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Category: LRNT 524

Guiding Principles for Instructional Design

Published by Stephen on 8 January 2025

Instructional design must balance theory with practice to create meaningful experiences and effective learning outcomes. These principles reflect my commitment to learner-centred, engaging, and adaptable design. They are grounded in established theories, personal insights, classroom observations, and conversations with students, aiming to guide actionable design decisions and foster impactful educational experiences.

Learning Needs Meaning

  • Design learning experiences that connect new knowledge to learners’ lives, passions, and existing understanding.
  • Anchored in Constructivist Theory (Piaget, 1950), this principle supports deep engagement through reflective practice and personalised applications.
  • Actionable Design Decision: Provide flexible activities and assignments that allow learners to bring their own perspectives and passions into the material.

Learning Needs Foundations

  • Establish essential skills and concepts as a foundation for more complex topics. Mastery is gained from iteration and intentional practice of the fundamentals.
  • Rooted in Bloom’s Taxonomy (Bloom, 1956), this principle ensures that higher-order thinking builds on well-understood basics.
  • Actionable Design Decision: Use scaffolded activities that build and reinforce core concepts, ensuring learners progress with confidence.

Learning is Something You Do

  • Learning happens through doing, experimenting, and applying concepts. It is an active process of engagement, not something passively absorbed.
  • Guided by Experiential Learning Theory (Kolb, 1984), this principle emphasises action and reflection.
  • Actionable Design Decision: Design hands-on activities and opportunities for learners to experiment with ideas and practise skills in realistic contexts. For instance, include project-based learning or simulations that mirror real-world scenarios.

Learning Should be Memorable

  • Infuse joy, humour, and humanity into learning experiences. Joy and humour make education approachable, helping learners navigate challenges. Memorable moments anchor knowledge in emotional experiences, enhancing retention.
  • Supported by research on Affective Learning (Krathwohl et al., 1964), this principle acknowledges the emotional dimensions of learning.
  • Actionable Design Decision: Incorporate relatable examples, clever commentary, or lighthearted elements (e.g., a humorous quiz) to create memorable, engaging experiences.

Learning Needs Rest Periods

  • Learning can be hard, and that’s okay. Include moments for learners to pause, reflect, and reset during challenging sessions. Spaced learning—revisiting content over time—further enhances retention and understanding by allowing learners to build knowledge gradually.
  • Informed by Cognitive Load Theory (Sweller, 1988) and research on Spaced Learning (Ebbinghaus, 1885), this principle ensures learners can process and internalise new information effectively.
  • Actionable Design Decision: Incorporate planned breaks and design activities that revisit key concepts at intervals within lessons and across a broader timeline. Schedule periodic opportunities for learners to revisit and apply knowledge over days or weeks to reinforce long-term retention.

Learning Must be Accessible

  • Design with accessibility in mind to ensure all learners, regardless of their abilities or circumstances, can fully engage with the content. Inclusive design fosters equitable access and benefits all learners.
  • Rooted in Universal Design for Learning (UDL) (Meyer et al., 2014), this principle promotes inclusivity in both design and delivery.
  • Actionable Design Decision: Use multimodal formats, clear instructions, and a conversational tone to create a supportive environment for all learners.

Learning is Social

  • Create opportunities for collaboration, discussion, and shared exploration. Knowledge grows through interaction and co-construction.
  • Anchored in Sociocultural Learning Theory (Vygotsky, 1978), this principle highlights the importance of community in education.
  • Actionable Design Decision: Encourage informal discussions to deepen connections and build a sense of community. Lead informal discussions (“talk shop”) on concepts and industry trends, and encourage peer teaching, study groups, and knowledge-sharing opportunities.

These principles aim to guide thoughtful instructional design, fostering inclusive, engaging, and effective learning experiences that inspire and empower learners to achieve their potential.

References

Bloom, B. S. (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals. Longman.

Ebbinghaus, H. (1885). Memory: A Contribution to Experimental Psychology. Dover Publications.

Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Prentice Hall.

Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1964). Taxonomy of Educational Objectives: The Classification of Educational Goals, Handbook II: Affective Domain. David McKay Co., Inc.

Meyer, A., Rose, D. H., & Gordon, D. (2014). Universal Design for Learning: Theory and Practice. CAST Professional Publishing.

Piaget, J. (1950). The Psychology of Intelligence. Routledge.

Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12(2), 257–285. Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press.

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Exploring Design Models and FrameworksAerial view of a river meandering through a lush, green forest

Exploring Design Models and Frameworks

Published by Stephen on 1 December 2024

I have been diving deeper into learning about Instructional Design (ID). When learners sit in a classroom, they might not realise that the teacher leading the class is not simply improvising, sharing knowledge, and handing out tests. The delivery of instruction likely underwent a systematic process of pedagogy-informed planning and design—this is Instructional Design.

It was fascinating to learn that ID has its roots in World War II, when efforts were made to improve military training programs. Reiser (2001) noted that psychologists and educators employed by the U.S. military studied recruits who excelled in certain disciplines. Tests were developed to assess relevant skills, enabling the identification of recruits suited for specific roles where they could perform best.

There is no one-size-fits-all method for designing effective learning content and delivery. ID is deeply contextual and varies depending on factors like whether the instruction is in a classroom or online, the average age of learners, and social and cultural influences. Naturally, the subject matter also plays a critical role. With so many intersecting conditions, every instructional project must be approached as unique.

ADDIE is an acronym that appeared frequently in my research. It describes the underlying process common to most ID models: Analyse, Design, Develop, Implement, and Evaluate. Within the ADDIE framework, a variety of ID models exist—many dating back to the 1960s. While they share similarities, these models are not interchangeable; some are better suited to curriculum design or lesson planning, while others are ideal for performance-based training.

Regardless of the model, iteration is critical. By evaluating how a solution performs for learners (users) and making improvements, the likelihood of achieving learning outcomes increases. Without measurement and refinement, learners may fail to meet outcomes—a risk that, in some industries, could lead to serious consequences.

Parallels Between Instructional Design and Software Development

For those with experience in software development or user experience, ID approaches will feel familiar. Iteration is a common thread—creating, testing, and refining a product in cycles. Features are released, feedback is gathered, and improvements are made, fostering incremental refinement. Instructional design follows a similar process, using feedback loops to improve learning outcomes.

Interestingly, the ADDIE framework reflects processes I’ve encountered in my work as a software developer. In software development, a need for a feature or change is analysed, a solution is designed and developed, and the feature is implemented for users. Evaluation might involve user testing, A/B testing, or analysing usage data. This feedback informs further analysis and refinement, creating an iterative cycle of improvement.

Models in Practice

When I began teaching, I was introduced to Bloom’s Taxonomy (Anderson & Krathwohl, 2001), which I’ve since integrated into my instruction. In my web coding classes, I ask learners to solve problems, explain code in plain language, or create features using new concepts. They do this in their independent assignments, and as we engage in interactive demos while I continually prompt their thinking by seeking their input. By aligning activities and assignments to Bloom’s Taxonomy, I’ve found it well-suited to the study of web development.

In my diverse classrooms, I aim to adopt Universal Design for Learning (Rose & Meyer, 2002) principles to accommodate the diverse needs of my learners by offering multiple means of engagement, representation, and expression. Self-study material is offered in a variety of contexts, like videos and articles, but students are encouraged to find what works for them. Though there are submission requirements for assessments, there is flexibility in giving learners choice in their implementations and content themes. To keep learners engaged and motivated, we often talk about the “why” of what we are doing: how it fits into the work, increases value in their skillsets, and prepares them for industry.

While I have experienced ADDIE principles in practice, I have also experienced the drawbacks of neglecting them. Reluctance to iterate on instructional design—even when data supports change—can leave learners frustrated and ill-prepared for industry. While ongoing improvement requires investment, iteration is the cornerstone of successful instructional frameworks.

As I explore ID models and reflect on my experiences in software and education, I have started to wonder how I might structure an instructional design model of my own. This is something I am eager to contemplate further.

References

Adobe Stock. (n.d.). River and green forest in Tuchola natural park, aerial view [Stock image]. Retrieved November 29, 2024, from https://t.ly/CxO4p

Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives: complete edition. Addison Wesley Longman, Inc.

Reiser, R. A. (2001). A history of instructional design and technology: Part II: A history of instructional design. Educational technology research and development, 49(2), 57-67.

Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. Association for Supervision and Curriculum Development (ASCD).

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PechaKucha: Understanding the Learning Challenge of Delivering Fast Results

Published by Stephen on 23 November 2024

Fellow grad student Alex Nwokoukwo and I got together to examine a learning challenge through a design-thinking lens. We quickly discovered that we share the same challenge in our classrooms: learners desire quick results with minimal time commitment and low-effort interaction.

Alex and I interviewed each other to gain empathy for one another’s challenge, seeking to deeply learn about and understand the nuances of the problem. We discussed our different approaches and the various ways in which learners will seek learning shortcuts in our particular educational contexts. We did not set out to find a solution; our goal was to simply examine the challenge.

We share our thoughts through a PechaKucha presentation. The structure of this format is 20 image-only slides that are each exactly 20 seconds in length. Please enjoy, and feel free to share your thoughts.

PechaKucha

References

Kohler, T. J. (2023). Caught In The Loop: The Effects of The Addictive Nature Of Short-form Videos On Users’ Perceived Attention Span And Mood (Bachelor’s thesis, University of Twente).
Murre, J. M., & Dros, J. (2015). Replication and analysis of Ebbinghaus’ forgetting curve. PloS one, 10(7). https://doi.org/10.1371/journal.pone.0120644

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