The Impact of Data-Driven Learning on Medical Students\' English Academic Writing

Abstract

Introduction: Data-driven learning has emerged as a powerful method in language teaching and has drastically changed the traditional methods of teaching and learning, especially in academic writing. So, the present study was conducted to investigate the effect of data-driven learning on the English academic writing of medical students. Method: In this quasi-experimental intervention study, 29 MSc students of different fields of medical sciences participated in a training intervention program using a data-driven learning approach focusing on self-generated corpora of academic articles related to their fields of study. Students were asked to write an academic text related to their fields at the beginning and end of the course. Written texts were graded based on the Hemp-Lyons academic writing rubric, and the data collected from the pre-and post-test assessments were analyzed using the paired-sample t-test. Results: Comparison of pre-test scores with a mean of 59 (SD = 2.26) and post-test scores with a mean of 78.53 (SD = 2.33) indicated a significant improvement in the writing of the participants of this study (p < 0.05). Conclusion: In light of confirming the educational suitability of the data-driven approach and recognizing the need for students to engage in English article writing and publication, these findings serve as a valuable aid to MSc students studying medical sciences, facilitating their academic writing in English.

Keywords

• writing
• learning
• graduate education
• technology
• data-driven learning