Engaging and Supporting Problem Solving in Online Learning

Abstract

Based on my observations of numerous examples of online learning environments, more often than not, online learning functions and activities replicate face-to-face instruction. The pedagogies employed in online learning are similar if not identical to those typically employed in face-to-face instruction. Teachers or professors organize readings related to the topics being studied, tell the students their understandings about the topic, answer questions if any are raised, and then assess whether students remember what the textbook and the teacher told them. Most online learning begins with traditional assumptions and conceptions of learning and knowledge. The teach-and-test ontology and the reliance on traditional subject-matter ontologies ensure a lack of innovation in online learning. To the degree that online learning is perceived by educators as innovative, I believe that online learning provides the opportunity, if not the obligation, to innovate. Online learning can and should escape the limitations of conventional, face-to-face instruction.Why is innovation in online learning necessary? Because the conceptions of learning in formal educational contexts that online learning emulates and those in everyday and professional contexts (what some refer to as the real world) are diametrically opposed. In schools, universities, and corporate training contexts, learning is knowledge- (content-) based, highly organized and structured by rules and abstract formalisms. Learning is conceived of as “knowledge acquisition,” which depends on how much “knowledge” was transmitted from online instruction to the learner. The knowledge that supposedly is acquired by learners is determined by an examination of the knowledge that did not leak out of the heads of the learners. This knowledge transmission paradigm assumes an absolutist epistemology where content is assumed to represent the truth (Jonassen, Marra & Palmer, in press).In formal learning contexts, students or workers “learn about” the content they are studying. For example, in sociology classes, students learn about sociology. In biology classes, they learn about biology. However, they rarely, if ever, learn how to perform any of the functions of sociologists or biologists; that is, to think and act like sociologists or biologists. Again, learning in formal face-to-face or online learning contexts is diametrically opposed to how learning occurs in real world settings.Learning in the real world, where people live and work, is omnipresent and essential to survival, let alone progress. In order to advance their culture (be it community or corporate) in any conceivable way, individuals and societies must learn. However, their learning cannot be described by any ontology of concepts and rules that supposedly describe the world in a hierarchical fashion. They learn as individuals and communities while learning to make sense of phenomena in order to solve problems. Their learning is activity-based, not content-based. Learning is situated in the problems they are solving, so the learning issues are emergent from those problems. Those issues cannot be predicted or prescribed in any curriculum. Learning and solving problems in the real world rely on knowledge distributed throughout a community of practice. The medium of learning is social negotiation of meaning, not content as determined by some agency, so knowledge emerges in the discourse of the community. Perhaps the most important difference is that in the real world, individuals and communities learn how to do things, not about the things. Most curricula describe the topics that teachers and professors teach about.Here are the important questions: In corporate training and in higher education, the primary users of online learning, which world should we model in our instruction? Which skills need developing? Which conception of learning should drive our instruction? What should our students be learning?My position is clear. Telling students about the world and quizzing their recall of what we told them is not only an insult to our learners (should not we expect more of them?), but that pedagogy retards their epistemological development, preventing them from developing knowledge-seeking skills needed to learn how to do something useful (Jonassen, Marra, and Palmer, in press). I believe that the only legitimate goal of professional education, either in universities or in corporate training, is problem solving. The rationale is simple. People in the real world (in everyday and professional contexts) are not expected to memorize information as presented. Rather, they are expected to solve problems and are rewarded for doing so. Therefore, we should focus instruction on how to solve problems. Content is the information, advice, and strategic knowledge needed to solve problems. Before describing how to do that, I need to articulate briefly what problem solving is.Problem solving has two critical attributes. First, problem solving requires the mental representation of a situation in the world. That is, human problem solvers construct a mental representation of the problem, known as the problem space (Newell & Simon, 1972). Second, problem solving requires some active manipulation of the problem space. When we manipulate the problem space, we represent the components and dimensions of the problem, generate hypotheses about how to find the unknown, test those possible solutions, and draw conclusions. So, manipulation of the problem space, be it an internal mental representation or an external physical representation, necessarily engages conscious activity.Having provided a generic definition, it is important to point out that problems are not the same and that different kinds of problems engage different problem-solving processes. Problems and problem solving vary in their structuredness, complexity, dynamicity, and domain specificity (abstractness).Jonassen (1997) distinguished well-structured from ill-structured problems and recommended different design models for each. The most commonly encountered problems, especially in schools and universities, are well-structured problems. Typically found at the end of textbook chapters, these well-structured problems require the application of a finite number of concepts, rules, and principles being studied to a constrained problem situation. Well-structured problems typically present all elements of the problem; engage a limited number of rules and principles that are organized in a predictive and prescriptive arrangement; possess correct, convergent answers; and have a preferred, prescribed solution process. Ill-structured problems, on the other hand, are the kinds of problems that are encountered in everyday practice. Ill-structured problems have many alternative solutions; vaguely defined or unclear goals and constraints; multiple solution paths; and multiple criteria for evaluating solutions; so they are more difficult to solve.Just as ill-structured problems are more difficult to solve than well-structured problems, complex problems are more difficult to solve than simple ones. 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.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.A final dimension of problems and problem solving that is somewhat orthogonal to the other dimensions is domain specificity. In contemporary psychology, there is a common belief that problems within a domain rely on cognitive strategies that are specific to that domain (Mayer, 1992; Smith, 1991). Traditional conceptions of problem solving have been domain independent. That is, problem solving was conceived as a mental skill that could be generalized across domains. We now know that is not the case.Jonassen (2000) described a typology of problems. This typology assumes that there are similarities in the cognitive processing engaged within these classes of problems. This range of problem types describes a continuum of problems from well-structured to ill-structured. Within each category of problems that are described, problems can vary with regard to abstractness, complexity, and dynamicity.Logic problems: Logic problems are abstract tests of logic that puzzle the learner. They are used in research studies to assess mental acuity, clarity, logical reasoning, and problem solving, despite their lack of authenticity. Examples of logic problems include Tower of Hanoi or Cannibals and Missionaries. Because this type of problem is used only for laboratory research on human reasoning, it should not be further analyzed or developed.Algorithmic problems: Most common in mathematics courses, algorithmic problems are those in which students are taught to solve algorithms using a rigid set of procedures. Algorithmic problems, according to McCloskey, Caramaza, and Basili (1985), require comprehension of the operations (e.g. associative and commutative properties and concepts of multiplication and division), execution procedures for calculating, and retrieval of arithmetic facts (e.g. times tables). The primary limitation of algorithmic approaches to problem solving is the over-reliance on procedural knowledge structures and the lack or absence of conceptual understanding of the objects of the algorithm and the procedures engaged. Examples of algorithms include factoring quadratic equations, calculating the derivative of an equation, or bisecting any given angle. Procedural problems also exist in the real world (e.g. starting a computer, checking out a book, or entering data in an accounting system.Story problems: In an attempt to situate algorithms in some kind of context, many textbook authors and teachers employ story problems. This usually takes the form of embedding the values needed to solve an algorithm into a brief narrative or scenario. Learners are required to access a problem schema and apply that schema to the current problem. If they access the correct schema, the solution procedure is usually embedded within that schema. However, numerous difficulties occur in extracting the values from the narrative and inserting them into the correct formula, and solving for the unknown quantity. Unfortunately, the story covers for the problems are too often uninteresting and irrelevant to students. So when they attempt to transfer story problem skills to other problems, they focus too closely on surface features or recall familiar solutions from previously solved problems (Woods, Hrymak, Marshall, Wood, Crowe, Hoffman, Wright, Taylor, Woodhouse, & Bouchard, 1997). Story problems include elementary combine, cause/change, or compare problems in math (Joe has three marbles; Jane gave him three more; how many does he have?); calculating resistance given voltage and amperage; calculating reagents needed to form a specific precipitate in a chemical reaction; or calculating interest accrued on a savings account.Rule-using problems: Many problems have correct solutions but have multiple solution paths or multiple rules governing the process. They tend to have a clear purpose or goal that is constrained but not restricted to a specific procedure or method. Rule-using problems can be as simple as expanding a recipe to accommodate more guests and as complex as completing tax return schedules or evaluating a loan application. Using an online search system to locate relevant information on the World Wide Web is an example of rule-using problems. The purpose is clear: find the most relevant information in the least amount of time. That requires selection of search terms, constructing effective search arguments, implementing the search strategy, and evaluating the utility and credibility of information found. Because there is little, if any, research on rule-using, knowledge about this kind of problem solving must derive from rule induction research.Decision-making problems: Decision-making problems usually require making a decision from a limited number of alternative options. For instance, which marketing strategy will be most effective? Which health plan do we select? How am I going to pay this bill? What is the best way to get to the interstate during rush hour? Though these problems have a limited number of solutions, the number of factors to be considered in deciding among those solutions as well as the weights assigned to them can be very complex. More vexing, decisions often entail personal beliefs and biases. Decision problems usually require comparing and contrasting the advantages and disadvantages of alternate solutions.Troubleshooting problems: Troubleshooting is one of the most common forms of everyday problem solving. Maintaining automobiles, aircraft, or any complex system requires troubleshooting skills. Debugging a computer program requires troubleshooting. The primary purpose of troubleshooting is fault state diagnosis. That is, some part of a system is not functioning properly, resulting in a set of symptoms that have to be diagnosed and matched with the user’s knowledge of various fault states. Troubleshooters use symptoms to generate and test hypotheses about different fault states. This kind of problem will be described more completely later.Diagnosis-solution problems: Diagnosissolution problems are similar to troubleshooting problems. Most diagnosis-solution problems require identifying a fault state, just like troubleshooting. However, in troubleshooting, the goal is to repair the fault and get the system back online as soon as possible, so the solution strategies are more restrictive. Diagnosis-solution problems usually begin with a fault state similar to troubleshooting (e.g. symptoms of a sick person). The the and making an diagnosis. 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