Beyond Adult Learning Theory: Motivations of Adult Learners

While training may be necessary to employers, the employees will often balk. So, you have to ask, “How do I motivate the reluctant learner?" Like all things instructional design, the answer to this question is, “It depends.”

First, we assume that the employee is otherwise engaged in their job. How to motivate an employee to work, much less actively engage in training, is beyond the scope of this article.

Second, let’s get a working definition of motivation. Richard Clark (2019) defined it as “the willingness to get the job done by starting rather than procrastinating, persisting in the face of distractions, and investing enough mental effort to succeed.”

There are multiple reasons an employee may not be motivated, but let’s address the obvious ones – the ones I didn’t find in the academic literature – first. Don’t be the company that sends employees to safety training one week and have them violate all that they learned in the next week. You’ve not only negated that training, but you’ve also set up the mindset that training doesn’t matter and that employees will ignore anything they are told in training. A related tactic is to send an employee to training but still expect a whole week’s work.  With mixed messages and limited resources, the employee can’t do it all and will fail in one area or the other.

That brings us to self-efficacy, a person’s belief in their ability to succeed in a given task. Bandura’s (1982, 1997) Social Cognitive Theory posits that self-efficacy drives the exertion of mental effort (the cognitive resources used and allocating for learning). In other words, why bother if you won’t succeed? If you want someone to engage in their training, set them up to succeed. Because past successes can increase self-efficacy, make sure your training embeds small victories early on. But you have to balance it – an over-inflated self-efficacy can result in exerting too little effort, probably because they underestimate the amount of mental effort – the cognitive resources used and allocated for learning – required to complete the task. The anticipated level of mental effort is essential. Its graph looks like an inverted “u.” If someone expects too much mental effort (the task is too challenging), they won’t try. However, if they anticipate the task to take minimal cognitive action, they again will minimize how much they put into it. In practicality, this means you should be straightforward about the time and difficulty level of a class. An accurate description allows your learners to plan accordingly and avoid suffering the crisis of confidence that can come when they don’t succeed in something they expected to be easy. Additionally, well-designed training, especially those based on Cognitive Load Theory, can break even complex constructs into manageable sections.

This brings to mind attribution errors, one of Clark’s (2019) four reasons employees lose motivation. Attribution errors come about when a learner is trying to figure out why something negative and unexpected happened. Those who place the blame on something outside their control (e.g., I’m too stupid; the trainer’s test was too hard) are likely to quit trying. You can help motivate this employee by assisting them in concluding that the task is doable, but they hadn’t put in enough effort.

Disruptive emotions such as anxiety, depression, and anger can also impede learning (Clark, 2019). If there is test anxiety, a discussion with the employee is in order. Explain the purpose of the training is to teach and not to fail. Describe the testing process (pen and paper, computer-based, or hands-on) and assure the employee that passing is possible if they pay attention and invest effort. If the course is quite tricky and failing is a possibility, then addressing this will generally fall outside the realm of the training department, along with other types of anxiety, anger, and depression. Your organization probably has a process in place that perhaps involves the supervisor, human resources, and maybe an employee assistance program.

Along with lack of self-efficacy, attribution errors, and disruptive emotions, Clark (2019) discusses a values mismatch – when an employee doesn’t care enough to learn.  Assuming they are otherwise engaged in their job, there are several ways to address this. First, find a way to get the employee interested – perhaps as a challenge or in some way linked to the tasks the employee likes to do.  Another tact is to emphasize the importance and utility of the training, its impact on the employee, the group, or the company.

Most educational theories of motivation involve the constructs of self-efficacy, persistence, and mental effort. Well-designed training can assist in all these. Good training with a balanced cognitive load is doable, even if it takes time and effort. Radiant Digital is well-equipped to help you with your training needs. Reach out to us and see how we can improve your employee’s training.



Clark, R. E., & Saxberg, B. (2019). 4 Reasons Good Employees Lose Their Motivation. Harvard Business Review.

Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122–147.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Macmillan.

Using Complex Learning and Instructional Design when things get complicated

Complex learning involves the integration of qualitatively different knowledge and skills along with their relationships and interaction rules. All the following tasks are complex:

  • Psychotherapy
  • Selling
  • Troubleshooting
  • Hardware design
  • Selecting the most appropriate statistical test for a given set of circumstances
  • Balancing competing priorities, such as prioritizing worker safety while maximizing return on investment and minimizing costs


As part of the “real world,” complex tasks frequently have novel variations and uncertainty. Every customer is a new challenge; almost no business models factored in a pandemic. These skills require knowledge transfer: taking what you know, making adaptions, and applying it in different situations.

Especially when dealing with uncertainty, complex learning is not well-suited for a linear instructional design model. Unfortunately, education and traditional training do just that: They teach a string of tasks sequentially and then place the onus of mastering the portion on the learner. Because these tasks and interactions are merely too much to learn at one time, they overload learners’ cognitive processes. The result is wasted training time, increased employee stress, burnout, and turnover while yielding less-than-optimal results.

When faced with complex tasks, I base my training on the Four-Component Instructional Design Model (4C/ID). Explicitly designed to reduce overall cognitive load and courage transfer, this nonlinear model developed by van Merriënboer and his colleagues breaks training down into components: (1) learning tasks, (2) supportive information, (3) procedural information, and (4) part-task practice.

Learning tasks

When using 4C/ID, I start with the two fundamental questions, “What do the learners need to do?” and “What do they need to know to do this?”

Learning tasks include projects, problems, case studies, etc. You would start with fully formed, authentic tasks. These learning tasks should start as straightforward as possible while still being faithful to avoid overload. As the learner masters the simple cases, add complexity.

Say, for example, you are trying to train a cohort of spokespersons for a large corporation. The first learning task could be telling a friendly press corps that the Company is adding 1,000 jobs to a new area. It would involve receiving the information, facing the press, giving the news, and responding to questions. To truly master the role, the spokesperson must also prepare their script from primary sources and raw documents. This should be the second learning task. The third task could be going on a financial radio show and discussing missed financial milestones.  These are all authentic tasks, and, for most jobs, they should be practiced in as realistic a setting as possible. Notice, too, that they are hugely divergent. This is by design: the variability in the tasks encourages knowledge transfer.

Not all complex tasks are as nebulous as the spokesperson. Teaching hardware design would have a different approach to whole task practice. In this case, the learner is guided through Worked Examples. These examples would highlight the complexities and interactivity of the information. The next step would be to provide similar examples, except with small portions of the solution removed. The learner would complete the highly scaffolded problems, and progress with each iteration require more significant input from the learners. For transfer, the difficulties should still vary.

Supportive Information

The next component is Supportive Information. This information is provided to help with the less common variants of the task. This isn’t a simple blurb on a computer screen. This type of information offers cognitive strategies: how to approach a situation, the way a given set of circumstances fits into the overall knowledge base that the task or job requires. You work on the best way to begin and the best way to do it into their mental model. One of the more difficult instructional design tasks is working with true experts to get the best mental model.

Procedural Information

The third component is procedural information, which supports the ordinary, routine tasks of the job. This information is provided just in time. In other words, you don’t present the learners with formula until they need the procedure. Doing so ahead of time adds noise, or more accurate, extraneous cognitive load. This type of information can be just a blurb on a pop-up window. It explains a basic how-to, with the facts, principles, and rules associated. For the spokesperson, this can be how to gather the press for a press conference or announcement.

For our hardware designer, the procedural information could be formulas or standard bits of circuitry repeatedly.

Part-Task Practice

Finally, you have part-task practice. Part-task practice usually involves some particularly tedious or difficult tasks and an enormous amount of repetition. For physicians, it could be tying sutures on a vein. For our spokespersons, this is most likely dealing with hostile questions while thinking on their feet. They will spend a lot of time being peppered with aggressive or leading questions, irrelevant details, and erroneous leaps of logic.

Standard differential equations and integrations are the most likely candidates for part-task practice. After mastering math, the designer can automate large portions of the design workload.

Although 4C/ID is an excellent model, it’s still just a model and isn’t suitable for every situation. Instructional design is complex, like the tasks we have been discussing. To get the training your organization deserves, you need instructional designers who can blend models, adapt models, or let the content drive the learning.

Radiant Digital can design and build the training your organization needs, all the way from specific compliance training to consequence-critical complex learning.





[Webinar] Cognitive Task Analysis in Practice: Exploring Methods

The conclusion to our CTA webinar series. Dr. Sheila Mitchell explores methods of cognitive task analysis in practice. With standard interview techniques, experts omit up to 70% of their decision points. She explains how an expert’s knowledge can be more fully captured, allowing organizations to maintain their knowledge base and provide better quality training.

Our guest speaker Dr. Sheila Mitchell is a Senior Instructional Designer at Radiant. After earning her doctorate at the University of Virginia, she has worked in diverse fields such as biosecurity, safety, and the energy sector. Sheila’s goal is to create efficient training firmly rooted in Cognitive Load Theory.

[Webinar] Apply Cognitive Task Analysis to improve training

This webinar series explains how an expert’s knowledge can be more fully captured, allowing organizations to maintain their knowledge base and provide better quality training.

Our guest speaker Dr. Sheila Mitchell is a Senior Instructional Designer at Radiant. After earning her doctorate at the University of Virginia, she has worked in diverse fields such as biosecurity, safety, and the energy sector. Sheila's goal is to create efficient training firmly rooted in Cognitive Load Theory.

Capturing Continuity of Knowledge Through Cognitive Task Analysis

Most of the work in the US today is mental labor as opposed to manual labor. As individuals get more skilled in their work and become experts, their value to your organization grows. Once an employee reaches relative expertise, their absence (or loss) becomes a significant detriment. But capturing their knowledge allows businesses to reduce the impact of absence substantially. However, effective knowledge capture is difficult because experts tend to have a high omission rate when attempting to detail and record their decision-making. Cognitive task analysis (CTA) is an empirically proven method of producing the most complete knowledge capture possible.

CTA is a method of interviewing experts to get fine-grained knowledge from them. It is particularly well-suited for safety-critical, sensitive tasks and large-scale training initiatives. Before proceeding further, we need to learn more about expertise, automaticity, and the human cognitive structure.


Knowledge levels in a subject run from novice to expert. While novice is easy to understand, expert is a more ambiguous term. Compared to novices, experts have not only more knowledge but also a better quality of knowledge. They perceive large patterns within their domain, primarily because their knowledge is so well organized. This pattern recognition allows large amounts of information to be perceived and processed quickly that it almost appears to be intuition.


Cognitive psychology operates on the premise that humans have a severely limited amount of conscious memory (working memory), almost unlimited long-term memory, and an organizational system that allows the memories to function together and facilitate learning. Working memory is referred to as one’s focus, attention, or “consciousness.” This memory is where the mental work is done. Because it is so limited, we must use it efficiently. Chunking is one such way.

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A chunk (or element) is anything that requires a slot in working memory. Probably different for each person, chunks vary in the amount of information they hold. This amount is a function of the individual’s acquired knowledge in schemas, with one schema taking up a single chunk in working memory. More complex schemas contain and integrate more information into a single slot of working memory.

Consider, for example, a chessboard set at the starting position, except that the white King and Queen have been place-switched. For someone who has never seen a chessboard, the information present could easily overload available working memory because each piece’s location occupies one chunk. However, many or all the piece locations could occupy a single chunk for someone well-acquainted with the game. This increase in the information held in each chunk is possible because of the way information is associated with long-term memory over time.

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Development of Expertise

After extensive practice, a schema can become automated so that a task is carried out quickly, effortlessly, and with few errors without occupying any working memory. This leaves space available to address more difficult or unfamiliar tasks.

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Expert performance of cognitive tasks is not due to innate ability but is acquired through deliberate practice. Deliberate practice is tailored to a task, provides immediate feedback, and is repeated. It requires conscious, focused exertion to improve performance and is so fatiguing that it cannot last long.

In the workplace, getting the deliberate practice necessary for the development of expertise is not straightforward. The time spent on other work duties takes away from deliberate practice. However, training derived from CTA is a proven method for accelerating expertise development.

Cognitive Task Analysis (CTA) is a generic name for various techniques used to elicit the information, knowledge, and method of performing a given task from experts. As an extension of time and motion studies of manual labor, CTA addresses an observable action by determining the procedural (how-to) and declarative (facts) knowledge, decision points, and strategies necessary for its appropriate completion.

CTA Process

Several authors describe a general procedure for CTA that encompasses roughly the same steps. These steps assume that a preliminary needs analysis has been performed. It indicates a need for the type of in-depth knowledge garnered from CTA, complex Tasks, have high consequences,  or whose performance impacts the safety or large amounts of capital or operating expenditures are good candidates. The process proceeds as follows:

  1. Preliminary Phase: Experts are selected, and the analyst takes time to learn the vernacular of the experts’ knowledge domain.
  1. Identify Knowledge Representations: Establish the framework of the type of knowledge needed, e.g., procedural, declarative, etc. Map it into tasks, sub-tasks, and supporting knowledge.
  1. Elicit Knowledge: Knowledge elicitation is typically in the form of structured or semi-structured interviews, live or recorded observation, self-report through, autonomic response, or some combination of these techniques.
  1. Analysis and Verification of Data: Transcriptions of the interviews are formatted and verified by the experts. The use of multiple experts produces better results.
  1. Formatting and Use: This information may become the primary source document during the instructional design of a class or training session. It may become the basis for a checklist or questionnaire for an evaluation or assessment.


When describing the procedures necessary for a task, experts omit up to 70% of the decisions and vital information required to complete the task. However, through the iterative interviews, a compilation of procedures, and expert feedback, the procedural knowledge gathered through CTA far exceeds individual free recall.

From an instructional viewpoint, the procedures' completeness is irrelevant unless those procedures can be utilized to provide students with higher-quality instruction. If properly applied, the instruction developed using CTA-derived information will lead to improved performance from the cognitive perspective. When learners are given complete information, their cognitive resources are available for developing schemas and learning instead of expended in the instructional gaps and struggling to grasp the content.

In multiple studies comparing CTA-based instruction and traditional instruction, students receiving CTA-based instruction have demonstrated superior performance to those receiving traditional instruction in medicine, radar system troubleshooting, landmine detection, and computer spreadsheet usage. They have even demonstrated improvements in undergraduate biology coursework. Additionally, a meta-analysis found that, overall, instruction based on CTA offers an improvement over traditional instructional methods with a large effect size.  The same meta-analysis also revealed that studies in military and university settings demonstrated CTA-based instruction to be highly effective.

In Practice

The cost of performing a CTA is primarily a factor of time (of practitioner and expert) and the expert's opportunity cost. If the task is crucial or safety-critical, the cost will be negligible compared to the impacts of ineffective training. Furthermore, if the training is delivered to many learners, the cost per person trained diminishes rapidly. Radiant Digital is ready to help you with your advanced training by performing CTA-based instructional design —enabling you to build and maintain operational excellence.