Defence Sector – Instructional Design Principles

In this course, I explored the history of design thinking in instructional design as well as popular frameworks and models in the context of my industry, defence, while also analyzing applications in healthcare, which was the focus of one of my fellow students, Chris Henderson. Our application of Design Thinking focuses on its first two stages, Define and Empathy, allowing me to gain a deeper understanding of my students’ wants, needs, frustrations, and motivations. This process helped me grasp the “what” of the student’s needs and uncovered the “why,” enabling me to reframe problems more effectively.

Through an in-depth analysis of my findings from applying design thinking within the defence industry, I identified the following key issues, as discussed in Assignment Three: Jigsaw Reflection. This assignment is not publicly available; however, the issues below were identified through a vignette that explored a potential scenario reflecting challenges in training programs within the defence industry.

• Lack of engaging courseware content due to an over-reliance on PowerPoint presentations.
• Assessments that fail to measure student comprehension accurately.
• Limited funding directly impacts the quality of training content.
• Outdated courseware that lacks accuracy or relevance.
• Disconnection between training design, student engagement, and real-world applications.
• Loss of expertise caused by staff retention challenges and retirements.

To address these challenges, I developed the following design principles as a guiding framework for courseware and instructional design. My research into various frameworks that address these root issues and aspects of the Canadian Armed Forces’ training approaches informed these principles, emphasizing increased training throughput and a crawl, walk, and run approach. These principles aim to mitigate the challenges outlined above, specifically within the defence industry, while offering potential applications in other sectors.

Design Principles

Student Centric

Focus on learners’ needs, wants, frustrations, and motivations, and prioritize designs focusing on their experiences and outcomes. 

Inspired by Design Thinking, which fosters the collaboration between end users and the designer, leading to a deeper and more meaningful design that meets both the organization and end user's goals and needs (Baker & Moukhliss, 2020, p. 309). With this mindset, training design will be relevant and critical design considerations will be uncovered.

Modular 

Design in isolated, self-contained units of instruction which can be combined to support unique learning pathways.

The distribution of unique training experiences relies on partitioning training into logical chunks, allowing learners to receive relevant information tailored to their needs. Advanced Distributed Learning's Total Learning Architecture (2020), inspired by systems thinking, highlights modular learning design as key to enabling dynamic, personalized learning. This approach also reduces costs by avoiding redundant content development across programs.

Adaptive 

Design for dynamic instruction which responds to users’ unique preferences and needs.

We are inspired by constructivism, which views knowledge as developed through experiences, beliefs, and social interaction (Heaster-Ekholm, 2020). Our service members engage in collective training exercises, which are inherently dynamic learning environments that require adaptable facilitation and training content to maximize learning opportunities.

Insights Driven

Proactively collect data that answers questions and informs improvements to refine and improve effectiveness.

This approach maximizes the application of the ADDIE model (Molenda, 2003) by ensuring that the Analyze and Design stages focus on capturing critical insights to evaluate training effectiveness and student performance. Fostering a proactive approach to data collection helps prevent the reactive practice of seeking answers from inadequate data, ensuring relevant and purposeful information is gathered from the outset.

Student Control

Allow students the autonomy to control their learning experiences to align with their unique goals and motivations.

Inspired by the ARCS-V model (Ucar & Kumtepe, 2020), which aims to enhance student engagement and subsequently student performance by augmenting the existing ARCS  model (Keller & Suzuki, 2004). The ARCS-V model focuses on Attention, Relevance, Confidence, Satisfaction, and Volition. By considering volition, a student's motivations can be better measured, and training design can be improved by fostering autonomy, as Ucar and Kumtepe (2020) highlighted. 

Scaffolded

Iterate on instruction by gradually increasing complexity as learners gain confidence and competency.

The Canadian Armed Forces employ a fundamental design strategy known as the crawl-walk-run approach (Canadian Defence Review, n.d.), which sequentially layers skill complexities as each level is mastered. This method reduces cognitive load, fosters a deep understanding of individual skills, and prepares individuals for collective training exercises where team performance depends on individual competency.

Motivation

Design engaging, relevant, meaningful learning experiences to enable intrinsic and extrinsic motivations.

According to Keller and Suzuki (2004), their extensive review of educational performance highlights a correlation between motivation and student achievement. By applying Keller and Suzuki’s (2004) ARCS model, which focuses on Attention, Relevance, Confidence, and Satisfaction, we can enhance student motivation and, in turn, improve performance.

Effective

Design meaningful, evidence-based instruction that prepares students for operation in high-stress, high-stakes environments.

High student performance holds little value without effective instruction. According to the Chief Review Services (2005, p. 16), significant opportunities exist to enhance educational effectiveness by focusing on both insights and performance. This can be achieved by leveraging design principles alongside assessment frameworks such as Kirkpatrick’s Four Levels of Evaluation. This model evaluates the effectiveness of training programs through four dimensions: engagement, knowledge acquisition, skill application, and outcomes (Reio et al., 2017).

Conclusion

Based on literature reviews, these design principles serve as a foundational approach to instructional design within the context of the defence industry and require validation and revisions upon application in real-world scenarios. To remain vigilant in aligning design approaches with the needs of our clients, the following infographic, which can be printed, summarizes these principles (see Appendix A), providing physical reminders of the thoughtful approaches to instructional design that can guide improved training programs in the defence industry.

Appendix A

References

Advanced Distributed Learning Initiative. (2020, April). 2019 total learning architecture report (Publication No. 2020-04). https://adlnet.gov/publications/2020/04/2019-Total-Learning-Architecture-Report/

Baker, F. W., & Moukhliss, S. (2020). Concretising design thinking: A content analysis of systematic and extended literature reviews on design thinking and human-centred design. Review of Education, 8(1), 305–333. https://doi.org/10.1002/rev3.3186 

Canadian Defence Review. (n.d.). Training and simulation report. Retrieved from https://canadiandefencereview.com/training-simulation-report/

Carter, J. (n.d.). We must adjust to changing times and still hold to unchanging principles. BrainyQuote. Retrieved January 11, 2025, from https://www.brainyquote.com/quotes/jimmy_carter_121086

Chief Review Services. (2005). Evaluation of military individual training and education: Final report, November 2005 (p. 16). Department of National Defence, Canada. https://publications.gc.ca/collections/collection_2016/mdn-dnd/D58-142-2005-eng.pdf

Heaster-Ekholm, K. L. (2020). Popular instructional design models: Their theoretical roots and cultural considerations. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 16(3), 50–65. https://eric.ed.gov/?id=EJ1275582   

Keller, J. M., & Suzuki, K. (2004). Learner motivation and e-learning design: A multinationally validated process. Journal of Educational Media, 29(3), 229–239. https://doi.org/10.1080/1358165042000283084 

Molenda, M. (2003). The ADDIE model. In A. Kovalchick & K. Dawson (Eds.), Educational technology: An encyclopedia. ABC-Clio. https://noraastrid.wordpress.com/wp-content/uploads/2010/08/m1-the-addie-model_encyclo.pdf.

Reio, T. G., Jr., Rocco, T. S., Smith, D. H., & Chang, E. (2017). A critique of Kirkpatrick’s evaluation model. New Horizons in Adult Education and Human Resource Development, 29(2), 35–53. https://doi.org/10.1002/nha3.20178Ucar, H., & Kumtepe, A. T. (2020). Effects of the ARCS-V-based motivational strategies on online learners’ academic performance, motivation, volition, and course interest. Journal of Computer Assisted Learning, 36(3), 335–349. https://doi.org/10.1111/jcal.12404