Breaking the Stereotype of Data Driven Learning: Students’ Experiences of Mobile Data-Driven Learning in a Developing Country

This study investigated students’ attitudes towards using mobile- data-driven learning (M-DDL) to support their writing instruction within resource-constrained environments. While corpora and DDL are recognised for their potential in enhancing language awareness and writing skills, challenges hinder widespread adoption. These challenges include infrastructure limitations and insufficient teacher training. Such issues are particularly prominent in developing country settings. To overcome these barriers, we introduced an M-DDL approach integrating corpus consultation into regular EFL classrooms. By creating a supportive environment using smartphones, non-DDL syllabi, the KWIC (keyword in context) mode, and minimal training, we aimed to facilitate seamless implementation. A purposive sample of 26 intermediate-level EFL students participated in this study. This study assessed their experiences and perceptions of a mobile-based corpus approach. Data were collected through a 26-item Likert-scale questionnaire and focus group interviews. Questionnaire data were analysed using descriptive statistics, calculating means and standard deviations for each of items, while a thematic analysis was applied to the interview transcripts. The results indicated that students generally had positive attitudes toward M-DDL, reporting increased motivation, improved vocabulary and grammar understanding, and better error correction abilities. However, some students expressed frustration with the difficulty of concordance analysis and the presence of incomplete sentences in the corpus. These findings suggest that M-DDL has potential for supporting EFL writing, even with limited resources, but highlight the need to address challenges related to corpus data interpretation and quality. Plain Language Summary Students’ Experiences of Mobile Data-Driven Learning in a Developing Country Background: Using large collections of text (corpus) and data-driven learning (DDL) can improve writing skills in second language (L2) classes. DDL is an approach to language learning and teaching that utilises authentic language data from corpus. However, these methods are often difficult to use because of technology needs, resources and training requirements. Objectives: This study created a simpler, more accessible version of DDL that uses smartphones and fits into regular classes. This makes DDL easier to use in developing countries. Methods: We studied 26 intermediate English as a Foreign Language (EFL) students in a developing country. We used surveys and focus groups interviews to learn about their experiences using mobile data-driven learning (M-DDL) for writing. Results: Students felt that using text corpora significantly improved their writing. Most students had positive experiences with M-DDL. They liked using corpus in their writing classes as a learning tools to know how to use the target language. Conclusion: Students had positive views of using M-DDL. This suggests that smartphones are a good tool for using text corpora to improve writing in developing countries, because students already use them widely