Session Information
11 ONLINE 51 A, Quality of higher education: Students' perception
Paper Session
MeetingID: 924 2827 8203 Code: 5sZdVt
Contribution
Evidence suggests that on average the number of young tertiary graduates in Europe experiencing field-of-study mismatch in engineering, manufacturing, and construction is over 30 % (OECD, 2020). This number is even higher in Lithuania and is more than 40 % (OECD, 2021). Additionally, the number of dropouts in Lithuanian universities is relatively high, averaging around 19% in 2019 (Pranskevičiūtė-Amoson et al., 2020). The termination of studies or failure to prepare qualified professionals is very harmful to economic growth given that essential human resources and funds invested in the student (in Lithuania, most students are funded by the state) are lost. Therefore, such statistics raise questions of whether the quality of engineering tertiary education is effective and what can be done to improve both retention and preparation of competent specialists, especially during and after the COVID-19 pandemic.
Therefore, a key research question in this paper is to determine what factors contribute to the academic success of first-year engineering university students. The objective of the study is to identify these success factors in Lithuanian universities and to provide recommendations for a more successful transition to higher education.
York et al. (2015), defines academic success as ‘inclusive of academic achievement, attainment of learning objectives, acquisition of desired skills and competencies, satisfaction, persistence, and post-college performance’. The activity and quality of higher education are influenced by many stakeholders. According to Marshall (2018), these include students, academic faculty, alumni, donors, parents, accreditation agencies, vendors and suppliers, employers, taxpayers, government and non-government organizations, and other groups such as unions and advocacy bodies, other institutions or providers. We based our theoretical framework on four main levels that influence student academic success: the state level; the institutional level (including the learning institution and business); and the individual student level.
At the state level, teaching quality and performance in secondary education (Jankus & Šarpienė, 2020), as well as the availability of non-formal education (Jankus & Šarpienė, 2020) may positively influence academic success in university.
At the institutional university level, the teaching ability of the lecturers (NSSE, 2013), the willingness to listen to the feedback of the students (university reports), and the academic and social integration of the first year (Marra et al., 2015) are factors that promote academic success.
At the institutional business level, we have identified vocational guidance (Krupovič & Žeimys, 2017) and business financial incentives (Richardson et al., 2012) as factors that could influence academic success.
Many individuals’ academic success factors are related to motivation (Richardson et al., 2012; Robbins et al., 2004). In this study, self-determination theory (SDT) was used to assess student motivation, as it is one of the most well-known theories of motivation and encompasses various aspects of it, namely intrinsic and extrinsic motivation. According to SDT, increasing intrinsic motivation, which is positively correlated with grade point average (GPA)(Richardson et al., 2012), is facilitated by three innate human psychological needs, namely autonomy, competence, and relatedness (Ryan & Deci, 2000). The aspects of this theory are further explored in this study.
Method
To achieve the research goal, which is to determine the factors of academic success of engineering students, both international and national literature was analyzed. Based on theoretical findings, a quantitative survey-based research design was chosen, and a pilot study was done. While there are a variety of ways to evaluate academic success, in this article the term was operationalized as the GPA of the participants. The 19 items of the questionnaire that were established as factors of academic success based on both national circumstances and theory were measured by a 5-point Likert scale. The study participants were students who had successfully completed their first year in an engineering study programme in Lithuania during 2020/2021. The data was gathered during the late summer of 2021 by sending the questionnaire to the students through email directly or with the help of faculties administrators. In total, there were 83 responses. The internal consistency reliability of the questionnaire was evaluated with the Cronbach alpha coefficient, which was equal to 0.77. Since most of the data are of ordinal scale, methods used for data analysis included descriptive statistics; Fisher's exact test for sample independence (since the expected values between measured variables were frequently less than 5); nonparametric Mann-Whitney-U and Kruskal-Wallis tests for examining the differences between measured categories; Spearman correlation analysis and linear regression, in which ordinal variables were treated as continuous. For linear regression assumptions, the Shapiro-Wilk test was used to test the normality of the residuals and the Breusch-Pagan test to test for homoscedasticity. Outliers were detected with studentized residuals and multicollinearity was checked with the variance inflation factor (VIF).
Expected Outcomes
When evaluating the descriptive statistics of the survey, it was found that of the 19 academic success items, the surveyed students valued the cooperation with other students, their competence to achieve goals, and the applicability of studies in professional activities the most. When examining self-determination theory, the results indicated that competence factors were perceived as more important for students’ success compared with the factors expressing connectedness and autonomy. The correlation analysis showed that the GPA is significantly correlated mainly with the previous GPA obtained at school (r=0.66, p<0.001), the mathematics state maturity exam (r=0.62, p<0.001), the ability to successfully perform study activities (r=0.25, p<0.05), autonomy in university activities (r=0.23, p<0.05) and students’ perceptions of their readiness for university studies (r=0.22, p<0.05). Regression analysis showed that 51.58% of the variance in university GPA can be explained with previous school GPA (standardized β=.31) and the mathematics state maturity exam (standardized β=.45). These two factors were the only significant regressors in the model. Together these results provided important insights for a study, which will be conducted in February-March and will be of a bigger sample. The results of this study would be presented at the conference.
References
Jankus, T., & Šarpienė, J. (2020). STEAM ugdymas Lietuvoje: Atviros prieigos centrų steigimas ir bendradarbiavimas. Lietuvos Švietimo, mokslo ir sporto ministerija ir “Kurk Lietuvai.” http://kurklt.lt/wp-content/uploads/2020/03/STEAM-esamos-situacijos-analiz%C4%97.pdf. Krupovič, G., & Žeimys, P. (2017). Profesinio orientavimo vizitų „ateities inžinieriai“ dalyvių apklausos rezultatai. Kurk Lietuvai. http://kurklt.lt/wp-content/uploads/2017/04/Dalyvi%C5%B3-apklausos-rezultatai.pdf. Marra, R. M., Tsai, C.-L., Bogue, B., & Pytel, J. L. (2015). Alternative Pathways To Engineering Success –Using Academic And Social Integration To Understand Two-Year Engineering Student Success. American Journal of Engineering Education (AJEE), 6(2), 69–84. https://doi.org/10.19030/ajee.v6i2.9503. Marshall, S. J. (2018). Internal and External Stakeholders in Higher Education. In Shaping the University of the Future: Using Technology to Catalyse Change in University Learning and Teaching (pp. 77–102). Springer. https://doi.org/10.1007/978-981-10-7620-6_4. NSSE. (2013). NSSE’s Conceptual Framework. https://nsse.indiana.edu/nsse/about-nsse/conceptual-framework/index.html. OECD. (2020). The changing labour market for graduates from medium-level vocational education and training (OECD Social, Employment and Migration Working Papers No. 244; OECD Social, Employment and Migration Working Papers, Vol. 244). https://doi.org/10.1787/503bcecb-en. OECD. (2021). OECD Skills Strategy Lithuania: Assessment and Recommendations. OECD. https://doi.org/10.1787/14deb088-en. Pranskevičiūtė-Amoson, R., Pusevaitė, I., Pauliukaitė-Gečienė, Ž., & Skirmantas, R. (2020). Lietuvos studijų būklė. STRATA. https://strata.gov.lt/images/tyrimai/2020-metai/svietimo-politika/20200901-Lietuvos-studiju-bukle.pdf. 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. Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004). Do Psychosocial and Study Skill Factors Predict College Outcomes? A Meta-Analysis. Psychological Bulletin, 130(2), 261–288. https://doi.org/10.1037/0033-2909.130.2.261. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68. York, T. T., Gibson, C., & Rankin, S. (2015). Defining and Measuring Academic Success. Practical Assessment, Research & Evaluation, 20(Article 5). https://doi.org/10.7275/HZ5X-TX03
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