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In English for Specific Purposes (ESP), an area which focuses on learners’ acquisition of language used to carry out tasks in specific contexts, digital mediation proves invaluable, since, among other things, it allows learners’ exposure to subject-specific terminology and genre. Netflix, a subscription-based streaming service that allows members to watch TV shows, movies and documentaries on internet-connected devices, has been gaining popularity among young audiences. Even though the use of videos in the ESP teaching and learning process is not new, research that describes the experience of using Netflix in ESP is limited. This paper presents an action research study, conducted during the COVID-19 pandemic, in the context of an English for Specific Academic Purposes (ESAP) course for first year students of Commerce, Finance and Shipping at the Cyprus University of Technology. The purpose of the study was to find a way in which students would be exposed to more authentic-like language related to their field of study and to increase motivation, amidst social distancing and online instruction; for this purpose, the course facilitator utilised Netflix. The participants in the study were 42 first year students of the Department of Commerce, Finance and Shipping. Data were collected through a mixed methods approach, an online questionnaire and student reflections. The results of the study showed students’ overall satisfaction with the integration of Netflix in the learning process. Despite certain challenges they faced, students noted that generally the experience was beneficial in terms of language skills improvement as well as professional skills development, among others.

In this article we present an knowledge extraction approach that can be used in systems that implement teaching in a fully automated manner. These systems are called Intelligent Tutoring Systems (ITS) and are conceived around the idea of one-to-one teaching. Many such systems use natural language processing to improve the communication interface between student and the system. These techniques can be also used on the content creator side to semi- automate or fully automate the task of teaching content creation. In such systems the knowledge representation plays a crucial role to successfully implement teaching and encourage learning. The output of the knowledge extraction phase is a knowledge in the form of a hyper graph that can be used for adaption to the students current knowledge level. We present a deep neural network architecture for precise POS tagging of words written in languages that are morphologically rich. Using sparse representations for words in this task increases the vector space and makes learning more complex. This problem can be solved to some extent by using traditional vector representations but there is also the problem with representing words that are ambiguous. Proposed architecture uses a Bidirectional Encoder Representations from Transformers (BERT) model that is pre-trained on Croatian language to achieve state-of-the-art accuracy for POS tagging.