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Current Editor: Dr. Robert T. Howell  bhowell@fhsu.edu
Volume 37, Number 4
Summer 2000


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Linking Cognitive Science Theory and Technology Education Practice: A Powerful Connection Not Fully Realized

Michael A. DeMiranda
Colorado State University
James E. Folkestad
Colorado State University

Not since the launching of Sputnik in late 1950s have proponents been so vocal about the scientific and technological preparation of today's youth. The final decades of the 20th century witnessed a flurry of national reports lamenting the problems confronting science and the study of technology in this country (U.S. House of Representatives, One Hundred Fifth Congress, Second Session., March 24, 1998). Most reports suggested that a majority of students are losing interest in science and technology and are falling behind their worldwide peers in achievement in these school subjects (Exxon Education Foundation, 1984; International Association for the Evaluation of Educational Achievements, 1987 & 1988; National Assessment of Educational Progress [NAEP], 1996; National Science Board Commission on Pre–college Education in Mathematics, Science, & Technology, 1983; National Science Foundation & the U.S. Department of Education, 1980).

Since 1969, the National Assessment of Educational Progress, also known as "the Nation's Report Card," has assessed the academic performance of fourth, eighth, and twelfth graders in a range of subjects. The National Assessment of Educational Progress report presents the results of a national assessment of what the nation's 4th–, 8th–, and 12th–grade students know and can do in areas like science and technology. Results are reported for 44 participating states and jurisdictions at the 8th grade level. Students are assessed on their knowledge of earth, physical, and life sciences, as well as for their conceptual understanding, ability to conduct scientific investigations, and practical reasoning skills related to science and technology. The NAEP assessment is comprised of multiple–choice andextended–response questions and requires students to perform at least one hands–on activity.

The NAEP study provides evidence that most students can recall simple facts but serious deficiencies occur at the higher levels of scientific and technological thinking. The NAEP study differentiates five levels of student proficiency in the subject matter tested, ranging from level 150, which in the case of science and technology indicates basic proficiency in simple recall of facts, to level 350, which would indicate advanced multi–step problem–solving and reasoning. NAEP results confirm that students have command of lower–level (150 to 200) rote skills such as recalling facts or simple computation in math. However, far fewer students are able to apply their knowledge to solve more complex problems requiring multiple steps that have no obvious immediate answers. The importance of the NAEP results for teacher educators is that this report documents weakness in students' abilities to apply the facts they know, interpret data, evaluate experimental designs, and use specialized scientific and technologic knowledge to draw conclusions.

Bruer (1993) argues that "the NAEP results indicate that current curricula, teaching methods, and instructional materials successfully impart facts and rote skills to most students but fail to impart high–order reasoning and learning skills" (p. 5). He concludes that the new expectations for higher level outcomes will require teacher education programs to incorporate new methods of teaching, new innovative instructional materials, and new approaches to learning and instruction. This paper examines four cognitively–based models of instruction that when linked to exemplary practice in technology teacher education can form a powerful connection not yet realized in our field.

Traditional Instruction

Goodlad (1993) conducted one of the most recent and comprehensive studies of American schooling. Goodlad and his team of 20 trained data collectors observed over 1,000 classrooms representative of all grade levels across elementary, middle, and secondary schools. The content of school instruction and how teachers teach constituted one focus of Goodlad's research.

Goodlad found, in general, that teachers are in control, the center of activity, and they out–talk the entire class by a ratio of three–to–one. Teachers were observed spending most of their time lecturing or monitoring students engaged in individualized seatwork. Observational data collected during the study confirmed that student passivity, individual performance, and teacher control were emphasized, while student participation, cooperation, and peer–learning were de–emphasized.

Goodlad concludes that there is an emphasis on direct instruction with strong teacher control throughout traditional instructional practice. The teacher lectures and the students listen. There is little discussion and few opportunities for students to contribute their own feelings, ideas, or concerns during the course of instruction.

In technology education, Petrina, (1993, 1994) argued similarly that using a modular approach to technology education is simply a contemporary exhibition of teaching machines and programmed learning of the 1960s. These teaching approaches, he asserts, emphasize stability, certainty, and cast the student in a passive role subject to predetermined outcomes. While the assertions of Petrina and the findings from Goodlad are congruent, as stated, this paper seeks to focus on the potential of technology educators to look beyond the myopic view of the modular debate and influence future technology teachers by connecting practice in technology education with well–researched theories of learning and instruction grounded in the cognitive sciences.

A Need to Connect

Through experience in classroom teaching and refining instructional methods by trial and error, technology educators have witnessed the success of hands–on lab–based problem solving instruction. However, they have lacked a powerful connection between well–researched theories on learning and instruction that could help validate their experiences and support their instructional methods. Research grounded in theory of the cognitive sciences can provide technology educators with a strong understanding and foundation in support of their experiences.

Instruction grounded in cognitive science transfers the self–regulation and monitoring of cognitive functions like memory, process, control of thinking process, appropriate application, and the "cognitive tools for thinking and learning" from the teacher to the student. A central theme resonating across the cognitive science literature that is applicable to technology education is, when instruction and instructional materials are designed, they should be designed to help students acquire and integrate the cognitive and metacognitive strategies for using, managing, assessing, reorganizing, and discovering knowledge. This implies that all students and indeed technology students must be active, collaborative participants in framing technology related questions, designing and participating in data collection and analysis procedures, and free to predict and inquire about observed outcomes (Brown, 1992).

Technology education curriculum has evolved from a rich tradition of hands–on, team–oriented, project–based learning commonly associated with activities in industrial arts. These teaching methods have for years been touted as the strength of technology programs. Although cognitive science research has provided evidence to support the validity of this claim, technology education instructors continue to focus on their antidotal evidence, which they have collected throughout time. Technology education instructors need to focus on current cognitive research by seizing the opportunity to illustrate the significance and importance of their programs and instructional methods (Sirotnik & Soder, 1999).

Connecting Instructional Practice in Technology Education to Cognitive Science Research on Learning and Instruction

The consonance between research recommendations from the cognitive sciences and models of learning and instruction derived from exemplary practice in technology education is shown in Figure 1. The foundation of cognitively–based models hold three elements of learning and instruction common across the various instructional strategies:

  1. the learner actively engages the learning process and content;
  2. the instructional design requires the learner to reflect on and use existing structures of knowledge to guide and further his or her learning; and
  3. classrooms are communities of learning where knowledge and information are shared openly in an environment that values participation and interaction between students, teachers, and external sources of knowledge outside the classroom.

It is important for technology educators to understand these three elements and internalize them so that each becomes an integral part of their instruction. To make this connection and understanding its importance relative to teaching each model of instruction is reviewed and practical examples are given of how each model is applied in a educational context.

Figure 1
Comparison of exemplary technology education models of instruction
to research recommendation from the cognitive sciences

table 1
table 1 (continued)

Collaborative Learning

Resnick (1987) asserts that instructional programs that emphasize social interaction demonstrate promising results in fostering higher–order thinking skills. Resnick argues that thinking aloud in front of peers and teachers fosters a classroom ethos that encourages discourse and critical analysis. She contends that higher–order discourse and thought are cultivated by participation in social communities that value thinking and judgment. Resnick commented that "communities communicate these values by making available many occasions for such activity and responding encouragingly to expressions of questioning and judgment" (p. 43).

Minstrell (1984) provides an example of a cooperative classroom ethos in his report on discussion periods in high school physics that are applicable to technology education. He found that during discussion periods, students offer explanations to observed physics or technological problems or phenomena. They are encouraged to present counter–arguments to problem solutions and explanations offered by other students. Minstrell discovered that during discussion time he is strictly a facilitator, offering no facts, opinions, or arguments himself. When the counter–arguments and the responses have run their course, he transitions into new strategies such as demonstration of plausible solutions or student participation in collaborative laboratory experiments. Minstrell argues that the collaborative activities or demonstrations are designed to induce the conceptual change required to correct student misconceptions of the physical and technological phenomena they observed or experienced.

Resnick (1987), Boyer, (1995), Brown (1992), and Minstrell (1984) affirm that collaborative learning and a cooperative classroom ethos are designed to foster and encourage student reflection and communication. In many forms of collaborative learning emphasis is placed on the student's ability to discover, share, and use knowledge, rather than just retain it. Thus, technology teachers who use collaborative learning in their classrooms work to encourage students to be partially responsible for creating, monitoring, and evaluating their progress. Collaborative learning strategies that extend past structured modular time periods that free students to make inquiries and create without curricular boundaries, when employed in technology education classrooms, are powerful cognitively–based tools for instruction.

Models of instruction that encourage collaboration between learners are varied, yet most rely on a change of the classroom learning environment and instructional materials to create learning communities and a classroom ethos designed to foster active–collaborative learning. As is illustrated in Figure 1, technology education instruction can be consistent with this classroom ethos. Roper (1989) provides evidence that collaborative learning activities can make up more than 40% of allocated instructional time in technology related subjects. Specifically, students in technology education can work in collaborative learning environments, in the conduct of research, in which their functions and knowledge bases are differentiated between the members of the group and among the groups themselves. This form of collaboration and socially–distributed expertise reduces the cognitive load placed on individual students. Their coming together with differentiated knowledge and functions facilitates the building of knowledge structures larger than a student could cognitively construct him or herself while working independently.

Socially–Distributed Expertise

Brown, Ash, Rutherford, Nakagawa, Gordon, and Campione (1993) report that ways of knowing are strongly connected to the social, cultural, and physical situations students experience in learning. A cognitively–based model of instruction emphasizing socially–distributed expertise in the classroom is designed to foster a community of learners who harbor several qualities. Brown et al. describe the first quality of a classroom ethos associated with socially distributed expertise as having an atmosphere of individual responsibility coupled with communal sharing. This implies that students and teachers each have ownership of certain forms of expertise, but no one has it all. The responsible members of the group share among and between themselves the expertise they have and are responsible for finding out about needed knowledge. Through group participation and collaborative forums, the group collectively uncovers and unifies their expertise to develop a cohesive body of knowledge possessed by no one individual. The atmosphere of joint responsibility and a classroom ethos of collaboration are essential for this enterprise.

Technology education classrooms can capitalize on socially–distributed models of instruction where students come to recognize that even experts do not always know the answers. Thus, respect between peers, teachers, and experts outside the classroom are earned through responsible participation in the knowledge–building process. Students, teachers, and outside experts listen to each other and come to respect the contributions each bring to the group.

Discourse, constructive discussion, questioning, and criticism is the prevailing mode rather than the exception in socially–distributed classrooms. Meaning, problem solutions, and the framing of critical issues are negotiated and re–negotiated as members of the group develop and share expertise. For example, in a socially distributed learning group in technology education, students come to construct new understandings, developing together a common body of knowledge and a common voice that can be shared with other groups in the class. In the process of participating in socially–distributed learning activities, students contribute to authentic learning of technological content while fostering a sense of ownership as they assume roles within learning groups.

An example of a socially–distributed model of instruction that is well suited for a technology education classroom is the Jigsaw Method adapted from Aronson (1978). In the jigsaw method of cooperative learning students are assigned part of a classroom topic to research and learn, then teach to others in a group. In effect, students are responsible for doing collaborative research and sharing their expertise with their peers within and between classroom groups. In the jig saw method, each of the sub–groups within a class regroup to form a collective body of experts. For example, if a class were divided into five learning groups with the task of researching, designing, and constructing a model of a light rail transportation system for their city, each group would hold one–fifth of the information related to the task. Each student in a learning group is a resident expert on one part of the transportation system; they teach it to others and prepare questions for other groups on topics related to their area of responsibility. Collectively, all five learning groups take on the completed task of designing the transportation system.

Brown et al. (1993) found that students in socially–distributed models of learning are far from being passive recipients of incoming information, rather, students take on the role of active researcher and teacher, monitoring their own progress and that of others when they adopt the role of a constructive critic. The constructive critic role in technology education is the self–regulating role students assume when they assess, evaluate, question, and control the work in which they are engaged. Teachers in socially–distributed classrooms change from managers and didactic teachers to models of active learning, by coaching and guiding students. Therefore, socially–distributed expertise in classrooms can contribute to cognitively–based models of instruction in technology education.

Design/Engineering

diSessa (1992) asserts that viewing and cultivating the student as a designer/engineer in science and technology supports productive–based activities in classrooms. diSessa argues that activities requiring designing and constructing engage the student in the externalization of physical artifacts that present many opportunities for reflection, debugging, and keeping the goals of the activity in focus. diSessa found that students engage in self–evaluation as a result of the activity structure by determining how well their designed object works. He reports that a design/engineering approach provides ample opportunities for students to collaborate and share. diSessa explains that the type of sharing encountered in the design/engineering approach is consonant with the instructional benefits of socially–distributed instructional models.

A benefit of engaging the student as designer/engineer is the student's personal investment in the learning enterprise. diSessa (1992) and Brown (1993) confirm that the development of a product or expert knowledge can generate sustaining effects through personal or group pride in ownership. The development of products in the design/engineering model of instruction provides multiple opportunities for students to cooperate and share. Analogous with socially–distributed methods of classroom instruction, some design and construction activities are too big for any individual to accomplish alone. diSessa found that big design projects allow many slots for individuals with different skills and expertise to participate effectively. Technology education design and technology (D&T) activities are consistent with the design/engineering approaches to instruction in the cognitive science tradition. In D&T activities some students may take on primary responsibility for design, others for construction, and others for evaluation and testing. Thus, the design/engineering model represents a method of instruction applicable to technology education that is consonant with a cognitive approach to teaching learning.

Technology education students can be engaged in design/engineering activities that require them to design and construct models that aid in the explanation of technological principles being studied. For example, in the study of resistance, students would be challenged to design and construct a small sliding sled capable of holding a 5–kg weight. Students would be responsible for collecting data on the amount of force required to pull the sled at a constant speed across the surface of rough and smooth materials using a spring balance. Students would be required to explore, apply, and present solutions for reducing the resistance created by the varying surface conditions. In the process, the power of design/engineering approaches resides in motivating learners to build on ideas and intuitions they bring to the learning environment to construct functional, qualitative representations that work in demonstrating the physics principles effecting the technology under study.

Project–Based Learning

Pea and Gomez (1993) argue that science and technology instruction must focus on understanding and supporting learning in entirely new ways. Pea and Gomez assert that the model of most educational settings is learning–before–doing. They maintain that attention in science and technology education classrooms must be focused on learning–in–doing. Learning–in–doing is a model in which learners are increasingly involved in the authentic practice of applying technology through learning conversations with other students and activities that include and extend past educators and peers to expert practitioners in the field who support work based learning outside the classroom.

Learning–in–doing requires interaction among groups that traditionally have been separated by the institutional boundaries of work and school. Pea (1993) argues that advances in high–performance computing and Internet communication offer enormous potential for linking these communities in meaningful ways for learning. Pea found that relying on information networks and multimedia services create what he calls distributed multimedia learning environments (DMLE). Pea reports that DMLE's in science and technology support project–enhanced technological learning. This means that experts in the field can be accessed by students and consulted with during the completion of a project.

This project–based approach to learning establishes collaborative technology learning environments, or "collaboratories," that enable project–enhanced science/technology learning (PES/TL) among remote project partners using advanced telecommunication networks. PES/TL extends the collaborative reach of technology education classrooms to include widely dispersed expertise among learners, teachers, scientists, and learning researchers. For example, a collaborative project with technologists at the National Center for Super Computing Applications or NASA provides learners with access to subject–matter experts, visualization tools, and vast databases in the field of atmospheric technology. Students work collaboratively in project investigations on topics such as severe storms, weather fronts and air pressure systems; ozone depletion trends; and global warming.

Student participants in PES/TL environments refine questions and select project topics, design procedures for data collection, and conduct sense–making activities with their data, culminating in results such as multimedia reports of their project investigations that can be shared among technology education classrooms or student research teams across the country.

The authentic activity structure of technology education combined with a large portion of instructional time allocated to student collaboration fosters a classroom environment that favors project–based student inquiry assignments. Pea (1993) reported that in a "project–enhanced" learning environment students are able to frame questions and propose solutions based upon their own research and experimentation. One of Pea's primary assertions is that scientific and technologic inquiry should extend beyond the classroom and engage students in project–based activities in which teams of student researchers and off–school–site experts interact to examine common problems collaboratively and seek solutions to problems.

The Challenge

Although a link between cognitive science research on teaching and learning and the potential for exemplary technology education instruction has been illustrated above; it is unlikely that all technology education programs meet the standard of moving theory into practice as is described. Furthermore, most would agree that there is always room for improvement in classroom instruction. The challenge of connecting cognitive science theory to technology education teaching requires those teacher education programs to teach new methods of instruction and adopt new approaches to student learning in technology education. This education should include an assessment tool that will indicate the degree to which instructors are applying cognitive science theory in the classroom and instruction on how to overcome obstacles that prevent the use of these teaching models. Additional research and development should be conduct on this topic. Two fundamental questions central to this issue include:

  1. To what degree is cognitive science theory being practiced in the classroom?
  2. How do I overcome obstacles to using cognitive science principles in the classroom?

Determining the Degree to Which Cognitive Science Theory Is Being Practiced in the Classroom.

In order to make a self–evaluation of how your course teaching is linked with the cognitively–based model of instruction, teachers and teacher educators should reflect on the following questions. Higher estimations indicate a higher degree of alignment with cognitive model.

  1. What percent of time does each student spend working with classmates?
  2. What percent of assignments foster student reflection and shared knowledge?
  3. What percent of your time (as instructor) do you spend facilitating versus lecturing?
  4. What percent of classroom activities are project–based and design–based?
  5. What percent of your classroom activities are learning–in–doing versus learning–before–doing?
  6. What is directing the learner, scripted curriculum, or student generated inquiry?

Primary Obstacles to Using Cognitive Science Principles for Instruction

Sternberg and Williams (1998) and Sternberg (1986) assert that the marriage between cognition and instruction in the cognitive sciences requires teachers to adopt two theories, one of cognition and one of instruction. For technology teachers this means adopting a cognitive perspective that students are capable of more than following a scripted curriculum with defined learning times, questions, and answers. Rather, students are capable of self–regulation and monitoring of cognitive functions like memory, process, control of thinking process, framing questions, and appropriate application, use, and management of technology.

A second obstacle teachers must overcome is the adoption of a cognitive perspective to instruction. Instruction should be designed to help students acquire and integrate the cognitive and metacognitive strategies for using, managing, assessing, reorganizing and discovering knowledge. This implies that all students and indeed technology students must be active, collaborative participants in framing technology related questions, designing and participating in data collection, analysis procedures, and free to predict and inquire about observed outcomes (Brown, 1992).

This perhaps represents our greatest challenge. Attempts to apply psychological theories to education, particularly in teacher education programs can falter on the translation of the theory into educational practice. It is not often clear whether the lack of success is due to the inadequacy of the theory or the inadequacy of the implementation of the theory. Sternberg (1998) and Bransford and Vye (1989) suggest some characteristics that educators can instill in teacher candidates that will aid them in developing the teaching skills necessary to implement the previously discussed cognitive instructional methods. Several recommendations are directly applicable to teaching technology from a cognitive perspective.

  1. Technology teachers must assume the role of teacher as coach. This requires teachers to monitor and regulate student attempts at problem solving so they don't go too far into the wrong solution yet allowing students to have the opportunity to experience the complex process and emotions of real problem solving.
  2. Technology teachers help students learn to reflect on the processes used while designing, constructing, testing, and solving problems,(learning by doing), and contrast their approaches with those used by others in the class.
  3. The role of the technology teacher as computer–based instructional tutor with students working in structured groups of two is not being a facilitator or coach. Technology teachers must learn to use a classroom resource that is often underused—other students. By learning to create climates that foster cooperative learning, it becomes possible to help students engage in active problem solving and reflection even though there is only one teacher and many students (see also Slavin 1987, Whimbey & Lochhead 1980).

While these recommendations are far from exhaustive they do represent a point of departure from which to stimulate discourse and reflection among technology teacher educators about grounding the teaching of technology in the well–researched tradition of the cognitive sciences.

Conclusion

In summary, the design characteristics and instructional practice found in many exemplary technology education classrooms accord closely with the cognitive science view of learning, knowledge, and instruction. Specifically, technology education can align itself with the view of the student as an active participant in the instructional process, free to reflect, monitor, evaluate, and engage in self–regulation. Extensive uses of socially–distributed expertise in the classroom and the use of projects in the form of student design constructions support a collaborative classroom ethos. The ability of a student to manipulate and create design solutions, participate in collaborative projects with his or her peers and connect technology–based activities inside the classroom to authentic practice outside of school represent powerful evidence that learning and instruction in technology education can meet the current reform demands recommended by research on learning and instruction in the cognitive sciences. Therefore, technology education instructional must continue to focus on the process of learning–in–doing rather than just doing. Society now demands that students think critically, consider all options, evaluate their choices, and develop the process to achieve the purpose or outcome of the lesson.

Although technology education has originated apart from the cognitive science research tradition, it appears remarkably suited to adopt many characteristics of a cognitive science perspective at a general level and in terms of their views of the learner, knowledge, and instruction. Specifically, there is considerable accord between technology education and how one can use the power of cognitively–based instructional models and instructional materials design such as collaborative learning, socially distributed expertise, design/engineering, and project based instruction to support the student learning of technology.

In technology education the adoption of the role of the student and teacher consistent with cognitively–based models/roles related to learning and instruction are critical to bridging theory into practice. Technology education instruction must require that the learner be highly active in the learning process and exercise considerable control in monitoring their own progress in accord with metacognitive processes. The role of the teacher must not be to monitor computers transmitting knowledge to tabula rasa receivers, but rather to nurture the building of and correcting often erroneous student representations about technology through authentic application. While the instructional characteristics that make up a technology education classroom have long been known to those who practice teaching technology, the powerful connections that exist between well researched theories on learning and instruction in the cognitive sciences and technology education are often missing. Teachers of technology have long employed instructional practices that have motivated and extended student learning in exciting classroom environments. It is no wonder that many before us have long asserted that the structure of technology classrooms engages a broad range of students in exciting ways. Perhaps, the connection between how research is intended to inform practice can come full circle in technology education. To date, though, we remain not fully connected.

Author

DeMiranda is an Associate Professor in the Department of Manufacturing Technology and Construction Management at Colorado State University, Fort Collins.

Folkestad is an Assistant Professor in the Department of Manufacturing Technology and Construction Management at Colorado State University.

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