A Microlearning-Based Digital Storytelling Model to Enhance English Speaking Skills

Abstract

This study aimed to develop a microlearning-based digital storytelling instructional model (MICRO-DST) to enhance English speaking skills among Grade 9 EFL students and to examine its implementation effects. Employing a research and development methodology with a one-group pretest–posttest quasi-experimental design, the study selected a sample of 35 students from a population of 383 at Seka School, Thailand, via cluster sampling. The model was developed through a systematic synthesis of 14 studies published between 2019 and 2025 and validated by five experts. The resulting model consists of three core elements: conceptual foundations, six instructional components, and a five-step learning process. Data from a rubric-based English-speaking assessment were analyzed using descriptive statistics, dependent-samples t-test, and Cohen's d effect size. Expert evaluation indicated the highest level of model appropriateness (M = 4.56, SD = 0.52). The findings revealed that students' posttest speaking scores (M = 14.52, SD = 2.92) were significantly higher than their pretest scores (M = 10.61, SD = 2.94), t(34) = 8.92, p < .001. The effect size was remarkably large (Cohen's d = 1.51), with students demonstrating a 47.60% overall proficiency gain across all five CEFR speaking dimensions, led by language Range (51.0%). These findings indicate that the MICRO-DST model effectively enhances multidimensional speaking proficiency and offers a practical instructional innovation for secondary EFL education.