117 Studies in Second Language Learning and Teaching Department of English Studies, Faculty of Pedagogy and Fine Arts, Adam Mickiewicz University, Kalisz SSLLT 12 (1). 2022. 117-141 http://dx.doi.org/10.14746/ssllt.2022.12.1.6 http://pressto.amu.edu.pl/index.php/ssllt Investigating academic achievement of English medium instruction courses in Turkey Mehmet Altay Kocaeli University, Turkey https://orcid.org/0000-0001-7227-5685 mehmet.altay@kocaeli.edu.tr Samantha Curle University of Bath, United Kingdom https://orcid.org/0000-0003-3790-8656 samanthamcurle@gmail.com Dogan Yuksel Kocaeli University, Turkey https://orcid.org/0000-0001-9131-3907 doganyuksel@gmail.com Adem Soruç University of Bath, UK https://orcid.org/0000-0003-4165-6260 a.soruc@bath.ac.uk Abstract This article reports a quantitative study that investigated academic achieve- ment in English medium instruction (EMI) courses at a public university in Tur- key. Student test score data on EMI and Turkish medium instruction (TMI) courses as well as general English proficiency scores were collected in two ac- ademic divisions: the mathematical, physical, and life sciences (MPLS, N = 357); and the social sciences (N = 359). Analysis conducted at the macro (academic division), meso (academic department), and micro levels (academic program) showed subtle differences at each level. Overall, results were consistent: English Mehmet Altay, Samantha Curle, Dogan Yuksel, Adem Soruç 118 language proficiency was a strong predictor of academic achievement of social sci- ence participants, whereas success in TMI courses predicted EMI success of MPLS participants. These results reinforce the notion that more language support should be given to social science students, whereas learning some content through TMI should be prioritized for MPLS students. Implications for language professionals and EMI practitioners are discussed, and suggestions are made for further research. Keywords: English medium instruction; academic success; Turkish medium in- struction; general English proficiency; higher education 1. Introduction When the Bologna Declaration was adopted in Europe in 1999, the aim was to “culti- vate and develop multilingualism” (Doiz et al., 2013, p. 345) and “motivate and pro- duce a highly-skilled plurilingual, pluricultural workforce” (Coyle 2008, p. 99). Despite this focus on multi- and plurilingualism, English has become the dominant foreign language used as the medium of instruction (MOI) at universities in the European Higher Education Area (EHEA, Doiz et al., 2013). This trend has spread globally in higher education (HE; see Macaro et al., 2018). Commonly known as English medium instruction (EMI), this global phenomenon is defined here as “the use of the English language to teach academic subjects other than English itself in countries or jurisdic- tions where the first language of the majority of the population is not English” (Macaro, 2018, p. 19). The context of this study, Turkey, falls within this description. While there is a growing research interest in student learning outcomes of EMI programs (Li, 2018; Rose et al., 2020; Terraschke & Wahid, 2011; Xie & Curle, 2022), no studies have examined EMI success at three levels: division level (macro: mathematical, physical and life sciences (MPLS) and social sciences), de- partment level (meso: engineering and economics, and administrative sciences), and program level (micro: four programs per department; see the appendix). This study, therefore, makes an original contribution to the field by investigating the influence of general English proficiency, success in Turkish medium instruc- tion (TMI), and academic subject on EMI academic achievement. 2. Literature review 2.1. The role of the first language in academic success in English medium instruction The effect of students’ first language (L1) on their academic attainment in EMI courses has not been explored until recently (see Curle et al., 2020). From a linguistic theoretical perspective, in his interdependence hypothesis, Cummins (2017) Investigating academic achievement of English medium instruction courses in Turkey 119 proposes two language-independent transfer types, namely: (a) conceptual el- ements and (b) strategic knowledge transfer from the first language (L1) to the second language (L2). He postulates that these two transfer types might facili- tate success in learning a second language (Cummins, 2017). The transfer of conceptual elements (a) is known as the transfer of knowledge. This is the trans- fer of declarative knowledge from the L1 to the L2. Declarative knowledge can be made overt or explicit, “teachable” knowledge that is statically stored in memory (Ullman, 2005). This is conceptual or descriptive knowledge, as op- posed to “implicit” knowledge, or knowledge of performance or operation (Wat- son et al., 2021). It is therefore hypothesized that bilingual learners transfer knowledge from their L1 to perform academically in the L2 (Olivares, 2002). One example could be transferring the understanding of the concept of photosyn- thesis from the L1 to the L2. The transfer of strategic knowledge (b) occurs when learners become aware of their learning process and apply that process to a new learning circumstance. For example, if a student has learned to use graphic or- ganizers or mnemonic devices to learn new vocabulary in their L1, the same strategy could be applied and used to learn vocabulary in the L2 (Wolfsberger, 2012). The main argument behind transfer of knowledge theory is that learning strategies can be used irrespective of the language/content being learned. Empirical research has been conducted and provides evidence for the transfer of knowledge. Lemberger and Vinogradova (2002) examined a group of bilingual students’ transfer of science literacy skills from their L1 (Russian) to their L2 (English) and concluded that bilingual instruction helped them maintain and build on prior science learning together with well-developed reading and writing skills. In the Korean EMI setting, Kang and Park’s (2005) study revealed that students required some form of preparation course in their L1 (Korean) be- fore commencing their EMI academic programs to ensure their studies were successful. Similarly, Turkish students in Curle et al. (2020) noted that their L1 courses helped them understand basic background knowledge in their academic discipline, thus mediating learning and facilitating comprehension of abstract concepts in their EMI courses. The current study takes the theory of transfer of knowledge further by taking academic achievement in L1 courses into account. More specifically, it explores whether knowledge acquired in TMI courses influ- ences EMI success and whether this differs according to academic discipline. 2.2. The role of English language proficiency in academic success in English medium instruction Recently, there has been an increased interest in EMI academic achievement. A hand- ful of studies have explored significant predictors of EMI academic achievement (i.e., Mehmet Altay, Samantha Curle, Dogan Yuksel, Adem Soruç 120 EMI success: Curle et al., 2020; Li, 2018; Rose et al., 2020; Terraschke & Wahid, 2011). Rose et al.’s (2020) study with 139 second-year students in Japan found that scores of English language proficiency and English for academic purposes (EAP) were significant predictors of EMI success in an international business course (a so- cial science subject). These findings were echoed in Thompson et al.’s (2019) study in Japan with a similar group of students (i.e., 139 second-year students): English language ability and EAP scores predicted EMI international business administration success. Xie and Curle (2022) developed this line of research in China and found that business English proficiency predicted EMI academic achievement of 106 sec- ond-year students studying business management administration. Only two studies on EMI success have been conducted in Turkey, and the results were contrary to previous study findings. Curle et al.’s (2020) study found that instead of English pro- ficiency, success in TMI courses predicted the EMI academic success of 159 fourth- year economics students (a social science subject). The investigation of social science subjects has so far dominated the EMI success literature, and analysis has focused solely on the micro level (academic program). The current study builds on this EMI academic success literature by comparing two academic divisions (macro level): social science and MPLS. Such a comparison might be critical because previous research that has examined the differences between the micro and macro levels has identified significant gaps between these two levels (Aizawa & Rose, 2019; Hu et al., 2014). 2.3. Discipline-specific language differences: Differences according to academic division EMI is used to teach and learn a range of academic disciplines in higher education, from physical and life sciences to the humanities and social sciences. Previous studies have categorized and named these disciplines (at a macro level) differ- ently; from numeric-based subjects to arts and humanities (Dearden & Macaro, 2016), hard versus soft sciences (Dafouz et al., 2014; Neumann, 2001), natural sciences versus social sciences and humanities (Kuteeva & Airey, 2014) to STEM versus sumanities (Roothooft, 2019). In this study, we categorize and compare ac- ademic disciplines at three levels: division level (macro: MPLS and social sciences), department level (meso: engineering and economics, and administrative sci- ences), and program level (micro: four programs per department). When the nuances of language used in different academic disciplines have been investigated, both in the EMI and the English for specific purposes (ESP) literature, many research studies have adopted a descriptive approach, specifi- cally examining lecturers’ perceptions (e.g., Dearden & Macaro, 2016; Kuteeva & Airey, 2014; Roothooft, 2019) and learners’ perceptions (e.g., Kuteeva & Airey, Investigating academic achievement of English medium instruction courses in Turkey 121 2014; May & Casazza, 2012) of linguistic complexity in various academic disci- plines. For example, Kuteeva and Airey (2014) found that learners in social sci- ences depend greatly on their English proficiency because their EMI courses de- mand that students use language flexibly and creatively. Similarly, in Dearden and Macaro’s (2016) study, social science EMI lecturers reported that they deal with numerous language-related issues. MPLS lecturers, on the other hand, stated that they rely more on formulae; they also considered general English proficiency less important than content knowledge (Dearden & Macaro, 2016). Support for this argument also comes from two studies conducted by Ward (1999, 2009). Moreover, Ward (1999) examined five foundation-level engineer- ing textbooks to determine the number of words students need to know to be able to read efficiently. He found that 2000 word families covered up to 95% of the texts. In another study, Ward (2009) identified 299 word types that provided good coverage of the vocabulary used across five engineering subjects. These results demonstrate that a limited number of words (or word groups) are com- monly used in MPLS discipline coursebooks. In their book on the nature of lan- guage in science and science education, Wellington and Osbourne (2001) argue that the intensity of language used in technical disciplines is low. This is due to learners primarily relying on “a combination and interaction of words, pictures, diagrams, images, animations, graphs, equations, tables and charts” in the pure and applied sciences (Wellington & Osbourne, 2001, p. 6). In their longitudinal study, Yuksel et al., (2021) investigated whether lan- guage improvement over a four-year period of studying through English impacts academic achievement by comparing the scores of the students in a business administration program (a social science subject, N = 81) with those in a mech- atronics engineering (a mathematics, physical and life sciences subject, N = 84). Their results revealed that language proficiency statistically significantly pre- dicted EMI academic achievement in business administration courses but not in mechatronics engineering courses. The current study expands this line of re- search by exploring whether general English proficiency plays a role in EMI aca- demic achievement and whether this differs according to macro (academic divi- sion), meso (academic department), and micro (academic program) levels. 2.4. English medium instruction in Turkey Although Turkey does not have a colonial past, it has adopted English as the medium of instruction (MOI) in higher education. EMI in Turkey has been expo- nentially growing in recent years. According to Dearden et al. (2016), 110 out of 178 higher education institutions in Turkey use English to teach academic subjects. Furthermore, Turkey has seen an incremental expansion of the multilingual model Mehmet Altay, Samantha Curle, Dogan Yuksel, Adem Soruç 122 of EMI (a hybrid rather than an “English only” use of language; Macaro, 2018). As of 2019, out of 10,396 undergraduate programs offered by 193 universities in Turkey, 2542 programs provide students with full EMI education, while 378 provide partial EMI. 28% of all EMI programs are, therefore, partial EMI pro- grams (OSYM Manual, 2019). Similar to the exponential growth in the number of the EMI programs (OSYM Manual, 2019), numerous studies have been conducted on various as- pects of the use of English to teach academic subjects in Turkey. When these studies are reviewed, we can find some empirical investigations which have ex- amined students’ motivations for choosing EMI programs (Kırkgöz, 2005; Turhan & Kırkgöz, 2018) or studies that have undertaken policy-level analysis of the EMI programs (Karakas, 2016). There are some other studies that have investigated the challenges faced by Turkish EMI students and strategies used to overcome those challenges (Soruç & Griffiths, 2018; Soruç et al., 2018). However, few quantitative studies have been conducted. One example is the study by Macaro and Akıncıoğlu (2018), which explored Turkish EMI students’ perceptions using year group, gender, and university type as variables. To our best knowledge, no prior study has explored the influence of general English language proficiency, TMI academic achievement, and academic subject on EMI success, at three dif- ferent levels. This innovative study aims to fill this gap in the literature. Motivated by these gaps in the literature, the current study seeks to ad- dress the following research questions: 1. To what extent do general English proficiency, TMI academic success, and academic division predict EMI academic achievement? 2. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in engineering and economics, and administrative sciences? 3. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in the following academic subjects: civil engineering, electronics and communication engineering, mecha- tronics engineering, environmental engineering, business administra- tion, international relations, labor economics, and political science and public administration? 3. Methodology This study adopted a quantitative research approach. First, details about the context and participants of this study are given. Next, the procedural details of data collection and analysis are explained. Investigating academic achievement of English medium instruction courses in Turkey 123 3.1. Context EMI programs at higher education institutions in Turkey are classified into two types: partial EMI and full EMI. The current study investigates partial EMI programs, also known as the implementation of a “multilingual model” of EMI (Macaro, 2018). This means that not the entire program is taught through English; rather, students are re- quired to take a minimum of two EMI courses per semester. In these programs, courses such as introduction to political science, basic concepts in law, history of Turk- ish politics (in international relations program) and linear algebra and engineering ap- plications, and electric circuit theory and differential equations (in electronics and communications engineering program) are offered in Turkish and account for a mini- mum of 70% of all the courses, whereas the rest (e.g., research methods in interna- tional relations, sociology and business English in the international relations program, and introduction to electronics and telecommunications engineering, linear algebra and engineering applications and computer programming, in the electronics and communication engineering program) are offered in English. Newly admitted stu- dents are required to take a general English language proficiency exemption test, that is, Cambridge Preliminary English Test (PET) at the B1 level of difficulty (Cambridge ESOL, 2014). If this test is failed, students then need to complete a one-year intensive general English as a foreign language course alongside their EMI studies. 3.2. Participants Data were collected at a major public university in Turkey that offers 13 EMI programs in three departments. Departments included in this sample were: En- gineering (from the MPLS division), and Economics and Administrative Sciences (from the social science division). The remainder of the sample (i.e., of the total data collected) could not be included in this study due to a lack of a sufficient sample size from each program (two in the humanities, a further two in MPLS, and one in social science). The Department of Engineering offers six EMI programs, while the Depart- ment of Economics and Administrative Sciences offers five. Consent forms were sent out to a total of 1,343 students in 13 academic programs. 908 students gave consent for their scores to be used in this study. A final sample of 716 were in- cluded: 357 students from four EMI programs in the Engineering Department, and 359 students from four EMI programs in the Economics and Administrative Sciences Department. All participants were Turkish and had similar learning ex- periences of English as a foreign language. They were exposed to English only during their EMI classes, and most of them did not get much opportunity to improve their English out of class. Mehmet Altay, Samantha Curle, Dogan Yuksel, Adem Soruç 124 Using a purposive sampling technique (see Rose & McKinley, 2020), par- ticipants were included in the sample if they met the following criteria: The par- ticipant had completed three and a half years of their degree program, the par- ticipant had completed a minimum of 18 EMI courses and 35 TMI courses, the participant had completed the one-year intensive general English program. Background information of participants according to their academic division, de- partment and program is provided in Table 1. Table 1 Demographic information of participants according to academic divi- sion, department and program Division: Mathematical, physical and life sciences (MPLS) Department: Engineering (N = 357) Programs Civil engineering Electronics and commu- nication engineering Mechatronics engineering Environmental engineering Gender (%) M: 68 (73) F: 25 (27) M: 64 (75) F: 21 (25) M: 72 (79) F: 19 (21) M: 59 (67) F: 29 (33) Total students 93 85 91 88 Age range (M) 22-27 (24.6) 21-29 (23.9) 22-28 (25.1) 21-28 (24.7) Division: Social science Department: Economics and administrative sciences: (N = 359) Programs Business administration International relations Labor economics Political science and public administration Gender (%) M: 38 (46) F: 45 (54) M: 37 (42) F: 52 (58) M: 54 (59) F: 38 (41) M: 45 (47) F: 50 (53) Total students 83 89 92 95 Age range (M) 22-31 (25.2) 22-30 (24.5) 21-30 (26.2) 23-31 (25.3) Note. M = males, F = females 3.3. Data collection Quantitative data on four variables were collected for statistical analyses: EMI academic success (i.e., general grade point average [GPA] scores for English-me- dium taught courses), TMI academic success (i.e., general GPA scores for Turk- ish-medium taught courses), general English proficiency (i.e., English language test scores), and the academic subject students were studying. After the univer- sity had granted all the necessary ethical and legal permissions, and when stu- dents gave their informed consent, data were obtained from the University Reg- istrar Office. The four variables were measured as follows: · EMI academic success was calculated by dividing the sum of final course scores for all courses taken in English by the total number of English- medium courses each student took. Final course scores for the students were generated by combining students’ mid-term and final exam scores, as well as grades for presentations, projects, and quizzes. All assessment tools used in each course are publicly available on the course list server Investigating academic achievement of English medium instruction courses in Turkey 125 of the university. To gain a comprehensive overview of students’ EMI ac- ademic success, a minimum of 18 English-medium courses was used as a unit threshold to be included in this study. · TMI academic success was calculated by dividing the sum of final course scores for all courses taken in Turkish by the total number of courses each student took in Turkish. Similar to EMI academic success, final course scores were derived from various assessment tools including mid-term and final exam scores, presentations, projects and quizzes. A minimum of 35 Turkish-medium courses was used as a unit threshold to be included in this study. · General English proficiency: A version of the Cambridge Preliminary Eng- lish Test (PET) at the B1 difficulty level (Cambridge ESOL, 2014) was used to measure general English language proficiency. This included sections on all four language skills: reading, writing, listening, and speaking. PET exam reports include scores in each skill as well as a single final score. The final scores of the students were used in this study. The validity and reliability of each component of the PET was verified in a series of stud- ies: Shaw and Weir (2007, writing), Khalifa and Weir (2009, reading), Tay- lor (2011, speaking) and Geranpayeh and Taylor (2013, listening). · The framework for academic divisions and departments, as adopted by the University of Oxford (ODDF; University of Oxford, 2020), was used as a model to classify and group academic subjects into two academic divi- sions: MPLS, and social sciences. Academic subject is a general term that encompasses the different levels of academic division, academic depart- ment, and academic program. 3.4. Data analysis Using the computing software R, we performed multiple linear regressions on the dataset to determine if general English proficiency, TMI academic success, and academic subject predict EMI academic achievement. Separate models were run at the macro level (division, RQ1), the meso level (department, RQ2), and the mi- cro level (program, RQ3). Levels of analysis are illustrated in Figure 1. This was done to determine the unique variance in EMI academic success as explained by each of the predictor variables at each level. Multi-level modelling was deter- mined to be inappropriate due to the sample size (Cohen, 1998). There were missing data, and each model met the assumptions for multiple linear regressions (i.e., linearity, normality, multicollinearity, correlation, and homo- scedasticity; Field, 2013). As the variables in each model (at each level) were very Mehmet Altay, Samantha Curle, Dogan Yuksel, Adem Soruç 126 similar, the hypothesis was that the results of each model would be similar. How- ever, the next section demonstrates that this was not the case. Figure 1 Levels of data analysis 4. Results To answer each research question, we ran multiple linear regressions on the da- taset at three different levels: the macro level (academic division), the meso level (academic department), and the micro-level (academic program). Research ques- tions 2 and 3 are presented separately according to division, with the results grouped for MPLS subjects and social science subjects. 4.1. To what extent do general English proficiency, TMI academic success, and academic division predict EMI academic achievement? Model 1 (see Table 2) showed that all three predictors statistically significantly predicted EMI academic success (F(3, 712) = 63.69, p = .000). This included: gen- eral English proficiency (beta = .145, t = 4.275, p = .000), TMI academic success (beta = .384, t = 11.082, p = .000), and academic division (beta = .129, t = 3.686, p = .000). The adjusted R2 showed that these three predictors explained 20% of the variance in EMI academic success. Table 2 Model 1: General English proficiency, TMI academic success, and aca- demic division predicting EMI success according to division B Estimate Std. error t Constant .000 12.588 4.921 2.558* General English proficiency .145 .248 .058 4.275*** TMI academic success .384 .406 .036 11.082*** Academic division .129 3.232 .876 3.686*** Note. Adjusted R2 = .20***; significance codes: * p < .05, ** p < .01, *** p < .001 Investigating academic achievement of English medium instruction courses in Turkey 127 4.2. MPLS academic subjects 4.2.1. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in engineering? (division level) The model presented in Table 3 shows that TMI academic success was the only statistically significant predictor of EMI academic success in the Engineering De- partment (F(2, 354 = 193.7, p = .000). The adjusted R2 showed that TMI aca- demic success explained 51.9% of the variance in EMI engineering academic success (beta = .727, t = 19.584, p = .000). Table 3 Model 2: General English proficiency, TMI academic success, and aca- demic division predicting EMI success in engineering B Estimate Std. error t Constant .000 11.555 5.544 2.084*** General English Proficiency -.036 -.068 .069 -.983*** TMI academic success .727 .860 .043 19.58*** Note. Adjusted R2 = .519***; significance codes: * p < .05, ** p < .01, *** p < .001 4.2.2. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in civil engineering? (program level) Model 3 (see Table 4) showed that both predictors statistically significantly pre- dicted EMI academic success in civil engineering (F(2, 90) = 76.27, p = .000). This included: general English proficiency (beta = -.182, t = -2.738, p = .007) and TMI academic success (beta = .820, t = 12.341, p = .000). The adjusted R2 showed that these two predictors explained 62% of the variance in EMI civil engineering academic success. Table 4 Model 3: General English proficiency, TMI academic success, and aca- demic division predicting EMI success in civil engineering B Estimate Std. error t Constant .000 -23.706 12.450 -1.904*** General English Proficiency -.182 -.407 .148 -2.738*** TMI academic success .820 1.752 .142 12.341*** Note. Adjusted R2 = .62***; significance codes: * p < .05, ** p < .01, *** p < .001 Mehmet Altay, Samantha Curle, Dogan Yuksel, Adem Soruç 128 4.2.3. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in electronics and communication engineering? (program level) Table 5 illustrates that TMI academic success was the only statistically significant predictor of EMI academic success in electronics and communication engineering (F(2, 82 = 68.62, p =.000). The adjusted R2 showed that TMI academic success explained 61.6% of the variance in EMI electronics and communication engineer- ing academic success (beta = .793, t = 11.636, p = .000). This strong, positive, linear relationship is illustrated in Figure 2; the more successful students were in their TMI courses (see the incremental rise on the y axis), so too were they more suc- cessful in their EMI courses (see the incremental rise on the x axis). Figure 2 Scatterplot of EMI academic success in electronics and communication engineering and TMI academic success Table 5 Model 4: General English proficiency, TMI academic success, and academic division predicting EMI success in electronics and communication engineering B Estimate Std. error t Constant .000 -.433 .252 -.047*** General English Proficiency -.015 -.024 .109 -.223*** TMI academic success .793 .925 .079 11.636*** Note. Adjusted R2 = .616***; significance codes: * p < .05, ** p < .01, *** p < .001 Investigating academic achievement of English medium instruction courses in Turkey 129 4.2.4. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in mechatronics engineering? (program level) Model 5 (see Table 6) highlighted that TMI academic success was the only sta- tistically significant predictor of EMI academic success in mechatronics engi- neering (F(2, 88 = 120.2, p = .000). The adjusted R2 showed that TMI academic success explained 72.5% of the variance in EMI mechatronics engineering aca- demic success (beta = .855, t = 15.212, p = .000). Table 6 Model 5: General English proficiency, TMI academic success, and aca- demic division predicting EMI success in mechatronics engineering B Estimate Std. error t Constant .000 13.377 8.150 1.641*** General English Proficiency .001 .003 .107 .031*** TMI academic success .855 .863 .056 15.212*** Note. Adjusted R2 = .725***; significance codes: * p < .05, ** p < .01, *** p < .001 4.2.5. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in environmental engineering? (program level) In Table 7, Model 6 demonstrates that TMI academic success was the only sta- tistically significant predictor of EMI academic success in environmental engi- neering (F(2, 85 = 304.6, p = .000). The adjusted R2 showed that TMI academic success explained 87.4% of the variance in EMI environmental engineering aca- demic success (beta = .940, t = 24.643, p = .000). Table 7 Model 6: General English proficiency, TMI academic success, and aca- demic division predicting EMI success in environmental engineering B Estimate Std. error t Constant .000 3.103 5.359 .579*** General English Proficiency -.044 -.079 .068 -1.162*** TMI academic success .940 1.017 .041 24.643*** Note. Adjusted R2 = .874***; significance codes: * p < .05, ** p < .01, *** p < .001 Mehmet Altay, Samantha Curle, Dogan Yuksel, Adem Soruç 130 4.3. Social sciences 4.3.1. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in economics and administrative sciences? (department level) In Table 8 Model 7 displays general English proficiency as the only statistically sig- nificant predictor of EMI academic success in the Economics and Administrative Sciences Department (F(2, 356 = 22.74, p = .0000). The adjusted R2 showed that English proficiency explained 10.8% of the variance in EMI economics and admin- istrative sciences academic success (beta = 0.337, t = 6.741, p = .0000). Table 8 Model 7: General English proficiency, TMI academic success, and aca- demic division predicting EMI success in economics and administrative sciences B Estimate Std. error t Constant .000 30.065 6.165 4.877*** General English Proficiency .337 .508 .075 6.741*** TMI academic success -.033 -.31 .047 -.664*** Note. Adjusted R2 = .108***; significance codes: * p < .05, ** p < .01, *** p < .001 4.3.2. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in business administration? (program level) Table 9 (Model 8) shows both predictors were statistically significant for busi- ness administration (F(2, 80) = 21.22, p = .000). This included: general English proficiency (beta = .559, t = 6.169, p = .000) and TMI academic success (beta = - .239, t = -2.644, p = .009). The adjusted R2 showed that these two predictors explained 33% of the variance in EMI business administration academic success. Table 9 Model 8: General English proficiency, TMI academic success, and aca- demic division predicting EMI success in business administration B Estimate Std. error t Constant .000 22.003 9.973 2.206*** General English Proficiency .559 .802 .130 6.169*** TMI academic success -.239 -.200 .075 -2.644*** Note. Adjusted R2 = .33***; significance codes: * p < .05, ** p < .01, *** p < .001 Investigating academic achievement of English medium instruction courses in Turkey 131 4.3.3. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in international relations? (program level) Table 10 shows the results from Model 9. General English proficiency was the only statistically significant predictor of EMI academic success in international relations (F(2, 86 = 3.79, p = .000). The adjusted R2 showed that English profi- ciency explained 5.9% of the variance in EMI international relations academic achievement (beta = .274, t = 2.617, p = .01). Table 10 Model 9: General English proficiency, TMI academic success, and aca- demic division predicting EMI success in international relations B Estimate Std. error t Constant .000 14.244 18.248 .781* General English Proficiency .274 .489 .187 2.617* TMI academic success .136 .180 .138 1.302* Note. Adjusted R2 = .059***; significance codes: * p < .05, ** p < .01, *** p < .001 4.3.4. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in labor economics? (program level) Model 10 (see Table 11) shows that general English proficiency was the only statistically significant predictor of EMI academic success in labor economics (F(2, 89 = 11.81, p = .000). The adjusted R2 showed that English proficiency ex- plained 19% of the variance in EMI labor economics academic success (beta = .458, t = 4.85, p = .000). This relationship is illustrated in Figure 3. This scatterplot shows a strong, positive, linear association between that English proficiency and EMI labor economics academic success. Table 11 Model 10: General English proficiency, TMI academic success, and ac- ademic division predicting EMI success in labor economics B Estimate Std. error t Constant .000 10.687 12.402 .862*** General English Proficiency .458 .719 .148 4.856*** TMI academic success .038 .034 .085 .408*** Note. Adjusted R2 = .191***; significance codes: * p < .05, ** p < .01, *** p < .001 Mehmet Altay, Samantha Curle, Dogan Yuksel, Adem Soruç 132 Figure 3 Scatterplot of EMI academic success in labor economics and general English proficiency 4.3.5. To what extent do general English proficiency and TMI academic success predict EMI academic achievement in political science and public admin- istration? (program level) Model 11 (Table 12) highlights general English proficiency as the only statistically significant predictor of EMI academic success in political science and public ad- ministration (F(2, 92 = 4.4, p = .013). The adjusted R2 showed that English profi- ciency explained 6% of the variance in EMI political science and public admin- istration academic success (beta = .280, t = 2.786, p = .006). Table 12 Model 11: General English proficiency, TMI academic success, and aca- demic division predicting EMI success in political science and public administration B Estimate Std. error t Constant .000 33.422 10.966 3.048** General English Proficiency .280 .368 .132 2.786** TMI academic success .062 .059 .095 .624** Note. Adjusted R2 = .06; significance codes: * p < .05, ** p < .01, *** p < .001 Investigating academic achievement of English medium instruction courses in Turkey 133 5. Discussion 5.1. RQ1: To what extent do general English proficiency, TMI academic success, and academic division predict EMI academic achievement? This study investigated the influence of general English language proficiency, TMI success, and academic discipline on EMI academic achievement at three different levels (i.e., macro, meso, and micro). When the dataset was analyzed at division level, all three independent variables statistically significantly pre- dicted EMI academic success, explaining 20% of the variance. This finding par- tially supports previous studies that have examined the impact of these varia- bles independently (e.g., Rose et al., 2020; Soruç et al., 2021; Yuksel et al., 2021) but contradicts Curle et al., (2020), who only found L1 MOI success to be a sig- nificant predictor of EMI academic success. General English language proficiency was found to be a strong predictor of EMI success in the social sciences (at the macro, meso and micro levels). It cannot be ignored that the Matthew effect (Merton, 1968) might be present here: stu- dents most likely to achieve high grades in social science EMI programs are those who might already be highly proficient in English. Nevertheless, this finding may be due to the role that English plays in this discipline. Kuteeva and Airey (2014) argue that English-taught programs in social sciences rely heavily on L2 skills be- cause of the need to use the language flexibly and creatively. Evidence related to this was reported by Dearden and Macaro (2016). In their study, EMI lecturers in this discipline reported that they focused on language issues to a large extent. In addition, Bolton and Kuteeva (2012) state that the teaching and learning of social sciences involve more interactive, small group seminars, which leads to a heavy reliance on language (i.e., the use of, practice, and need for English). The overall finding that general English language proficiency did not pre- dict EMI academic achievement in MPLS disciplines may be explained from two perspectives, that is, the language used in EMI materials, and also, actual lan- guage practices in EMI classrooms. Firstly, Ward’s (2009) analysis of five engi- neering textbooks found that students only need to know 299 word types for comprehension. EMI students may, therefore, have mastered these word types, which broadly are MPLS-related jargon. It stands to reason, therefore, that gen- eral English language proficiency would play less of a role in this academic disci- pline. This may, however, vary from MPLS discipline to MPLS discipline. An anomaly in this study was civil engineering, where both general English profi- ciency and TMI success were significant predictors. Therefore, to further under- stand these subtle disparities in the influence of general versus academic vocab- ulary knowledge in EMI MPLS disciplines, further MPLS discipline-comparative Mehmet Altay, Samantha Curle, Dogan Yuksel, Adem Soruç 134 research (at micro level) needs to be conducted. Secondly, findings of Roothooft’s (2019) study illustrated that humanities EMI lecturers were stricter about limiting the use of the mother tongue in the EMI classroom than STEM lecturers. This discipline-specific, in-classroom use of language may provide in- sight into why, in this study, TMI academic achievement was a strong predictor of MPLS students’ EMI academic achievement. MPLS students may be benefit- ing from the L1 in TMI not only in terms of knowledge transfer, but also through their daily EMI learning experience, thus possibly explaining the heavy influence of L1 success. However, the situation may vary from one EMI context to another; therefore, it is necessary to conduct further research into how in-classroom EMI language practices directly influence EMI academic achievement. 5.2. RQ2: To what extent do general English proficiency and TMI academic success predict EMI academic achievement in: Engineering and economics, and administrative sciences? When each department was analyzed, results indicated that academic achieve- ment in TMI courses played a significant role in EMI academic achievement in engineering (a MPLS department), whereas general English language profi- ciency influenced EMI success in economics and administrative sciences (a social sciences department). These findings are in line with previous studies that have explored EMI academic achievement in the social sciences. For example, Curle et al. (2020) found that TMI was a statistically significant predictor while general English proficiency of the students was also found to have a significant impact. With respect to general English proficiency, Kuteeva and Airey (2014) argue that students rely heavily on language in the social sciences because they need to use it flexibly and creatively. On the other hand, Dearden and Macaro (2016) suggest that lectures in MPLS depend more on formulae and downplay the sig- nificance of the medium of instruction. 5.3. RQ3: To what extent do general English proficiency and TMI academic success predict EMI academic achievement in the following academic subjects: civil engineering, electronics and communication engineering, mechatronics engineering, environmental engineering, business administration, international relations, labor economics, and political science and public administration? When each program was analyzed individually, nuances in the data emerged. Results showed that general English language proficiency and TMI achievement predicted achievement in EMI business administration courses (33% of the var- iance). This is somewhat in line with Curle et al.’s (2020) study of economics Investigating academic achievement of English medium instruction courses in Turkey 135 students in Turkey, which found TMI success rather than English proficiency pre- dicted student EMI achievement in economics. These subtle differences in find- ings need to be further explored in various EMI contexts in order for results to be generalizable. Furthermore, even though Halliday (2004) states that the so- cial sciences make greater use of narrative or expository language, some sub- jects (such as economics and business studies) may rely more on numbers and formulae rather than language as such. More research is therefore called for in this respect. Furthermore, the type of support we provide for EMI students should be tailored at the micro-level (program). Providing economics and busi- ness students with some courses through their L1 appears to positively affect their EMI academic achievement. When academic programs in the MPLS division were analyzed separately, results revealed that academic achievement in Turkish-medium courses was the strongest, most consistent predictor of EMI success, explaining overall 51.9% of the variance in EMI success scores. These findings therefore indicate that EMI academic achievement is enhanced when MPLS students study some courses through their L1 alongside their EMI courses. The phenomenon of “transfer of knowledge” might explain this influence (Olivares, 2002). As students are ac- quiring knowledge in their L1 at the same time as learning similar (or even more advanced) concepts through English, understanding of abstract concepts is fa- ciliated (Cummins, 2017), thus positively affecting learning outcomes. Applying the knowledge acquired in the L1 helps students to become more academically successful in the L2 (Brooks & Danserau, 1987; Dong, 2002; Lemberger & Vinogradova 2002). Cummins’ (2017) model of multilingual transfer, therefore, provides theoretical support for the cognitive transfer of conceptual elements as well as metacognitive and metalinguistic learning strategies from L1 to L2. This issue deserves further research, particularly in contexts employing multilin- gual models of EMI (Macaro, 2018). 6. Limitations Our results should be evaluated taking the limitations of this study into account. Firstly, only partial EMI students were sampled since the effect of TMI over EMI academic achievement could have only been investigated in this EMI model. In addition, only Turkish students were included. Future studies might attempt to undertake multiple-country comparisons to increase generalizability. Further- more, the effect sizes in this study might be another limitation. First, although Cohen (1988) argues that power, significance criterion, sample size, and effect size are a function of the other “which means that when any three of them are fixed, the fourth is completely determined” (p. 14). Nevertheless, replication Mehmet Altay, Samantha Curle, Dogan Yuksel, Adem Soruç 136 studies are called for to compare effect sizes. Second, this study adopted a quan- titative research approach, reporting only the nuanced statistical significance of the influence of certain variables on EMI academic success. Future studies might adopt a qualitative or mixed-method approach. Interview or classroom obser- vation data might shed further light on the role of English language proficiency and the mother tongue in EMI students’ academic success, as well as other pos- sible influencing variables. Finally, limitations of the measures used in this study should be recognized. In particular, EMI academic success has been operation- alized as a single number in this study. However, the complexity of this construct should be acknowledged and thus future studies might take a more nuanced measurement approach to capture this complexity. 7. Conclusion This study examined the influence of general English proficiency, success in TMI, and academic subject (or discipline), on EMI academic achievement. It pre- sented evidence of the influence of these variables at three different academic levels: macro (division), meso (department), and micro (program). Findings il- lustrated subtle differences at each level. This has clear implications for EMI pol- icymakers. Based on this evidence, these stakeholders should consider disci- pline-specific issues faced by EMI lecturers and students at the three different levels. Aizawa and Rose (2019) argue that EMI policy and practice usually do not overlap and top-down policies usually ignore academic discipline-based prac- tices. To have sound practices, policies should be tailored according to the needs of each academic discipline in terms of the number and role of L1 courses stu- dents take alongside their EMI courses as well as the English language support offered to students. Results from this study also have implications for EMI prac- titioners. Lecturers teaching the academic subjects examined in this paper may draw on this evidence to further support their students. For example, civil engi- neering lecturers are now aware that not only do courses taught through Turkish enhance EMI learning, but that students may also require more, sustained Eng- lish language support (compared to, for example, mechatronics engineering stu- dents). These academic disciplinary differences in EMI contexts need to be taken into account when designing EMI student support programs. Investigating academic achievement of English medium instruction courses in Turkey 137 References Aizawa, I., & Rose, H. (2019). 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Applied Linguistics Re- view. https://doi.org/10.1515/applirev-2020-0097 Investigating academic achievement of English medium instruction courses in Turkey 141 APPENDIX The University of Oxford’s divisions and departments framework (University of Oxford, 2020) HUMANITIES MATHEMATICAL, PHYSICAL AND LIFE SCIENCES SOCIAL SCIENCES MEDICAL SCIENCES Classics Computer Science Anthropology and Museum Ethnogra- phy Biochemistry English Language and Literature Chemistry Archaeology Clinical Medicine History Earth Sciences Government Clinical Neurosciences History of Art Engineering Science Economics Experimental Psychology Medieval and Mod- ern Languages Materials Education Medicine Music Mathematics Geography and the Environment Obstetrics and Gynecology Oriental Studies Physics Interdisciplinary Area Studies Oncology Philosophy Plant Sciences International Development Orthopedics, Rheumatology and Mus- culoskeletal Sciences Theology and Reli- gion Statistics Technology and the Internet Pediatrics Art Zoology Business Pathology Law Pharmacology Politics and International Relations Physiology, Anatomy and Genetics Social Policy and Intervention Population Health Sociology Psychiatry Surgical Sciences Primary Care Health Sciences University of Oxford. (2020). Divisions and departments, University of Oxford. Retrieved 30 January 2020, from http://www.ox.ac.uk/about/divisions-and-departments