by Rachel Pledger
October 30, 2022

When I was asked to help build the first-ever onboarding program for our manufacturing technicians, I knew I wanted to start by talking to the target audience. What did they think about this effort? What were they hoping to see? What kind of performance outcomes were they anticipating? How could they imagine this program helping them to do their jobs better? 

If you’ve ever been on the receiving end of compulsory corporate training, their answers might not shock you:

“I’m really glad you guys are doing this, I just hope it’s not ‘death by PowerPoint’.”

“I’m not sure we can really teach someone to do that by sitting in a classroom all day.”

“We’d like it to be as hands-on as possible, but we’ll trust your judgment.”

There was a real appetite to develop an onboarding program, but it was clear they were apprehensive about the type of training we–a bunch of non-technicians–would be creating. The ghosts of Bad Trainings Past were still haunting them–doldrum lectures, hours-long eLearnings, arbitrary tests with even more arbitrary success criteria, and a pitiful lack of on-the-job training. What’s worse, they assumed these subpar training experiences were the norm, just how people are taught. Given the chance, they wanted to ensure this onboarding program did the work of setting their team up for success, not sentencing them to death by PowerPoint. 

I couldn’t agree more. And, here’s where we, as instructional designers, can leverage our knowledge of adult learning theory, a framework for understanding how human beings learn. 

What is cognitive learning theory?

My training audience consisted of manufacturing technicians whose daily work soldering, welding, forming, and assembling asks them to engage in job tasks that are process-driven and systematic. But, technical training doesn’t necessarily equate to “hard skills”. Ruth Colvin Clark (2008) defines technical training as “a structured learning environment engineered to improve workplace performance in ways that are aligned with the bottom-line business goals” [4, p. 10]. You may find that the training you develop falls into this category, too! To do this work, I recommend using cognitive learning theory to guide your design decisions.

Cognitivism is rooted in the twentieth century with thinkers whose works were primarily concerned with how learners acquire, process, store, retrieve, and activate knowledge [14, 15]. Cognitivism sees the learner as an information processor, receiving input that is then interpreted to yield a subsequent action or new way of thinking [9]. The goal of any cognitivist learning design, then, is to engineer a learning experience to activate processes that reliably lead to the desired action. 

But how do we do this?

Applying Cognitivist Learning Strategies to Training Design

Social psychologists explored a constellation of theories that aggregate under the definition of cognitivism we have today. Their discoveries, though sprawling and complex, can be boiled down to several guiding principles which can help us peer behind the veil of human beings’ cognitive architecture. We’ll dive into three of those principles here, exploring what each means and how we can apply them in our technical training design.  

1. People are goal-oriented. Behavioral psychologist Edward C. Tolman found that all behavior is purposeful and that our actions are motivated by an imperative to move us toward some desired outcome [5]. For instance, you may exercise regularly to keep your heart healthy. Or, you may spend a few hours each week practicing the guitar to prepare for that upcoming recital. In short, we do things for a reason. As learning professionals, we can immediately unpack the significance of this finding: if we fail to let learners know what they will get out of a particular learning experience, then we can expect a workforce whose performance winds up largely unchanged. Tolman called this effect “latent learning” [15]–and, within the workplace development realm, it’s certainly something we want to avoid!

The most reliable way to do this is the ensure that learners perceive the training as relevant [7]. Are the instructional requirements consistent with learners’ professional goals? Is the modality compatible with their learning style? Is there an effort to connect the learning experience to their prior knowledge and past experiences? A robust audience analysis will allow you to answer these questions with confidence and build a relevant, motivating learning experience that drives engagement. Another easy way to tap into learners’ goal-oriented mindset is to begin your design by drafting learning objectives using Bloom’s Revised Taxonomy [2]. This framework classifies the cognitive domain of learning from lower- to higher-order, allowing us to craft goals that home in on what exactly we want learners to be able to do by the end of the program. Don’t forget the most important step of sharing these objectives with your learners!

2. People tend to organize experiences in similar and predictable ways: Gestalt psychologists [1] found that individuals are predisposed to organize information in particular ways, generally preferring structures that are simple, concise, systematic, and complete. They found, for instance, that people tend to perceive things that are chunked together in a close space or similar to one another as a single unit. Tolman called this mental organization “cognitive maps” and posited that learning results in an organized body of information that the learner can retrieve at will [11, 12]. 

Harnessing this innate cognitive architecture isn’t as difficult as it sounds, especially if you apply Gagné’s Nine Events of Instruction [10] model to your design. This model was developed in the 1960s by Robert Gagné, a preeminent voice in the cognitivism movement. He believed that by creating a learning environment that met particular conditions, learners would be better equipped to meet their instructional goals. Examples of those conditions include gaining students’ attention, sharing learning objectives, and providing adequate learning guidance in the form of scaffolds and performance support tools. 

3. People learn by watching others and practicing what they see: By observing others, learners can acquire the knowledge, rules, skills, strategies, beliefs, and attitudes to perform competently on the job. Albert Bandura, who pioneered social cognitive theory, found that learning could be fostered by observing others’ behaviors, attitudes, and emotional reactions [13]. Through deliberate observation of and reflection on the facilitator’s performance of a given skill or task, learners can be made aware of their own performance expectations. Subsequent rehearsal of that skill is informed by the actions they just observed. Vygotsky’s seminal work on the zone of proximal development reinforces these findings, arguing that learning is maximized when the learner is asked to perform a task in collaboration with others [8]. 

Translating this principle into a classroom setting can take several forms, but the broad strokes comprise three steps: 

• Step One: The facilitator demonstrates a skill or task while the learner observes.
• Step Two: The learners perform the task while the facilitator observes, providing just-in-time coaching, redirection, and feedback.
• Step Three: The learner performs the taks independently while the facilitator evaluates their competency.

You can riff on this model in a number of ways. For instance, you can break the demonstration into more granular sub-tasks, provide time to practice in small groups, provide learners with the opportunity to demonstrate their acumen through reciprocal teaching, allow students to watch the demonstration as often as desired by supplying them with their own access to videos or job aides [3]. The list goes on! Feel free to get creative and find the formula that works best for your learners.

When we are able to tap into these mental processes, we are doing the work of engineering learning by way of instructional design. As instructional designers, it’s our job to ensure that the training we create yields measurable results. Those results can only be achieved if our training audience learns the intended information, skills, and attitudes. Allowing cognitivism to inform our design is therefore key.

References

1. Bustamante, N. (2021, November 15). What is Gestalt psychology? SimplyPsychology. Retrieved on October 23, 2022 from https://www.simplypsychology.org/what-is-gestalt-psychology.html 

2. Center for Instructional Technology and Training. (n.d.). Bloom’s Taxonomy. University of Florida Information Technology. Retrieved October 22, 2022 from https://citt.ufl.edu/resources/the-learning-process/designing-the-learning-experience/blooms-taxonomy/  

3. Cerretti, K. (2014). Improving the Learning Experience. Collector Magazine, 80(4), 42-44. 

4. Clark, R. C. (2008). Developing technical training. John Wiley & Sons.  

5. Gelhaus, N. (2018, March 8). Using training audience analysis to inform training design. eLearning Industry. Retrieved October 23, 2022 from https://elearningindustry.com/training-audience-analysis-inform-use-training-design  

6. Johnson, M. L., EdD., & Lambeth, J. M., EdD. (2017). Redefining Instruction in Apprenticeship Training. Benefits Magazine, 54(1), 42-47,6. 

7. Keller, J.M. (2017). The MVP Model: Overview and Application. New Directions for Teaching and Learning, Dec 2017(152), 13-26. https://doi.org/10.1002/tl.20265   

8. Mcleod, S. (2019). The Zone of Proximal Development and Scaffolding. SimplyPsychology. Retrieved October 22, 2022 from https://www.simplypsychology.org/Zone-of-Proximal-Development.html 

9. Merriam, S. B., & Bierema, L. L. (2013). Adult learning: Linking theory and practice. John Wiley & Sons. 

10. Northern Illinois University Center for Innovative Teaching and Learning. (2020). Gagné’s nine events of instruction. In Instructional guide for university faculty and teaching assistants. Retrieved October 19, 2022, from https://www.niu.edu/citl/resources/guides/instructional-guide 

11. Pritchard, A. (2009). Ways of learning: learning theories and learning styles in the classroom (2nd ed.). Routledge.

12. Rothwell, W. J. (2020). Adult learning basics. (2nd ed.). ATD Press. 

13. Saurabh, G., Bostrom, R.P., & Huber, M. (2010). End-User training methods: What we know, need to know. The Data Base for Advances in Information Systems, 41(4), 9-39. 

14. Taylor, G. R., & MacKenney, L. (2008). Improving human learning in the classroom: theories and teaching practices. Rowman & Littlefield Education. 

15. Yilmaz, K. (2011). The cognitive perspective on learning: Its theoretical underpinnings and implications for classroom practices. The Clearing House, 84(5), 204-212. https://doi.org/10.1080/00098655.2011.568989