6 Guidelines to Design Training to Accelerate Complex Problem Solving Skills

Through 6 research-based guidelines, this article explains why it is important to acquire complex problem-solving in today’s job environment and how training experts can design a training curriculum that ensures acquiring complex problem-solving skills in any complex domain.

Global jobs, especially technical ones are becoming more complex day by day. Complex jobs are the jobs that are characterized by the complexity of decision-making, complexity of problems, complexity of problem-solving, complexity and ambiguity of the tasks, uncertainty in the environment, and complexity of interactions it entails. Task complexity is another key factor to determine job complexity. TaskManagementGuide website defines task complexity as “a collection of properties inherited by a task. These properties (like a priority, due date, duration, and urgency) define the difficulty of this task and its significance to a performer (a person who should do the task)”.

Several jobs require employees to handle critical and complex technical issues almost on daily basis. The job ranges from problem-solving responsibility being a part of the job to the main job itself. Examples of such jobs are Equipment repair service, internal organ medical surgery, Network and database administration, Cyber security, Aircraft maintenance, Airplane piloting, Oil and gas exploration, Air Traffic Control, Civil engineering, Biomedical Engineering, Strategic military operations, Satellite and rocket control, Space and astronautically missions to name a few (Onetoonline, n.d). All the complex jobs have one thing in common – complex problems. What makes a problem complex? The complexity of a problem is a function of the number of issues, functions, or variables involved in the problem; the number of interactions among those issues, functions, or variables; and the predictability of the behavior of those issues, functions, or variables (Xu et.al, 2007). Jonassen (2000) maintains that dynamicity is another dimension of complexity. In dynamic problems, the relationships among variables or factors change over time. Changes in one factor may cause variable changes in other factors. The more intricate these interactions, the more difficult is any solution.

These kinds of jobs require employees to have the ability to resolve problems of any complexity and order quickly and efficiently. In today’s environment, employees are expected to possess proficiency in top-order problem-solving and troubleshooting skills. General strategies for developing expertise in other contexts are seen to be not working effectively in such jobs. Developing the expertise of individuals and developing it faster is an extremely challenging task.

Complex Problem Solving (CPS)

Technical problems are typically very complex in nature due to the nature of the domain and far more reaching effects than business problems. This goes beyond the general problem-solving we talk about in day-to-day life. There is a well-developed body of knowledge called Complex Problem Solving (CPS). This moment was originally started in Europe. For those who find CPS as a new term, let me define it briefly:

Quesada et.al (2005) presented a compact characterization of complex problem solving based on Frensch and Funke (1995b):“Complex problem solving tasks are situations that are: (1) dynamic because early actions determine the environment in which subsequent decision must be made, and features of the task environment may change independently of the solver’s actions; (2) time dependent, because decisions must be made at the correct moment in relation to environmental demands; and (3) complex, in the sense that most variables are not related to each other in a one-to-one manner. In these situations, the problem requires not one decision, but a long series, in which early decisions condition later ones. For a task that is changing continuously, the same action can be definitive at moment t1 and useless at moment t2.”

There is a distinction between CPS and general problem-solving. Some researchers believe that complex problem-solving competency may not be an extension of the general problem-solving process to complex situations; rather it is a separate competency (OECD, 2003). According to Brehmer (1995), “Complex problem solving is concerned with people’s ability to handle tasks that are complex, dynamic (in the sense that they change both autonomously) and as a consequence of the decision-maker’s actions), and opaque (in the sense that the decision-maker may not be able to directly see the tasks states or structure).” On the other hand, General Problem solving refers to a state of desire for reaching a definite ‘goal’ from a present condition that either is not directly moving toward the goal, is far from it, or needs more complex logic to find a missing description of conditions or steps toward the goal (Robertson, 2001, p2). There is some evidence (though not conclusive) that complex problem-solving competency is a separate construct and not just the application of “normal” problem‐solving processes to complex situations.

With technological advances, more and more complex problems surfaced which are technical in nature. Such problems require different strategies termed troubleshooting. Troubleshooting is a form of problem-solving, often applied to repair a failed product or process. In general, troubleshooting is the identification of or diagnosis of “trouble” in the management flow of a corporation or a system caused by a failure of some kind. Troubleshooters then search for actions that will efficiently eliminate the discrepancy. It is believed that troubleshooting requires highly specific strategies too. In the technical domain, troubleshooting refers to searching for the most likely cause of a fault in a larger set of possible causes (Schaafstal et al., 2000). Wikipedia states it as “a logical, systematic search for the source of a problem so that it can be solved, and so the product or process can be made operational again.” Troubleshooters then search for actions that will efficiently eliminate the discrepancy. Within complex problem spaces, troubleshooting is also considered a separate construct in itself but highly integrated with CPS. In several instances of complex problems mainly in the technical domain, complex problem-solving and troubleshooting work hand to hand. Troubleshooting is seen to require highly specific strategies over and above general and complex problem-solving. 

Acquiring CPS skills

Both complex problem-solving and troubleshooting are complex processes that require a range of cognitive and metacognitive skills to be used by the problem solver to identify and resolve a problem. Complex technical problem-solving and troubleshooting remain complex, even for highly experienced individuals. Complex Problem solving and troubleshooting is a complex process that requires a range of cognitive and metacognitive skills to be used by the problem solver to identify and resolve a problem. Research has shown that there are several competencies and strategies which are used by proficient problem solvers and those are generally acquired by them while working on the issues. Lyn (2011) lists the abilities learners need to deal with complex systems for success beyond the school:  “Such abilities include: constructing, describing, explaining, manipulating, and predicting complex systems; working on multi-phase and multi-component component projects in which planning, monitoring, and communicating are critical for success; and adapting rapidly to ever-evolving conceptual tools (or complex artifacts) and resources (Gainsburg,2006; Lesh & Doerr, 2003; Lesh & Zawojewski, 2007)”. 

Complexity is one of the several factors which may result in several levels of performance for the same task and thus can affect how a person is deemed competent, proficient and expert. During learning, the novice completes a simple version of tasks and as skill increases, he can move to more and more complex tasks. By acquiring more skills, the learner gains skill and becomes skillful in more complex tasks, and can process several factors at the same time. Merrill, (2006) states that “adequate measurement of performance in complex real-world tasks requires that we can detect increments in performance demonstrating gradually increased skill in completing a whole complex task or solving a problem.”

Complex technical problem-solving and troubleshooting remain complex, even for highly experienced individuals. However, experts have the advantage of experience. For example, expert troubleshooters have more well-developed cognitive schemas and strategic knowledge than novices’ schemas do (Chi, Glaser, & Rees, 1982; Larkin, McDermott, Simon, & Simon, 1980).  When troubleshooting familiar systems, experts can use the prior knowledge they gained from experience. They form a schema of their mental representation during their experience. When faced with unfamiliar systems troubleshooting, their prior schema and mental representation help them to quickly develop a mental representation of that system faster than less experienced troubleshooters can (Egan & Schwartz, 1979). These sophisticated mental representations are used by proficient troubleshooters to reason why a system may not be working. There are several competencies and strategies which are used by experienced problem solvers and those are generally acquired by them while working on the issues. Proficient troubleshooters have well-developed metacognitive knowledge and tested strategies like a structured approach to troubleshooting (Schaafstal et al., 2000).

Designing Training for CPS Skills

There is surmounting challenge when it comes to building proficiency in complex jobs involving complex tasks, Complex decisions, and complex problem-solving through training courses. Not only does it require trainers to deliver the knowledge, skills, and competencies required to solve real-world problems, but also at the same time needs to develop learners with strategies appropriate for that domain. Historically, most of the traditional training models assumed solving all problems in the same way. Recent theories have established that different contexts and different domains require different approaches to solve the problem. Thus solving the same problem in two different situations or disciplines may altogether be different (Mayer, 1992; Sternberg & Frensch, 1991).

When it comes to designing and teaching problem-solving skill-based training programs, Hung (2009) quoted how current training strategies are not working, “Traditional pedagogies, such as lecturing and demonstrating solutions to problems, very often result in students being capable of solving “textbook problems,” but unable to apply the knowledge to solve real life problems” (Brown, Collins, & Duguid, 1989; Mayer, 1996; Perkins & Salomon, 1989).

Coming to complex problem solving, three fundamental questions spring up in regard to these methods:

1. Do the common methods from general problem-solving skill training like Problem Based Learning, Case-Based Method or Simulation-Based Learning, etc. work in designing and delivering training on highly complex and mission-critical skills?
2. Can any of those training methods be used to accelerate expertise or proficiency in acquiring complex problem-solving and troubleshooting skills?
3. How to implement or use applicable training methods to design complex training, particularly in problem-solving and troubleshooting skills of a higher order?

In order to deliver a real-world experience on complex problem solving, the problem or scenarios need to be built on real-world competencies. There are basically three broader steps involved in integrating problems in the training curriculum to develop proficiency in complex problem-solving:

• Gather and select the types of problems faced by real-world problem solvers
• Identify and analyze the competencies actually used by real-world problem solvers to solve the selected problems
• Develop a mechanism to transform the competencies into the real-world CPS curriculum

Most of the training designed on Problem based learning, scenario-based learning, and case-based approach fails to give results in developing expertise faster. In my experience, the issue is right up front at steps 1 and 2 rather than at step 3. The reason for the failure to get any acceleration of expertise through any problem-based learning, scenario-based learning, case-based method, or simulation-based training is the “unrealistic” or “non-real-world problems”. Without the “reality” involved in well-designed PBL/ SCL/ Simulated or Case-based training, end up learning at their own rate and developing expertise in a much longer time than originally expected. 

6 Guidelines To Design Training for Complex Problem-Solving Skills

Based on some research evidence as well as experimentation, I came up with the following 6 guidelines to design a curriculum particularly to impact complex problem skills in a relatively shorter time:

#1: Go Real – Select the Correct Real-world Problem

The effectiveness of a complex problem-solving curriculum is determined by the selection of the correct problem for teaching real-world troubleshooting to the students (Jonassen and Hung, 2008). However, this seemingly simple statement is not simple to execute. Four studies showed that the correspondence rates between instructors’ objectives and students’ generating learning issues were only about 62% (Coulson & Osborne, 1984; Dolmans, Gijselaers, Schmidt, & van der Meer, 1993; O’Neill, 2000; van Gessel, Nendaz, Vermeulen, Junod, & Vu, 2003). These low correspondence rates signal that the design of problems (or the framework to design those) might have contributed to some ineffective problem-based learning implementations in the past. There is another challenge in building proficiency in complex problem-solving and complex tasks right during training. Instructional design and trainers are limited in their choice of real-world cases, a number of different cases they can teach in a training class, and the methodology being deployed could be far from the real-world methodology. For example, a manager’s job environment, pressure, and ambiguities in the real job may not be possible to simulate in a training class even though the trainer is able to bring real-world issues and challenges he would face. The most fundamental issue is the ability of the educator to “Define Objectives” rather than the fact that how these objectives are being taught. Since these objectives are taught through the problems, the correct design of the problem is a crucial requirement. The challenge is: How to integrate correct field-specific real-world competencies into a technical training course design targeted to develop complex cognitive and metacognitive skills of participants? My research revealed an important postulation. It has been seen that most of the inquiry-based, case-based or simulated training generally ends up ‘tweaking’ real-world problems to match with objectives rather than matching objectives to real-world problems. This is the real reason your training course meant to deliver expertise on complex problem solving or troubleshooting may not be working.

#2: Get Your Hands Dirty – Choose Real-world Environment

The design of environments for learning is very important to teach complex skills. Such an environment can be created using several techniques which could foster collaboration, discussion, and reflection (NRC, 2000). However, my research reveals that trainers and instructional designers tend to ‘replicate’ or tend to ‘simulate’ the real-world environment in a classroom to a certain degree. I have seen trainers teaching a project management course with “some” real-world scenarios in classroom settings. But a project manager’s job does not happen in the classroom environment. A car mechanic’s real job is in the car service workshop rather than bringing a workshop to the classroom. The way I see is that the issue might be the way the analysis/design phase of the training curriculum leads to the use of a ‘simulated’ environment. If something can be simulated, the question is why not use the real-world environment itself? Or why not create the uncontrolled real-world environment as it is in controlled conditions? The actual long-lasting learning that can accelerate the expertise happens in reality, not in controlled conditions. Admittedly some environments have to be simulated due to one-time things, costs, and risks. But point is to simulate the reality closer to the reality and make sure the environment reflects the uncontrolled conditions to the learner in which he is actually supposed to work.

#3: Select tough and complex problems

When applied in a complex problem-solving context, this approach has some limitations in regard to what kind of problems can be introduced in a given training program. The complexity of problems and the process of impacting complex learning has more to it than just the method of problem-based learning. It has been seen that in order to build expertise in complex problem-solving, learners need to be working on tough cases of higher complexity. Hoffman et al. (2014) and Soule (2016) specifically suggest that tough cases are the keys to accelerating expertise in complex domains. This approach has its challenges in selecting the correct problem for teaching real-world troubleshooting to the students (Jonassen and Hung, 2008). The real-world problems should be selected if we want to use inquiry-based learning to accelerate these skills. Without properly designed complex problems, PBL alone cannot do any wonder.

#4: Draw objectives from the problem rather than drawing problems from objectives

As I mentioned earlier, to reap the true benefits of this approach, the problems need to be designed correctly and objectives should be drawn out of the problem rather than problems defined around the objectives. This is the key guideline if you want to apply inquiry-based learning in a complex problem-solving space. In my experience designers miss this part and tend to look for the problems around the objectives while the real intent of complex problem-solving training should be to teach solving the process of problem-solving itself rather than the content. Content plays a secondary or supporting role in inquiry-based learning.

#5: Focus on the problem-solving process rather than the solution

The problem usually has a pre-determined outcome. Therefore it is necessary to ensure that training material clearly states the final outcome expected. But take note that the solution may not be that important but what is important is the “process” of problem-solving and how learners acquire or recognize various knowledge pieces required to solve the problem. Literature has good support in regard to the process of problem-solving. Usually, a problem solver has to actively acquire knowledge about the complex problem by systematically interacting with it (Funke, 2001) as the initial assumptions about the structure of the problem are mostly false or incomplete (Dörner, 1989). Often the problem solver has to define one or more of the problem’s components him- or herself based on aspects like prior knowledge (e.g., experience with analogous problems, or generalized schemas for this kind of problem) and features of the task (Novick & Bassok, 2005) and usually building a viable internal representation of a complex problem involves processes like rule induction (Simon & Lea, 1974), generating and testing hypotheses (Klahr & Dunbar, 1988) and causal learning (Buehner & Cheng, 2005).  While designing and implementing the problem-solving process itself in the training, be cognizant of how experts would solve the same problem if the goal is to accelerate proficiency in problem-solving. After all, the goal of acceleration is to achieve ‘expert-like’ performance in a shorter time.

#6: Pre-test the story and the process

While you develop that process to incorporate into the training, think of a real-world context for the concept under consideration. Develop a storytelling aspect to an end-of-chapter problem, or research an actual case that can be adapted, adding some motivation for learners to solve the problem. The problem needs to be introduced in stages so that students will be able to identify learning issues that will lead them to research the targeted concepts. It may be a good idea to have a few dry runs of the problem through the pilot group to ensure that problem is understood and the process is validated to ensure that various pieces of knowledge and skills required to solve the problem are well integrated into the problem. The emphasis is on testing and presenting the problems in a well-designed story. It has been seen that learners can relate quickly to a story-based approach, no matter whether the problem is complex. This lays foundations for accelerating proficiency in complex problem-solving skills when using any inquiry-based learning method.

SUGGESTED CITATION

Attri, RK (2018), ‘6 Guidelines to Develop Training for Acquiring Complex Problem-Solving Skills’, [Blog post], Speed To Proficiency Research: S2PRo©, Available online at <https://get-there-faster.com/blog/acquiring-complex-problem-solving-skills/>.

REFERENCES

1. Brehmer, B., 1992, Dynamic decision-making—human control of complex-systems. Acta Psychologica, 81, 211–241.
2. Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18, 32–42.
3. Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence, Vol. 1 (pp. 7-76). Hillsdale, NJ: Erlbaum.
4. Coulson, R. L., & Osborne, C. E. (1984). Insuring curricular content in a student directed problem-based learning program. In H. G. Schmidt & M. L. de Volder (Eds.), Tutorial in problem-based learning: A new direction in teaching the health professions (pp. 225–229). Assen, The Netherlands: Van Gorcum
5. Dolmans, D. H. J. M., Gijselaers, W. H., Schmidt, H. G., & van der Meer, S. B. (1993). Problem effectiveness in a course using problem-based learning. Academic Medicine, 68(3), 207–213.
6. Egan, D. E., & Schwartz, B. J. (1979). Chunking in recall of symbolic drawings. Memory and Cognition, 7, 149-158.
7. Frensch, P. A., & Funke, J. (Eds.). (1995b). Complex problem solving: The European Perspective. Hillsdale, NJ: Lawrence Erlbaum Associates.
8. Hoffman, RR, Ward, P, Feltovich, PJ, DiBello, L, Fiore, SM & Andrews, DH 2014, Accelerated Expertise: Training for high proficiency in a complex world, Expertise: Research and Applications Series, Psychology Press, New York, http://dx.doi.org/10.4324/9780203797327.
9. Hung, W., Jonassen, D. H., & Liu, R. (2008). Problem-based learning. In J. M. Spector, M. D. Merrill, van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (pp. 441–456). New York: Routledge.
10. Jonassen, D. (2000), Nature of Problems, Centre for the study of Problem Solving, available at http://csps.missouri.edu/proposal.php
11. Jonassen, D. H., & Hung, W. (2008). All Problems are Not Equal: Implications for Problem-Based Learning. Interdisciplinary Journal of Problem-based Learning, 2(2). < http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1080&context=ijpbl>
12. Larkin, J, McDermott, J, Simon, D & Simon, H 1980, “Expert and novice performance in solving physics problems,” Science, vol. 208, no. 4450, pp. 1335–1342, http://dx.doi.org/10.1126/science.208.4450.1335 and < https://www.researchgate.net/publication/6064271>
13. English, LD 2011, “Complex learning through cognitively demanding tasks,” The Mathematics Enthusiast, vol. 8, no. 3, pp. 483–506, viewed 24 June 2017, <http://eprints.qut.edu.au/47327/>.
14. Mayer, R. E. (1992). Thinking, problem solving, cognition (2nd ed). New York: Freeman
15. Mayer, R. E. (1996). Learning strategies for making sense out of expository text: the SOI model for guiding three cognitive processes in knowledge construction. Educational Psychology Review, 8, 357–371.
16. Merrill, MD 2006, Hypothesized performance on complex tasks as a function of scaled instructional strategies, in J Enen & R Clark (ed.), Handling complexity in learning environments: Research and theory, Elsevier, Amsterdam, pp. 265–282, viewed 24 June 2017, <http://www.mdavidmerrill.com/Papers/Scaled_Instructional_Strategies.pdf>.
17. (2003b). The definition and selection of competencies (DeSeCo): Executive Summary of the final report. Paris: OECD. < http://www.oecd.org/pisa/35070367.pdf>
18. O’Neill, P. A. (2000). The role of basic sciences in a problem-based learning clinical curriculum. Medical Education, 34, 608–613.
19. O*NET Online available at http://www.onetonline.org/find/descriptor/result/2.B.2.i
20. Perkins D N, Salomon G 1989 Are cognitive skills context bound? Educational Researcher 18 (1): 16-25.
21. Quesada, J, Kintsch, W & Gomez, E 2005, “Complex problem-solving: a field in search of a definition?,” Theoretical Issues in Ergonomics Science, vol. 6, no. 1, pp. 5–33, http://dx.doi.org/10.1080/14639220512331311553 and < http://lsa.colorado.edu/papers/manuscriptTIES.3.9.postReview.pdf>
22. Schaafstal, AA, Schraagen, JM, van Berlo, M & van Berlo, M 2000, “Cognitive task analysis and innovation of training: the case of structured troubleshooting,” Human Factors, vol. 42, no. 1, pp. 75–86, http://dx.doi.org/10.1518/001872000779656570 and < https://www.researchgate.net/publication/286623558>
23. Soule, RT 2016, “The learning experience of tough cases: a descriptive case study,” PhD thesis, The George Washington University, Ann Harbor, MI, viewed 24 June 2017, <http://pqdtopen.proquest.com/doc/1751007250.html?FMT=AI>.
24. Sternberg, R. J., & Frensch, P. A. (Eds.). (1991). Complex problem solving: Principles and mechanisms. Hillsdale, NJ: Lawrence Erlbaum Associates.
25. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257-285.
26. Sweller, J, Van Merrienboer, JJ & Paas, FG 1998, “Cognitive architecture and instructional design,” Educational Psychology Review, vol. 10, no. 3, pp. 251–296, http://dx.doi.org/10.1023/A:1022193728205 and < http://www.davidlewisphd.com/courses/EDD8121/readings/1998-Sweller_et_al.pdf>
27. Xu, J., G. Alan Wang, G.A., Li, J., Michael Chau, M., (2007), Complex Problem Solving: Identity Matching Based on Social Contextual Information, Journal of the Association of Information System, Volume 8, Issue 10, Article 2, pp. 525-545
28. Van Gessel, E., Nendaz, M. R., Vermeulen, B., Junod, A., & Vu, N. V. (2003). Basic science development of clinical reasoning from the basic sciences to the clerkships: A longitudinal assessment of medical students’ needs and self-perception after a transitional learning unit. Medical Education, 37, 966–974.

A version of this article was originally published on 23 Sept 2014.

Image credits: Pixabay CC0 attribution