Enhancing Learning Outcomes Through the 70:30 Blended Learning Model in Digital Marketing and Analytics

The rise of digital tools and artificial intelligence (AI) has reshaped education, necessitating learning models that balance theoretical knowledge with practical application (Fantinelli et al., 2024). In response, the Specialist Diploma in Digital Marketing and Analytics (NSDMA) adopts a 70:30 blended learning model, adapted from the blended learning framework (Zahedi et al., 2023), to enhance self-directed learning (Robinson & Persky, 2020), AI literacy (Long & Magerko, 2020), and industry readiness (Singapore Economic Development Board, 2017). In this model, 70% of learning occurs online, facilitated by microlearning packages (McKee & Ntokos, 2020) that enhance retention and knowledge acquisition (Mohammed et al., 2018), AI-integrated formative assessments, and structured quizzes that reinforce foundational concepts. The remaining 30% takes place in-person, incorporating tutorials, collaborative learning, and project-based applications (Doolittle et al., 2023) to support active engagement and workplace-relevant skills (Rahman et al., 2023). This study examines how microlearning, AI-integrated assessments, and project-based learning collectively impact student engagement, AI literacy, and workplace preparedness. The online component includes bite-sized microlearning modules, AI-driven formative quizzes, and MCQ-based assessments to reinforce understanding. The face-to-face component involves tutorials, where students refine AI-generated outputs through human intervention, fostering critical thinking and AI moderation (Vasconcelos & dos Santos, 2023), as well as project-based learning, where students apply their knowledge to real-world business scenarios. Preliminary findings indicate that tutorial discussions enhance collaboration between individuals, strengthen analytical and problem-solving skills, while project-based learning reinforces practical knowledge for workplace readiness. The 70:30 blended learning model effectively promotes self-directed learning, AI literacy, and industry preparedness by integrating structured microlearning, AI-assisted assessments, and project-based applications. This study offers value to practitioners, institutions, and Institutes of Higher Learning (IHLs) by demonstrating an AI-integrated, industry-aligned framework that enhances digital competencies and critical engagement. Future iterations will focus on refining AI feedback mechanisms and expanding real-world case applications to optimize learning outcomes and better prepare learners for AI-driven workplaces.<p></p>