Research Project
Background
With the advent of mobile applications, educators have exploited this novel tool in teaching in a bid to engender better educational outcomes. The benefits of mobile applications include helping students access information quickly and easily, encouraging them to share knowledge with each other, as well as facilitating practice and training. Intriguingly, Lee (2015) found that mobile applications provided potential benefits over computer-based web learning for discussion activities in nursing education.
Scholarly research on mobile app usage in higher education is often concerned with gamification elements. As revealed by LeitĆ£o et al. (2022), the integration of mobile applications as a learning resource facilitated the acquisition of knowledge. Furthermore, gamification design elements are seen as essential tools to boost user enthusiasm and engagement. For instance, the underlying features of the interaction process within the application, such as the sensation of progression or completion, challenge, the aesthetic experience, the sounds and the tangible interactions, can transmit fun emotions.
Generative AI (GenAI) has become a hot topic in recent years, and its utilization in language teaching and learning has been widely demonstrated to be beneficial. Students reported that ChatGPT was user-friendly and highly effective in identifying spelling, grammar, and formatting errors in their paragraph writing (Schmidt-Fajlik, 2023:116). GenAI tools also boost learning motivation, interest, engagement, and studentsā writing creativity. Additionally, multiple studies have suggested that GenAI has the potential to offer advantages such as personalized learning, rapid responses, and autonomy (Law, 2024).
While a multitude of studies have delved into the above issues, only a tiny minority of them are dedicated to the acquisition of linguistic knowledge, let alone to a particular field of this discipline, e.g. corpus linguistics. In view of this research gap, this study aims to examine whether the aforementioned tools help students learn corpus linguistics.
Goals of the research
(a) To determine if mobile applications facilitate the learning of corpus linguistics
(b) To ascertain if gamification boosts studentsā passion for learning corpus linguistics
(c) To examine if students benefit from the incorporation of GenAI in learning corpus linguistics
General methodology
Students majoring in Language Studies, Linguistics and Applied Linguistics, will be recruited to take part in this research. They will be divided into two groups: an experimental group and a control group. The experimental group will learn corpus linguistics with the mobile application designed for this study, whereas the control group will learn it in a conventional setting without the app. This study employs a mixed-methods approach with an explanatory design. Initially, quantitative data (questionnaire scores) will be collected and analyzed, followed by qualitative data (interviews) to explain and elaborate on the quantitative findings.
Questionnaire
Students in both groups will be administered a questionnaire during the first lesson of the courses (pre-test). In the last lesson, students will be asked to fill out the same questionnaire again (post-test). The questionnaire contains questions concerning their motivation for learning corpus linguistics, satisfaction with learning it, and knowledge of this field. A 5-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree), is used for each question.
Interviews
Individual interviews will be conducted with participating university instructors. Subsequently, a focus-group interview will be conducted with students from the experimental group. The principal project supervisor (PPS) facilitates the discussion, ensuring that the conversation remains on track and all participants have the opportunity to contribute. The PPS uses a set of prepared questions but also allows for natural dialogue and exploration. A subset of the questions is pertinent to the changes observed in pre- and post-test questionnaires. Both types of interviews will be audio-recorded.