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Implementing Technology Education Problem-Solving Activities V. William DeLuca Teaching students how to solve problems is an important goal of education and indus- trial arts/technology education has had a long history of providing an environment for developing these skills. The congruence of technology education and problem solving is based on the fact that technologies are, in many ways, a product of problem solving. Technological problems require the applica- tion of knowledge from many different disci- plines and the laboratory provides a medium to develop and test solutions. Greenfield (1987, p. 20) suggests that students do not acquire thinking skills sim- ply by practice in problem solving, drill, or osmosis. Problem-solving activities must be implemented with careful planning to insure intended student outcomes. Curriculum plan- ning must involve careful consideration of the goals of problem-solving instruction, how an activity fits in relation to the goals, and the teaching style that would best facil- itate goal attainment. Also, there is a dif- ference between the product and the process when considering the value of problem-solving activities. Perkins (1986, p. 7) cautions against focusing on the products we produce and only indirectly the process by which we produce them. Specifically, how to proceed in a stepwize fashion to reach a goal. The es- sence of problem-solving is the application of knowledge and process that leads to a sol- ution. Like any skill, the problem solver must acquire knowledge related to the prob- lem, thinking skills needed to process this knowledge, and the ability to identify and apply appropriate processes to reach a sol- ution. PROBLEM-SOLVING PROCESSES Problem solving is a process of resolv- ing a known difficulty. Anderson (1980) em- phasizes the processes undertaken during the act of problem solving by defining this be- havior as goal directed sequence of oper- ations-- an organized sequence of mental steps. Accordingly, several different problem-solving processes have been docu- mented. Brightman (1981) discussed a process model first proposed by John Dewey in 1933. The three step process included the diagnosis phase, analysis phase, and solution phase. Other, more specific, models have been de- scribed by Polya (1971), Soloway (1988), Bransford & Stein (1984), Hatch (1988), Seymour (1987), and Devore (1987). Following are summaries of these problem-solving proc- esses. 1. Troubleshooting/Debugging: Isolate the problem, identify possible cause, test, implement solution, test solution. 2. Scientific Process: Observation, develop hypothesis, experimentation, draw conclu- sion. 3. Design Process: Ideation/brainstorm, identify possible solution, prototype, finalize design. 4. Research and Development: Conceptualize the project, select research procedure, finalize research design, develop pro- posal, conduct research, analyze result, report result, evaluate research project. 5. Project Management: Identify project goal, identify tasks to reach the goal, develop a plan to accomplish the tasks, implement the plan, evaluate the plan. The problem type determines the appro- priate process to select and use. Therefore, the task of the problem solver is to select the best process for a given problem. To se- lect from these processes, the problem solver must understand each process and how and when to use the appropriate one. Advanced problem solvers perceive the process of solving prob- lems as a cycle and selected processes or subprocesses are used when needed. THINKING SKILLS The mental abilities needed to solve problems are not fully understood because of the many levels and integrations of knowledge sets that are manifested in the act of solv- ing problems. In its simplest form, problem- solving involves the application of recalled knowledge. Woods (1987, p. 55) discusses the importance of a knowledge base pertinent to the content of the problem and further ex- plains the value of the problem solver's ability to identify, locate, and evaluate missing information needed in the problem- solving process. These thinking skills, as they relate to technology education, may be classified as follows: 1. Prior Technological Knowledge: Knowledge and skills gained from previous study in technology education class. 2. Related Knowledge: Knowledge gained from classes other than technology education such as math and science. 3. Knowledge Seeking: Ability to identify missing information, and locate and ob- tain relevant information. Higher order thinking skills involve the processing of knowledge in memory. In this respect, thinking is the process of changing knowledge. Comparing ordinary thinking and good thinking, Lipman (1988, p. 40) uses terms such as estimating, evaluating, classi- fying, assuming, and hypothesizing to define good thinking. Similar thinking processes have been identified by Bloom (1956); Duke (1985); Kurfman & Cassidy (1977); and Feuerstein, Rand, Hoffman, & Miller (1980). Presseisen (1985, p.45) classified thinking skills as follows: 1. Qualifications -- finding unique characteristics: units of basic identity, definitions, facts, problem/task recogni- tion. 2. Causations -- establishing cause and ef- fect, assessment: predictions, infer- ences, judgments, evaluations. 3. Transformation -- relating known to un- known characteristics, creating meanings: analogies, metaphors, logical inductions. 4. Relationships -- Detecting regular operations: parts and wholes, patterns, analysis and synthesis, sequence and or- der, logical deduction. 5. Classification -- determining common qualities: similarities and differences, grouping and sorting, comparisons, either/or distinctions. This list encompasses the thinking skills presented in the literature. The five categories describe ways people mentally process knowledge to change its form and function. TEACHING METHODS AND STYLES When implementing problem-solving activ- ities, the level of achievement is determined by the teaching methods used to initiate and maintain students' goal directed behaviors. Maley (1978) describes 15 teaching methods appropriate for industrial arts. Nader (1984) and Costa (1984) also referenced similar methods in addition to several other commonly used teaching methods. Refer to Table 2 col- umn 4 for a listing of these methods. Which of these methods are best for de- veloping students' problem-solving skills? Given the diversity of technology education content and the need to teach basic content and skills, this question is not easily an- swered. When students have had no experience with the subject matter, recall is the start- ing point. Basic knowledge and skills may best be taught with a lecture-demonstration teaching approach. To develop problem-solving skills, Sternberg & Martin (1988), and Nickerson, Perkins, & Smith (1985) recommend deemphasizing lecture. These researchers point out the value of encouraging inter- action between student and teacher and main- taining a balance between structure and unstructured learning environments. The teaching style defines the inter- action of student and teacher. The steps in- volved in developing problem-solving skills move the student from teacher dependence to independence. Sternberg & Martin (1988, p. 569) describe a four step process beginning with direct instruction followed by intra- group problem solving, intergroup problem- solving, and individual problem solving. The process begins by fostering teacher-to- student interaction then encouraging student- to-student interaction. When students internalize the problem-solving skills, indi- vidual problem-solving skills can be devel- oped. Problem-solving activities implemented in technology education are characterized by the problem-solving processes and thinking skills that are taught. The teaching method and teaching style determine the environment in which learning occurs. The interactions of these variables define the level of student development on the continuum of problem- solving performance. Problem solving, whether direct or indi- rect, has long been a part of technology edu- cation because of the nature of technological content. To continue to develop and improve technology education problem-solving activ- ities, it is worthwhile to establish a baseline that quantifies the best in current practices. The purpose of this study was to identify and describe problem-solving proc- esses, thinking skills, teaching methods, and teaching styles typically used by technology education teachers that were recognized for their teaching excellence. METHODOLOGY SUBJECTS The sample consisted of 44 technology education teachers from the population of teachers recognized for their teaching excel- lence. Two groups of teachers were identified to participate in this study. One group con- sisted of the International Technology Educa- tion Association's 1989 Teacher of the Year award winners and members of the other group were nominated by state directors for technology/vocational education. State direc- tors were asked to nominate teachers from their state who were noted for providing in- struction of high quality and developing and/or implementing innovative learning expe- riences related to problem solving. Since the intent of this study was to describe the best in current practices, teachers of each group were asked to participate if they had suc- cessfully implemented innovative problem- solving activities. Twenty-two of the 44 ITEA Teachers of the Year award winners participated in the study. Twenty-two teachers nominated by state supervisors participated. Twenty teachers taught high school students, 15 taught middle school students and 5 taught students at both the middle and high school level. Four teach- ers did not respond to the question regarding grade level. INSTRUMENTATION A survey instrument was designed to identify problem-solving activities that teachers had successfully implemented and variables associated with the implementation process. The survey consisted of two parts. In the first part, participants were asked to list and briefly describe one or more innova- tive problem-solving activities that they found to be positive student learning experi- ences. The second part of the survey con- tained 33 items. These items, included the variables that affect implementation of problem-solving activities as identified in the review of literature. A verbal frequency scale was used to measure the frequency of use of the five problem-solving processes de- scribed by Polya (1971), Soloway (1988), Bransford & Stein (1984), Hatch (1988), Seymour (1987), and Devore (1987); the eight thinking skills described by Woods (1987, p. 55) and Presseisen (1985, p.45); and the 17 teaching methods described by Maley (1978), Nader (1984) and Costa (1984). Four questions were used to measure the continuum of teacher-to-student interaction as described by Sternberg & Martin (1988), and Nickerson, Perkins, & Smith (1985). Participants re- corded their responses to the second part of the survey on a CompuTest form using the fol- lowing verbal frequency scale: A = always, B = usually, C = occasionally, D = seldom and E = never. For data analysis, these response categories were coded on a one (i.e always) to five (i.e never) point ordinal scale. RESULTS The participants identified and briefly described 109 activities, an average of 2.5 activities per participant. Sixty-nine of these activities were different in title and description. The activities listed were used in a variety of grade levels ranging from 8th grade to post secondary. The subject area also varied. Teachers of CAD, construction, drafting, electronic communication, engineer- ing, exploring technology, general technology education, graphics communication, industrial technology, introduction to industry, intro- duction to technology, manufacturing, power and energy, product design, transportation, and woodworking reported the activities. Survey items were categorized according to problem-solving processes, thinking skills, teaching methods, and teaching styles. These items were used to determine typical techniques used by the teachers sur- veyed when they implemented problem-solving activities. A cluster analysis, the Ward's Method, was used to classify the set of variables into homogeneous groups based on similarity of response. With this analysis, the mean verbal frequency scores of each item were grouped to minimize the overall sum of squared within-cluster distances. Therefore, the clusters represent questionnaire items that shared similar frequency of use when teachers implemented problem-solving activ- ities. To understand the similarity of the items in each cluster and the differences be- tween the five clusters, Table 1 shows the characteristic response that items in each cluster share. For clarity the clusters were labeled according to mean rank of cluster characteristics, therefore cluster one re- presents items most frequently used and clus- ter five represents items least frequently used. TABLE 1 CLUSTER CHARACTERISTICS --------------------------------------------- Cluster Mean MDN SD --------------------------------------------- 1 2.30 2.0 .966 2 2.68 3.0 1.11 3 3.07 3.0 1.20 4 3.18 3.0 1.12 5 3.96 4.0 1.08 --------------------------------------------- The five clusters are summarized in Ta- ble 2. Cluster one contained eight items. One problem-solving process, the design proc- ess, was a member of this cluster. The think- ing skills in this cluster included application of related knowledge gained from classes other than technology education and prior technological knowledge gained from technology education class. The teaching style clustered in this group was described as the teacher shared goals and objectives with the student and decisions TABLE 2 CLUSTER GROUPINGS OF SURVEY ITEMS ------------------------------------------------------------------------------- Cluster PS Process Thinking Skills Teaching Methods Teaching Style ------------------------------------------------------------------------------- 1 Design Process Related Knowledge Discussion Goals are shared by teacher. Decisions Prior Demonstration reached through Technological agreement. Knowledge Experimentation Lecture ------------------------------------------------------------------------------- 2 Individual Goals are set by Instruction teacher. Teacher facilitates goal Media attainment. ------------------------------------------------------------------------------- 3 Troubleshooting Discovery Teacher directs all learning experiences. Scientific Simulation Project Management Readings Research & Develop Game-Structured Competition ------------------------------------------------------------------------------- 4 Classification Competency-based Causations Qualifications Relationships Knowledge Seeking ------------------------------------------------------------------------------- 5 Seminar Student develops goals and means Scenario to reach them. Contract Case Study Panel Discussion Role Play ------------------------------------------------------------------------------- were reached through agreement. The charac- teristics of this cluster, listed in Table 1, indicate that these methods were the most frequently used by technology teachers with a mean of 2.30. Sixty-one percent of the teachers surveyed used the items listed in this clus- ter usually or always and 98.3% used them at least sometimes. Cluster two was characterized by mean of 2.68. Four items were always or usually used by 43.9% of the teachers. Individualized in- struction and media were teaching methods grouped in this cluster. The teaching style, like the teaching method, was teacher di- rected with goals and objectives set by the teacher and the teacher guided goal attain- ment. These methods and this style are condu- cive to attainment of basic level knowledge that is a prerequisite to successful problem solving. Cluster three contained items typically used often by the teachers surveyed. The mean response for items in this cluster was 3.07 with 34.8% of the teachers using them always or usually. Four of the five problem-solving processes were part of this cluster. They included troubleshooting/debugging, scien- tific process, research and development and project management. Teaching methods included in this cluster were discovery, simulation, and reading. The teaching style that was close to the mean of this cluster was one where the teacher directed all learning expe- rience. Six of the eight thinking skills were grouped in cluster four. Competency based instruction was also grouped in this cluster. The characteristics of cluster four were similar to cluster three with 33.8% of the teachers using the members of this clus- ter usually or always. The items with the lowest frequency, typically seldom used, were grouped in clus- ter five. This cluster was characterized by a mean of 3.96 with 9.7% of the teachers sur- veyed indicating that they used the teaching methods and style usually or always. Seminar, scenario, contract, case study, panel dis- cussion and role play were members of this cluster. Also, the teaching style that was defined as students develop goals and objec- tives and the means to reach them was seldom used by the teachers surveyed. DISCUSSION Problem-solving activities develop im- portant skills. They teach students how to think and provide them with opportunities to experience knowledge seeking, selection, ap- plication, and evaluation. Implementing problem-solving activities means more than just giving students assignments. The out- comes of activities are dependent on the problem-solving processes and thinking skills that are taught and applied. The environment that fosters problem solving is created by the teaching methods and styles that define the teacher-to-student and student-to-student interaction. This study identified elements of problem-solving activities that were fre- quently used by a sample of technology educa- tion teachers recognized for their teaching excellence. The inferential qualities of the data are limited due to the sample size, but the cluster analysis does establish norms for describing the characteristics of technology education problem-solving activities. The typical activities required students to apply knowledge gained in technology education class as well as other classes. The design process was used to structure a procedure for reaching a solution. Lecture, discussion, demonstration, and experimentation were meth- ods most frequently used to implement activ- ities. Teachers typically shared the goals of the activity with students and decisions were reached through agreement. The results represent a hierarchal paradigm that emphasizes the design process and application of knowledge learned in school. Four of the five problem-solving processes and six of the eight thinking skills were typically used occasionally. In- creasing the application of those elements less frequently used could be the focus for improving technology education problem- solving activities. Relating to thinking skills, Feuerstein, Miller, Hoffman, Rand, Mintzker & Jensen (1981) have shown that the development of thinking skills increases problem-solving performance. Narrol, Silverman & Waksman (1982) have shown that remedial students in vocational education programs benefit from thinking skill instruc- tion. The teaching methods used by teachers represent techniques that are associated with teaching low as well as high level cognitive skills. As discussed by Nickerson, Perkins, & Smith (1985, p. 327), the use of several teaching methods is common when implementing problem-solving activities. Often students need to gain basic knowledge to apply to the solution especially in a new area of study. The sequence of instruction then leads stu- dents to methods such as experimentation, game structured competition, and discovery that give them a more active role in know- ledge seeking. The teaching methods listed in cluster five were seldom used by the teachers surveyed. These methods are associated with developing cognitive skills associated with effective problem solving. Likewise, the teaching style used least frequently (cluster five) is associated with high-level perform- ance. Methods such as case study, contract and scenario could be used to focus activ- ities on current technological problems. This study showed that technology educa- tion is providing students with experiences, as defined by the literature cited, that de- velop valuable problem-solving skills. To im- prove technology education problem-solving activities, the intent of instruction and scope of problem-solving skill developed are the issues. If the intent of instruction is to focus on certain elements and treat others as subsets then a hierarchal paradigm should be the focus for further development. If the elements are to be treated with equal value then a paradigm representing a balance in scope should be pursued. With this paradigm, students should be taught to identify the problem type and select the appropriate proc- ess. As problem-solving activities continue to evolve, educators must insure that appro- priate processes and thinking skills are taught and teaching methods and styles allow students to grow. Curriculum developers should consider the variables identified and described in this study to analyze the paradigm that characterizes the learning po- tential of problem-solving activities within the scope and sequence of technology educa- tion instruction. ---------------- V. William DeLuca is Assistant Professor, De- partment of Occupational Education, North Carolina State University, Raleigh, North Carolina. REFERENCES Anderson, J. (1980). COGNITIVE PSYCHOLOGY AND ITS IMPLICATIONS. San Francisco: W. H. Freeman & Co. Bloom, B. (1956). TAXONOMY OF EDUCATIONAL OBJECTIVES. New York: David McKay. Bransford, J., & Stein, B. (1984). THE IDEAL PROBLEM SOLVER: A GUIDE FOR IMPROV- ING THINKING, LEARNING AND CREATIVITY. San Francisco: W. H. Freeman. Brightman, H. J. (1981). PROBLEM SOLVING: A LOGICAL AND CREATIVE APPROACH. Atlanta: Business Publication Division, College of Business Administration. Costa, A. L. (1984). Mediating the metacognitive. EDUCATIONAL LEADERSHIP, 42(3), 57-62. Devore, P. (1987). A perspective for tech- nical research. In E. Israel & R. Wright (Eds.), CONDUCTING TECHNICAL RESEARCH. Mission Hills, CA: Glencoe Publishing Co. Duke, L. E. (1985). Seven cardinal princi- ples for teaching higher-order thinking. THE SOCIAL STUDIES, 76(3), 129-132. Feuerstein, R., Rand, Y., Hoffman, M., & Miller R. (1980). INSTRUCTIONAL ENRICHMENT. Baltimore: University Park Press. Feuerstein, R., Miller, R., Hoffman, M., Rand, Y., Mintzker, Y., & Jenson, M. (1981). Cognitive modifiability in adolescence: Cognitive structure and the effect of intervention, THE JOURNAL OF SPECIAL EDUCATION, 1(2), 269-287. Greenfield, L. B. (1987). Thinking teaching through problem solving. In James E. Stice (Ed.), DEVELOPING CRITICAL THINKING AND PROBLEM-SOLVING ABILITIES. San Francisco: Jossey Bass Inc., pp. 5-22. Hatch, L. (1988). Problem Solving Approach. In W. Kemp and A. Schwaller (Eds), In- structional Strategies for Technology Edu- cation. Mission Hills, CA: Glencoe Publishing Company. Kurfman, D., & Cassidy, E. (1977). DEVELOP- ING DECISION-MAKING SKILLS. Arlington, VA: The National Council for the Social Studies. Lipman, M. (1988). Critical thinking -- what can it be? EDUCATIONAL LEADERSHIP, 46(1), 38-43. Maley, D. (1978). THE INDUSTRIAL ARTS TEACHER'S HANDBOOK. Boston: Allyn & Bacon Inc. Nader, L. (1984). THE HANDBOOK OF HUMAN RE- SOURCE DEVELOPMENT. New York: John Wiley & Sons. Narrol, H., Silverman, H., & Waksman, M. (1982). Developing cognitive potential in vocational high school students. JOURNAL OF EDUCATIONAL RESEARCH, 76(2), 107-112. Nickerson, R., Perkins D., & Smith E. (1985). The teaching of thinking. Hillsdale, NJ: Lawrence Erlbaum Associ- ates. Perkins, D. N. (1986). Thinking frames, ED- UCATIONAL LEADERSHIP. 43(8), 4-10. Polya, G. (1971). HOW TO SOLVE IT. Princeton: Princeton University Press. Presseisen, B. Z. (1985). Thinking skills: Means and models. In Arthur L. Costa (Ed.), DEVELOPING MINDS: A RESOURCE BOOK FOR TEACHING THINKING. Alexandria, VA: Association for Supervision and Curriculum Development. Seymour, R. D. (1987). A model of the tech- nical research project. In E. Israel & R. Wright (Eds.), CONDUCTING TECHNICAL RE- SEARCH. Mission Hills, CA: Glencoe Pub- lishing Co. Soloway, A. (1988). Do you know what your children are learning? In R. Nickerson (Ed.), TECHNOLOGY IN EDUCATION: LOOKING TOWARD 2020. Hillsdale, NJ: Lawrence Erlbaum Associates. Sternberg, R., & Martin, M. (1988). When teaching thinking does not work, what goes wrong? TEACHERS COLLEGE RECORD89(4), 555-578. Woods, D. R. (1987). How might I teach problem solving? In James Stice (Ed.), DEVELOPING CRITICAL THINKING AND PROBLEM-SOLVING ABILITIES. San Francisco: Jossey Bass Inc. Permission is given to copy any article or graphic provided credit is given and the copies are not intended for sale. Journal of Technology Education Volume 2, Number 2 Spring 1991