While studies about the integration and academic success of refugees in higher education are scarce, the challenges arising from this issue are apparent (Crea, 2016). Enabling refugees’ direct and unbureaucratic access to higher education and supporting and promoting their study success are the main objectives of the new research project SUCCESS(Study Success and Study Opportunities of Refugees)[1]. To overcome entrance barriers to higher education that refugees often face, e.g. language difficulties or missing certificates, refugees can study by participating in an online study program based on Massive Open Online Courses. Kiron Open Higher Education(Kiron) promotes digital approaches to enable refugees’ access to free higher education Kiron combines up to two years of online studies with two years of on-campus studies at one of Kiron’s partner state universities in many European countries. The aim is for students to apply and to change to a regular offline study program at a European university, where they complete their third and fourth year of a bachelor’s degree.

The refugees can enroll in one of five study tracks: mechanical engineering, business and economics, computer science, political science and social work. It is evident that successful integration into higher education is dependent on students’ entrance preconditions. In this paper, we present the results from the empirical analysis during the onboarding process of 1376 students into the Kiron program. Besides analyzing the background questionnaire, including personal factors and education level, we focus on study preconditions such as the level of English skills, personal characteristics and self-reported educational level. The theoretical model underlying SUCCESS is based on educational research on students with a migration background (e.g. Tjaden & Hunkler, 2017) – both the initial studies on the educational integration of refugees (e.g., Schroeder & Seukwa, 2017) and research on academic success (e.g., van Herpen et al., 2017) are considered. Based on the developed model, a number of academic success and failure indicators are defined, including both valid assessments of domain-specific knowledge and language skills, as well as further personal characteristics of Kiron students.

[1] Grant no. 16HLQ007, funded by the Federal Ministry of Education and Research.

The evaluation approach, following an evidence-centered design (ECD) by Mislevy et al. (2003) and the triangle model by Pellegrino et al. (2001), includes test-based entire and process diagnostics, addressee-appropriate feedback systems and study-related support measures in order to improve study opportunities, teaching-and-learning processes (particularly curriculum and instruction) and the academic success of refugees participating in Kiron. SUCCESS is developing an effective, multi-perspective and method-integrative analysis conducted over two phases: at Kiron and at partner universities. The research design encompasses status analyses at the beginning of studies in KIRON, formative assessments during, and summative analyses at the end of studies. The combined longitudinal and cross-sectional design (Dielman, 1989) with three students’ cohorts includes test-based analyses of learning progress based on the Kiron platform and student surveys at three points during their studies (upon enrollment, after one year, and upon switching to partner institutions). The presented data was acquired from the entrance surveys at Kiron (June-October 2017). Valid assessments of students’ English skills are conducted using the C-test (Norris, 2006). The results of the C-tests were embedded into the internationally established (North, 2014) Common European Framework of Reference for Languages (CEFR; Verhelst et al.2009)[2]. The findings on study related previous knowledge presented here are based on domain-specific knowledge tests. The US-American internationally established Test of Economic Literacy (4th ed.) of the Council for Economic Education (CEE) (Walstad et al., 2013) was used to assess economic knowledge. Mechanical engineerings knowledge was assessed using the Representational Variant of the Force Concept Inventory (R-FCI; Nieminen et al., 2010). Furthermore, a version adapted for English of the Computertest für die Personalauswahl (C-PA; Wagener, 2013) was used for computer science and the Knowledge Mental Illness Test (MC-Komit; Furnham et al., 2016) for social work. All these tests were pretested with Kiron students who were not part of the SUCCESS cohort. For assessing the level of self-reported secondary and higher education obtained in the country of origin, UNESCO’s International Standard Classification of Education (ISCED; UNESCO, 2012) was used. [2]The CEFR differentiates between six levels of language skills (A1 and A2: Basic User A1: Beginner (Breakthrough) A2: Fundamentals (Waystage); B1 and B2: Independent User B1: Advanced Use of Language (Threshold) B2: Independent Use of Language (Vantage); C1 and C2: Proficient User: C1: Expert Language Skills (Advanced) C2: Nearly Native Speaker Language Skills (Mastery).

The SUCCESS cohort comprises 1.376 refugees originating from 54 countries: about half the students come from the Syrian Arab Republic (37%), Somalia (8%) and Afghanistan (6%). The status analyses showed extreme differences regarding the level of education and the study-related preconditions upon entrance into Kiron. Almost half of the students (53%) stated they already had study experience at different tertiary levels. 71% of students with study experience reported to have graduated, mostly with a bachelor degree (56%). Although 45% study participants had reported English to be the language of study, the results of the language test showed overall major weaknesses in the language proficiency of Kiron students. Two percent of students in the SUCCESS sample had a C1 or higher language proficiency level, 18% had B2, 58% had B1, and 22% had A2 or below. Remarkably, we found no statistically significant effects of the country of origin, of upper secondary education or of completed tertiary education on language proficiency at the p < .05 level. For results on knowledge tests, we present the findings from the pre-test study. Overall, the results of the domain-specific tests indicate that the cohort has a solid level of previous knowledge in computer science (M=18.4 (24);SD=3.9) ; in economics (M=8.5 (15);SD =2.8) and political science (M=7 (15);SD=3.4) , approximately half of the tasks or more were solved. In social work (M=4.2 (12);SD=5.7) and mechanical engineering (M=6.4 (18);SD=2.9), study-related knowledge levels are rather low on average. Despite self-reported prior study experience, present results point to a lack of language proficiency and study-related knowledge for refugees studying at Kiron. It implies that students with a refugee background need more appropriate and specific approaches and measures to promote their academic success, including development of their personal and corresponding learning- or study-related values and attitudes.

Crea, T. M. (2016). Refugee higher education: Contextual challenges and implications for program design, delivery, and accompaniment. International Journal of Educational Development, 46, 12–22. doi:10.1016/j.ijedudev.2015.11.005 Dielman, T. E. (1989). Pooled cross-sectional and time series data analysis. New York: Dekker. Furnham, A., Gee, M., & Weis, L. (2016). Knowledge of mental illnesses: Two studies using a new test. Psychiatry research, 244, 363-369. Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003). On the Structure of Educational Assessments. Measurement: Interdisciplinary Research and Perspectives, 1(1), 3–62. Nieminen, P., Savinainen, A., & Virri, J. (2010). Force Concept Inventory-based multiple-choice test for investigating students’ representational consistency. Physical Review Special Topics - Physics Education Research, 6(2), 020109. doi:10.1103/PhysRevSTPER.6.020109 Norris, J. M. (2006). Development and evaluation of a curriculum-based German C-test for placement purposes. In R. Grotjahn (Ed.), The C-Test: Theory, empirical research, applications (pp. 45–83). Frankfurt: Lang. North, B. (2014). Putting the Common European Framework of Reference to good use. Language Teaching, 47(2), 228–249. doi:10.1017/S0261444811000206 Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2001). The nature of assessment and reasoning from evidence. In J. W. Pellegrino, N. Chudowsky, & R. Glaser (Eds.), Knowing what students know: The science and design of educational assessment (pp. 37–54). Washington, DC: National Academy Press. Schroeder, J., & Seukwa, L. H. (2017). Access to Education in Germany. In A. Korntheuer, P. Pritchard, & D. B. Maehler (Eds.), Structural Context of Refugee Integration in Canada and Germany (Schriftenreihe 15) (pp. 59–65). Köln: GESIS. Retrieved from http://nbn-resolving.de/urn:nbn:de:0168-ssoar-52101-0 Tjaden, J. D., & Hunkler, C. (2017). The optimism trap: Migrants’ educational choices in stratified education systems. Social Science Research. Advance online publication. doi:10.1016/j.ssresearch.2017.04.004 United Nations Educational, Scientific and Cultural Organization (UNESCO) (2012). International Standard Classification of Education ISCED 2011. Montreal, Quebec: UNESCO. van Herpen, S. G. A., Meeuwisse, M., Hofman, W. H. A., Severiens, S. E., & Arends, L. R. (2017). Early predictors of first-year academic success at university: pre-university effort, pre-university self-efficacy, and pre-university reasons for attending university. Educational Research and Evaluation, 23(1-2), 52–72. doi:10.1080/13803611.2017.1301261 Verhelst, N., Van Avermaet, P., Takala, S., Figueras, N. & North, B. (2009). Common European Framework of Reference for Languages: learning, teaching, assessment. Cambridge: University Press Wagener, D. (2013). Computerwissenstest für die Personalauswahl: C-PA. Hogrefe. Walstad, W. B., Rebeck, K., & Butters, R. B. (2013). Test of economic literacy: Examiner’s manual (4th ed.). New York: Council for Economic Education.