Tag: instructional design

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 Frameworks

Aerial 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).

The Great Media Debate in 2024

Published by Stephen on 19 September 2024

This post was co-authored with Heidi St. Hill.

The Great Media Debate is a decades-long discussion about whether the medium through which educational content is delivered directly affects learning outcomes. Richard E. Clark sparked the debate in 1983, arguing that the quality of the instructional method is what influences learning, and that the medium (video, text, computer, etc.) is merely a vehicle for content delivery. Clark asserted that while various media have different attributes, instructional content can be adapted to any medium so learning outcomes are comparably met.

Eight years later, in 1991, Robert Kozma countered Clark’s argument, suggesting that different media have varying attributes that allow for the enhancement of learning alongside effective pedagogical approaches. Clark vigorously reasserted his stance in 1994, with Kozma following suit in the same year, and The Great Media Debate had found its footing. Thirty years later, it would be interesting to know if Clark and Kozma would defend their positions in the same way, given how dramatically the educational-technology space has evolved since 1994. This ongoing debate is particularly relevant as educational technologies are often marketed as revolutionary. Understanding these different viewpoints helps us to better evaluate the promises made by creators and advocates of new technologies (2020, Weller, p. 181).

The Future of Educational Media

According to Clegg (2023), Meta, the parent company of the social-media platform Facebook, believed that the next big evolution of the internet is its “metaverse”, which leverages virtual reality (VR), augmented reality (AR), and mixed-reality (MR) experiences, collectively known as Extended Reality (XR). Clegg argued that VR can influence comprehension, retention, engagement, and motivation amongst students, and that the metaverse offers immersive environments that facilitate meaningful interactions with content and peers, bridging geographic and economic divides (Clegg, 2023).

Clark could be sceptical of Clegg’s claims, and might argue that XR is simply a variant of video. He might further claim that VR, AR, and MR are merely media attributes whose successes are owed to the instructional methods used rather than the technologies themselves. Kozma might counter that the XR medium shatters Clark’s rigid perspective of media by incorporating the psychomotor domain of learning in ways previously never thought possible—pushing beyond the dimension of video and allowing learners to physically move through space and interact with objects. Kozma could cite modern research, such as that of Lin et al. (2024), to support claims of XR’s efficacy in learning over other media. Regardless of one’s stance, XR and the metaverse are poised to be disruptors in education, reshaping how learners can interact with educational content.

One of the biggest new revolutionary promises is artificial intelligence (AI) and its possibility to reshape education and society as a whole. In “The rise of AI-enhanced learning: Education for the digital age,” Tewari (2024) explored the transformative potential of AI in education, projecting significant integration by 2027, with the e-learning market expected to exceed $460 billion. He asserted that AI technologies will be considered the linchpin in the evolution of education, as it seamlessly integrates with traditional teaching methods to provide engaged, dynamic, and personalised learning experiences. Tewari (2024) noted that one of the key strengths of AI is its ability to collect and apply data on the learner’s performance, preferences and past experiences to create customised learning paths. He emphasised that one of the unique opportunities that AI provides is that it can make learning more accessible by enhancing opportunities for marginalised and remote populations. (Tewari, 2024).

Clark might critique Tewari’s optimism for AI, maintaining that while AI can enhance efficiency and engagement, its impact on learning outcomes depends on the pedagogical strategies employed rather than the technology’s features. Conversely, Kozma may be more supportive of the transformative potential of AI in education, which aligns with his belief that media technologies can actively influence learning through their unique capabilities (Kozma, 1994). Modern media considered, while Clark might see AI as just another tool in the shed, Kozma would possibly argue it’s the Swiss Army knife of educational technology—if used correctly, of course.

The Future of The Great Media Debate

If 2024 were to see another round of The Great Media Debate between Clark and Kozma, it would sound much different today. Clark’s (1983, 1994) consideration of computers’ abilities reflects the technological zeitgeist of the 1980s and ‘90s. In the four decades since The Great Media Debate commenced, a learner can now virtually visit Rome and experience a detailed exploration of the Pantheon as it looked during its prime, explore the vast savannahs of Kenya, and dive to the coral reefs of Raja Ampa. (Joseph, n.d.). Troves of high-quality information is available at internet users’ fingertips. AI is teeming with potential as widespread adoption has taken root. Kozma would likely draw upon contemporary evidence to bolster his argument that media themselves influence learning outcomes. He might point to how AI’s ability to provide personalised learning experiences and XVR’s capacity to create immersive educational environments aligns with his view that the characteristics of some media can actively enhance learning where other media simply cannot. Clearly, this debate now lies against the backdrop of a vastly different media landscape than what was thought possible in 1994. The sophistication of modern media might finally force Clark to reassess his previous stance on the role of media in learning.

Should The Great Media Debate continue for another 30 years, it will certainly continue to be swayed by revolutionary technology and an evolving understanding of pedagogy and instructional methods. Given the rapid pace at which our modern, technology-infused world changes, it is difficult to imagine how this space might look three decades from now. As current breakthroughs suggest that extending the human lifespan and healthspan is becoming more feasible (Garmany et al., 2021), Clark and Kozma might find themselves engaged in this debate for much longer than they ever thought possible.

References

Adobe Stock. (n.d.). Chess faceoff of both knights horses on top of a chess board in front of a black background surrounded by pawns of both sides [Stock image]. Adobe Stock. https://t.ly/8aTuO

Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445-459. https://doi.org/10.3102/00346543053004445

Clark, R. E. (1994). Media will never influence learning. Educational Technology Research and Development, 42(2), 21-29. https://t.ly/l37Eu

Clegg, N. (2023, April 12). How the metaverse can transform education. Meta. https://t.ly/gJU0s

Garmany, A., Yamada, S., & Terzic, A. (2021). Longevity leap: mind the healthspan gap. npj Regenerative Medicine 6(57). https://doi.org/10.1038/s41536-021-00169-5

Joseph, P. (n.d.). 10 of the best virtual reality travel experiences. TravelMag. https://t.ly/rN7W_

Kozma, R. B. (1991). Learning with media. Review of Educational Research, 61(2), 179-211. https://doi.org/10.3102/00346543061002179

Kozma, R. B. (1994). Will media influence learning: Reframing the debate. Educational Technology Research and Development, 42(2), 7-19. https://www.jstor.org/stable/30218683

Lin, X., Li, B., Yao, Z., Yang, Z., & Zhang, M. (2024). The impact of virtual reality on student engagement in the classroom: a critical review of the literature. Frontiers in Psychology(15)1360574. https://doi.org/10.3389/fpsyg.2024.1360574

Tewari, G. (2024, February 13). The rise of AI-enhanced learning: Education for the digital age. Forbes. https://t.ly/J0UXJWeller, M. (2020). 25 Years of Ed Tech. Athabasca University Press.

Weller, M. (2020). 25 Years of Ed Tech. Athabasca University Press.