This week’s blog challenged learners not only to explore multiple models of design but to also ask how these models can be used and/or adapted in individualized context, be that from the point of view of corporate learning, K-12, or whatever scenario they deem applicable. I always appreciate taking new knowledge and ideas out of the theoretical and into practice; in other words, the ‘what’s in it for me’ and ‘why does it matter’ of the learning experience. This was an excellent exercise in exploring what design models are available and allowed the novice (meaning me) to dedicate time to examine the options, correlate and contrast, and weigh pros and cons, creating a solid foundation of understanding of models of design.
From the perspective of design models being selected to meet corporate or industry requirements, in a perfect world the design decisions would be made with the learner’s needs prioritized along with the desired outcomes or measurables. My experience in corporate learning leads me to believe that that is not the case; between mastery of a subject or measurability of training, measurables or ‘check box’ requirements are prioritized as they are often seen as tangible evidence that can prove success. ADDIE (Analysis, Design, Development, Implementation, Evaluation) is the design model used within my company and while ‘most instructional design models are built upon the ADDIE model’ (Goksu et al, 2017, p.86), based on the readings it seems other models that have an ADDIE foundation have evolved and adapted to account for inclusivity; therefore I assumed inclusivity to be an opportunity with the ADDIE model. Further research led me to a 2022 article by Halkiyo that used the ADDIE model specifically to increase equity and inclusivity for international students within an engineering program. By focusing the design and develop aspects of ADDIE to include ‘address the needs’ and ‘meet the needs’ of the learners (Halkiyo, 2022, p.5) inclusivity was baked into the model from the start! This led me to believe it’s not a question of the design model lacking inclusivity but instead the user of the design model.
A quote from Wilson as cited in Dousay that really spoke to me regarding this corporate learning conundrum of design model selection highlights what I see to be the missing piece, the human element:
Think about what good instruction means. Are you following a sound design procedure, e.g., ADDIE? Are you adhering to best practices of the professional community? Are your strategies supported by learning theory? Are design decisions validated by demonstrated gains on pre- and post- measures? Each of these has a role in creating good instruction, but don’t forget to meet the needs of learners, especially those at the margins. (2017,Chapter 22)
I appreciate that industry priorities and focus will drive decision making regarding design model selection, after all, ‘leaders in individual organizations determine which competencies are most relevant for specific positions and career advancement’ (Giacumo & Breman, 2020, p.1) and adult learners within the corporate workspace will likely conform to place themselves comfortably ahead when it comes time for promotion. I am hopeful that a better understanding of design model options and uses and the benefits of adapting to inclusivity and learner’s needs will help the focus move design model selection from measurables to mastery within my company and other industries.
Dousay. T. A. (2017). Chapter 22. Instructional Design Models. In R. West (Ed.), Foundations of Learning and Instructional Design Technology (1st ed.).
Giacumo, L. A., & Breman, J. (2021). Trends and Implications of Models, Frameworks, and Approaches Used by Instructional Designers in Workplace Learning and Performance Improvement. Performance Improvement Quarterly, 34(2), 131–170. https://doi.org/10.1002/piq.21349
Göksu, I., Özcan, K. V., Çakir, R., & Göktas, Y. (2017). Content Analysis of Research Trends in Instructional Design Models: 1999-2014. Journal of Learning Design, 10(2), 85-109.
Halkiyo, J. B. (2022, August). Enhancing the Equity and Inclusivity of Engineering Education for Diverse Learners through an Innovative Instructional Design, Delivery, and Evaluation: International Students in Focus. In 2022 ASEE Annual Conference & Exposition.
November 23, 2023 at 5:16 pm
Thank you for sharing your blog post, Jessica; we are glad you found this activity and assignment 1A’s process to be a practical exercise in supporting your developing knowledge around ID models in context.
We can appreciate the challenge of humanizing corporate learning experiences and that the goal of organizational training (often a checkbox) can exclude considerations of learners’ needs. How do you envision moving from “measurables to mastery” in your context? And what opportunities exist to understand your current learner audience?
Reflecting more deeply on the quote that resonated with you, how will you ensure the “human element” is present in your design, especially if learner needs are not prioritized or aligned with the institutional goals? What evidence, support, resources, or advocacy might be needed to shift culture towards more learner-centred approaches?
November 27, 2023 at 11:15 am
Thank you for your prompts! When I think of transitioning from measurables to mastery, I envision a corporation that prioritizes the amount and use of knowledge vs. the completion of training. I don’t think it’s realistic to remove measurement from corporate learning all together – we all have stakeholders to answer to and auditors to appease! – but WHAT we measure seems to be a variable that can be adjusted to account for the ‘human element’. I think measuring the knowledge of the individuals and the team may be part of that required transition. Some key points to back this up from Matošková are that “measuring the knowledge of individuals supports identifying key workers in an organization, their further development and stabilization in the organization” and “measuring knowledge can be also helpful for prediction of future performance of individuals, groups, or organizations” (2016, p.5-6). I believe spending more time on reviewing the amount of knowledge sharing happening via hard data and opinion surveys would highlight opportunities and gaps within the organization that could be addressed.
Interestingly enough, there is plenty of data that shows the ROI on people-first training, which is how I would classify mastery vs. measurement as it’s a people focus vs. number focus. Two good examples are a Deloitte article that, amongst other points, highlights that “companies that prioritise knowledge transfer are perceived by employees as more competitive with respect to revenue growth and client satisfaction” and “workers see them as more innovative and more attractive for employment” (2021,n.p.) and a study by that identified “human capital in the organization can be enhanced by knowledge management which includes knowledge creation, identification, sharing, and application” (2021,p.13). All this to say, there is a business case to ensuring we are managing knowledge, and sharing of knowledge versus measuring compliance and completion. For myself, I will continue to value add by presenting the business case to leadership in an attempt to change the culture, as well as challenge learners on how they will use and share the knowledge they receive to hopefully instill some accountability in the individuals as well.
Behme, F. & Becker, S. (2021, February 3). The New Knowledge Management. Deloitte Insights. https://www2.deloitte.com/xe/en/insights/focus/technology-and-the-future-of-work/organizational-knowledge-management.html
Matošková, J. (2016). Measuring knowledge. Journal of Competitiveness.https://publikace.k.utb.cz/handle/10563/1007085
Rezaei, F., Khalilzadeh, M., & Soleimani, P. (2021). Factors affecting knowledge management and its effect on organizational performance: Mediating the role of human capital. Advances in Human-Computer Interaction, 2021, 1-16.https://www.hindawi.com/journals/ahci/2021/8857572/
November 28, 2023 at 7:21 pm
Hi Jessica,
Thank you for the thoughtful response. I thoroughly enjoyed learning from the knowledge articles you referenced. This topic is intriguing as it led me to reflect on design using formative assessments as early alerts for measuring knowledge to predict future performance. If you are interested in exploring further, I suggest reading a book by Williamson (2017) as the future of digital learning is highlighted in “Big Data in Education.” There are so many considerations such as data protection, privacy, and policy just to name a few.
Reference
Williamson, B. (2017). Big data in education: The digital future of learning, policy, and practice. SAGE.
November 29, 2023 at 8:54 am
Hi Marni, thanks for your reply.
I will definitely look into the book you referenced, appreciate it. I think my personal success with culture change will rely heavily on ROI regarding productivity and performance so any resources that highlight the business case that can be made that knowledge can predict success or “future performance” is a win – thank you!