In today’s environment, employees are expected to possess proficiency in top order problem solving and troubleshooting skills, particularly the complex problem solving skills. Complex problem solving is considered as a different construct compared to general problem-solving. General strategies for developing expertise in other contexts are seen to be not working effectively in complex problem solving. The aim of this post is to address the challenge to design training to enable people acquiring complex problem solving skills.
Global jobs, especially the technical ones have become complex day by day. Complex jobs are the jobs that are characterized by the complexity of the 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 the job complexity. TaskManagementGuide website defines task complexity as “a collection of properties inherited by a task. These properties (like the priority, due date, duration, and urgency) define the difficulty of this tasks 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 jobs to the main job itself. Examples of such jobs are: Equipment repair service, internal organ medical surgery, Network and database administration, Cybersecurity, 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). These kinds of jobs require its employees with the ability to resolve problems of any complexity and order quickly and efficiently.
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 it is any solution.
Technical problems are typically very complex in nature due to nature of the domain and far more reaching effects than the business problems. This goes beyond the general problem solving we talk in day-to-day life. There is a well-developed body of knowledge called Complex Problem Solving (CPS). This movement 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 characteristic 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.”
Some researchers believe that complex problem-solving competency may not an extension of the general problem-solving process to the complex situation; rather it is a separate competency (OECD, 2003).
Take moment to differentiate between general problem solving and complex problem solving: 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 for finding a missing description of conditions or steps toward the goal (Robertson, 2001, p2).
Within complex problem space, troubleshooting is also considered a separate construct in itself but highly integrated with CPS. 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. In several instances of complex problems mainly in the technical domain, the complex problem solving and troubleshooting works hand to hand. Troubleshooting is a “special case” of the larger field of complex problem solving, mostly in the technical domain, which refers to searching 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.” Though troubleshooting is seen to require highly specific strategies over and above general and complex problem-solving, 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.
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, novice completes simple version of tasks and as skill increases, he can move to more and more complex tasks. Acquiring more skills, the learner gains skill and he becomes skillful in more complex tasks and can process several factors at the same time. Merrill (2006) state 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 Problem solving and troubleshooting is a complex process which 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 the 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)”.
Complex technical problem solving and troubleshooting remains complex, even for highly experienced individuals. However, experts have the advantage of experience. For example, expert troubleshooters have 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 their 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 the reason why a system may not be working. There are several competencies and strategies which are used by the experienced problem solvers and those are generally acquired by them while working on the issues. Also, proficient troubleshooters have a well developed metacognitive knowledge and tested strategies like structured approach to troubleshooting (Schaafstal et al., 2000). On the other hand, the novices rely on weaker strategies which use domain-general heuristics (Sweller, 1988; Sweller et al., 1998).
Nevertheless, differences in novice vs. expert apart, the real organization challenge is how training experts in large organizations can develop the expertise of their employees through training as proficient problem solvers to solve complex problems.
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 it requires trainers to deliver 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. The recent theories have established that different context and different domains require a different approach to solve the problem. Thus solving the same problem in two different situations or disciplines may altogether require different approaches (Mayer, 1992; Sternberg & Frensch, 1991).
Hung (2009) quoted on 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). The general reason for the failure of training curriculum from any problem-based learning, scenario-based learning, or case-based approach is the “unrealistic” or “non-real-world problems” that are selected to teach a curriculum. Without such real-world “reality”, even a well-designed problem-based, case-based, scenario-based or simulated-based training (all collectively called inquiry-based learning) would end up with a learning curve that takes longer time.
Based on some research evidence as well as experimentation, I came up with following 6 guidelines to design a curriculum particularly to impact complex problem skills in relatively shorter time:
The effectiveness of 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 on 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 methodology being deployed could be far from the real-world methodology. For example, a manager’s job environment, pressure, and ambiguities on the real-job may not be possible to simulate in a training class even though 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 with 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 end 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.
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 analysis/design phase of training curriculum leads to the use of ‘simulated’ environment. If something can be simulated, a question is why not use the real-world environment itself? Or why not create the uncontrolled real-world environment as-it in the 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 thingy, cost, and risks. But point is to simulate the reality closer to the reality, and making sure environment reflects the uncontrolled conditions to the learner in which he is actually supposed to work.
When applied in complex problem solving context, this approach has some limitation in regards 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.
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 problem defined around the objectives. This is the key guideline if you want to apply inquiry-based learning in 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 content. Content plays secondary or supporting role in inquiry-based learning.
The problem usually have pre-determined outcome. Therefore it is necessary to ensure that training material clearly states the final outcome expected. But take a note that solution may not be that important but what is important is the “process” of problem solving and how learner acquire or recognize various knowledge pieces required to solve the problem. Literature has good support in regards 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 problems) 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 how experts would solve same problem if goal is to accelerate proficiency in problem solving. After all, goal of acceleration is to achieve ‘expert-like’ performance in shorter time.
While you develop that process to incorporate in 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 good idea to have few dry runs of the problem through pilot group to ensure that problem is understood and process is validated to ensure that various pieces of knowledge and skills required to solve the problem are well integrated in 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 with a story-based approach, no matter the problem is complex. This lays foundations for accelerating the proficiency in complex problem solving skills when using any inquiry-based learning method.
Let us know what you think about this article. Comments are highly welcomed.
A version of this article was originally published on 23 Sept 2014.
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