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THINKING ABOUTTHINKING:Learning How to Learn

Compiled by Marc Imhotep Cray, M.D. [12-13-V1]

For Imhotep Virtual Medical School

ContentsArticles

Metacognition 1Deductive reasoning 8Inductive reasoning 11Problem solving 16Decision-making 25Logical reasoning 33

ReferencesArticle Sources and Contributors 34Image Sources, Licenses and Contributors 36

Article LicensesLicense 37

Metacognition 1

MetacognitionMetacognition is defined as "cognition about cognition", or "knowing about knowing".[1] It can take many forms; itincludes knowledge about when and how to use particular strategies for learning or for problem solving. There aregenerally two components of metacognition: knowledge about cognition, and regulation of cognition.Metamemory, defined as knowing about memory and mnemonic strategies, is an especially important form ofmetacognition.[2] Differences in metacognitive processing across cultures have not been widely studied, but couldprovide better outcomes in cross-cultural learning between teachers and students.[3]

Some evolutionary psychologists hypothesize that metacognition is used as a survival tool, which would makemetacognition the same across cultures. Writings on metacognition can be traced back at least as far as De Animaand the Parva Naturalia of the Greek philosopher Aristotle.[4]

DefinitionsJ. H. Flavell first used the word "metacognition".[5] He describes it in these words:

Metacognition refers to ones knowledge concerning one's own cognitive processes and products or anythingrelated to them, e.g., the learning-relevant properties of information or data. For example, I am engaging inmetacognition if I notice that I am having more trouble learning A than B; [or] if it strikes me that I shoulddouble check C before accepting it as fact.J. H. Flavell (1976, p. 232).

A. Demetriou, in his theory, one of the neo-Piagetian theories of cognitive development, used the termhypercognition to refer to self-monitoring, self-representation, and self-regulation processes, which are regarded asintegral components of the human mind.[6] Moreover, with his colleagues, he showed that these processes participatein general intelligence, together with processing efficiency and reasoning, which have traditionally been consideredto compose fluid intelligence.Metacognition also thinks about one's own thinking process such as study skills, memory capabilities, and the abilityto monitor learning. This concept needs to be explicitly taught along with content instruction. Metacognitiveknowledge is about our own cognitive processes and our understanding of how to regulate those processes tomaximize learning. Some types of metacognitive knowledge would include: 1. Person knowledge (declarativeknowledge) which is understanding one's own capabilities. 2. Task knowledge (procedural knowledge) which is howone perceives the difficulty of a task which is the content, length, and the type of assignment. 3. Strategic knowledge(conditional knowledge) which is one's own capability for using strategies to learn information. Young children arenot particularly good at this; it is not until upper elementary where students start to develop the understanding ofstrategies that will be effective.Different fields define metacognition very differently. Metacognition variously refers to the study ofmemory-monitoring and self-regulation, meta-reasoning, consciousness/awareness andauto-consciousness/self-awareness. In practice these capacities are used to regulate one's own cognition, to maximizeone's potential to think, learn and to the evaluation of proper ethical/moral rules.In the domain of experimental psychology, an influential distinction in metacognition (proposed by T. O. Nelson &L. Narens) is between Monitoringmaking judgments about the strength of one's memoriesand Controlusingthose judgments to guide behavior (in particular, to guide study choices). Dunlosky, Serra, and Baker (2007) coveredthis distinction in a review of metamemory research that focused on how findings from this domain can be applied toother areas of applied research.In the domain of cognitive neuroscience, metacognitive monitoring and control has been viewed as a function of the prefrontal cortex, which receives (monitors) sensory signals from other cortical regions and through feedback loops

Metacognition 2

implements control (see chapters by Schwartz & Bacon and Shimamura, in Dunlosky & Bjork, 2008).Metacognition is studied in the domain of artificial intelligence and modelling. Therefore, it is the domain of interestof emergent systemics. It has been used, albeit off the original definition, to describe one's own knowledge that wewill die. Writers in the 1990s involved with the musical "grunge" scene often used the term to describeself-awareness of mortality.[citation needed]

ComponentsMetacognition is classified into three components:1. Metacognitive knowledge (also called metacognitive awareness) is what individuals know about themselves and

others as cognitive processors.2. Metacognitive regulation is the regulation of cognition and learning experiences through a set of activities that

help people control their learning.3. Metacognitive experiences are those experiences that have something to do with the current, on-going cognitive

endeavor.Metacognition refers to a level of thinking that involves active control over the process of thinking that is used inlearning situations. Planning the way to approach a learning task, monitoring comprehension, and evaluating theprogress towards the completion of a task: these are skills that are metacognitive in their nature.Metacognition includes at least three different types of metacognitive awareness when considering metacognitiveknowledge:1. Declarative Knowledge: refers to knowledge about oneself as a learner and about what factors can influence

one's performance. Declarative knowledge can also be referred to as "world knowledge".2. Procedural Knowledge: refers to knowledge about doing things. This type of knowledge is displayed as

heuristics and strategies. A high degree of procedural knowledge can allow individuals to perform tasks moreautomatically. This is achieved through a large variety of strategies that can be accessed more efficiently.

3. Conditional knowledge: refers to knowing when and why to use declarative and procedural knowledge. It allowsstudents to allocate their resources when using strategies. This in turn allows the strategies to become moreeffective.

Similar to metacognitive knowledge, metacognitive regulation or "regulation of cognition" contains three skills thatare essential.1. Planning: refers to the appropriate selection of strategies and the correct allocation of resources that affect task

performance.2. Monitoring: refers to one's awareness of comprehension and task performance3. Evaluating: refers to appraising the final product of a task and the efficiency at which the task was performed.

This can include re-evaluating strategies that were used.Similarly, maintaining motivation to see a task to completion is also a metacognitive skill. The ability to becomeaware of distracting stimuli both internal and external and sustain effort over time also involves metacognitive orexecutive functions. The theory that metacognition has a critical role to play in successful learning means it isimportant that it be demonstrated by both students and teachers.Students who demonstrate a wide range of metacognitive skills perform better on exams and complete work moreefficiently. They are self-regulated learners who utilize the "right tool for the job" and modify learning strategies andskills based on their awareness of effectiveness. Individuals with a high level of metacognitive knowledge and skillidentify blocks to learning as early as possible and change "tools" or strategies to ensure goal attainment. Swanson(1990) found that metacognitive knowledge can compensate for IQ and lack of prior knowledge when comparingfifth and sixth grade students' problem solving. Students with a high-metacognition were reported to have used fewerstrategies, but solved problems more effectively than low-metacognition students, regardless of IQ or prior

Metacognition 3

knowledge.Metacognologists are aware of their own strengths and weaknesses, the nature of the task at hand, and available"tools" or skills. A broader repertoire of "tools" also assists in goal attainment. When "tools" are general, generic,and context independent, they are more likely to be useful in different types of learning situations.Another distinction in metacognition is executive management and strategic knowledge. Executive managementprocesses involve planning, monitoring, evaluating and revising one's own thinking processes and products. Strategicknowledge involves knowing what (factual or declarative knowledge), knowing when and why (conditional orcontextual knowledge) and knowing how (procedural or methodological knowledge). Both executive managementand strategic knowledge metacognition are needed to self-regulate one's own thinking and learning.[7]

Finally, there is no distinction between domain-general and domain-specific metacognitive skills. This means thatmetacognitive skills are domain-general in nature and there are no specific skills for certain subject areas. Themetacognitive skills that are used to review an essay are the same as those that are used to verify an answer to a mathquestion.Metacognitive experience is responsible for creating an identity that matters to an individual. The creation of theidentity with meta-cognitive experience is linked to the identity-based motivation (IBM) model. The identity-basedmotivation model implies that "identities matter because they provide a basis for meaning making and for action."[8]

A person decides also if the identity matters in two ways with meta-cognitive experience. First, a current or possibleidentity is either "part of the self and so worth pursuing"[9] or the individual thinks that the identity is part of theirself, yet it is conflicting with more important identities and the individual will decide if the identity is or is not worthpursuing. Second, it also helps an individual decide if an identity should be pursued or abandoned.Usually, abandoning identity has been linked to meta-cognitive difficulty. Based on the identity-based motivationmodel there are naive theories describing difficulty as a way to continue to pursue an identity. The incrementaltheory of ability states that if "effort matters then difficulty is likely to be interpreted as meaning that more effort isneeded."[10] Here is an example, a woman who loves to play clarinet has come upon a hard piece. She knows thathow much effort she puts into learning this piece is beneficial. The piece had difficulty so she knew the effort wasneeded. The identity the woman wants to pursue is to be a good clarinet player, having a metacognitive experiencedifficulty pushed her to learn the difficult piece to continue to identify with her identity. The entity theory of abilityrepresents the opposite. This theory states that if "effort does not matter then difficulty is likely to be interpreted asmeaning that ability is lacking so effort should be suspended." Based on the example of the woman playing theclarinet, if she did not want to identify herself as a good clarinet player, she would not have put in any effort to learnthe difficult piece which is an example of using metacognitive experience difficulty to abandon an identity.[11]

Relation to sapienceMetacognologists believe that the ability to consciously think about thinking is unique to sapient species and indeedis one of the definitions of sapience.[citation needed] There is evidence that rhesus monkeys and apes can make accuratejudgments about the strengths of their memories of fact and monitor their own uncertainty, while attempts todemonstrate metacognition in birds have been inconclusive.[12] A 2007 study has provided some evidence formetacognition in rats,[13] but further analysis suggested that they may have been following simple operantconditioning principles, or a behavioral economic model.

Metacognition 4

Metacognitive strategiesMetacognitive-like processes are especially ubiquitous when it comes to the discussion of self-regulated learning.Being engaged in metacognition is a salient feature of good self-regulated learners[citation needed]. Groups reinforcingcollective discussion of metacognition is a salient feature of self-critical and self-regulating social groups[citationneeded]. The activities of strategy selection and application include those concerned with an ongoing attempt to plan,check, monitor, select, revise, evaluate, etc.Metacognition is 'stable' in that learners' initial decisions derive from the pertinent fact about their cognition throughyears of learning experience. Simultaneously, it is also 'situated' in the sense that it depends on learners' familiaritywith the task, motivation, emotion, and so forth. Individuals need to regulate their thoughts about the strategy theyare using and adjust it based on the situation to which the strategy is being applied. At a professional level, this hasled to emphasis on the development of reflective practice, particularly in the education and health-care professions.Recently, the notion has been applied to the study of second language learners in the field of TESOL and appliedlinguistics in general (e.g., Wenden, 1987; Zhang, 2001, 2010). This new development has been much related toFlavell (1979), where the notion of metacognition is elaborated within a tripartite theoretical framework. Learnermetacognition is defined and investigated by examining their person knowledge, task knowledge and strategyknowledge.Wenden (1991) has proposed and used this framework and Zhang (2001) has adopted this approach and investigatedsecond language learners' metacognition or metacognitive knowledge. In addition to exploring the relationshipsbetween learner metacognition and performance, researchers are also interested in the effects ofmetacognitively-oriented strategic instruction on reading comprehension (e.g., Garner, 1994, in first languagecontexts, and Chamot, 2005; Zhang, 2010). The efforts are aimed at developing learner autonomy, interdependenceand self-regulation.Metacognition helps people to perform many cognitive tasks more effectively. Strategies for promotingmetacognition include self-questioning (e.g. "What do I already know about this topic? How have I solved problemslike this before?"), thinking aloud while performing a task, and making graphic representations (e.g. concept maps,flow charts, semantic webs) of one's thoughts and knowledge. Carr, 2002, argues that the physical act of writingplays a large part in the development of metacognitive skills.Strategy Evaluation matrices (SEM) can help to improve the knowledge of cognition component of metacogntion.The SEM works by identifying the declarative (Column 1), procedural (Column 2) and conditional (Column 3 and 4)knowledge about specific strategies. The SEM can help individuals identify the strength and weaknesses aboutcertain strategies as well as introduce them to new strategies that they can add to their repertoire.A regulation checklist (RC) is a useful strategy for improving the regulation of cognition aspect of onesmetacognition. RCs help individuals to implement a sequence of thoughts that allow them to go over their ownmetacogntion. King (1991) found that fifth-grade students who used a regulation checklist outperformed controlstudents when looking at a variety of questions including written problem solving, asking strategic questions, andelaborating information.Metacognitive strategies training can consist of coaching the students in thinking skills that will allow them tomonitor their own learning. Examples of strategies that can be taught to students are word analysis skills, activereading strategies, listening skills, organizational skills and creating mnemonic devices.

Metacognition 5

Meta-Strategic KnowledgeMeta-Strategic Knowledge (MSK) is a sub-component of metacognition that is defined as general knowledge abouthigher order thinking strategies. MSK had been defined as general knowledge about the cognitive procedures thatare being manipulated. The knowledge involved in MSK consists of making generalizations and drawing rulesregarding a thinking strategy and of naming the thinking strategy.[14]

The important conscious act of a meta-strategic strategy is the conscious awareness that one is performing a formof higher order thinking. MSK is an awareness of the type of thinking strategies being used in specific instances andit consists of the following abilities: making generalizations and drawing rules regarding a thinking strategy, namingthe thinking strategy, explaining when, why and how such a thinking strategy should be used, when it should not beused, what are the disadvantages of not using appropriate strategies, and what task characteristics call for the use ofthe strategy.[15]

MSK deals with the broader picture of the conceptual problem. It creates rules to describe and understand thephysical world around the people who utilize these processes called Higher-order thinking. This is the capability ofthe individual to take apart complex problems in order to understand the components in problem. These are thebuilding blocks to understanding the big picture (of the main problem) through reflection and problem solving.[16]

Characteristics of Theory of Mind: Understanding the mind and the "mental world":1.1. False beliefs: understanding that a belief is only one of many and can be false.2. Appearancereality distinctions: something may look one way but may be something else.3.3. Visual perspective taking: the views of physical objects differ based on perspective.4.4. Introspection: children's awareness and understanding of their own thoughts.

Mental Illness and Metacognition

Sparks of InterestIn the context of mental health, metacognition can be loosely defined as the process that "reinforces one's subjectivesense of being a self and allows for becoming aware that some of one's thoughts and feelings are symptoms of anillness.[17]" The interest in metacognition emerged out of a concern for an individuals ability to understand theirown mental status compared to others as well as the ability to cope with the source of their distress.[18] Theseinsights into an individual's mental health status can have a profound affect on the over-all prognosis and recovery.Metacognition brings many unique insights into the normal daily functioning of a human being. It also demonstratesthat a lack of these insights compromises normal functioning. This leads to less healthy functioning. In the Autismspectrum, there is a profound inability to feel empathy towards the minds of other human beings.[19] In people whoidentify as alcoholics, there is a belief that the need to control cognitions is an independent predictor of alcohol useover anxiety. Alcohol may be used as a coping strategy for controlling unwanted thoughts and emotions formed bynegative perceptions.[20] This is sometimes referred to as self medication.

ImplicationsWells and Matthews theory proposes that when faced with an undesired choice, an individual can operate in two distinct modes: object and Metacognitive. Object mode interprets perceived stimuli as truth, where Metacognitive mode understands thoughts as cues that have to be weighted and evaluated. They are not as easily trusted. There are targeted interventions unique of each patient, that gives rise to the belief that assistance in increasing metacognition in people diagnosed with schizophrenia is possible through tailored psychotherapy. With a customized therapy in place clients then have the potential to develop greater ability to engage in complex self-reflection.[21] This can ultimately be pivotal in the patient's recovery process. In the Obsessive Compulsive Disorder spectrum, cognitive formulations have greater attention to intrusive thoughts related to the disorder. "Cognitive Self-Consciousness" are

Metacognition 6

the tendencies to focus attention on thought. Patients with OCD exemplify varying degrees of these intrusivethoughts. Patients also suffering from Generalized Anxiety Disorder also show negative thought process in theircognition.[22]

With any metacognition strategy, the general consensus is to believe that they are good. But in all actuality somemay be very harmful. Cognitive-Attentional Syndrome (CAS) characterizes a Metacognitive model of emotiondisorder. CAS is consistent with the constant with the attention strategy of excessively focusing on the source of athreat. This ultimately develops through the clients own beliefs. Metacognitive therapy attempts to correct thischange in the CAS. One of the techniques in this model is called Attention Training (ATT). It was designed todiminish the worry and anxiety by a sense of control and cognitive awareness. Also ATT trains clients to detectthreats, test how controllable reality appears to be.[23]

Works of art as metacognitive artifactsThe concept of metacognition has also been applied to reader-response criticism. Narrative works of art, includingnovels, movies and musical compositions, can be characterized as metacognitive artifacts which are designed by theartist to anticipate and regulate the beliefs and cognitive processes of the recipient, for instance, how and in whichorder events and their causes and identities are revealed to the reader of a detective story. As Menakhem Perry haspointed out, mere order has profound effects on the aesthetical meaning of a text. Narrative works of art contain arepresentation of their own ideal reception process. They are something of a tool with which the creators of the workwish to attain certain aesthetical and even moral effects.[24]

References[1] Metcalfe, J., & Shimamura, A. P. (1994). Metacognition: knowing about knowing. Cambridge, MA: MIT Press.[2] Dunlosky, J. & Bjork, R. A. (Eds), Handbook of Metamemory and Memory. Psychology Press: New York.[3] Wright, Frederick. APERA Conference 2008. 14 Apr. 2009. http:/ / www. apera08. nie. edu. sg/ proceedings/ 4. 24. pdf. . >[4] Oxford Psychology Dictionary;metacognition[5][5] Nisbet, Shucksmith (1984). The Seventh Sense (p6) SCRE Publications[6] Demetriou, A., Efklides, A., & Platsidou, M. (1993). The architecture and dynamics of developing mind: Experiential structuralism as a

frame for unifying cognitive developmental theories. Monographs of the Society for Research in Child Development, 58, Serial Number 234.[7][7] Hartman, 2001.[8] Oyserman & Destin 1011, 2010.[9] Oyserman & Destin 1013, 2010.[10] Oyserman & Destin 1014, 2010.[11] Oyserman, D., & Destin, M. (2010) Identity-Based Motivation: Implications for Intervention. The Counseling Psychologist, 38 (7),

10011043.[12] Metacognition: Known unknowns (http:/ / www. newscientist. com/ channel/ being-human/ mg19225821.

600-metacognition-known-unknowns. html). Issue 2582 of New Scientist magazine, subscribers only.[13] Rats Capable Of Reflecting On Mental Processes (http:/ / www. sciencedaily. com/ releases/ 2007/ 03/ 070308121856. htm)[14] Zohar, A., & Ben David, A. (2009). Paving a clear path in a thick forest: A conceptual analysis of a metacognitive component.

Metacognition And Learning, 4(3), 177-195. doi:10.1007/s11409-009-9044-6[15] Veenman, M. V. J. (2006). Metacognition: Definitions, constituents, and their intricate relation with cognition. Symposium organized by

Marcel V. J. Veenman, Anat Zohar, and Anastasia Efklides for the 2nd conference of the EARLI SIG on Metacognition (SIG 16), Cambridge,UK, July 1921.

[16] Beer, N., & Moneta, G. B. (2012). Coping and perceived stress as a function of positive metacognitions and positive meta-emotions.Individual Differences Research, 10(2), 105116.

[17] Lysaker, P. H., Dimaggio, G., Buck, K. D., Callaway, S. S., Salvatore, G., Carcione, A., & ... Stanghellini, G. (2011). Poor insight inschizophrenia: Links between different forms of metacognition with awareness of symptoms, treatment needed, and consequences of illness.Comprehensive Psychiatry, 52(3), 253-260.

[18] Semerari, A., Carcione, A., Dimaggio, G., Falcone, M., Nicol ` o, G., Procacci, M., & Alleva, G. (2003). How to evaluate Metacognitivefunction in psychotherapy? The Metacognition Assessment Scale and it's applications. Clinical Psychology & Psychotherapy, 10, 238261.

[19] Lysaker, P. H., Gumley, A., & Dimaggio, G. (2011). Metacognitive disturbances in people with severe mental illness: Theory, correlateswith psychopathology and models of psychotherapy. Psychology And Psychotherapy: Theory, Research And Practice, 84(1), 1-8.doi:10.1111/j.2044-8341.2010.02007.x

Metacognition 7

[20] Spada, M. M., Zandvoort, M., & Wells, A. (2007). Metacognitions in problem drinkers. Cognitive Therapy And Research, 31(5), 709-716.doi:10.1007/s10608-006-9066-1

[21] Lysaker, P. H., Buck, K. D., Carcione, A., Procacci, M., Salvatore, G., Nicol, G., & Dimaggio, G. (2011). Addressing metacognitivecapacity for self-reflection in the psychotherapy for schizophrenia: A conceptual model of the key tasks and processes. Psychology AndPsychotherapy: Theory, Research And Practice, 84(1), 58-69.

[22] Jacobi, D. M., Calamari, J. E., & Woodard, J. L. (2006). Obsessive-Compulsive Disorder Beliefs, Metacognitive Beliefs and ObsessionalSymptoms: Relations between Parent Beliefs and Child Symptoms. Clinical Psychology & Psychotherapy, 13(3), 153-162.doi:10.1002/cpp.485

[23] Wells, A., Fisher, P., Myers, S., Wheatley, J., Patel, T., & Brewin, C. R. (2009). Metacognitive therapy in recurrent and persistentdepression: A multiple-baseline study of a new treatment. Cognitive Therapy And Research, 33(3), 291-300. doi:10.1007/s10608-007-9178-2

[24][24] Lng 1998, p. 88.

Further readingAnnual Editions: Educational Psychology. Guilford: Dushkin Pub., 2002. Print.

Barell, J. (1992), Like an incredibly hard algebra problem: Teaching for metacognition In A. L. Costa, J. A. Bellanca, & R. Fogarty (eds.) Ifminds matter: A foreword to the future, Volume I (pp.257266). Palatine, IL: IRI/Skylight Publishing, Inc.

Beck, G. M. (1998) The Impact of a Prescriptive Curriculum on the Development of Higher Order Thinking Skills in Children, UnpublishedMA dissertation, University of Leicester.

Brown, A. (1987). Metacognition, executive control, self-control, and other mysterious mechanisms. In F. Weinert and R. Kluwe (Eds.),Metacognition, Motivation, and Understanding (pp.65116). Hillsdale, NJ: Erlbaum.

Burke, K. (1999), The Mindful School: How to Assess Authentic Learning (3rd ed.), SkyLight Training and Publishing, USA. ISBN1-57517-151-1

Carr, S.C. (2002). "Assessing learning processes: Useful information for teachers and students". Intervention in School and Clinic 37:156162.

Chamot, A. (2005). The Cognitive Academic Language Learning Approach (CALLA): An update. In P. Richard-Amato and M. Snow (eds),Academic Success for English Language Learners (pp.87101). White Plains, NY: Longman.

Dunlosky, John & Metcalfe, Janet (2009). Metacognition. Los Angeles: SAGE. ISBN 978-1-4129-3972-0 Fisher, Peter & Wells, Adrian (2009). Metacognitive Therapy: Distinctive Features. London: Routledge. ISBN 978-0-415-43499-7 Flavell, J. H. (1976). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The nature of intelligence (pp.231236). Hillsdale,

NJ: Erlbaum

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, v34n10 p906-11 Oct 1979.

Hartman, H. J. (2001). Metacognition in Learning and Instruction: Theory, Research and Practice. Dordrecht: Kluwer Academic Publishers Niemi, H. (2002). Active learninga cultural change needed in teacher education and schools. Teaching and Teacher Education, 18, 763-780. Rasekh, Z., & Ranjbary, R. (2003). Metacognitive strategy training for vocabulary learning, TESL-EJ, 7(2), 1-18. Shimamura, A. P. (2000). "Toward a cognitive neuroscience of metacognition". Consciousness and Cognition 9: 313323. H. S. Terrace & J. Metcalfe (Eds.), The Missing Link in Cognition: Origins of Self-Reflective Consciousness. New York: Oxford University

Press.

Metcalfe, J., & Shimamura, A. P. (1994). Metacognition: knowing about knowing. Cambridge, MA: MIT Press. Papaleontiou-Louca, Eleonora (2008). Metacognition and Theory of Mind. Newcastle: Cambridge Scholars Publishing. ISBN

978-1-84718-578-5

Smith, J. D., Beran, M. J., Couchman, J. J., Coutinho, M. V. C., & Boomer, J. B. (2009). Animal metacognition: Problems and prospects,WWW (http:/ / psyc. queensu. ca/ ccbr/ Vol4/ SmithABS. html), Comparative Cognition and Behavior Reviews, 4, 4053.

Wenden, A. L. (1987). "Metacognition: An expanded view on the cognitive abilities of L2 learners". Language Learning 37 (4): 573594. Wenden, A. (1991). Learner Strategies for Learner Autonomy. London: Prentice Hall. Zhang, L. J. (2001). Awareness in reading: EFL students' metacognitive knowledge of reading strategies in an input-poor environment.

Language Awareness, WWW (http:/ / www. multilingual-matters. net), 11 (4), 268-288. Zhang, L. J. (2010). A dynamic metacognitive systems account of Chinese university students knowledge about EFL reading. TESOL

Quarterly, WWW (http:/ / www. ingentaconnect. com/ content/ tesol/ tq/ 2010/ 00000044/ 00000002/ art00006), 44

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(2), 320-353.

External links

The International Association for Metacognition (http:/ / www. personal. kent. edu/ ~jdunlosk/ metacog/ ) Metacognition (http:/ / www. instructionaldesign. org/ concepts/ metacognition. html) in Learning Concepts (http:/ /

www. instructionaldesign. org/ concepts/ index. html) Metacognition: An Overview (http:/ / www. gse. buffalo. edu/ fas/ shuell/ cep564/ Metacog. htm) by Jennifer A. Livingston

(1997) at Buffalo.edu

Unskilled and Unaware of It: How Difficulties in Recognizing One's Own Incompetence Lead to Inflated Self-Assessments (http:/ / www.apa. org/ journals/ features/ psp7761121. pdf) by Justin Kruger, David Dunning, Cornell University, Journal of Personality andSocial Psychology, vol 77, no 6, p 1121-1134, American Psychological Association (1999)

Metacognitive knowledge (http:/ / wik. ed. uiuc. edu/ index. php/ Metacognitive_knowledge) Metacognition in Computation overview (http:/ / www. cs. umd. edu/ ~anderson/ MIC/ ) links Developing Metacognition (http:/ / www. ericdigests. org/ pre-9218/ developing. htm) ERIC Digest Workshops on Metacognition and Self-Regulated Learning in Educational Technology (http:/ / www. andrew. cmu. edu/ user/ iroll/

workshops/ index. html)

Deductive reasoningDeductive reasoning, also deductive logic or logical deduction or, informally, "top-down" logic,[1] is the processof reasoning from one or more general statements (premises) to reach a logically certain conclusion.Deductive reasoning links premises with conclusions. If all premises are true, the terms are clear, and the rules ofdeductive logic are followed, then the conclusion reached is necessarily true.Deductive reasoning (top-down logic) contrasts with inductive reasoning (bottom-up logic) in the following way: Indeductive reasoning, a conclusion is reached reductively by applying general rules that hold over the entirety of aclosed domain of discourse, narrowing the range under consideration until only the conclusion is left. In inductivereasoning, the conclusion is reached by generalizing or extrapolating from initial information (and so induction canbe used even in an open domain, one where there is epistemic uncertainty. (Note, however, that the inductivereasoning mentioned here is not the same as induction used in mathematical proofs - mathematical induction isactually a form of deductive reasoning.)

Simple exampleAn example of a deductive argument:1.1. All men are mortal.2.2. Aristotle is a man.3.3. Therefore, Aristotle is mortal.The first premise states that all objects classified as "men" have the attribute "mortal". The second premise states that"Aristotle" is classified as a "man" a member of the set "men". The conclusion then states that "Aristotle" must be"mortal" because he inherits this attribute from his classification as a "man".

Deductive reasoning 9

Law of detachmentThe law of detachment (also known as affirming the antecedent and Modus ponens) is the first form of deductivereasoning. A single conditional statement is made, and a hypothesis (P) is stated. The conclusion (Q) is then deducedfrom the statement and the hypothesis. The most basic form is listed below:1. PQ (conditional statement)2.2. P (hypothesis stated)3.3. Q (conclusion deduced)In deductive reasoning, we can conclude Q from P by using the law of detachment.[2] However, if the conclusion (Q)is given instead of the hypothesis (P) then there is no definitive conclusion.The following is an example of an argument using the law of detachment in the form of an if-then statement:1. If an angle satisfies 90

Deductive reasoning 10

Validity and soundnessDeductive arguments are evaluated in terms of their validity and soundness.An argument is valid if it is impossible for its premises to be true while its conclusion is false. In other words, theconclusion must be true if the premises are true. An argument can be valid even though the premises are false.An argument is sound if it is valid and the premises are true.It is possible to have a deductive argument that is logically valid but is not sound. Fallacious arguments often takethat form.The following is an example of an argument that is valid, but not sound:1.1. Everyone who eats carrots is a quarterback.2.2. John eats carrots.3.3. Therefore, John is a quarterback.The example's first premise is false there are people who eat carrots and are not quarterbacks but the conclusionmust be true, so long as the premises are true (i.e. it is impossible for the premises to be true and the conclusionfalse). Therefore the argument is valid, but not sound. Generalizations are often used to make invalid arguments,such as "everyone who eats carrots is a quarterback." Not everyone who eats carrots is a quarterback, thus provingthe flaw of such arguments.In this example, the first statement uses categorical reasoning, saying that all carrot-eaters are definitelyquarterbacks. This theory of deductive reasoning also known as term logic was developed by Aristotle, but wassuperseded by propositional (sentential) logic and predicate logic.Deductive reasoning can be contrasted with inductive reasoning, in regards to validity and soundness. In cases ofinductive reasoning, even though the premises are true and the argument is "valid", it is possible for the conclusionto be false (determined to be false with a counterexample or other means).

EducationDeductive reasoning is generally thought of as a skill that develops without any formal teaching or training. As aresult of this belief, deductive reasoning skills are not taught in secondary schools, where students are expected touse reasoning more often and at a higher level. It is in high school, for example, that students have an abruptintroduction to mathematical proofs which rely heavily on deductive reasoning.

References[1] Deduction & Induction, Research Methods Knowledge Base (http:/ / www. socialresearchmethods. net/ kb/ dedind. php)[2] Guide to Logic (http:/ / www. jgsee. kmutt. ac. th/ exell/ Logic/ Logic12. htm#25)

Further reading Vincent F. Hendricks, Thought 2 Talk: A Crash Course in Reflection and Expression, New York: Automatic

Press / VIP, 2005, ISBN 87-991013-7-8 Philip Johnson-Laird, Ruth M. J. Byrne, Deduction, Psychology Press 1991, ISBN 978-0-86377-149-1jiii Zarefsky, David, Argumentation: The Study of Effective Reasoning Parts I and II, The Teaching Company 2002

External links Deductive reasoning (http:/ / philpapers. org/ browse/ deductive-reasoning) at PhilPapers Deductive reasoning (https:/ / inpho. cogs. indiana. edu/ idea/ 636) at the Indiana Philosophy Ontology Project Deductive reasoning (http:/ / www. iep. utm. edu/ ded-ind) entry in the Internet Encyclopedia of Philosophy

Inductive reasoning 11

Inductive reasoningInductive reasoning (as opposed to deductive reasoning) is reasoning in which the premises seek to supply strongevidence for (not absolute proof of) the truth of the conclusion. While the conclusion of a deductive argument issupposed to be certain, the truth of an inductive argument is supposed to be probable, based upon the evidencegiven.[1]

DefinitionThe philosophical definition of inductive reasoning is much more nuanced than simple progression fromparticular/individual instances to broader generalizations. Rather, the premises of an inductive logical argumentindicate some degree of support (inductive probability) for the conclusion but do not entail it; that is, they suggesttruth but do not ensure it. In this manner, there is the possibility of moving from general statements to individualinstances (for example, statistical syllogisms, discussed below).Though many dictionaries define inductive reasoning as reasoning that derives general principles from specificobservations, this usage is outdated.

DescriptionInductive reasoning is inherently uncertain. It only deals in degrees to which, given the premises, the conclusion iscredible according to some theory of evidence, for example a many-valued logic, DempsterShafer theory, orprobability theory with rules for inference such as Bayes' rule. Unlike deductive reasoning, it does not rely onuniversals holding over a closed domain of discourse to draw conclusions, so it can be applicable even in cases ofepistemic uncertainty (technical issues with this may arise however; for example, the second axiom of probability isa closed-world assumption).[2]

A statistical syllogism is an example of inductive reasoning:1.1. Almost all people are taller than 26inches2.2. Gareth is a person3.3. Therefore, Gareth is almost certainly taller than 26inchesAs a stronger example:

100% of biological life forms that we know of depend on liquid water to exist.Therefore, if we discover a new biological life form it will probably depend on liquid water to exist.

This argument could have been made every time a new biological life form was found, and would have been correctevery time; this does not mean it is impossible that in the future a biological life form that does not require watercould be discovered.As a result, the argument may be stated less formally as:

All biological life forms that we know of depend on liquid water to exist.All biological life probably depends on liquid water to exist.

Inductive reasoning 12

Inductive vs. deductive reasoningUnlike deductive arguments, inductive reasoning allows for the possibility that the conclusion is false, even if all ofthe premises are true.[3] Instead of being valid or invalid, inductive arguments are either strong or weak, whichdescribes how probable it is that the conclusion is true.A classical example of an incorrect inductive argument was presented by John Vickers:

All of the swans we have seen are white.Therefore, all swans are white.

Note that this definition of inductive reasoning excludes mathematical induction, which is a form of deductivereasoning.

CriticismInductive reasoning has been criticized by thinkers as diverse as Sextus Empiricus[4] and Karl Popper.[5]

The classic philosophical treatment of the problem of induction was given by the Scottish philosopher David Hume.Hume highlighted the fact that our everyday habits of mind depend on drawing uncertain conclusions from ourrelatively limited experiences rather than on deductively valid arguments. For example, we believe that bread willnourish us because it has done so in the past, despite no guarantee that it will do so. Hume argued that it isimpossible to justify inductive reasoning: specifically, that it cannot be justified deductively, so our only option is tojustify it inductively. Since this is circular he concluded that it is impossible to justify induction.[6]

However, Hume then stated that even if induction were proved unreliable, we would still have to rely on it. Soinstead of a position of severe skepticism, Hume advocated a practical skepticism based on common sense, wherethe inevitability of induction is accepted.[7]

BiasesInductive reasoning is also known as hypothesis construction because any conclusions made are based on currentknowledge and predictions.[citation needed] As with deductive arguments, biases can distort the proper application ofinductive argument, thereby preventing the reasoner from forming the most logical conclusion based on the clues.Examples of these biases include the availability heuristic, confirmation bias, and the predictable-world bias.The availability heuristic causes the reasoner to depend primarily upon information that is readily available tohim/her. People have a tendency to rely on information that is easily accessible in the world around them. Forexample, in surveys, when people are asked to estimate the percentage of people who died from various causes, mostrespondents would choose the causes that have been most prevalent in the media such as terrorism, and murders, andairplane accidents rather than causes such as disease and traffic accidents, which have been technically "lessaccessible" to the individual since they are not emphasized as heavily in the world around him/her.The confirmation bias is based on the natural tendency to confirm rather than to deny a current hypothesis. Researchhas demonstrated that people are inclined to seek solutions to problems that are more consistent with knownhypotheses rather than attempt to refute those hypotheses. Often, in experiments, subjects will ask questions thatseek answers that fit established hypotheses, thus confirming these hypotheses. For example, if it is hypothesizedthat Sally is a sociable individual, subjects will naturally seek to confirm the premise by asking questions that wouldproduce answers confirming that Sally is in fact a sociable individual.The predictable-world bias revolves around the inclination to perceive order where it has not been proved to exist, either at all or at a particular level of abstraction. Gambling, for example, is one of the most popular examples of predictable-world bias. Gamblers often begin to think that they see simple and obvious patterns in the outcomes and, therefore, believe that they are able to predict outcomes based upon what they have witnessed. In reality, however, the outcomes of these games are difficult to predict and highly complex in nature. However, in general, people tend

Inductive reasoning 13

to seek some type of simplistic order to explain or justify their beliefs and experiences, and it is often difficult forthem to realise that their perceptions of order may be entirely different from the truth.[8]

Types

GeneralizationA generalization (more accurately, an inductive generalization) proceeds from a premise about a sample to aconclusion about the population.

The proportion Q of the sample has attribute A.Therefore:The proportion Q of the population has attribute A.

ExampleThere are 20 ballseither black or whitein an urn. To estimate their respective numbers, you draw a sample offour balls and find that three are black and one is white. A good inductive generalization would be that there are 15black, and five white, balls in the urn.How much the premises support the conclusion depends upon (a) the number in the sample group, (b) the number inthe population, and (c) the degree to which the sample represents the population (which may be achieved by taking arandom sample). The hasty generalization and the biased sample are generalization fallacies.

Statistical syllogismA statistical syllogism proceeds from a generalization to a conclusion about an individual.

A proportion Q of population P has attribute A.An individual X is a member of P.Therefore:There is a probability which corresponds to Q that X has A.

The proportion in the first premise would be something like "3/5ths of", "all", "few", etc. Two dicto simpliciterfallacies can occur in statistical syllogisms: "accident" and "converse accident".

Simple inductionSimple induction proceeds from a premise about a sample group to a conclusion about another individual.

Proportion Q of the known instances of population P has attribute A.Individual I is another member of P.Therefore:There is a probability corresponding to Q that I has A.

This is a combination of a generalization and a statistical syllogism, where the conclusion of the generalization isalso the first premise of the statistical syllogism.

Inductive reasoning 14

Argument from analogy

The process of analogical inference involves noting the shared properties of two or more things, and from this basisinferring that they also share some further property:

P and Q are similar in respect to properties a, b, and c.Object P has been observed to have further property x.Therefore, Q probably has property x also.

Analogical reasoning is very frequent in common sense, science, philosophy and the humanities, but sometimes it isaccepted only as an auxiliary method. A refined approach is case-based reasoning. For more information oninferences by analogy, see Juthe, 2005 [9].

Causal inferenceA causal inference draws a conclusion about a causal connection based on the conditions of the occurrence of aneffect. Premises about the correlation of two things can indicate a causal relationship between them, but additionalfactors must be confirmed to establish the exact form of the causal relationship.

PredictionA prediction draws a conclusion about a future individual from a past sample.

Proportion Q of observed members of group G have had attribute A.Therefore:There is a probability corresponding to Q that other members of group G will have attribute A when nextobserved.

Bayesian inferenceAs a logic of induction rather than a theory of belief, Bayesian inference does not determine which beliefs are apriori rational, but rather determines how we should rationally change the beliefs we have when presented withevidence. We begin by committing to a prior probability for a hypothesis based on logic or previous experience, andwhen faced with evidence, we adjust the strength of our belief in that hypothesis in a precise manner using Bayesianlogic.

Inductive inferenceAround 1960, Ray Solomonoff founded the theory of universal inductive inference, the theory of prediction based onobservations; for example, predicting the next symbol based upon a given series of symbols. This is a formalinductive framework that combines algorithmic information theory with the Bayesian framework. Universalinductive inference is based on solid philosophical foundations[10] and can be considered as a mathematicallyformalized Occam's razor. Fundamental ingredients of the theory are the concepts of algorithmic probability andKolmogorov complexity.

Inductive reasoning 15

References[1] Copi, I. M., Cohen, C., & Flage, D. E. (2007). Essentials of logic (2nd ed.). Upper Saddle River, NJ: Pearson Education, Inc.[2] Bart Kosko, Fuzziness vs. Probability, International Journal of General Systems, vol. 17, no. 1, pp. 211-240, 1990.[3] John Vickers. The Problem of Induction (http:/ / plato. stanford. edu/ entries/ induction-problem/ ). The Stanford Encyclopedia of Philosophy.[4] Sextus Empiricus, Outlines Of Pyrrhonism. Trans. R.G. Bury, Harvard University Press, Cambridge, Massachusetts, 1933, p. 283.[5] Karl R. Popper, David W. Miller. "A proof of the impossibility of inductive probability." Nature 302 (1983), 687688.[6] Vickers, John. "The Problem of Induction" (http:/ / plato. stanford. edu/ entries/ induction-problem/ #2HumIndJus) (Section 2). Stanford

Encyclopedia of Philosophy. 21 June 2010[7] Vickers, John. "The Problem of Induction" (http:/ / plato. stanford. edu/ entries/ induction-problem/ #IndJus) (Section 2.1). Stanford

Encyclopedia of Philosophy. 21 June 2010.[8][8] Gray, Peter. Psychology. New York: Worth, 2011. Print.[9] http:/ / www. cs. hut. fi/ Opinnot/ T-93. 850/ 2005/ Papers/ juthe2005-analogy. pdf[10] Samuel Rathmanner and Marcus Hutter. A philosophical treatise of universal induction. Entropy, 13(6):10761136, 2011

Further reading Herms, D. "Logical Basis of Hypothesis Testing in Scientific Research" (http:/ / www. dartmouth. edu/ ~bio125/

logic. Giere. pdf) (PDF). Kemerling, G. (27 October 2001). "Causal Reasoning" (http:/ / www. philosophypages. com/ lg/ e14. htm). Holland, J. H.; Holyoak, K. J.; Nisbett, R. E.; Thagard, P. R. (1989). Induction: Processes of Inference, Learning,

and Discovery. Cambridge, MA, USA: MIT Press. ISBN0-262-58096-9. Holyoak, K.; Morrison, R. (2005). The Cambridge Handbook of Thinking and Reasoning. New York: Cambridge

University Press. ISBN978-0-521-82417-0.

External links Confirmation and Induction (http:/ / www. iep. utm. edu/ conf-ind) entry in the Internet Encyclopedia of

Philosophy Inductive Logic (http:/ / plato. stanford. edu/ entries/ logic-inductive) entry in the Stanford Encyclopedia of

Philosophy Inductive reasoning (http:/ / philpapers. org/ browse/ induction) at PhilPapers Inductive reasoning (https:/ / inpho. cogs. indiana. edu/ taxonomy/ 2256) at the Indiana Philosophy Ontology

Project Four Varieties of Inductive Argument (http:/ / www. uncg. edu/ phi/ phi115/ induc4. htm) from the Department of

Philosophy, University of North Carolina at Greensboro. Properties of Inductive Reasoning (http:/ / faculty. ucmerced. edu/ sites/ default/ files/ eheit/ files/ heit2000.

pdf)PDF(166KiB), a psychological review by Evan Heit of the University of California, Merced. The Mind, Limber (http:/ / dudespaper. com/ the-mind-limber. html) An article which employs the film The Big

Lebowski to explain the value of inductive reasoning.

Problem solving 16

Problem solvingProblem-solving consists of using generic or ad hoc methods, in an orderly manner, for finding solutions toproblems. Some of the problem-solving techniques developed and used in artificial intelligence, computer science,engineering, mathematics, medicine, etc. are related to mental problem-solving techniques studied in psychology.

DefinitionThe term problem-solving is used in many disciplines, sometimes with different perspectives, and often withdifferent terminologies. For instance, it is a mental process in psychology and a computerized process in computerscience. Problems can also be classified into two different types (ill-defined and well-defined) from whichappropriate solutions are to be made. Ill-defined problems are those that do not have clear goals, solution paths, orexpected solution. Well-defined problems have specific goals, clearly defined solution paths, and clear expectedsolutions. These problems also allow for more initial planning than ill-defined problems.[1] Being able to solveproblems sometimes involves dealing with pragmatics (logic) and semantics (interpretation of the problem). Theability to understand what the goal of the problem is and what rules there are key to solving the problem. Sometimesthe problem requires some abstract thinking and coming up with a creative solution.

PsychologyIn psychology, problem solving refers to a state of desire for reaching a definite 'goal' from a present condition thateither is not directly moving toward the goal, is far from it, or needs more complex logic for finding a missingdescription of conditions or steps toward the goal.[2] In psychology, problem solving is the concluding part of alarger process that also includes problem finding and problem shaping.Considered the most complex of all intellectual functions, problem solving has been defined as a higher-ordercognitive process that requires the modulation and control of more routine or fundamental skills. Problem solvinghas two major domains: mathematical problem solving and personal problem solving where, in the second, somedifficulty or barrier is encountered.[3] Further problem solving occurs when moving from a given state to a desiredgoal state is needed for either living organisms or an artificial intelligence system.While problem solving accompanies the very beginning of human evolution and especially the history ofmathematics, the nature of human problem solving processes and methods has been studied by psychologists overthe past hundred years. Methods of studying problem solving include introspection, behaviorism, simulation,computer modeling, and experiment. Social psychologists have recently distinguished between independent andinterdependent problem-solving (see more [4]).[5]

Clinical PsychologySimple laboratory-based tasks can be useful in explicating the steps of logic and reasoning that underlie problemsolving; however, they usually omit the complexity and emotional valence of "real-world" problems. In clinicalpsychology, researchers have focused on the role of emotions in problem solving (D'Zurilla & Goldfried, 1971;D'Zurilla & Nezu, 1982), demonstrating that poor emotional control can disrupt focus on the target task and impedeproblem resolution (Rath, Langenbahn, Simon, Sherr, & Diller, 2004). In this conceptualization, human problemsolving consists of two related processes: problem orientation, the motivational/attitudinal/affective approach toproblematic situations and problem-solving skills. Working with individuals with frontal lobe injuries,neuropsychologists have discovered that deficits in emotional control and reasoning can be remedied, improving thecapacity of injured persons to resolve everyday problems successfully (Rath, Simon, Langenbahn, Sherr, & Diller,2003).

Problem solving 17

Cognitive SciencesThe early experimental work of the Gestaltists in Germany placed the beginning of problem solving study (e.g., KarlDuncker in 1935 with his book The psychology of productive thinking ). Later this experimental work continuedthrough the 1960s and early 1970s with research conducted on relatively simple (but novel for participants)laboratory tasks of problem solving.[6] Choosing simple novel tasks was based on the clearly defined optimalsolutions and their short time for solving, which made possible for the researchers to trace participants' steps inproblem-solving process. Researchers' underlying assumption was that simple tasks such as the Tower of Hanoicorrespond to the main properties of "real world" problems and thus the characteristic cognitive processes withinparticipants' attempts to solve simple problems are the same for "real world" problems too; simple problems wereused for reasons of convenience and with the expectation that thought generalizations to more complex problemswould become possible. Perhaps the best-known and most impressive example of this line of research is the work byAllen Newell and Herbert A. Simon. Other experts have shown that the principle of decomposition improves theability of the problem solver to make good judgment.

Computer Science and AlgorithmicsIn computer science and in the part of artificial intelligence that deals with algorithms ("algorithmics"), problemsolving encompasses a number of techniques known as algorithms, heuristics, root cause analysis, etc. In thesedisciplines, problem solving is part of a larger process that encompasses problem determination, de-duplication,analysis, diagnosis, repair, etc.

EngineeringProblem solving is used in engineering when products or processes fail, so corrective action can be taken to preventfurther failures. It can also be applied to a product or process prior to an actual fail event, i.e., when a potentialproblem can be predicted and analyzed, and mitigation applied so the problem never actually occurs. Techniquessuch as Failure Mode Effects Analysis can be used to proactively reduce the likelihood of problems occurring.Forensic engineering is an important technique of failure analysis that involves tracing product defects and flaws.Corrective action can then be taken to prevent further failures.Reverse engineering attempts to discover the original problem-solving logic used in developing a product by takingit apart.

Cognitive Sciences: Two SchoolsIn cognitive sciences, researchers' realization that problem-solving processes differ across knowledge domains andacross levels of expertise (e.g. Sternberg, 1995) and that, consequently, findings obtained in the laboratory cannotnecessarily generalize to problem-solving situations outside the laboratory, has led to an emphasis on real-worldproblem solving since the 1990s. This emphasis has been expressed quite differently in North America and Europe,however. Whereas North American research has typically concentrated on studying problem solving in separate,natural knowledge domains, much of the European research has focused on novel, complex problems, and has beenperformed with computerized scenarios (see Funke, 1991, for an overview).

EuropeIn Europe, two main approaches have surfaced, one initiated by Donald Broadbent (1977; see Berry & Broadbent, 1995) in the United Kingdom and the other one by Dietrich Drner (1975, 1985; see Drner & Wearing, 1995) in Germany. The two approaches share an emphasis on relatively complex, semantically rich, computerized laboratory tasks, constructed to resemble real-life problems. The approaches differ somewhat in their theoretical goals and methodology, however. The tradition initiated by Broadbent emphasizes the distinction between cognitive

Problem solving 18

problem-solving processes that operate under awareness versus outside of awareness, and typically employsmathematically well-defined computerized systems. The tradition initiated by Drner, on the other hand, has aninterest in the interplay of the cognitive, motivational, and social components of problem solving, and utilizes verycomplex computerized scenarios that contain up to 2,000 highly interconnected variables (e.g., Drner, Kreuzig,Reither & Studel's 1983 LOHHAUSEN project; Ringelband, Misiak & Kluwe, 1990). Buchner (1995) describes thetwo traditions in detail.

North AmericaIn North America, initiated by the work of Herbert A. Simon on "learning by doing" in semantically rich domains(e.g. Anzai & Simon, 1979; Bhaskar & Simon, 1977), researchers began to investigate problem solving separately indifferent natural knowledge domains such as physics, writing, or chess playing thus relinquishing their attemptsto extract a global theory of problem solving (e.g. Sternberg & Frensch, 1991). Instead, these researchers havefrequently focused on the development of problem solving within a certain domain, that is on the development ofexpertise (e.g. Anderson, Boyle & Reiser, 1985; Chase & Simon, 1973; Chi, Feltovich & Glaser, 1981).Areas that have attracted rather intensive attention in North America include: Reading (Stanovich & Cunningham, 1991) Writing (Bryson, Bereiter, Scardamalia & Joram, 1991) Calculation (Sokol & McCloskey, 1991) Political decision making (Voss, Wolfe, Lawrence & Engle, 1991) Problem Solving for Business (Cornell, 2010) Managerial problem solving (Wagner, 1991) Lawyers' reasoning (Amsel, Langer & Loutzenhiser, 1991) Mechanical problem solving (Hegarty, 1991) Problem solving in electronics (Lesgold & Lajoie, 1991) Computer skills (Kay, 1991) Game playing (Frensch & Sternberg, 1991) Personal problem solving (Heppner & Krauskopf, 1987) Mathematical problem solving (Plya, 1945; Schoenfeld, 1985) Social problem solving (D'Zurilla & Goldfreid, 1971; D'Zurilla & Nezu, 1982) Problem solving for innovations and inventions: TRIZ (Altshuller, 1973,1990,1995]

Characteristics of Difficult ProblemsAs elucidated by Dietrich Drner and later expanded upon by Joachim Funke, difficult problems have some typicalcharacteristics that can be summarized as follows: Intransparency (lack of clarity of the situation)

commencement opacity continuation opacity

Polytely (multiple goals) inexpressiveness opposition transience

Complexity (large numbers of items, interrelations and decisions) enumerability connectivity (hierarchy relation, communication relation, allocation relation) heterogeneity

Problem solving 19

Dynamics (time considerations) temporal constraints temporal sensitivity phase effects dynamic unpredictability

The resolution of difficult problems requires a direct attack on each of these characteristics that are encountered.[7]

Problem-Solving StrategiesProblem-solving strategies are the steps that one would use to find the problem(s) that are in the way to getting toones own goal. Some would refer to this as the problem-solving cycle. (Bransford & Stein, 1993) In this cycle onewill recognize the problem, define the problem, develop a strategy to fix the problem, organize the knowledge of theproblem, figure-out the resources at the user's disposal, monitor one's progress, and evaluate the solution foraccuracy. Although called a cycle, one does not have to do each step in order to fix the problem, in fact those whodont are usually better at problem solving.[citation needed] The reason it is called a cycle is that once one is completedwith a problem another usually will pop up. Blanchard-Fields (2007) looks at problem solving from one of twofacets. The first looking at those problems that only have one solution (like math problems, or fact based questions)which are grounded in psychometric intelligence. The other that is socioemotional in nature and are unpredictablewith answers that are constantly changing (like whats your favorite color or what you should get someone forChristmas).The following techniques are usually called problem-solving strategies:[citation needed]

Abstraction: solving the problem in a model of the system before applying it to the real system Analogy: using a solution that solves an analogous problem Brainstorming: (especially among groups of people) suggesting a large number of solutions or ideas and

combining and developing them until an optimum solution is found Divide and conquer: breaking down a large, complex problem into smaller, solvable problems Hypothesis testing: assuming a possible explanation to the problem and trying to prove (or, in some contexts,

disprove) the assumption Lateral thinking: approaching solutions indirectly and creatively Means-ends analysis: choosing an action at each step to move closer to the goal Method of focal objects: synthesizing seemingly non-matching characteristics of different objects into something

new Morphological analysis: assessing the output and interactions of an entire system Proof: try to prove that the problem cannot be solved. The point where the proof fails will be the starting point for

solving it Reduction: transforming the problem into another problem for which solutions exist Research: employing existing ideas or adapting existing solutions to similar problems Root cause analysis: identifying the cause of a problem Trial-and-error: testing possible solutions until the right one is found

Problem solving 20

Problem-Solving Methodologies Eight Disciplines Problem Solving GROW model How to Solve It KEPNERandFOURIE Incident and Problem Investigation Kepner-Tregoe Problem Solving and Decision Making PDCA (plandocheckact) Productive Thinking Model RPR Problem Diagnosis (rapid problem resolution) Thinking Dimensions - Problem Solving TRIZ (in Russian: Teoriya Resheniya Izobretatelskikh Zadatch, "theory of solving inventor's problems")

Notes[1] Schacter, D.L. et al. (2009). Psychology, Second Edition. New York: Worth Publishers. pp. 376[2] "In each case "where you want to be" is an imagined(or written) state in which you would like to be. We might use the term 'Problem

Identification' or analysis in order to figure out exactly what the problem is. After we have found a problem we need to define what theproblem is. In other words, a distinguished feature of a problem is that there is a goal to be reached and how you get there is not immediatelyobvious.", What is a problem? in S. Ian Robertson, Problem solving, Psychology Press, 2001, p.2

[3] Bernd Zimmermann, On mathematical problem solving processes and history of mathematics (http:/ / www. icme-organisers. dk/ tsg18/S12BerndZimmermann. pdf), University of Jena

[4] https:/ / sites. google. com/ site/ markrubinsocialpsychresearch/ -independent-interdependent-problem-solving-scale[5] Rubin, M., Watt, S. E., & Ramelli, M. (2012). Immigrants social integration as a function of approach-avoidance orientation and

problem-solving style. International Journal of Intercultural Relations, 36, 498-505.[6] For example Duncker's "X-ray" problem; Ewert & Lambert's "disk" problem in 1932, later known as Tower of Hanoi.[7][7] resolver.scholarsportal.info.myaccess.library.utoronto.ca/resolve/02692821/v34i0003/221_pstics

References Altshuller, Genrich (1973). Innovation Algorithm. Worcester, MA: Technical Innovation Center.

ISBN0-9640740-2-8. Altshuller, Genrich (1984). Creativity as an Exact Science. New York, NY: Gordon & Breach.

ISBN0-677-21230-5. Altshuller, Genrich (1994). And Suddenly the Inventor Appeared. translated by Lev Shulyak. Worcester, MA:

Technical Innovation Center. ISBN0-9640740-1-X. Amsel, E., Langer, R., & Loutzenhiser, L. (1991). Do lawyers reason differently from psychologists? A

comparative design for studying expertise. In R. J. Sternberg & P. A. Frensch (Eds.), Complex problem solving:Principles and mechanisms (pp. 223-250). Hillsdale, NJ: Lawrence Erlbaum Associates. ISBN978-0-8058-1783-6

Anderson, J. R., Boyle, C. B., & Reiser, B. J. (1985). "Intelligent tutoring systems". Science 228 (4698): 456462.doi: 10.1126/science.228.4698.456 (http:/ / dx. doi. org/ 10. 1126/ science. 228. 4698. 456). PMID 17746875(http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 17746875).

Anzai, K., & Simon, H. A. (1979) (1979). "The theory of learning by doing". Psychological Review 86 (2):124140. doi: 10.1037/0033-295X.86.2.124 (http:/ / dx. doi. org/ 10. 1037/ 0033-295X. 86. 2. 124). PMID493441 (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 493441).

Beckmann, J. F., & Guthke, J. (1995). Complex problem solving, intelligence, and learning ability. In P. A.Frensch & J. Funke (Eds.), Complex problem solving: The European Perspective (pp. 177-200). Hillsdale, NJ:Lawrence Erlbaum Associates.

Berry, D. C., & Broadbent, D. E. (1995). Implicit learning in the control of complex systems: A reconsideration of some of the earlier claims. In P.A. Frensch & J. Funke (Eds.), Complex problem solving: The European

Problem solving 21

Perspective (pp. 131-150). Hillsdale, NJ: Lawrence Erlbaum Associates. Bhaskar, R., & Simon, H. A. (1977). Problem solving in semantically rich domains: An example from

engineering thermodynamics. Cognitive Science, 1, 193-215. Brehmer, B. (1995). Feedback delays in dynamic decision making. In P. A. Frensch & J. Funke (Eds.), Complex

problem solving: The European Perspective (pp. 103-130). Hillsdale, NJ: Lawrence Erlbaum Associates. Brehmer, B., & Drner, D. (1993). Experiments with computer-simulated microworlds: Escaping both the narrow

straits of the laboratory and the deep blue sea of the field study. Computers in Human Behavior, 9, 171-184. Broadbent, D. E. (1977). Levels, hierarchies, and the locus of control. Quarterly Journal of Experimental

Psychology, 29, 181-201. Bryson, M., Bereiter, C., Scardamalia, M., & Joram, E. (1991). Going beyond the problem as given: Problem

solving in expert and novice writers. In R. J. Sternberg & P. A. Frensch (Eds.), Complex problem solving:Principles and mechanisms (pp. 61-84). Hillsdale, NJ: Lawrence Erlbaum Associates.

Buchner, A. (1995). Theories of complex problem solving. In P. A. Frensch & J. Funke (Eds.), Complex problemsolving: The European Perspective (pp. 27-63). Hillsdale, NJ: Lawrence Erlbaum Associates.

Buchner, A., Funke, J., & Berry, D. C. (1995). Negative correlations between control performance andverbalizable knowledge: Indicators for implicit learning in process control tasks? Quarterly Journal ofExperimental Psychology, 48A, 166-187.

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Problem solving 23

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Problem_solving-Elementary_level_320d. html)

Decision-making 25

Decision-making

Sample flowchart representing the decisionprocess to add a new article to Wikipedia.

Decision making can be regarded as the cognitive process resulting inthe selection of a course of action among several alternative scenarios.Every decision making process produces a final choice. The output canbe an action or an opinion of choice.

Overview

Human performance in decision terms has been the subject of activeresearch from several perspectives. From a psychological perspective, it is necessary to examine

individual decisions in the context of a set of needs, preferences anindividual has and values they seek.

From a cognitive perspective, the decision making process must beregarded as a continuous process integrated in the interaction with the environment.

From a normative perspective, the analysis of individual decisions is concerned with the logic of decision makingand rationality and the invariant choice it leads to.

Yet, at another level, it might be regarded as a problem solving activity which is terminated when a satisfactorysolution is reached. Therefore, decision making is a reasoning or emotional process which can be rational orirrational, can be based on explicit assumptions or tacit assumptions. Decisions are likely to be involuntary andfollowing the decision, we spend time analyzing the cost and benefits of that decision. This is known as "RationalChoice Theory," which encompasses the notion that we maximize benefits and minimize the costs.Some have argued that most decisions are made unconsciously. Jim Nightingale, Author of Think Smart-Act Smart,states that "we simply decide without thinking much about the decision process." In a controlled environment, suchas a classroom, instructors might try to encourage students to weigh pros and cons before making a decision. Thisstrategy is known as Franklin's Rule. Because such a rule requires time, cognitive resources and full access torelevant information about the decision, Franklin's Rule may not best describe how people naturally make theirdecisions. [citation needed]

Logical decision making is an important part of all science-based professions, where specialists apply theirknowledge in a given area to make informed decisions. For example, medical decision making often involvesmaking a diagnosis and selecting an appropriate treatment. SomeWikipedia:Avoid weasel words research usingnaturalistic methods shows, however, that in situations with higher time pressure, higher stakes, or increasedambiguities, experts use intuitive decision making rather than structured approaches, following a recognition primeddecision approach to fit a set of indicators into the expert's experience and immediately arrive at a satisfactory courseof action without weighing alternatives. Recent robust decision efforts have formally integrated uncertainty into thedecision making process. However, decision analysis, recognized and included uncertainties with a structured andrationally justifiable method of decision making since its conception in 1964.A major part of decision making involves the analysis of a finite set of alternatives described in terms of evaluative criteria. Information Overload is when there is a substantial gap between the capacity of information and the ways we adapt. The overload of information can be related to problems processing and tasking, which impacts decision making.[1] These criteria may be benefit or cost in nature. Then the problem might be to rank these alternatives in terms of how attractive they are to the decision maker(s) when all the criteria are considered simultaneously. Another goal might be to just find the best alternative or to determine the relative total priority of each alternative (for instance, if alternatives represent projects competing for funds) when all the criteria are considered simultaneously. Solving such problems is the focus of multi-criteria decision analysis (MCDA) also known as multi-criteria decision

Decision-making 26

making (MCDM). This area of decision making, although it is very old and has attracted the interest of manyresearchers and practitioners, is still highly debated as there are many MCDA / MCDM methods which may yieldvery different results when they are applied on exactly the same data. This leads to the formulation of a decisionmaking paradox.

Rational and irrational decision makingIn economics, it is thought that if humans are rational and free to make their own decisions, then they would behaveaccording to the rational choice theory. This theory states that people make decisions by determining the likelihoodof a potential outcome, the value of the outcome and then multiplying the two. For example, with a 50% chance ofwinning $20 or a 100% chance of winning $10, people more likely to choose the first option.However, in reality, there are some factors that affect decision making abilities and cause people to make irrationaldecisions, one of them being availability bias. Availability bias is the tendency for some items that are more readilyavailable in memory to be judged as more frequently occurring. For example, someone who watches a lot of moviesabout terrorist attacks may think the frequency of terrorism to be higher than it actually is.

Information overloadInformation overload is "a gap between the volume of information and the tools we need to assimilate it." It isproven in some studies Wikipedia:Avoid weasel words that the more information overload, the worse the quality ofdecisions made. There are 5 factors concerning information overload: Personal Information Factors: personal qualifications, experiences, attitudes etc. Information Characteristics: information quality, quantity and frequency Tasks and Process: standardized procedures or methods Organizational Design: organizations' cooperation, processing capacity and organization relationship Information Technology: IT management, and general technologyHall, Ariss & Todorov with an assistant Rashar phinyor (2007) described an illusion of knowledge, meaning that asindividuals encounter too much knowledge it actually interferes with their ability to make rational decisions.[2]

Problem analysis vs decision makingIt is important to differe