Surveys indicate that university education fails in appropriately enhancing these ... this course on TRIZ thinking tools impacted students' problem solving abilities ...
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Teaching Thinking and Problem Solving at University: A Course on TRIZ Iouri Belski Thinking and problem solving skills are considered to be of significant importance in many professions. Surveys indicate that university education fails in appropriately enhancing these skills. This paper presents a concept of teaching thinking and problem solving as a separate course, based on the Theory of Inventive Problem Solving (TRIZ). Student surveys showed that students’ perception of their abilities in problem solving changed vastly as a consequence of the course. Students reflected that they would never have expected themselves to come up with the ideas they eventually thought of and suggested while conducting their final project, had they not been formally taught about the tools of problem solving. It was also found that this course on TRIZ thinking tools impacted students’ problem solving abilities much more than discipline-based courses, supporting the superiority of the ‘enrichment’ over the ‘infusion’ approach.
Introduction
R
ecently, the need for graduates with welldeveloped thinking and problem solving skills has emerged strongly in many professions. Australian graduate recruiters list thinking and problem solving skills among the nine generic employability skills they expect a graduate to possess in addition to appropriate academic results (Graduate Careers Australia, 2007). Engineering accrediting bodies are even more demanding and consider thinking and problem solving abilities to be exceptionally important skills for engineering graduates of the twenty-first century (National Academy of Engineering, 2005). Results of surveys conducted by Graduate Careers Australia identified that students rated their thinking and problem solving skills as the weakest of all nine generic employability skills (Graduate Careers Australia, 2007). This clearly demonstrates that the current university curriculum is unsuccessful in equipping students with the expected thinking and problem solving skills. To overcome this deficit, this paper presents the outcomes of the TRIZ thinking course conducted at the Royal Melbourne Institute of Technology (RMIT) in semester 2 of 2006. It investigates three issues: (1) the attributes that
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characterize an infusion and an enrichment approach for teaching thinking and problem solving skills; (2) how TRIZ thinking tools should be taught for a successful university course; and (3) what could be learned from an explorative investigation of the course’s students skills. In the third issue incorporated is the aspect whether the enrichment approach in teaching thinking and problem solving has any advantage over the infusion approach.
Infusion or Enrichment Approach? It is generally agreed that thinking and problem solving skills need to be taught explicitly. There are two main approaches which are normally used to guide such teaching: ‘enrichment’ and ‘infusion’. • With the enrichment approach, thinking modules are taught in parallel with existing domain-specific content. Cognitive Acceleration and Instrumental Enrichment programmes (Adey & Shayer, 1994; Shayer & Adey, 2002) are examples of this approach in non-engineering areas. Theory of Inventive Problem Solving (TRIZ) courses (Rivin & Fey, 1996; Belski, 2007) represent an
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example of the enrichment approach in engineering education. • The infusion approach embeds the teaching of thinking and problem solving in the context of discipline-based curricula (McGuinness, 2005). Engineering and science educators normally rely on this approach. This is due to the significant volume of discipline-specific knowledge to be taught and the inability of educators to devote sufficient time within the engineering curriculum to a separate subject which is specifically focused on thinking and problem solving skills. Bruer has shown that infusion across the curriculum is a good strategy for developing ‘intelligent’ novices (Bruer, 1993). Swartz and Parks (1994) have also asserted the benefits of the infusion over the enrichment approach. Engineering educators who adopt infusion strategies usually anticipate improved problem solving abilities of the students’ occurring as a by-product of a disciplinerelated course. Moreover, many academics trust that even while being involved in resolving problems related to a specific discipline (e.g., Electronics, Mechanical Engineering, Chemical Engineering), students are able to enhance their thinking and problem solving skills sufficiently well. Engineering educators utilize numerous approaches to ‘infuse’ thinking skills into their courses. Some of these approaches offer stepby-step procedures which students need to follow in their thinking and problem solving routines. For example, Polya (1988) proposed a four-step process in mathematical problem solving. Hayes (1981) expanded Polya’s conceptualization into a six-step procedure. Altshuller and Shapiro (1956) identified three main stages of problem solving based on their study of the psychology of invention. Hammond, Keeney and Raiffa (1999) suggested eight key elements of problem solving based on their review of the ideas most useful in guiding the procedure. Nickerson (1994) listed a number of heuristics and problemsolving methods that could be infused into discipline courses (e.g., problem decomposition or subgoaling, working backwards, hill climbing, mean-end analysis, forward chaining, considering analogous problems, specialization and generalization, considering extreme cases, and mixing strategies). More infusion strategies can be found in numerous publications on thinking, problem solving, and engineering creativity (Kivenson, 1977; Thring & Laithwaite, 1977; Altshuller, 1984; de Bono, 1990; Langrehr, 1994; King & Schlicksupp, 1998; Belski, 2007).
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Teaching TRIZ: A Basic Structure with Four Tools, Organized in Seven Steps A total of 42 engineering students in their second to fourth year of study were enrolled in an RMIT-wide elective course ‘Systematic and Inventive Problem-solving’ in the second semester of 2006 (July to November). During the 13-week semester, the students studied the following four thinking tools of TRIZ: Situation Analysis, Method of the Ideal Result, Systematized Substance-Field Analysis, and the 40 Innovative Principles with the Contradiction Table. These four TRIZ thinking tools have been chosen as the most suitable introductory modules for novices based on the author’s prior experience in teaching thinking to practising engineers. These tools cover the basic needs of engineering problem solving and can be used for problem and resources analysis, idea generation and failure prevention. The tools taught during the TRIZ thinking course will be discussed in more detail in the next section of the paper. During the semester every student had to complete four individual assignments, which were related to the aforementioned thinking tools, and also had to participate in group project work over a three-week period. The projects undertaken by the student groups were related to various needs of the Australian community. The following are some of the project titles: ‘Improving safety at traffic lights’, ‘Getting rid of cane toads’, ‘Detection of rip currents’. Projects had to be undertaken using the Seven Steps of Systematic Thinking procedure (Belski, 2002), which was used as a framework for the application of the TRIZ thinking tools.
Thinking Tools of TRIZ and the Seven Steps of Systematic Thinking TRIZ is the Russian acronym for Theory of Inventive Problem Solving. It is a wellestablished system of tools for problem solving, idea generation, failure analysis and prevention. TRIZ originated in Russia more than 50 years ago (Altshuller, 1984). TRIZ thinking tools branch out from the evolution of products and processes, which were revealed through the analysis of thousands of patents. Developed behind the iron curtain, TRIZ was used by Russian engineers and has contributed to many inventions. TRIZ entered the Western world in the early 1990s, and has already helped many Western companies to achieve enormous improvements. © 2009 The Author Journal compilation © 2009 Blackwell Publishing Ltd
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The following is a short description of the tools which were taught to students. Situation Analysis Situation Analysis (SA) was used by students as the first thinking step on the way to situation improvement. SA is designed to question the assumptions of a user and their perception of the problem. To solve a problem, it is imperative to understand what the problem is. Our perception of the situation and our entire outlook concerning its improvement often changes when various needs of the situation become apparent. When humans consider real problems (situations) they often mix many issues together. Technical matters are frequently blended with human emotions. These issues are indeed related, but they often refer to very different aspects of the situation and can therefore be dealt with differently. Usually, people who strive to improve the situation are in fact attempting to address all human and technical issues at once. This is not the most efficient method for achieving success. Proposed actions rarely lead to the expected outcome. This sort of approach often results in a waste of time and resources. The SA tool deployed in this study required students to answer a set of 11 questions. This was intended to achieve the following results: • to clarify the situation in question; • to separate human perceptions from the reality of the situation; • to identify different problems embedded in the situation; and • to formulate the tasks that were to be undertaken for situation improvement. Method of the Ideal Result The Method of the Ideal Result (MIR) has been developed by the author (Belski, 1998). The method is based on the TRIZ notion of the Ideal Ultimate Result (IUR). It has been established that when engineers face problematic situations and need to devise some improvements, two main issues are especially challenging. These issues are: • identifying the most problematic issue to focus on during the improvement; and • being able to utilize the resources which are available, with minimal additional expense. We often find ourselves in situations in which improvement is necessary, but the element that should be focused on is not always apparent. To identify the correct element upon which to focus is vital. Unless an engineer applies their © 2009 The Author Journal compilation © 2009 Blackwell Publishing Ltd
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skill and invests money into upgrading the system part which sustains overall performance foremost, the outcome of such improvements is likely to be quite unsatisfactory. The first part of the Method of the Ideal Result is designed to aid users in pinpointing the very element upon which to focus. Another common mistake made by many engineers is related to the introduction of additional resources (e.g., new parts, elements, substances, etc.) in order to improve the situation, without having a clear picture of what resources are originally available. Any new resource costs money. Most existing resources are either free or inexpensive. Moreover, existing resources are already available and do not have to be supplied. The second part of the Method of the Ideal Result procedure helps users to identify available resources and consider how these resources may be applied in improving the situation. Overall, the Method of the Ideal Result helps users to accomplish the following: • identify the direction towards an effective and simple solution; • separate different areas for improvement, and to identify the elements one has to focus on to deliver the most efficient improvement; • recognize all resources at hand; and • sift through all available resources, with the aim of seeing whether they could be of help in the improvement All students in the trial used the Method of the Ideal Result by employing the TRIZ4U MIR Pro-forma. Systematized Substance-Field Analysis Substance-Field Analysis represents any natural and man-made system as a set of interacting elements – a set of substances interacting with each other by means of fields, which are generated by the substances. Substances and fields in Substance-Field Analysis are not equal in representing systems – substances describe real system elements and fields show the interactions between these elements. Nevertheless, both substances and fields are represented in a similar manner: by circles. This ensures that vastly different real systems are modelled in a similar way – by means of circle-substances and circle-fields. Such generalizations enable a practitioner to represent complex systems by simple circle structures. This allows the user to consider different systems in a uniform way and to apply similar rules in order to resolve dissimilar problems. An example of a twodimensional representation of a system as a set of fields and substances is shown in Figure 1.
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S
F
S
F
S
F
S
F
S
Figure 1. A Two-Dimensional Representation of a System in Substance-Field Analysis. The circles marked with the letter S represent substances, the circles marked F symbolize the fields generated by these substances The real picture of a system is often more complicated and possesses more dimensions than those shown. Nevertheless, such models and generalizations of circle-substances and circle-fields help systematize our thinking. Substance-Field Analysis is a general tool for idea generation as well as failure analysis and prevention. It models a system through a set of interconnecting substances and fields. This converts the real task into its SubstanceField model and helps to clearly identify the conflict zones of the system. These conflict zones are broken down into conflict triads. Five model solutions are considered for each conflict triad. Eight fields of MATCEMIB (Mechanical, Acoustic, Thermal, Chemical, Electric, Magnetic, Intermolecular, Biological) are then deployed to ‘translate’ model solutions into real solutions. Students were taught the systematized Substance-Field Analysis procedure, consisting of five model solutions (Belski, 2007) which replace the classical 76 standard solutions (Salamatov, 1999). Most students also used systematized Substance-Field Analysis for idea generation as well as failure prevention. 40 Innovative Principles and Contradiction Table The 40 Innovative Principles are ‘solution recipes’ which have been applied successfully in thousands of patents. To derive the 40 Innovative Principles, more than 20,000 patents were analysed (Altshuller, 1984). It was found that dissimilar tasks from distinct areas of engineering and science were often solved in a
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similar manner. The 40 Innovative Principles combine these ideas into larger groups, called principles. The 40 Principles can be used separately, but they yield better solutions when used in combination with the Contradiction Table. The Russian versions of the TRIZ Contradiction Table and 40 Innovative Principles have remained unmodified for over 25 years. The Contradiction Table and the 40 Innovative Principles, if used together, represent another idea generating tool which models real systems. Unlike Su-Field, which provides the user with a wide range of general ideas for implementation, these tools offer solution ideas which are design-ready. The TRIZ4U CT Proforma was utilized by students to model systems accurately. Seven Steps of Systematic Thinking The Seven Steps of Systematic Thinking (Belski, 2002) were used as a framework for the four above-mentioned TRIZ thinking tools. All students were asked to conduct their practical work using the following steps: 1. Situation analysis. 2. Revealing the system’s stage of development. 3. Identifying the ideal solution. 4. Idea generation. 5. Failure prevention. 6. Adjusting the super-system and subsystems in accordance with the identified solution. 7. Reflection on the solution and the process of the solution. The student project teams were required to submit formal project reports identifying all seven steps. Reflection on the solution, the process of the solution, problems encountered during the solution process, changes in the thinking pattern, etc., were a compulsory part of the report.
Explorative Investigation of Students’ Thinking Skills After having introduced a basic structure for teaching TRIZ at university, now an investigation of the students’ thinking skills will be presented.
Three Sources of Data The results presented here reflect three different sources: (i) RMIT Course Experience Survey; (ii) pre- and post-course surveys specifically devoted to thinking and problem © 2009 The Author Journal compilation © 2009 Blackwell Publishing Ltd
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solving skills; as well as (iii) student reflections on their achievement and experience taken from the formal Project Reports. The RMIT Course Experience Survey was conducted by the university during classes in week 10 of the semester and was completed by 34 students. The survey represents the main means of independent evaluation of the course’s teaching quality. The Course Experience Survey has been conducted in every course at RMIT on a compulsory basis since early 2006. In semester 2 of 2006, the Course Experience Survey consisted of 21 statements that all RMIT students were expected to respond to. Although the RMIT Course Experience Survey offered official data, it lacked depth in terms of evaluating thinking and problem solving skills. Only one statement of the Course Experience Survey: ‘This course contributes to my confidence in tackling unfamiliar problems’ was closely related to thinking and problem solving skills. Bearing this in mind, the author designed pre- and post-course surveys that specifically focused on student thinking and problem solving skills. These two surveys were conducted by the author in week 1 and in week 13 of the semester and were completed by 30 and 32 students, respectively. In addition, student reflections were used to complete the overall picture. Students were required to individually reflect on their experiences and achievements in accordance with Step 7 of the Seven Steps procedure. Their individual reflections (completed by 42 students) were incorporated into the Project Report and submitted by every project team at the end of the semester.
RMIT Course Experience Survey Results regarding the TRIZ Course While responding to the Course Experience Survey, students had five options for every statement (Likert-type scale from 1 to 5): they could choose only one response from ‘strongly agree’ (identified as ‘5’) to ‘strongly disagree’ (identified as ‘1’). Students evaluated the course very highly. All but one of them either
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strongly agreed (25) or agreed (8) with the following Course Experience Survey statement: ‘Overall, I am satisfied with the quality of this course’. One student was unsure and gave the course a score of 3. A total of 22 students strongly agreed, 11 agreed and one student was unsure of the statement: ‘I can see how I’ll be able to use what I am learning in this course in my career’. The following quotations from the RMIT Course Experience Survey of 2007 represent student opinions on the effectiveness of the course in enhancing their thinking and problem solving skills: ‘It just makes you look at things from a wider angle and from all angles. Therefore it exercises your brain to think of things you do not think of.’ ‘The course is extremely useful in enabling a person to deal with unfamiliar problems with a systematic approach.’
RMIT Course Experience Survey Results regarding the Enrichment or Infusion Approach In order to judge the impact of disciplinebased (‘infusion’) versus specifically designed courses (‘enrichment’) on students’ problem solving ability, the data from the RMIT Course Experience Survey on all the discipline courses of all engineering degrees at RMIT and the TRIZ thinking course were compared. Over 200 engineering courses were conducted at RMIT in semester 2 of 2006. All of these courses, excluding the TRIZ thinking course, were discipline-focused and were not devoted to thinking and problem solving techniques. Approximately 5,600 engineering students responded to the RMIT Course Experience Survey that semester. With only 34 of the TRIZ thinking course students participating in the Course Experience Survey, the opinions of students enrolled in all engineering courses were considered as representing the data for the ‘average’ engineering course that deployed ‘infusion’ to enhance student thinking and problem solving skills. Table 1 depicts the
Table 1. Student Opinions on the Impacts of the TRIZ Thinking Course and Discipline-Related Courses in Terms of Problem Solving Ability Course TRIZ (enrichment) Discipline (infusion)
Strongly agree
Agree
Not sure
Disagree
Strongly disagree
65% 11%
32% 34%
3% 35%
0% 15%
0% 5%
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distribution of students’ reactions to the statement: ‘This course contributes to my confidence in tackling unfamiliar problems’ for both the ‘average’ engineering course and TRIZ thinking course. The results presented in Table 1 reveal a significant difference between the impact of the TRIZ thinking course and the discipline-based courses on student thinking and problem solving skills. Only 45 per cent of students enrolled in all engineering courses agreed or strongly agreed to the effectiveness of the infusion model in teaching thinking and problem solving skills. On the other hand, 97 per cent of the students explicitly learning thinking judged that the enrichment approach to teaching was successful. Moreover, the number of students who strongly agreed with the statement ‘This course contributes to my confidence in tackling unfamiliar problems’ shows a 6:1 difference: 11 per cent for an ‘average’ disciplinerelated course and 65 per cent for the TRIZ thinking course. A one-sample t-test revealed that the difference between the thinking course and the ‘average’ engineering course (as depicted in Table 1) is statistically significant (M = 4.62, SE = 0.095, t(33) = 13.937, p < 0.01, r = 0.92).
Pre- and Post-Course Survey Results and Student Reflections Table 2 depicts student opinions on their ability to resolve problems before and after the course. Data in Table 2 were collected over two surveys:
• in week 1, students confronted a statement related to their past problem solving experience: ‘So far I have been able to resolve every problem I faced’, • and in week 13 they responded to a statement focusing on their likely future achievements in solving problems: ‘I am certain that I will be able to resolve any problem I face’. The data in Table 2 reveal that the number of students who were confident in the effectiveness of their thinking and problem solving abilities increased nearly eightfold – from 9 per cent to 69 per cent – as a consequence of the course (including students who either agreed or strongly agreed with the respective statements). Furthermore, in week 1, 76 per cent of the students considered themselves unable to resolve every problem (representing those who either disagreed or strongly disagreed with the statement). This number was reduced to less than a fifth, i.e., to 14 per cent in week 13, after the course had been completed. An independent samples t-test showed that this change in student opinions from week 1 (M = 2.25, SE = 0.156) to week 13 (M = 3.66, SE = 0.159) was statistically significant, t(60) = -6.307, p < 0.01, r = 0.63. It is also of interest to consider the selfevaluation of students’ problem solving skills (shown in Table 3) which represents their responses to the statement: ‘I am very good at problem solving’. The shift from the average response of ‘Not Sure’ to the average of ‘Agree’ from week 1 (M = 3.28, SE = 0.103) to week 13
Table 2. Students’ Opinions on their Ability to Resolve Problems before and after the Course Week
1 13
Question
Strongly agree
Agree
Not sure
Disagree
Strongly disagree
So far I have been able to resolve every problem I faced I am certain that I am able to resolve any problem I will face
3%
6%
16%
63%
13%
10%
59%
17%
14%
0%
Table 3. Change in the Students’ Self-Assessment re. Problem Solving (‘I am very good at problem solving’) in Consequence of the Course Week 1 13
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Strongly agree
Agree
Not sure
Disagree
Strongly disagree
0% 14%
34% 83%
59% 3%
6% 0%
0% 0%
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(M = 4.10, SE = 0.076) is clearly visible and statistically significant, t(60) = -6.327, p < 0.01, r = 0.64. The students’ opinions as depicted in Tables 2 and 3 are very encouraging. They clearly show that the students’ perception of their thinking and problem solving skills improved significantly as a consequence of studying the tools of TRIZ and applying these tools in assignments and projects. These positive perceptions of the outcomes of the course are further supported by the following students’ answers to the questions ‘Do you think that your thinking changed as a result of this course?’ and ‘How did it change?’: ‘Yes. I can think more effectively.’ ‘Yes. I break problems into smaller tasks now.’ ‘. . . my thinking . . . broadened to look at previously ignored possible solutions.’ ‘. . . helped me to think outside the square that I usually think in.’ ‘Yes, the ability to look at problems from a different perspective. Not always looking for the technical solution.’ ‘Yes, my thinking mindset has become more structured.’ ‘Yes, it did. Ideas are more neatly formed and ways to come up with the solution are more systematic.’
Discussion The student opinions presented in this paper confirm that the course was well accepted by them. Students perceived that their problem solving skills had been enhanced by the TRIZ thinking course. These facts confirm that it is possible to structure a successful university course which is fully devoted to teaching thinking and problem solving skills solely around the thinking tools of TRIZ. The student responses indicate that they became more systematic in thinking and developed a more structured approach in solving problems. Human responses are often perception-based. Therefore, it is unclear just how reliably students’ opinions identify actual improvements in their thinking and problem solving skills. Strictly speaking, a measure of student thinking and problem solving skills that was capable of producing a reliable result quickly was necessary to make a judgement on skills’ improvement. Although such quick measures exist in other fields, the author was not aware of any gauge that could help to reliably evaluate changes in thinking and problem solving skills in the extremely complex field of engineering within a matter of hours. Nevertheless, students enrolled in the TRIZ thinking © 2009 The Author Journal compilation © 2009 Blackwell Publishing Ltd
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course were taught a number of efficient tools for systematic thinking and problem solving practically. They applied these tools in individual exercises and in team projects and were able to generate many elegant solution ideas. This doubtlessly assisted them in enhancing their thinking and problem solving skills beyond perception. However, the development of a rapid procedure to measure engineers’ thinking and problem solving skills is of importance in order to guarantee reliable judgements on any improvement in thinking and problem solving abilities. The TRIZ thinking course enhanced the thinking and problem solving skills of students significantly better than discipline courses did. Indeed, students’ responses to the statement ‘This course contributes to my confidence in tackling unfamiliar problems’ cannot fully measure the improvement in student thinking and problem solving skills during the course. These responses only cover one aspect of thinking and problem solving skills – the ability to confront unfamiliar situations. Nevertheless, the data presented in this paper still indicate that the enrichment approach should be preferred to the infusion strategies in teaching thinking and problem solving. In this study, infusion and enrichment strategies for teaching thinking and problem solving skills were examined by means of comparing formal students’ opinions on the TRIZ thinking course to the ‘average’ engineering course. Such a comparison gave rise to two limitations. First, it did not take into account the existence of the discipline-based courses, which use the infusion strategies significantly more effectively that the ‘average’ discipline course in engineering. Such courses are likely to exist and it would be of interest to analyse their ‘recipes for success’ in deploying infusion and to statistically compare their impact with that of enrichment courses. Second, this study did not consider changes in student thinking and problem solving skills over the duration of their four-year engineering degree. Engineering students experience up to 24 discipline-based courses in their years of university study. It is possible that, although one discipline-related course does not greatly influence thinking skills, the cumulative effect of all the discipline-based courses that students encounter is significant and is comparable with the impact of a specialized thinking course. Whilst the existing data from Graduate Careers Australia negates any significant impact of university courses on thinking and problem solving skills, investigating their cumulative effect may still be viable and worthwhile.
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References Adey, P. and Shayer, M. (1994) Really Raising Standards: Cognitive Intervention and Academic Achievement. Routledge, London. Altshuller, G. (1984) Creativity as an Exact Science. Gordon & Breach, New York. Altshuller, G.S. and Shapiro, R.B. (1956) On Psychology of the Inventive Process. Questions on Psychology, 6, 37–49 (in Russian). Belski, I. (1998) ‘I wish the work to be completed by itself, without my involvement’: The Method of the Ideal Result in Engineering Problem Solving. Proceedings of World of Innovation and Strategy Conference, Sydney. Belski, I. (2002) Seven Steps to Systems Thinking. Proceedings of the 13th Annual Conference and Convention, Australasian Association of Engineering Educators, Canberra. Belski, I. (2007) Improve your Thinking: SubstanceField Analysis. TRIZ4U, Melbourne. de Bono, E. (1990) Lateral Thinking. Penguin Books, London. Bruer, J.T. (1993) Schools for Thought: A Science for Learning in the Classroom. MIT Press/Bradford Books, Cambridge, MA. Graduate Careers Australia (2007) Snapshot: University and Beyond 2007. Graduate Careers Australia, Melbourne. Hammond, J.S., Keeney, R.L. and Raiffa, H. (1999) Smart Choices: A Practical Guide to Making Better Decisions. Harvard Business School Press, Boston, MA. Hayes, J.A. (1981) The Complete Problem Solver. Franklin Institute Press, Philadelphia, PA. King, R. and Schlicksupp, H. (1998) The Idea Edge: Transforming Creative Thought into Organizational Excellence. GOAL/QPC, Methuen, MA. Kivenson, G. (1977) The Art and Science of Inventing. Van Nostrand Reinhold Company, New York. Langrehr, J. (1994). Become a Better Thinker. Wrightbooks, Melbourne. McGuinness, C. (2005) Teaching Thinking: Theory and Practice. British Journal of Educational Psychology, Special Monograph Series, Pedagogy – Learning for Teaching, 3, 107–27. National Academy of Engineering (2005) Educating the Engineer of 2020: Adapting Engineering Education to the New Century. National Academy of Engineering, Washington, DC. Nickerson, R.S. (1994) The Teaching of Thinking and Problem Solving. Academic Press, San Diego, CA. Polya, G. (1988) How to Solve it: A New Aspect of Mathematical Method. Princeton University Press, Princeton, NJ.
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Rivin, E. and Fey, V. (1996) Use of the Theory of Inventive Problem Solving (TRIZ) in Design Curriculum. Innovations in Engineering Education, 1996 ABET Annual Meeting Proceedings. Salamatov, Y. (1999) TRIZ: The Right Solution at the Right Time. Insytec BV, Hattem, the Netherlands. Shayer, M. and Adey, P. (2002) Learning Intelligence: Cognitive Acceleration across the Curriculum from 5 to 15 Years. Open University Press, Buckinghamshire. Swartz, R. and Parks, S. (1994) Infusing the Teaching of Critical and Creative Thinking into Content Instruction: A Lesson Design Handbook for the Elementary Grades. Critical Thinking Press and Software, Pacific Grove, CA. Thring, M.W. and Laithwaite, E.R. (1977) How to Invent. The Macmillan Press Ltd, London.
Iouri Belski is an Associate Professor of Thinking and Problem Solving at the Royal Melbourne Institute of Technology (RMIT), Australia. He received a BEng and MSc in Automation and Electronics in 1981, and a PhD in Physics in 1989 from the Moscow Institute of Physics and Technology. Iouri spent over 15 years in R&D in Moscow, Russia, and has been granted over 20 patents. His research interests are in cognition, innovation, engineering problem solving and thinking, as well as in failure analysis and prevention. Iouri is specifically interested in application of TRIZ that he has been using since 1982. His book Improve your Thinking: Substance-Field Analysis, which systematized the classical Su-Field, was published in 2007. Iouri is the President of the Asia-Pacific TRIZ Association. Iouri has received numerous awards including the inaugural RMIT ViceChancellor’s Teaching Award for 2007. In 2006, the Australian Government awarded him the Citation for Outstanding Contribution to Student Learning: For the creation of innovative methodologies and imaginative resources which help students in enhancing thinking and problem solving skills.
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