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Nursing and midwifery students manage large volumes of course content and varying levels of importance, yet many feel overwhelmed. To support prioritization, a Traffic Light System (TLS) was introduced in three nursing and midwifery courses at an Australian university. The TLS classifies content as Green (supplementary), Amber (important), and Red (critical), guiding students toward high-impact learning areas. AIM: This study evaluated the TLS's effectiveness in reducing cognitive load, improving time management, and supporting academic performance. METHODS: A case study design examined the TLS implementation in an online neonatal care course and two blended Human Pathophysiology and Pharmacology courses. Quantitative data were analysed using descriptive statistics and effect size (Cohen's h) to compare positive agreement across cohorts. Qualitative feedback was thematically coded to identify key perceptions of TLS usefulness. The TLS was introduced in Care of the Neonate in 2018 and in the P&P courses in 2023. Data were collected across 13 institutional survey cycles (2018-2025) using Likert-scale and open-ended responses. Cumulative feedback included 146 neonatal and 192 P&P students. Analyses highlight the first neonatal course year (2018) and the most recent P&P cycle (2025). RESULTS: In 2018, 85.6% of neonatal students (n = 83) rated TLS positively, with 62.7% (n = 52) strongly agreeing it supported learning. In 2025 P&P data (n = 129), 82.2% rated the TLS positively, with 56.6% (n = 73) strongly agreeing. The difference between cohorts showed a small, stable effect size (h = 0.09), confirming consistency across time and courses. Qualitative feedback revealed five themes: clearer study prioritization, improved focus, ease of use, deeper engagement, and greater satisfaction. CONCLUSIONS: The TLS enhances curriculum clarity, time management, and cognitive load reduction. Integration of quantitative and qualitative outcomes shows the TLS is statistically consistent and valued by students, supporting targeted, high-impact learning across diverse courses.
Kain et al. (Mon,) studied this question.