New forms and dynamics of migration have led to an increased diversification of the student population in most Western countries in the last decades. One consequence is that teachers face the challenge of how to support students from homes where the language of instruction is not the dominant language. The language and literacy demands in school present challenges for all students, but particularly for multilinguals who are still in the process of learning the language of instruction and students growing up in socioeconomically disadvantaged families who may not receive a varied and stimulating linguistic input at home.

As several large-scale studies have shown that skills in the language of instruction (especially academic language) play a vital role for content learning, the preparation of students for the language, literature, and learning demands called for by teaching standards cannot be seen as the responsibility of a small group of language specialist (Bunch 2013). In Germany, many teaching standards and educational curricula nowadays expect teachers of all subjects and grades to focus on the development of their students’ language skills. However, many mainstream classroom teachers have had little or no preparation for providing learners with the types of assistance they need and the question arises of what teachers should know and be able to do in order to teach diverse learners in an effective way.

German research on teacher professionalism has been focussing on the impact of content knowledge (CK), pedagocial content knowledge (PCK) and pedagocial knowledge (PK) as well as teachers’ beliefs on teaching effectiveness (e.g. Blömeke et al. 2014; König & Kramer 2016). Empirical findings on the influence of teachers’ knowledge and beliefs on teaching practices in linguistically diverse contexts are scarce. It therefore does not come as a surprise that current models of teacher professionalism (e.g. Kunter & Baumert 2006) in Germany hardly mention language and literacy-related knowledge, skills, and beliefs.

While the German discussion about linguistically responsive teaching has been led (mostly) separatly from the discussion about teacher professionalism in general, a look beyond the country’s borders shows that other models for understanding teaching and learning (e.g. Bransford et al. 2005) recognize that knowledge about language should play a vital part in the preparation of teachers in a changing world: In order to be able to meet the language and literacy demands of current teaching standards, in addition to CK, PCK, and PK, teachers need to develop what Galguera (2011) and Bunch (2013) have described as pedagocial language knowledge (PLK). Even though it is still being discussed what kind of language-related knowledge and skills PLK should comprise, there seems to be no doubt that i) teachers need to be offered adequate opportunities to learn (OLT) in their preservice teacher preparation programs and in professional development in order to develop PLK (e.g. Hammer& Berkel-Otto 2019), and ii) that foundational knowledge about linguistics and second language acquisition (educational linguistics) is an important base for linguistically responsive pedagogical practices (Bunch 2013). The latter includes developing awareness of differences between spoken and written language and the ability to identify linguistic features (academic language features) of disciplinary texts and tasks that may pose a challenge especially for second language learners and that have been described by several authors for different languages (e.g. Duarte & Gogolin 2016).

Against the described backdrop, the current paper aims at answering two research questions:

1. To what extent are preservice teachers of different subjects able to identify linguistic complexity (academic language features) in a mathematical explanatory text?
2. Which role do opportunities to learn (OLT) and the pre-service teachers’ beliefs about linguistically responsive teaching play in explaining their ability to identify potential linguistic barriers?

In order to empirically address the research questions, a one shot online-survey was carried out among students being enrolled in the teacher-education program (primary and intermediate secondary school bound) at a University in Northern Germany in December 2020. The survey consists of six parts: First, the participating students were asked to carefully read a mathematical explanation and to rate its overall linguistic complexity. Second, the participants were exposed to five different parts of the explanatory text (consisting of three to five sentences each) and asked to identify all of the linguistic features that may potentially pose an obstacle to understanding the content for high school students. In part three, the pre-service teachers’ own understanding of the explanation (mathematical understanding) was tested. Part four focussed on opportunities to learn (OLT) in the area of linguistics and inclusive language education at university. Part five included Likert-type scales measuring the pre-service teachers’ beliefs on linguistically responsive teaching. In the last part, the participants were asked for information about their personal and professional background (e.g. age, languages spoken, migration background, qualifications in teaching German as a Second Language, type of school, subjects studied at university). Each of the text blocks that the students were asked to analyse (part II) included several academic language features which can be assigned to five sub-dimensions: i) syntactic features (e.g. subordination and complex sentence structure), ii) morphosyntactic features (e.g. genitive constructions), iii) impersonal expressions (e.g. passive voice), iv) referential ambiguity, and v) lexical features (subject specific as well as general academic language). The students’ responses are currently being coded. The coding process (including the calculation of inter-rater reliability) will be finished my March 2021. In a first step, descriptive analyses of the students’ ability to identify academic language features in the mathematical explanation. The results will be presented separately for the different dimensions of linguistic complexity (subscores for each category) and then differences in the total score of students with different professional and personal background characteristics will be compared using multiple linear regression analyses (including control variables which have proven to be if importance in the given context). In a second step, structural equation models (SEM) will be used in order to explore the role opportunities to learn (OLT) and the pre-service teachers’ beliefs about linguistically responsive teaching play when it comes to explaining the ability to identify linguistic complexity.

The sample consists of 135 pre-service teachers. 82.2 % are female (which is not unusual in German teacher education programmes). 11.1 % grew up with a language or dialect other than Standard German, e.g. Turkish, Russian, and Low German. Roughly half of the students (51.9 %) are completing their master’s degree. Two thirds (66.6 %) aim at becoming primary school educators. About half of the sample (50.4 %) was enrolled in German language and literature classes and one third (32.4 %) studied mathematics. 16.3 % have a qualification in teaching German as a second language. As the coding of the dependent variable has not been finished, results cannot be provided yet. A first glance, it seems that the students are generally quite aware of linguistic complexity on the word level (especially technical terms), but not on other levels. We expect students who study a language and/or those who have grown up multilingually to have a greater awareness of linguistic features that may impede the learning process than monolingual students and/or those who are not enrolled in language studies. We assume that opportunities to learn mediate the influence of personal pre-service teachers’ characteristics on their ability to identify language barriers in the text and test the latter hypothesis by calculating a structural equation model with OLT being the mediator and the total test score (identifying academic language features) being the dependent variable. In addition, we plan to test whether the students’ beliefs influence their likelihood to take OLTs (moderation). It can be expected that students with negative beliefs about linguistically responsive teaching engage less in learning opportunities about language and linguistically responsive teaching. However, the relationship between students’ beliefs and taking OLTs is not likely to be unidirectional in nature: More learning opportunities should be related to positive beliefs about teaching in linguistically diverse classrooms.

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