Problem–based Learning and e–Breakthrough Thinking
Problem–based Learning and e–Breakthrough ThinkingIntroduction
Problems and Paradigm Shifts in Learning Systems
Technology and Cognition for Problem Solving
Problem solving in real-world contexts involves multiple ways of knowing and learning. Intelligence in the real world is not only about the ability to learn how to do things and to actually do them, but also about the ability to deal with novelty as well as the capacity to select, shape, and adapt our interactions with the environment.
Designing a problem-based learning (PBL) system requires a shift in paradigm from the traditional analytic system to a holistic multidimensional thinking system. Stimulating cognitive processes is at the heart of the PBL process, which involves collecting, connecting, and communicating information. While there is consensus on the need to develop multiple intelligences in our students, few educators are aware that one of the best ways to do so is to utilize problem scenarios presented through rich media. We have seen in this volume how ePBL systems allow the harnessing of intelligences from individuals, from groups of people, and from the environment to solve problems through engagement in meaningful, relevant, and contextualized problem-solving activities using a variety of e-tools.
At the information collection stage in ePBL, the key cognitive functions of developing a systems perspective (e.g., a helicopter view), systematic scanning (e.g., environmental scanning), and building efficiency (e.g., accuracy and precision) are incorporated. The information connection stage deals with developing the connectivity of information for processing as the basis for creative problem solving and for the creation of new knowledge. Finally, the information communication stage is concerned with building cognitive output for communicating solutions. The ePBL approach also supports the idea of making thinking and the mind visible through the use of e-platforms for dialogue and inquiry. However, the development of scaffolding systems, learning objects, and collaborative platforms for facilitating thinking and inquiry is a major challenge.
The nature of the online environment is highly conducive for participants’ immersion in the problem as well as the collection, connection, and communication of information conducted over an extended period of time. Advances in e-learning environments and the inherent design of e-systems fit naturally into the PBL cycle and system.
From the psychological perspective, a problem triggers cognitive engagement by providing the context for engagement and stimulating curiosity, inquiry, and a quest to address a real-world issue. It leads to learning and cognition through (1) confronting unstructuredness, illstructuredness, and novelty; (2) active search for information; (3) proactive immersion in the task; (4) conscious and subconscious investment of time on the task; (5) the motivation to solve the problem arising from the desire to look for meaning and explanations; (6) goal orientation; and (7) the need for generative thinking, analytical thinking, divergent thinking, and information synthesis in solving the problem.
According to Hicks (1991), four psychological processes are implicit when we encounter a problem: (1) we recognize that there is a problem, (2) we do not know how to resolve the problem, (3) we want to resolve it, and (4) we perceive that we are able to find a solution. The types of problems that may trigger and stimulate the search for solutions include the following: (1) failure to perform, (2) situations in need of immediate attention or improvement, (3) the desire to find better and new ways to do things, (4) unexplained phenomena or observations, (5) gaps in information and knowledge, (6) decision-making problems, and (7) the need for a new design or invention (Tan, 2003). The e-environment provides an excellent platform for presenting problem scenarios of any of these forms.
One of the goals of PBL is to develop real-world problem-solving acumen. The solution of real-world problems often requires multiple ways of knowing and thinking. As Tan (2003) repeatedly emphasizes, in real-world problems, the context always appears unstructured in the first instance; as such, analytical thinking alone is not sufficient to solve them. The bulk of education and the modes of instruction and assessment often focus on training the mind to be analytical, particularly the ability to break things down into parts and to analyze things with a logical and critical mind. Indeed, the teaching of logic and analysis is important in all disciplines. Each discipline and field, whether it is mathematics, chemistry, economics, literature, or others, has established its unique way of knowing and system of analysis and thinking. All fields of knowledge are categorized for the convenience of dissemination and learning. There is also the legacy of specialization for developing and recognizing discipline experts— people supposedly possessing deep and comprehensive understanding of a particular field. All human models and theories, however, are at best approximation of reality. In solving real-world problems, we have to develop not only different perspectives but also different ways of thinking, such as “big picture” thinking, generative thinking, and divergent thinking. We need not only logical thinking but also “analogical” thinking— the ability to creatively or laterally transfer a whole set of ideas across to another situation. In problem solving, not only do we need to be able to draw on and integrate knowledge from multiple disciplines, we also need to be highly dexterous and flexible in employing diverse modes of thinking, such as seeing the big picture, generating ideas and viewpoints that are new and alien, as well as having a good sense of reality (e.g., being aware of the constraints of circumstances, the limitations of resources, and the differences in human perceptions).
With reference to technology and its impact on knowledge and thinking, a similar argument can be observed in the way knowledge has developed. Some 300 years ago, during the age of enlightenment, inventions such as the microscope reinforced the analytical mode of thinking in biology, chemistry, and physics. Consequently, the reduction of things to the smallest and simplest entity was the most fundamental concept in many fields of knowledge. That legacy remains in our education system, which requires students to learn the fundamental laws of chemistry and physics. The reductionist view of science can be said to prevail because of the technology and tools of the era. Over the centuries, many scholars have recognized the need to move from the reductionist approach to a more holistic one. In medicine, for example, in more recent times much more emphasis has been placed on holistic treatment and health care. Apart from a holistic physiological assessment, how patients feel and their emotional state, for example, are now an important part of diagnosis.
The advent of computer technology and the Internet with its World Wide Web offers a new dimension for looking at problems today. I would like to highlight just three observations about the benefits that technology can offer to problem solving.
Firstly, to solve a problem we often need to manage a large amount of information and data. Today, thanks to technology, we can present a problem scenario richly in multiple ways for retrieval across time and space.
Secondly, we need to recognize that a condition for competent problem solving is connectivity. The ability to connect data across domains, prior knowledge, contexts, and perspectives is key to creative problem solving. Connectivity is what the e-world provides with great expedience in many situations. Connectivity can be enhanced if we can scaffold reasoning processes and skills and make them more “visible,” like the way goods are cleverly displayed in a supermarket. Let us consider inductive reasoning as an example. Inductive reasoning involves a system of reasoning tasks (Klauer, cited in Haverty et al., 2000: 251):
- Generalization: Detecting similarities in attributes
- Discrimination: Detecting differences in attributes
- Cross-classification: Detecting similarities and differences in attributes
- Recognition of relationships: Detecting similarities in relationships
- Differentiation of relationships: Detecting differences in relationships
- System construction: Detecting similarities and differences in relationships
We can try to capture this cluster of subskills in a learning object on inductive reasoning. By raising the visibility, or awareness, of the types of reasoning, learning and thinking can be enhanced. The Internet connects us to vast amounts of useful information stored in a variety of formats, such as text, graphics, photographs, and video clips, which, with hypertext, can be easily accessed by simply pointing to a link and then clicking.
Thirdly, one of the powerful approaches to solving problems is to replicate the problem situation in some ways for analysis and study— in short, to simulate reality, a complicated task that computers excel in. Technology therefore allows us to deal with real-world problems from a different perspective. In dealing with a complex problem where we have to manage large quantities of a variety of data, human memory has its limitations. Salthouse and colleagues (1989) propose that our working memory consists of (1) a storage capacity sensitive to the number of items presented and (2) an operational capacity sensitive to the number of operations performed on each item. They have found that the speed of execution, such as the speed of comparison, determines the performance of the overall system of working memory (Salthouse & Babcock, 1991). They argue that a larger working memory capacity lays the foundation for a greater inductive reasoning ability. Extending the application of these findings, one may conclude that if learning systems can help with memory capacity we can enhance our reasoning capacity in problem solving. It is common knowledge that the human cognitive capacity is limited in nature, and remembering would take up the available working memory capacity if external representations in any form are not available. As such, when learning systems help capture problem representations, we would have free memory capacity for higher-order thinking. In solving problems, we may need to go through a systematic trial-and-error process, deal with ambiguity, and make predictions. Iterations that are needed in order to achieve accuracy and precision can be considerably speeded up with the use of technology. For example, life science today would not be what it is without supercomputers.
I would like to close this chapter with a story. Some 50 years ago, B. F. Skinner launched what was probably the first learning machine: a mechanical flash-card system. This was a precursor of programmed learning machines. It was designed based on the psychology of behaviorism of stimulus-response and reinforcement of learning. In a sense, it was a forerunner of today’s e-learning systems! Someone asked Skinner then: If technology continues to advance, would you foresee a day when teachers would be replaced by machines? Skinner’s reply was this: If a machine can replace a teacher, then the teacher ought to be replaced.
The ePBL system is not meant to replace the teacher, and teachers will always possess qualities that are irreplaceable by any system of learning. However, it is essential to recognize that educators today need to be able to design and make use of the e-learning environment as a tool not only to vary the mode of learning but, more importantly, to scaffold and enhance thinking and problem solving.
Haverty, L. A., Koedinger, K. R., Klahr, D., & Alibali, M. W. (2000). Solving inductive reasoning problems in mathematics: Not-so-trivial pursuit. Cognitive Science, 24 (2), 249-98. http://126.96.36.199:5150/yb/cse5393/abstracts/haverty.pdf.
Hicks, M. J. (1991). Problem solving in business and management: Hard, soft and creative approaches. London: International Thomson Business Press.
Salthouse, T. A., & Babcock, R. (1991). Decomposing adult age differences in working memory. Development Psychology, 27, 763-76.
Salthouse, T. A., Mitcheel, D. R. D., Skovronek, E., & Babcock, R. L. (1989). Effects of adult age and working memory on reasoning abilities. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 507-16.
Tan, O. S. (2003). Problem-based learning innovation: Using problems to power learning in the 21st century. Singapore: Thomson Learning.