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Engineering students’ perceptions of mobile-assisted language learning for English terminological competence in Kazakhstan
Автор(ы)
S. Bolatbek
Дата публикации
30 сентября 2026 г.
Тип
journal article
Издатель
Suleyman Demirel University
Выпуск
3(76)
Первая страница
50
Последняя страница
63
ISSN
2709-264X
Аннотация
This study investigates engineering students’ perceptions of the contribution of Mobile-Assisted Language Learning (MALL) tools to English terminological competence in Kazakhstan. The findings are interpreted through the Technology Acceptance Model (TAM), Self-Regulated Learning (SRL) Theory, and Mobile Learning Theory as theoretical frameworks. A quantitative survey design with supplementary open-ended responses was employed. The final analytic sample comprised 132 adult first- and second-year engineering students studying at higher education institutions in Kazakhstan after all responses from participants younger than 18 were excluded. Quantitative data were analysed using descriptive statistics, while qualitative responses were examined through thematic analysis. The findings indicate that students generally perceive MALL tools as beneficial for learning technical English terminology. Nearly half of the respondents reported daily use of mobile learning applications, with ChatGPT, Quizlet, Grammarly, and Google Classroom among the most frequently used tools. Students reported perceived benefits for understanding technical definitions, using engineering terminology accurately, and supporting academic writing. However, several challenges were identified, including the linguistic complexity of AI-generated explanations, limited access to digital resources, and insufficient pedagogical guidance. Students also described planning and monitoring behaviours consistent with aspects of self-regulated learning, whereas evidence of reflective evaluation was less consistent. The study highlights the importance of integrating mobile learning tools into English for Specific Purposes (ESP) curricula and providing students with structured AI literacy training. As the study relies on self-report data and descriptive analysis, its findings concern perceived contribution rather than objectively measured development of terminological competence.
Области науки (FoS)