIntosh, J. and Munk, M.D. (2007) ‘Scholastic ability vs family background in educational success: evidence from Danish sample survey data’, Journal of Population Economics, Vol. 20, No. 1, pp. 101–120.

  • Mohamadi, Z. (2018) ‘Comparative effect of online summative and formative assessment on EFL student writing ability’, Studies in Educational Evaluation, Vol. 59, pp. 29–40.

  • Montgomery, D.C. and Runger, G.C. (2011) Applied Statistics and Probability for Engineers. 5th ed. John Wiley & Sons.

  • Noha, E. (2015) ‘The concepts of quality, quality assurance and quality enhancement’, Quality Assurance in Education, Vol. 23, No. 3, pp. 250–261.

  • Okubo, K., Yamashita, T., Shimada, A. and Ogata, H. (2017) ‘A neural network approach for students’ performance prediction’, in Proceedings of the Seventh International Learning Analytics & Knowledge Conference. Canada, pp. 598–599.

  • Platt, L., Turocy, P., and Mc Glumphy, B. (2001) ‘Preadmission criteria as predictors of academic success in entry-level athletic training and other allied health educational programs’, Journal of Athletic Training, Vol. 36, No. 2, pp 141–144. 

  • Rigney T.J. (2003) ‘Relationship between Admission Grades and Academic Achievement’, The Irish Journal of Management, pp. 117–138.

  • Saliger, R. (2017) ‘Poznatky z výzkumu organizační kultury Armády České republiky – východisko k inovacím v edukaci leaderů ve vojenském školství’, in Vzdělávání dospělých 2016-východiska a inspirace pro teorii a praxi. Praha: Česká andragogická společnost, pp. 259–272.

  • Sapri, M., Kaka, A. and Finch, E. (2009) ‘Factors that influence student’s level of satisfaction with regards to higher educational facilities services’, Malaysian Journal of Real Estate, Vol. 4, No. 1, pp. 34–51.

  • Senge, P.M. (1990) The Fifth Discipline: The Art and Practice of the Learning Organisation. Doubleday, New York, NY.

  • Sedlacik, M., Cechova, I. and Doudova, L. (2013) ‘Be born as successful mathematics or language learner: myths, true or false?’, Journal on Efficiency and Responsibility in Education and Science, Vol. 6, No 3., pp. 155–166.

  • Shahiri, A.M., Husaina, W. and Rashida, N.A. (2015) ‘Review on Predicting Student’s Performance using Data Mining Techniques’, Procedia Computer Science, Vol. 72, pp. 414 – 422.

  • European Association for Quality Assurance in Higher Education (2005) Standards and Guidelines for Quality Assurance in the European Higher Education Area, [online], Available: [20 May 2018].

  • Stensaker, B., Langfeldt, L., Harvey, L., Huisman, J. and Westerheijden, D. (2011) ‘An in-depth study on the impact of external quality assurance’, Assessment & Evaluation in Higher Education, Vol. 36, No. 4, pp. 465–478.

  • Sultan, P. and Wong, H.Y. (2013) ‘Antecedents and consequences of service quality in a higher education context: a qualitative research approach’, Quality Assurance in Education, Vol. 21, No. 1, pp. 70–95.

  • Tam, M. (2014) ‘Outcomes-based approach to quality assessment and curriculum improvement in higher education’, Quality Assurance in Education, Vol. 22, No. 2, pp. 158–168.

  • UNESCO (1998) World Declaration on Higher Education for the Twenty-first Century: Vision and Action. Higher Education in the Twenty-First Century: Vision and Action. [online] [15 May 2018].

  • Vroeijenstijn, A.I. (1995) Improvement and accountability: Navigating between Scylla and Charybdis. Higher Education Policy Series, 30. London: J. Kingsley Publishers.

  • Wharrad, H., Chapple, M., and Price, N. (2003). ‘Predictors of academic success in bachelor of nursing course’. Nursing Education Today, Vol. 23, No. 4, pp. 54–246.

  • Winter. J.C.F. De and Dodou, D. (2011). ‘Predicting Academic Performance in Engineering Using High School Exam Scores’. International Journal of Engineering Education. Vol. 27, No. 6, 1343–1351.

  • Ullrich, D., Pokorny, V. and Ambrozova, E. (2017) ‘Leadership, Situational and Systemic Critical Thinking’, in Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth. Madrid Spain: International Business Information Management Association (IBIMA), pp. 1323–1332.

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    How to Cite

    Čechová, I., Neubauer, J. and Sedlačík, M. (2019) ’Tracking the University Student Success: Statistical Quality Assessment’, Journal on Efficiency and Responsibility in Education and Science, vol. 12, no. 1, pp. 12–25.



    Research Paper