Traditional whole-group teaching is prevalent in higher education but often ineffective for deep understanding. Practice Note No. 3 emphasizes that lectures are a design challenge. Active learning boosts student performance. By focusing on cognitive engagement and employing strategies like polling and discussions, educators can enhance lectures to foster real-time understanding.
Course design teams often rely too heavily on academic entry requirements, inadvertently building curricula for an idealised student cohort that
Revenue diversification efforts often fail due to persistent tuition dependence, which hampers institutional capacity. Governance structures and cultures focused solely on enrolment growth hinder diversification strategies. The key issue lies in governance reform; without it, institutions will struggle to effectively diversify their revenue beyond financial limits.
This Practice Note addresses why learning outcomes in higher education are often ignored and how educators can transform them into
The article emphasizes the importance of developing student personas in learning design, arguing against generic archetypes. It advocates for tailored course creation that accommodates diverse student capabilities. This proactive approach enhances inclusion, reduces structural friction, and improves efficiency, ultimately benefiting institutions while contradicting the notion that personas reinforce biases.
I’m delighted to announce the launch today of a series of weekly Practice Notes for learning and teaching faculty, lecturers,
The article emphasizes the urgent need for academic leaders to address curriculum inertia in the age of AI. It presents a 12-month action plan for Deputy and Pro Vice-Chancellors that includes naming outdated curricula as a key challenge, conducting thorough audits, and implementing rapid reform pathways.
The piece discusses how traditional academic governance is failing to adapt to the rapid impact of AI on curricula. It identifies three primary failures: slow review cycles, unclear ownership of AI initiatives, and a focus on compliance rather than transformation. Senior leadership must lead changes to effectively respond to these challenges.
Senior leadership discussions on AI often overlook the critical issue of curriculum inertia in higher education. As the labor market demands AI fluency, many graduates feel unprepared. Institutions risk falling behind in graduate employability, student retention, and reputation unless they adapt their degree programs swiftly to meet these changes.
Many universities continue to design courses focused on lecturers rather than learners. The latest Substack discusses shifting to a collaborative and transparent model, addressing topics like academic autonomy, neuro-inclusive design, the importance of institutional memory, and the challenges posed by the AI divide. This ongoing series supports an 8-Stage Learning Design Framework.
Following up on last week’s post about higher education focusing on the wrong AI emergency, my latest Substack shifts from
Higher education is shifting from a traditional transmission model to intentional learning design, emphasizing active knowledge construction and mastery. Research supports the effectiveness of active learning, while Learning Design emerges as a critical discipline. The pandemic and Generative AI have accelerated this transformation, redefining educators as Learning Architects focused on curated, purposeful learning experiences.
Many universities are currently focused on updating academic integrity policies and banning AI, viewing it as a cheating issue. However, this overlooks the need to redesign learning experiences for an AI-integrated world. The real crisis lies in faculty development and curriculum design, which are insufficiently addressed for modern labor market demands.
Many academics, while being subject-matter experts, lack training in course design, leading to ineffective curricula that reflect their own experiences rather than student needs. The 8-Stage Learning Design Framework (8-SLDF) addresses this issue, emphasizing Constructive Alignment and five development areas while encouraging honest engagement with AI in the design process.
The article discusses evolving curriculum development trends, advocating for a shift from static models to dynamic, modular approaches. It highlights the importance of granular mapping, preventing loss of educational resources through standardized tagging, and fostering collaborative content curation. This framework supports institutional agility and continuous course renewal.
The post emphasizes the importance of using student data to enhance course design rather than merely reporting past outcomes. It highlights the roles of Educational Data Mining, Academic Analytics, and Learning Analytics in understanding learner needs. Key strategies include building responsive pathways, designing supportive touchpoints, and anticipating emerging technologies to optimize learning experiences.
The 8th principle of learning design emphasizes the need for educators to adopt a proactive, future-oriented approach amid rapid technological changes. As skills quickly become outdated, the article advocates for developing Futures Literacy and focusing on transversal meta-skills. By leveraging real-time data, curriculum design can better prepare students for an evolving workforce.
Lifelong learning is enhanced by learner autonomy, which empowers individuals to shape their education. Educational design should shift focus to self-directed pathways, respect cultural contexts, and encourage personalized learning experiences. This approach emphasizes agency, cultural responsiveness, and authentic assessments, promoting a deeper understanding rather than mere memorization.