Purpose – This study examined the effectiveness of microlearning-based video tutorials in improving hair bun styling skills among students in vocational beauty education. The study addressed the limitations of conventional demonstration-based instruction, which often restricts repetition opportunities, learning flexibility, and independent procedural review. Design/methods/approach – A quasi-experimental one-group pretest–posttest design was employed involving 12 undergraduate students enrolled in a hair styling course. The intervention was conducted over four weeks using eight microlearning-based video tutorial segments, each lasting approximately 3–5 minutes and distributed through Google Classroom and WhatsApp to support flexible learning access. Data were collected using a performance-based assessment rubric covering preparation, technique, neatness, shape conformity, durability, and aesthetics. Instrument reliability testing produced a Cronbach’s Alpha coefficient of 0.82, indicating good internal consistency. Data analysis included descriptive statistics, Shapiro–Wilk normality testing, paired sample t-test, and N-gain analysis. Findings – The findings showed that the mean score increased from 65.42 in the pretest to 82.75 in the posttest. The paired sample t-test revealed a statistically significant improvement in students’ performance (p < 0.001). The average N-gain score of 0.63 indicated moderate effectiveness within the studied context. These findings suggest that microlearning-based video tutorials were associated with significant improvement in students’ practical hair bun styling skills. Research implications/limitations – Nevertheless, the small sample size and the absence of a control group limit broader causal interpretation and generalizability. Originality/value – This study contributes empirical evidence regarding the integration of microlearning strategies in vocational beauty education and offers a flexible instructional approach for supporting practical skill development in cosmetology learning environments.
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