JITE v36n4 - Toward a 'Unified Curriculum Framework' for Technology Education

Volume 36, Number 4
Summer 1999

Toward a "Unified Curriculum Framework" for Technology Education

Robert C. Wicklein
University of Georgia
Jay W. Rojewski
University of Georgia

Leading scientists, mostly physicists, have endeavored for decades to create what has been termed the unified field theory ( Hawking, 1990 ). This theory, if achieved, would explain the cohesive connection of the basic laws and properties of the universe with a single mathematical scheme, enabling humankind to ultimately understand the purpose of the cosmos. Proponents of a unifying theory assert that the deep questions philosophers have pondered through the centuries - What is the nature of the universe? What is our place in it? Where did it and we come from? Why is it the way it is? - would, ultimately, be clarified through a unification of physics theories. Indeed, Einstein spent the last years of his life preoccupied with formulating such a theory. Yet, while the unification of seemingly disparate views of the physical world is an appealing notion, scientists have not found this elusive "Theory of Everything" ( Davies, 1995 ).

Not unlike scientists working toward a unified field theory, technology educators have investigated the possibilities of creating a unifying conceptual framework for technology education curriculum for over a quarter century ( Wicklein, 1997 ). Serious attention from around the globe has brought to light the advantages of specific learning components that may prove to unite the curriculum into a comprehensive study of technology ( International Technology Education Association, 1996 ). However, it is also quite possible that efforts to unify diverse views about the contents and processes of technology education curriculum may prove as elusive as a unified theory of science.

Past efforts to determine common themes and goals of technology education curriculum have been difficult to attain, although many attempts have been made ( Zuga, 1989 ). A variety of curriculum strategies for technology education are found in the professional literature. Curriculum designs range from technical performance reflective of early 20th century manual arts such as woodworking, metal fabrication, and drafting to state-of-the-art learner-centered constructionist approaches to technology education such as problem solving, critical thinking, engineering, and multidisciplinary ( Satchwell & Dugger, 1996 ).

Curriculum designs for technology education may always remain varied and open to a variety of applications. However, if the profession is to achieve specific learning goals it is imperative that a foundational philosophy for the field be established. The questions, "What is the purpose of technology education?" and "What is technology education supposed to achieve?", must be answered with a significant degree of resolve by professionals within the field before clarity can be achieved for curriculum planning. A central goal for technology education, as it is for all of education, is to encourage and support students in becoming the best persons possible. This goal derives from the central tenets of democracy most pertinent to education and are plausibly stated in the following:

  1. freedom to be an individual, to dissent, to grow and to pursue one's own dreams are inalienable human rights;
  2. human capacities and freedoms are realizable in a society which seeks to protect and nourish them;
  3. humans have the capacity to devise institutions which honor the equal rights of all;
  4. a society which nourishes and protects human capacities and freedoms depends upon individual commitment to responsible participation in it;
  5. humans have the capacity to learn from and to improve themselves as a result of experience; and
  6. it is both possible and morally obligatory to think for oneself ( Raywid, Tesconi, & Warren, 1984, pp. 16-17 ).

As the purposes of technology education are considered, the tenets presented by Raywid et al. ( 1984 ) may serve as one basis for unifying technology education curriculum. In particular, the final tenet, it is both possible and morally obligatory to think for oneself addresses a central feature of technology education, for students to develop critical thinking and technological problem-solving skills. This underlying ideal served as a motivating factor for our research. Although the criteria for democracy may be debated as well as the unifying features of technology education, we believe that the ability to develop critical thinking and problem solving skills are paramount to the field and has been identified in the professional literature as primary goals of technology education ( International Technology Education Association, 1999 ).

In order to solve technological problems one must develop appropriate intellectual methods and processes. The question of determining what these intellectual processes are is pivotal to developing the unifying curriculum framework for technology education. We sought to examine the relative importance of mental methods of inquiry used by technologists (e.g., professional engineers, scientists, technicians, and business leaders) to solve technologically-based problems. By identifying the basic cognitive strategies employed when solving technology-based problems, technology educators could develop instructional strategies that incorporate these methods in a variety of learning activities. The mental processes are not developed as curriculum per se, however, they may serve as a basis for creating curriculum designs that may yield comprehensive and strategic means of employing critical thinking and problem solving strategies for students. Curriculum that emphasizes technical content tends to be rather short lived and is constantly changing due to the rapid accumulation of knowledge and techniques used in business and industry. In comparison, the mental processes and techniques used in solving technological problems could remain rather consistent over time. Thus, regardless of changes in tools or the type of technology, the underlying curriculum goals would remain consistent. Stability in curriculum design might be valued by teachers, administrators, and especially students involved in the volatile field of technology.

We prioritized and extended a list of select mental methods/processes of inquiry originally identified by Halfin ( 1973 ) considered essential for solving technological problems. The mental processes used in solving technological problems may provide a unifying framework for curriculum planning in technology education. Curriculum structured on identified mental processes (as opposed to curriculum focused on the use of technology) could provide teachers with the flexibility to employ many approaches and use numerous activities to support their learning environment, while consistently working on developing strong student-centered critical thinking and problem-solving skills. This type of curriculum would be united not by the types of technological devices present in the laboratory, but by the mental processes that were being developed, strengthened, and encouraged for each student. Thus, these results may contribute to a dialogue about the possibility and desirability of identifying a basic, unifying force for curriculum planning in technology education.



A three-round Delphi research method was used to garner consensus regarding the importance of previously established mental methods and processes ( Halfin, 1973 ). The Delphi technique provides a systematic method of soliciting and collating judgements on a select topic by presenting a questionnaire to the same group of participants over several iterations. Typically, a Delphi method starts by asking participants to generate responses to a specific question or issue. Each successive administration uses the same questionnaire which is interspersed with controlled opinion feedback from the previous round. For the second and all subsequent rounds, participants are asked to consider personal and group responses for each item. When respondents estimates for an item do not fall within the range of group response, they are asked to reconsider their position and, when justified, change their response. In this manner, an attempt is made to achieve consensus among panel members ( Sackman, 1975 ; Van de Ven & Delbecq, 1974 ; Weaver, 1971 ). The Delphi technique is an acceptable method for qualifying professional judgement and has been employed to gather expert opinion on numerous and diverse topics and concerns in education (e.g., Horadal, 1987 ; Rojewski & Meers, 1991 ).


The Delphi panel was composed of 25 individuals including professional engineers ( n =19), scientists ( n =2), educators ( n =3), and 1 inventor. Panel composition was purposefully weighted heavily in the direction of professional engineers because they often have the best opportunity to observe and discriminate mental processes employed in solving real-world technology-based problems. Panelists were considered well-informed, leading authorities in their respective fields, in addition to having considerable experience in the processes employed in technological problem solving. These criteria were posed to the executive directors of the American Institute of Plant Engineers and the International Technology Education Association who provided recommendations of potential participants. Final selection was based on individual experience and perceived ability to formulate thinking through writings and research.

Panelists averaged 23 years of work experience within their respective fields. Because the success of the Delphi technique relies upon the use of informed opinion by noted authorities, random selection was never considered when selecting participants. Since demographic, gender, and professional orientation were not included as selection criteria, our results might have been different had we used one or more of these descriptive criteria. Even so, Delphi panelists are typically selected, not for demographic representativeness, but for the perceived expertise that individuals can contribute to the topic under consideration.

Given this caveat, our participants were from 15 states with 22 being male and 3 being female. Returned instruments from the three-round Delphi probe yielded 100% return from probe 1 ( n =25), 96% return from probe 2 ( n =24), and 92% return from probe 3 ( n =23), respectively. While participant attrition can affect the results of a Delphi study, the two participants who withdrew from the study based their withdrawal on personal time conflicts rather than reactions to study content or results.


We used a modified Delphi procedure, meaning that specific criteria were supplied to participants during the initial probe. In this case, 17 specific mental processes and their definitions were provided for panelists initial review. The concept of mental processes as a curriculum development concern was presented. Halfin ( 1973 ) originally identified these processes through an examination of biographical and autobiographical literature of 10 high-level technologists (Thomas Alva Edison, Charles Goodyear, Elmer A. Sperry, Wilbur and Orville Wright, Frank Lloyd Wright, Charles Kettering, R. Buckminster Fuller, Arthur D. Moore, Edwin H. Land, & Robert R. Gilruth). Halfin submitted that mental processes are relevant to curriculum development because of potential improvements in student learning and the stability they may bring to a field of study.

There exists a need for the student to learn the [mental] processes of the scholar in the discipline as well as the [technical] content so that a better understanding of the discipline may result. [Technical] content is short lived and is constantly changing due to the rapid accumulation of knowledge; whereas, the [mental] processes of the scholar remain relatively stable. The study of [mental] processes appears to be an appropriate procedure for curriculum development. ( p. 1 )

Therefore, the 17 mental processes identified by Halfin produced the basis for the first round of Delphi inquiry ( see Table 1) .


The first probe provided participants with identified mental process descriptors followed by a definition of each process. Panelists used their technological expertise to respond to each of the mental processes. They were asked to rate their level of agreement with each process by marking the 17 statements according to a five-point Likert-type scale (1=not important, 2=slightly important, 3=moderately important, 4=important, 5=very important). Participants were also asked to identify other mental processes that they believed were important and should be considered when examining technological problem-solving. Any additional mental methods were integrated into subsequent probes for review and analysis. This same general process was employed for Rounds 2 and 3, with the added opportunity for each reviewer to compare their scores with the mean, median, and frequency distribution of the entire Delphi panel. If individual responses to any item differed more than one point from the group mean, they were asked to provide a rationale for their deviation.

Treatment of Data

The Delphi process is typically considered a qualitative research procedure ( Linstone & Turoff, 1975) . Therefore, most studies using the Delphi method employ both descriptive and ordinal statistics to relate findings from previous rounds back to the panel of experts ( Miller & Rojewski, 1992 ; Rojewski & Meers, 1991 ; Volk, 1993 ; Wicklein, 1992, 1993) . Data analysis relied on a variety of descriptive statistics to measure panel consensus including mean, median, standard deviation, and rank.

Table 1
Original mental methods for technology education curriculum used with the three round Delphi process

mental methods

Analyzing The process of identifying, isolating, taking apart, breaking down, or performing similar actions for the purpose of setting forth or clarifying the basic components of a phenomenon, problem, opportunity, object, system, or point of view.
Communicating The process of conveying information (or ideas) from one source (sender) to another (receiver) through a media using various modes. (The modes may be oral, written, picture, symbols, or any combination of these.)
Computing The process of selecting and applying mathematical symbols, operations, and processes to describe, estimate, calculate, quantity, relate, and/or evaluate in the real or abstract numerical sense.
Creating The process of combining the basic components or ideas of phenomena, objects, events, systems, or points of view in a unique manner which will better satisfy a need, either for the individual or for the outside world.
Defining problem(s) The process of stating or defining a problem which will enhance investigation leading to an optimal solution. It is transforming one state of affairs to another desired state.
Designing The process of conceiving, creating inventing, contriving, sketching, or planning by which some practical ends may be effected, or proposing a goal to meet the societal needs, desires, problems, or opportunities to do things better. Design is a cyclic or iterative process of continuous refinement or improvement.
Experimenting The process of determining the effects of something previously untried in order to test the validity of an hypothesis, to demonstrate a known (or unknown) truth or to try out various factors relating to a particular phenomenon problem, opportunity element, object, event, system, or point of view.
Interpreting data The process of clarifying, evaluating, explaining, and translating to provide (or communicate) the meaning of particular data.
Measuring The process of describing characteristics (by the use of numbers) of a phenomenon problem, opportunity, element, object, event, system, or point of view in terms which are transferable. Measurements are made by direct or indirect means, are on relative or absolute scales, and are continuous or discontinuous.
Modeling The process of producing or reducing an act, or condition to a generalized construct which may be presented graphically in the form of a sketch, diagram, or equation; presented physically in the form of a scale model or prototype; or described in the form of a written generalization.
Models/prototypes The process of forming, making, building, fabricating, creating, or combining parts to produce a scale model or prototype.
Observing The process of interacting with the environment through one or more of the senses (seeing, hearing, touching, smelling, tasting). The senses are utilized to determine the characteristics of a phenomenon, problem, opportunity, element, object, event, system, or point of view. The observer's experiences, values, and associations may influence the results.
Predicting The process of prophesying or foretelling something in advance, anticipating the future on the basis of special knowledge.
Questions/hypotheses Questioning is the process of asking, interrogating, challenging, or seeking answers related to a phenomenon, problem, opportunity element, object, event, system, or point of view.
Testing The process of determining the workability of a model, component, system, product, or point of view in a real or simulated environment to obtain information for clarifying or modifying design specifications.
Visualizing The process of perceiving a phenomenon, problem, opportunity, element, object, event, or system in the form of a mental image based on the experience of the perceiver. It includes an exercise of all the senses in establishing a valid mental analogy for the phenomena involved in a problem or opportunity.

Interquartile range scores (IQR) for each item were calculated after each round and were used as a primary measure of unanimity among panel members. IQR refers to the middle 50% of a group of scores and is related to the median. Since the IQR focuses on the middle range of scores, it is less likely to be influenced by extreme scores. The IQR is sensitive to the number, but not exact location of extreme scores, providing a relatively stable measure of variability in group response ( Gravetter & Wallnau, 1988 ; Minium, 1978 ).


Our efforts were to prioritize and verify the relevance of an existing conceptual base - Are Halfin's results still relevant today? - and determine if additional methods or processes should be considered in future developments of technology education curriculum. Thus, our intent was to both confirm and extend Halfin's original results.

A three-round modified Delphi process was employed to determine the relative importance of each method and to achieve consensus among a nationally-recognized panel members. First round questionnaires contained the 17 mental methods in technology education originally proposed by Halfin ( 1973 ). Round 1 responses also generated an additional 10 methods from participants and they were included in the second and third rounds.

Round 1 questionnaires included the 17 mental methods and processes originally studied by Halfin ( 1973 ). These methods can be loosely represented by broad categories of mental activity including analysis (analyzing, computing), conceptualization (defining problems, interpreting data, managing, questioning/ hypotheses, and predicting), creativity (creating, designing, modeling, models/prototypes), investigation (experimenting, measuring, observing, testing, visualizing), and social (communication) processes ( see Table 1 ). For the most part, the additional 10 items added by Delphi participants during the first round were easily assigned to one of the 5 broad categories we established. For example, understanding contexts was viewed as a social process, innovating a creative process, and establishing need for a conceptual process. Two items, establishing need and technology review, appeared to reflect conceptual processes. Four items (customer analysis, monitoring data, researching, searching for solutions) were seen as being investigative processes. The final two items seemed to represent potentially unique contributions to the broad categorization scheme we adopted; these included the practical application of technology (transfer/transformation) and values ( see Table 2 ). We made no attempt to remove duplicative items from the 10 items added during Round 1 which may have somehow altered panelists' responses in Rounds 2 and 3. In addition, members were only asked to indicate their perceptions of the importance of these newly added items, not to measure or compare their level of importance with previously established mental processes. These decisions were made because, "it is part of the very definition of a Delphi that the results from each round dictate the nature of the following round. The nature of all of the rounds cannot be dictated at the outset" ( Moore, 1987 ).

Table 2
Added mental methods for technology education curriculum used with the three round Delphi process

mental methods

Contexts Understanding the social, cultural, organizational, etc. context for the task.
Researching The process of becoming familiar with the background information necessary to investigate the problem. Knowing what type of information to look for and where to locate it.
Searching for solutions No definition provided.
Technology review The process of evaluating the performance of a solution at an appropriate time in the future.
Transfer/transformation To transfer a process across areas or fields to new situations.
Values Understanding the role of the technicians and other's values in deciding on courses of action.
Customer analysis The process of evaluating inputs of the receiver or technology.
Innovating Taking existing "know-how" and being able to implement it in new situations.
Monitoring data The process of collecting and recording data and time conditions related to problem occurrence.
Establishing need No definition provided.

Descriptive data for each mental process are displayed, by round, in Table 3. The responses gathered from the initial round of questionnaires remained fairly consistent throughout subsequent administrations. Differences in rank order were altered with the addition of the 10 additional methods or processes generated by panelists during Round 1. However, the relative position of each item, in terms of importance, appeared to remain fairly consistent from the first to final round. In almost every instance, indicators of variability (standard deviation and IQR) were reduced with each successive round, which is indicative of increased group consensus ( see Table 3 ).

Round 3 mean scores ranged from a low of 3.05 (moderate importance) to a high of 5.00 (very important). The overall mean score for the 27 mental methods (M=3.95) indicated that participants were not as discerning as we had originally hoped, i.e., none of the methods were deemed unimportant. Therefore, the group standard deviation (SD=.41) was used to help organize and distribute responses along a normal distribution. Three groupings were formed including very important - items with mean scores equal to or greater than 4.36 (1 standard deviation above the mean for all 27 mental methods), important - items with mean scores greater than 3.54 but less than 4.36 (within 1 standard deviation of the mean calculated for all mental methods), and moderately important - items with a mean score less than or equal to 3.53 (1 standard deviation below the mean for all 27 mental processes). Mental processes with mean scores within 1 standard deviation were further arranged into two subgroups to reflect items either above or below the mean score ( see Table 4 ).

Each of the five categories of mental processes - analysis, conceptualization, creativity, social, and investigative - are represented by one of the six highest ranked items (the fourth and fifth highest ranked items reflect creative processes). However, three of the next four ranked items (items ranked 7-10) belong to the investigative processes category. The two additional categories that emerged from the initial round and were considered to be potentially unique contributions to our tentative framework, practical application and values, were not included in those items rated most highly by participants.

Table 3
Response of Technology Panel Members to Proposed Mental Methods for Each Delphi Round
Mental Methods Round 1 Round 2 Round 3
M SD Mdn Mo IQR Rank M SD Mdn Mo IQR Rank M SD Mdn Mo IQR Rank
Analyzing 4.44 .65 5 5 4-5 2 4.57 .59 5 5 4-5 2 4.59 .50 5 5 4-5 2
Communicating 4.28 .84 4 5 4-5 3 4.39 .72 4 4 4-5 3 4.50 .60 5 5 4-5 3
Computing 3.28 .84 3 3 3-4 16 3.26 .69 3 3 3-4 26.5 3.05 .49 3 3 3 27
Creating 3.96 .98 4 4 4-5 6.5 3.96 .77 4 4 4 12 4.00 .54 4 4 4 11
Defining Problem (s) 4.68 .63 5 5 5 1 4.87 .34 5 5 5 1 5.00 .00 5 5 5 1
Designing 4.04 1.06 4 5 3-5 4.5 4.22 .85 4 5 4-5 5 4.27 .70 4 4 4-5 4
Experimenting 3.92 .81 4 4 3-4 8 4.00 .60 4 4 4 9 4.09 .43 4 4 4 8.5
Interpreting Data 3.96 .54 4 4 4 6.5 3.96 .37 4 4 4 12 3.96 .38 4 4 4 14.5
Managing 3.72 1.02 4 3 3-5 12 3.70 .77 4 3 3-4 19.5 3.91 .61 4 4 3-4 18
Measuring 3.56 .82 4 4 3-4 13.5 3.61 .66 3 3 3-4 23 3.64 .58 4 4 3-4 23.5
Modeling 3.56 1.08 3 3 3-4 13.5 3.30 .82 3 3 3-4 25 3.18 .80 3 3 3 3
Model (protorype) construction 3.08 1.12 3 2 2-4 17 3.26 1.01 3 3 3-4 26.5 3.27 .70 3 3 3-4 25
Observing 3.88 1.01 4 3 3-5 9 3.91 .79 4 4 3-4 15 4.09 .43 3 4 4 8.5
Predicting 3.44 .77 4 4 3-4 15 3.52 .59 4 4 3-4 24 3.68 .57 4 4 4 21.5
Questioning & hypothesizing 4.04 .74 4 4 4 4.5 4.09 .42 4 4 4 7.5 4.09 .29 4 4 4 8.5
Testing 3.80 1.04 4 4 3-5 10.5 3.70 .70 4 4 3-4 19.5 3.96 .64 4 4 4 14.5
Visualizing 3.80 .76 4 4 3-4 10.5 3.83 .58 4 4 3-4 17 3.96 .38 4 4 4 14.5
*Contexts - - - - - - 3.73 .46 4 4 3-4 18 3.73 .46 4 4 3-4 20
*Customer analysis - - - - - - 4.18 .59 4 4 4-5 6 4.18 .59 4 4 4-5 6
*Establishing need - - - - - - 3.96 .72 4 4 3-4 12 3.96 .72 4 4 3-4 14.5
*Innovation - - - - - - 4.23 .53 4 4 4-5 4 4.23 .53 4 4 4-5 5
*Monitoring data - - - - - - 3.68 .57 4 4 3-4 21 3.68 .57 4 4 3-4 21.5
*Researching - - - - - - 4.09 .61 4 4 4 7.5 4.09 .61 4 4 4 8.5
*Searching for solutions - - - - - - 3.86 .71 4 4 4 16 3.86 .71 4 4 3-4 19
*Technology review - - - - - - 3.96 .49 4 4 4 12 3.96 .49 4 4 4 14.5
- - - - - - 3.96 .58 4 4 4 12 3.96 .58 4 4 4 14.5
*Values - - - - - - 3.64 .58 4 4 3-4 22 3.64 .58 4 4 3-4 23.5
* These 10 items without descriptive data for Round 1 were added to the original 17 mental methods and processes by Delphi panel
members and included in the second and third rounds of data collection.
Table 4
Relative importance of proposed mental methods for technology education

Level of
Response Level Mental Methods

Very important 4.37 - 5.00 * Defining (5.00); Analyzing (4.59); Communicating (4.50)
Impotant 3.95 - 4.36 Designing (4.27); Innovation (4.23); Customer analysis (4.18); *Experimenting, *Observing, *Ques/Hypothese, * Research (4.09); Creating (4.00); Establish need , *Interpreting, * Technology review , *Testing, * Transfer , *Visualizing (3.96)
Important 3.54 - 3.94 Managing (3.91); * Searching (3.86); Contexts (3.73); Monitoring , *Predicting (3.68); Measuring, Values (3.64)
Moderately Important 0.00 - 3.53 Modeling/Prototype (3.27); Modeling (3.18); *Computing (3.05)

* Indicates that consensus was achieved on that particular mental method. Consensus was defined as an interquartile range (i.e., the spread of the middle 50% of scores) equal to or less than 1.00. Participants reached consensus on a total of 14 items. Underlined mental methods are those that were added by panel members during the first or second round of the Delphi process. Figures in parentheses represent the mean score for each item at the conclusion of the third and final Delphi round.

Panelists considered the mental process defining problems to be the most important method for technology problem solving. Analyzing and communicating processes were also deemed to be critical technology-related mental methods by participants. Conversely, three methods (model/prototype construction, modeling, and computing) were considered only moderately important. The additional 10 items added by panel members in the first round were, for the most part, not perceived as highly important. However, several exceptions were noted; the mental processes customer analysis, innovating, and researching each had a final round mean score greater than 4.00 and ranked 6, 5, and 8.5, respectively.


Often, curriculum developers in technology education start out to create state-of-the-art instructional activities, only to find that their curriculum materials are out of date soon after they are published. This process has been repeated over and over again with countless state-sponsored curriculum guides and materials. Taxpayers, through local, state, and national departments of education, have contributed millions of dollars over the past 10 years to support the latest forms of technology education within their communities. The learning environments created from these monies usually reflect a narrow type of vocationalism which concentrates primarily on technical skill preparation. This approach requires the curriculum to be constantly in flux, modified in an attempt to incorporate the latest emerging technologies. As a result, both teacher and students experience confusion and inconsistency in program delivery.

As a profession, technology educators remain enamored by the gadgetry of technology, with only limited reflection on the deeper educational needs of students. Rather than contribute to helping students develop the higher order thinking skills needed to solve problems within the broad technological aspects of our society, we concentrate on specific technical applications of a few select technologies (e.g., robotics, CAD, desktop publishing, lasers). Students are often left with minor technical skills and an unreflective assumption that all technology is good. Instead of helping students develop a balanced perspective of the impact that technology has on society, we often present it as an independent power in and of itself. In a sense, technology education might even contribute to the creation of a new form of totalitarianism. The idea of human progress has been replaced by technological progress. Therefore, the new goal of society is to accommodate ourselves to the requirements of technology or, in Postman's ( 1992 ) terms, the creation of a "technopoly".

It was the intent of this study to prioritize and extend the list of select mental methods/processes of inquiry considered essential for solving technological problems, i.e., to think for one's self. It is possible that the mental processes we examined might serve as one basis for development of a unifying conceptual framework for technology education curriculum. The stability that a mental processes approach to curriculum development could bring to the field makes this strategy a potentially powerful tool in creating appropriate learning objectives for all students. By focusing technology education curriculum goals on mental processes rather than specific technical procedures, students could be encouraged to develop a more reflective, transferable approach to solving problems. Students would learn to employ appropriate levels of technology to solve problems rather than relying on preset curriculum structures that prescribe simple step-by-step procedures. A mental processes-based curriculum could provide a stimulating learning environment where students would be intellectually challenged using a wide variety of laboratory configurations. The result would be a consistent curriculum that would give students the most important tool to achieve a successful career, a strong disciplined mind.


The mental processes examined in this study can serve as a foundational base for future curriculum planning for technology education. By integrating these processes, technology educators can create comprehensive approaches to problem solving that are not limited by tools, equipment, and laboratories. Further efforts are needed to establish the viability and practicality of a mental processes approach for technology education curriculum. The following recommendations are proposed as possible next steps in this design procedure.

  1. Distill or synthesize the 27 mental processes we examined into a smaller, more manageable group of items that can be incorporated into technology education curriculum. Efforts are underway to merge the current list of mental processes into a more inclusive list that will be practical for classroom teachers. A factor analysis approach could be used to establish a smaller number of representative mental processes from a nationwide input of technology educators.
  2. Develop specific instructional activities that incorporate mental processes with appropriate technologies for secondary technology education programs. Current efforts to create a series of learning activities that incorporate the mental processes for technology education students exist where students are actively engaged in the learning process.
  3. The mental methods included in this research could be used to evaluate current technology education curriculum. Existing curriculum could be analyzed to determine the extent that mental methods of inquiry are emphasized. Based on this evaluation, educators could enhance or revise their existing curriculum to better address identified mental methods. The degree to which this can be achieved is unknown at this time, however, further research has been proposed that will address this concern and, hopefully, provide specific data regarding this curriculum modification.

Finally, we did not intend to create a hierarchical mechanism for problem-solving or curriculum development. We do not consider the list of mental methods we examined as a necessarily exhaustive one, nor are all methods equally important or applicable in all technological problem situations. However, our analysis does provide educators with an indication of important mental processes that could be incorporated into technology education curriculum.


Wicklein and Rojewski are both Associate Professors in the Department of Occupational Studies at the University of Georgia, Athens.


Davies , P. (1995). About time: Einstein's unfinished revolution . New York: Touchstone.

Gravetter F. J., & Wallnau, L. B. (1988). Statistics for the behavioral sciences (2nd ed.). St. Paul: West.

Halfin H. H. (1973). Technology: A process approach . Unpublished doctoral dissertation, West Virginia University, Morgantown.

Hawking S. W. (1990). A brief history of time . New York: Bantam.

Horadal P. (1987). The future of vocational education in Thailand toward the year 2009 A. D. using the Delphi approach (Doctoral dissertation, Kent State University, 1987). Dissertation Abstracts International , 49 (04), 802A.

International Technology Education Association. (1999). Curriculum materials for the technology teacher . Reston VA: Author

International Technology Education Association. (1996). Technology for all Americans: A rationale and structure for the study of technology . Reston VA: Author.

Linstone H. A., & Turoff, M. (Eds.) (1975). The delphi method: Techniques and applications . Reading, MA: Addison-Wesley.

Miller M. T., & Rojewski, J. W. (1992). Integrating technology in the liberal arts: Perspectives of liberal arts administrators. Journal of Studies in Technical Careers , 14 (2), 115-126.

Minium E. W. (1978). Statistical reasoning in psychology and education (2nd ed.). New York: Wiley & Sons.

Moore C. M. (1987). Group techniques for idea building . Newbury Park, CA: Sage.

Postman N. (1992). Technopoly: A surrender of culture to technology . New York: Vintage.

Raywid M. A., Tesconi, C. A., & Warren, D. R. (1987). The challenge of purpose. In G. Hass (Ed.), Curriculum planning: A new approach (pp. 15-19). Boston: Allyn & Bacon.

Rojewski J. W., & Meers, G. D. (1991). Research priorities in vocational special needs education. Journal for Vocational Special Needs Education , 13 (2), 33-38.

Sackman H. (1975). Delphi critique . Lexington, MA: DC Heath.

Satchwell R., & Dugger, W. (1996). A united vision: Technology for all Americans. Journal of Technology Education , 7 (2), 5-12 .

Van de Ven A. H., & Delbecq, A. L. (1974). The effectiveness of nominal, Delphi, and interacting group decision making processes. Academy of Management Journal , 17 (4), 605-621.

Volk K. S. (1993). Technology education in developing countries: Curriculum guidelines for program design and evaluation. Journal of Industrial Teacher Education , 30 (4), 68-85.

Weaver W. T. (1971). The Delphi forecasting method. Phi Delta Kappan , 52 (5), 267-272.

Wicklein R. C. (1992). Curriculum development in technology education. The Technology Teacher , 51 (5), 2325.

Wicklein R. C. (1993). Developing goals and objectives for a process-based technology education curriculum. Journal of Industrial Technology Education , 30 (3), 66-80.

Wicklein R. C. (1997). Curriculum focus for technology education. Journal of Technology Education , 8 (2), 73-80 .

Zuga K. F. (1989). Relating technology education goals to curriculum planning. Journal of Technology Education , 1 (1), 34-58.