How the Academic Study of Problem Solving and Thinking Relates to Everyday Life - Essay Example

? How the Academic Study of Problem Solving and Thinking Relates to Everyday Life This paper explores the academic studies that have been done on problem solving and thinking and how these studies relate to people’s day-to-day life. The paper explores theories associated with problem solving and cognitive psychology. In particular, the theories discussed in this paper are the information processing approach to problem solving, Gestalt psychology theory, transfer of learning, logical reasoning and the biases that challenge reasoning. The paper is based mainly on the work of Robertson (2001) and his contribution of his work to the development of cognitive psychology and the techniques of problem solving. The paper has also an example that relates the art and science of problem solving to the day-to-day life application of problem solving knowledge. How the Academic Study of Problem Solving and Thinking Relates to Everyday Life The academic study of problem solving and thinking has a resounding relationship with day-to-day decision making in every aspect of life. Robertson (2001) explains how through the years, people and problem solving methods and skills have evolved owing to continuous changes in nature of the problems and the continued process of research to improve knowledge and skills. This paper covers the relationship of many studies carried out on problem solving and how people implement this knowledge and skills to solve the day-to-day problems as they are encountered. According to Davidson and Sternberg (2003), the formation quality of the way we feel, think about and perceive the world around us is emphasised by the fundamental nature of the Gestalt psychology. The quality of this structure is the wholeness. In order to perceive something we focus on part of it thus developing a background. This basic nature of people’s perception is based on the knowledge that each part is in existence as an asset of the whole and is seen as a whole by itself. By translating this to human nature, Gestalt psychologists made an assumption that there will always be an underlying structure within the way we perceive a task and experience a situation. They also consider the existence of relationships between parts and whole that constitute people’s productive thinking of the underlying structure. Mason (2011) says that to demonstrate how this theory works in daily life, it suggests, for example, when teaching students how to find the area of a parallelogram, the best way for them to discover how to go about the task is not by being taught the rule of algorithm. They need to find the underlying structure of the problem there after solving the problem meaningfully. The process of reasoning might run as defining a parallelogram as having a rectangle in the middle and two extra triangles on the sides. Since the area of the rectangle is known, we are left with two triangles that when fit together create another rectangle, and thus the problem is solved by having one large rectangle. According to Robertson (1999), this theory suggests that when one grasps the situation of a problem, its characteristics and the requirements, certain stresses and strains are set up in the thinker. So, when it comes to real thinking, the strains and stresses are trailed and create vectors in the direction of improving the situation and change it accordingly. Martin and Greene (2011) argue that this version of problem solving stresses the significance of the meaning of the problem for the problem solver. This theory, though, does not clearly explain how far it is applicable with other different structured kinds of problems like engineering or experimental problems. It was mainly researched for particular kinds of problems in mathematics, algebra and geometry. In addition, Overholt, Aaberg and Lindsey (2008) say that it also focuses so much on the problem and how the student perceives it. This is a major problem of this theory in that the student does not perceive the problem situation as the only problem; the context of the problem also plays an important role in problem solving. In Robertson (2001), contrast to Gestalt’s description of problem solving, information processing theory has its focus on the mechanism of the process of problem solving. In this theory, the procedure adopted by people while solving a problem is what is considered, and it integrates to a more deterministic version of how people solve their problems. The theory is based on the thought that the human cognition is subject by heuristic processes. Park (2004) adds up by saying that, by analysing this, they found out the heuristics which were possible to be represented in a computer program and are capable of getting to a solution of a problem in a similar way. When problems could not be solved, it was seen as a failure to apply the right heuristic or to use any one at all. Harnish and Cummins (2000) suggested that the common heuristics events like sub goals creation, means-end analysis or working forwards and backwards, as this theory states, can be applied to any kind of problem. When an individual is confronted with a new problem, this theory explains that general heuristic procedures are to be used in combination with a correct representation of the problem to come up with a specific solution. Information processing approach poses the very significant question of what is a suitable representation of the problem. Logan (2011) explains research that showed that a student can be more concerned with solving the problem in its context of education while paying little interest to the problem itself or the subject issue of the problem itself. Similarly, it is more probable that a baby will be stimulated to look at a person’s face than any other incentives. In transfer learning, learning in a context that is with certain set of materials affects the performance in another set of related materials or context. For example, learning mathematics helps students be prepared to learn another subject related to mathematics, like physics. This theory, learning transfer, is an important concept in education and also learning theory. This is because most of formal education seeks to transfer. According to Gentner (2001), normally, the context of learning differs significantly with the context of application, and ends expected in education are not achieved until the transfer occurs; this transfer is very important that it can not be taken for granted. Plenty of proof shows that from time to time the intended transfer from learning experiences do not occur, and thus, the conditions and prospects of transfer are critical educational issues. Evertson and Weinstein (2006) say that while bridging, instruction persuades the creation of abstractions, mindfulness, finding possible connections and meta-cognition. In an example of a teacher and students, a teacher might ask students to come up with an exam plan based on their precedent experience, and a job counsellor may ask the students to look back at their strengths and weaknesses and develop a plan to stress the previous and downplay the latter during an interview. Galotti (2010) also states the other problem solving process that involves premises, statements, arguments and axioms to define whether or not a statement is true is known as logical reasoning. Logical reasoning can be deductive whereby general rules are applied in specific situations solutions of which can be drawn. Pugalee (2004) explains how this process only rearranges what is known to arrive to a new conclusion. For example, considering the statement that states ‘all humans are mortal’, and ‘Socrates is a human’ then, a conclusion that ‘Socrates is mortal’, can be deducted. On the other hand, the opposite of deductive logical reasoning is inductive reasoning, whereby particular conclusions are generalised by broad conclusions. According to Mehlcr (2009), there is also the third form of logical reasoning known as abductive reasoning. This is more of inductive reasoning in that conclusions are drawn based on possibilities. So, it is assumed that the most probable conclusion is the acceptable one. Hughes (2007) explains that logical reasoning in everyday’s life is often applied in amplitude tests and IQ tests. This is because of the universality of logical reasoning, and it can be used in any profession and every job every day. For instance, logical reasoning skills are used to identify traces that can weaken an argument or acknowledge a certain assumption. Research has also gone ahead to demonstrate how deductive reasoning can be subject to bias, for example, in Carver and Klahr (2001). This is discussed in a dual process whereby one is automatic, rapid and effortless process of perception labelled as System 1, and the other one is slow but effortful and controlled process of reasoning tagged as System 2. A prevailing perspective has been a result of system 2 in terms of moral judgement. This is known as the Rationalist moral judgement model. However, in the recent past, some social psychologists proposed that some of the judgements we make morally are a result of System1. McMurran and McGuire (2005) have even argued that moral judgement take place mostly due to the spontaneous process and reasoning that materialises to give a post hoc and biased foundation of explanation rather than generate moral judgement. Anderson (2012) argues that moral judgements occur at first as a result of conscious involuntary intuitive processing and the conscious reasoning takes over particularly to try and justify whatever instinct that is presented to the consciousness in a non-truth seeking way. When a correct conclusion actually requires and individual to overcome a mismatch or more, normative performance can be impacted negatively. Another bias to normative reasoning comes up when a problem solver considers his or her personal beliefs. Baars and Gage (2010) say that belief bias is an effect that an individual’s personal beliefs happen to compete with the information that is contradictory with judgement of validity. For example, suppose a student breaks a mirror and then falls accidentally on his or her way back to school, the student can make an illogical conclusion that breaking glass causes bad luck. According to Hastie and Dawes (2009), isomorphic problems are described as having a similar formal structure, but the only difference is their contents. It is unlikely for the problem solver to realise isomorphism in similar but not identical problems. It is also quite hard to identify isomorphism when the surface features of the two problems differ sharply. This is where personal belief bias affects most of the judgements made when dealing with such problems. For example, physics students may have a hard time identifying the similarity in terms of structure when different materials are used in a variety of physics problems. In problem solving and thinking, it is clear that knowledge, judgement and decision making go hand in hand, according to Robertson (2001). This is because the knowledge acquired through the years has made it possible for scholars to conduct further research on problem solving and thinking issues in order to improve the perception of the process, thus making it better and simpler to every problem solver. Through the years, different disciplines have come into existence, and this includes the introduction of cognitive science that has influenced many other different points of view in that a problem can be viewed as discussed above. References Anderson, J. R. (2012) Psychological review. American Psychological Association. Baars, B. J., & Gage, N. M. (2010) Cognition, brain, and consciousness: introduction to cognitive neuroscience. Academic Press. Carver, S. M., & Klahr, D. (2001) Cognition and instruction. Routledge. Davidson, J., & Sternberg, R. J. (2003) The psychology of problem solving. Cambridg University Press. Evertson, C. M., & Weinstein, C. S. (2006) Handbook of classroom management: Research, practice, and contemporary issues. Routledge. Galotti, K. (2010) Cognitive psychology: In and out of the laboratory. Cengage Learning. Gentner, D. (2001) The analogical mind: Perspectives from cognitive science. Bradford Book. Harnish, R., & Cummins D. D. (2000) Minds, brains, and computers: An historical introduction to the foundations of cognitive science. Wiley. Hastie, R., & Dawes, R. M. (2009) Rational choice in an uncertain world: The psychology of judgment and decision making. SAGE. Hughes, J.W. (2007) Environmental problem solving: A how-to guide. UPNE. Logan, G. D. (2011) Cognitive psychology. 63. Martin, R. C., & Greene, R. L. (2011) Learning, memory and cognition bimonthly, beginning in January. Journal of Experimental Psychology. Mason, L. (2011) Learning and instruction. The Journal of the European Association for Research on Learning and Instruction. (EARLI) ELSEVIER. 28(6). McMurran, M., & McGuire, J. (2005) Social problem solving and offending: evidence, evaluation and evolution. Wiley Series in Forensic Clinical Psychology. John Wiley & Sons. Mehlcr, J. (2009) Cognition. International Journal of Cognitive Science. Elsevier Science. 113(3). Overholt, J.L., Aaberg, N.H. & Lindsey, J. (2008) Math stories for problem solving success: ready-to-use activities based on real-life situations, grades 6-12. John Wiley & Sons. Park, Y. (2004) .Teaching and learning of physics in cultural contexts: proceedings of the international conference on physics education in cultural contexts. World Scientific. Cheongwon, South Korea, 13-17 August 2001. Pugalee, D. K. (2004) A comparison of verbal and written descriptions of students’ problems Solving processes. Educational Studies in Mathematics, 55: 27–47. Robertson, S. I. (1999) Types of thinking: Psychology focus series, Routledge. Robertson, S. I. (2001) Problem solving. Psychology Press. Read More