Teaching technical university subjects in english to mixed-language student classroom

Abstract

This study examined the authors' experiences with teaching technical material in English to international students using mixed-language classrooms. The authors have identified both effective and ineffective ways of teaching these types of courses. Through systematic classroom observation and analysis, the authors identified what students had learned from each course taught entirely in English. In response to the complexities of implementing English Medium Instruction (EMI) in multiple languages and at multiple levels of academic studies, this longitudinal study tested and evaluated the use of the Language Responsive Delivery Model (LRDM). This model was used throughout all undergraduate and postgraduate Construction Management classes in four countries: the Czech Republic, New Zealand, Turkey, and the USA. Based on an Action Research methodology, the LRDM has three components of iterative phases as follows: prior to class delivery of the instructional materials including preliminary language assessments and a list of technical vocabulary terms; during class delivery using the "pause – paraphrase – probe" method and collecting data by minute-paper evaluations; and after class delivery, assessing learning outcomes through multimodal formats. This study explored how students with various levels of language proficiency affect their ability to comprehend instruction. Due to the differences in reading abilities among students, the authors found that traditional written exams were subject to biases related to reading literacy. The results indicated that using verbal testing and developing well-crafted rubrics for evaluating student performance provided a more equitable way of assessing a student's technical knowledge rather than their ability to communicate in English. The authors also outlined common pedagogical obstacles experienced by educators and suggested scalable approaches for universities. Lastly, the authors synthesized possible developmentally oriented directions and emerging trends that may enhance the model's long-term viability globally in engineering education such as integrating generative AI, large language models, micro-credential systems, and immersive digital twin labs.

Received:                                             08.02.2026

Received in revised form:                  09.06.2026

Accepted:                                            08.07.2026