Browsing by Author "Welgama, N"

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    Enhancing English Proficiency through Level-Based Flipped Learning: Insights from a First-Time Implementation in a Computing Faculty
    (Department of Linguistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Nanayakkara, L; Welgama, N
    This study reports on the first implementation of a flipped English module at the Faculty of Computing, Sri Lanka Institute of Information Technology (SLIIT), designed to improve students’ speaking proficiency through a custom mobile application integrated with AI-driven tasks. The flipped approach encouraged learners to engage with video lessons, quizzes, and practice activities before class, while classroom sessions were reserved for interactive assessments and communicative practice. Data were collected from 266 first-year undergraduates using a structured online survey consisting of Likert-scaleitems and open-ended questions. Quantitative data were analyzed using descriptive statistics, and qualitative responses were thematically coded to capture student perspectives in depth. The findings demonstrate high levels of engagement, particularly with quizzes and instructional videos, which students described as helpful in consolidating knowledge and preparing for class. Many participants reported improvements in pronunciation, confidence, and overall communication skills, indicating that the flipped model effectively supported oral language development. In-class sessions were also viewed positively, with students valuing the opportunities for practice and feedback, and recognizing the strong alignment between app-based tasks and classroom activities. Nonetheless, several challenges were identified, including technical glitches, loss of progress due to the lack of an autosave function, heavy workloads, and the need for more interactive speaking opportunities. These results highlight both the strengths and limitations of flipped CALL approaches in STEM-based higher education. Overall, the study suggests that integrating flipped learning with AI-driven tools can significantly enhance English proficiency, provided that issues of workload, app design, and feedback practices are carefully addressed.