Reconstructing Technical Instruction
Dennis R. Herschbach
University of Maryland
Technical instruction in the United States is undergoing a significant transformation. Older concepts of specific skill preparation are being replaced by a broader concept of work preparation emphasizing curriculum integration and the learning of higher order technical and academic skills. Most discussion surrounding the reform of technical instruction, however, centers on the kinds of skills that should be taught and on the various curricular models for integration (Grubb, 1995; Ramsey, Eden, Stasz, Ramsey, & Bodilly, 1995). The focus is on content and not on how content is taught and learned. Integration is conceived primarily as a curriculum problem of determining content selection.
However, it is not sufficient to identify curricular components without also addressing the specific pedagogical strategies required to engage students in the acquisition and use of higher-order learning. Largely absent from the discussion on reforming technical education is systematic attention to structuring an effective learning environment through which higher order skills can be acquired. It is an erroneous assumption that integration can be achieved through content selection alone, when probably the most important consideration is how content is taught. Integration and higher-order skill development is primarily a problem of instructional design, not content selection.
The teaching of higher-level mathematical concepts, for example, only results in rote learning if instruction is not presented in a way challenging enough to promote problem solving and higher order reasoning. Static, lock-step instruction results in rote learning regardless of the content. Conversely, even the simplest technical skills can be taught in intellectually challenging ways which engage students in inquiry and discovery. Some time ago John Dewey recognized this fact when he urged educators to eliminate the duality between the academic and technical (abstract and concrete) and between content and method and instead concentrate on the active application of learning to the solution of problems in their relevant context. Students had to learn to construct meaning, Dewey urged (1933).
Recent research in the field of cognitive psychology recognizes the importance of the learning task in promoting intellectual development. Learning is considered to be a process of knowledge construction and reconstruction, rather than knowledge recording and absorption (Resnick, 1987; 1989). The most important use of content lies not in the accumulation of information but in the distinct ways of thinking and intellectual processes reflected through its subsequent use as it is acquired and applied. This recognition has fueled the growth of cognitive research and has opened the way for the reconstruction of instruction along lines urged by Dewey. Technical instruction can be reconstructed through concepts of cognitive psychology which transform the way that learning environments are constructed and managed. The integration of academic and technical skills can be achieved in ways that engage students in the construction, use, and reformulation of knowledge across fields of inquiry.
In this article I examine the application of concepts of cognitive psychology to the reconstruction of technical instruction. A constructionist view of instructional design is presented. The purpose is to transpose concepts of cognitive psychology and instruction design onto a curricular and instructional framework for technical instruction. New ideas are emerging which promise to transform the way that we conceive of teaching and learning. Cognitive learning concepts provide a foundation on which to structure a learning environment which achieves integration and facilitates the acquisition of higher-order skills.
The general field of cognitive psychology is in a state of rapid development and flux. While there is agreement over the basic, broad theoretical outlines of how learning occurs, many specific conclusions are tentative and incomplete, and some applications of theory cannot be applied beyond a specific subject area. At the same time, some concepts also are being rapidly reformulated as research extends into new areas. Given these limitations, the attempt here is to synthesize major conclusions that have wide theoretical and experimental support, focus the findings, and relate them to the design of technical instruction. The dimensions of instruction based on cognitive concepts will be outlined.
The field of cognitive psychology is revolutionizing the way that teaching and learning are conceived (Resnick, 1989). New concepts have been introduced and old ones are being reformulated which strongly challenge traditional behaviorist concepts that have served to guide curriculum and instructional development in education and training for most of this century. In a sense, cognitive psychology is the psychology of the computer age. It provides concepts of learning and instructional vocabulary that interface with the language of the computer. The use of learning technology is facilitated through cognitive representations (Plyshyn, 1984). As Streibel (1995) observes, "the cognitive paradigm opens the door for conceptualizing teaching and learning in information processing terms" (p. 146). Cognitive psychology will play a major role in human resource development in the next century.
Constructionist Concepts of Learning
Cognitive psychology traces its origins back to the early part of the century and to the work of constructionists such as Werheimer Kohler, and Lewin (Hill, 1963), and to the theorizing of Dewey (1933). Both Piaget (Wadsworth, 1971) and Ausubel (1963) supplied important theoretical frameworks that advanced the use of cognitive psychology. Of recent interest is the work of Davydov (1988) and Vygotsky (1978). Resnick's (1987; 1989) writing has been influential.
The term "constructionist" has taken on broad meaning. While formerly it was primarily linked to the study of human learning by psychologists, today the term encompasses discussion of the meaning of knowledge itself, as well as how it is constructed and learned and how it is related to its philosophical siblings, social constructionism and the poststructural variant, constructivism. According to constructivism, knowledge is a construct relative to each individual or social/cultural group (Held, 1996). While the epistemological assumptions undergirding these positions are important to the construction of learning environments (McLellan, 1993; Streibel, 1995; Winn, 1991), cognitive psychologists in general are only beginning to delve into the philosophical ramifications of their own work (Steffe & Gale, 1995), but not without the risk of it becoming politicized (Wilson, 1998). This paper uses the more restricted meaning of constructionism, while acknowledging the importance of poststructural critique.
How Learning is Conceived
Cognitive psychology conceives of learning as a result of the organization of memory structures, referred to also as mental models or maps. Individuals actively make sense out of new knowledge by elaborating on the information already in their memory as they apply their existing understanding and previous experience to draw inferences, add new details, establish relationships, and reorganize concepts. Instruction is designed to strengthen what are thought to be classification, organizing, analytical, and problem solving structures (or networks) in the mind. These structures are thought to embody crucial process functions essential for learning and using knowledge. The specific activities that the individual engages in and the mental elaborations generated at the time of learning are thought to aid in acquiring new knowledge and facilitating the retrieval of existing knowledge. Knowledge is thus thought to be actively constructed and reconstructed by the individual in the process of acquiring and using it. As knowledge is absorbed into existing structures, it helps to reshape previously held concepts and understandings while at the same time it undergoes modification itself (Bonner, 1988; Idol & Jones, 1990; Marzano, et al., 1988; Merrill, 1991; Tennyson, 1988, 1990; P. H. Winn, 1991; Wood, 1995).
Cognitive theorists have developed a number of hypothetical models to explain learning and to guide the development of instruction (Anderson, 1983; Bonner, 1988; Idol & Jones, 1990; Tennyson, 1990; P. H. Winn, 1991). Although differences exist among theorists, most models include sensory, memory, processing, and response components (Figure 1) as a part of the overall cognitive processing system. The sensory component is the site through which information enters the cognitive system in the form of coded messages before being transferred, after a very brief period (milliseconds) to the memory. External information enters into the sensory system through auditory, visual, and tactile stimuli in response to text material, visuals, graphics, drawings, real objects, teacher direction, and other delivery forms. Incomplete learning is thought to occur when students do not pay attention, when information is poorly presented, or when
Figure 1 Model of cognitive processing
there are classroom distractions. The appropriate sensory signals simply are not received. Some students may also experience particular perception difficulties. Instruction should be clearly presented at a pace that can be absorbed by the student in order to facilitate effective reception.
The memory component is the location of both the short-term and long-term memory. The short-term, or working memory, is theorized to be the site where information is stored momentarily as it is processed and transferred into the long-term memory. The amount of information that can be stored in the short-term memory as well as the length that it can be retained is limited. It is thought that roughly three to eight items can be worked at one time, depending on the learner and the complexity of information, and information must be processed within 10 to 30 seconds or it will be lost. Information transferred and processed from permanent memory can be retrieved again, but information transferred from the external environment is permanently lost unless rapidly processed, provided again, or reconstructed.
A major instructional challenge, then, is to present information in the form and amount that facilitates rapid processing in the working memory while at the same time not exceeding capacity. The designer can build into instruction cognitive strategies which facilitate processing. These include, for example, providing cues and advanced organizers, helping students to recognize patterns, and "chunking" information into meaningful groupings.
The permanent, or long-term memory is theorized to be the site for both the storage and retrieval of all information that has been learned. Information is thought to be stored in highly organized associative structures or networks in the form of visual, auditory, tactile, olfactory, and semantic codes that assist in the retrieval and use of information. These storage structures vary among individuals in the amount or actual volume of information coded in the memory, in the structural connections making up the networks, and in the control strategies used to find and employ information. There appears, however, to be no limit on the amount of information that can be stored in the permanent memory. Assumed differences in general ability appear to be relative differences in the ability to code, form useful structures, retrieve, and employ information (P. H. Winn, 1991).
Different Kinds of Knowledge
According to cognitive psychologists, different kinds of knowledge are stored in the permanent or long-term memory. Declarative knowledge is best thought of as the collection of facts or concepts that make up formal knowledge. It is knowledge that something is the case (Derry,1990). When someone knows declarative knowledge they know the location of a carburetor venturi, the use of intransitive verbs, or what menu item to use to perform a specific word processing function. Procedural knowledge is knowing how to do something, and refers to the performance capabilities involved in symbolic manipulation. Procedural skills are performance capabilities, such as the ability to read and write, follow directions, or solve technical problems (Derry, 1990). Conditional knowledge includes knowing when to apply a given cognitive strategy or skill as well as why. Conditional knowledge contributes to the organization and accessibility of the other two forms of knowledge, and in a sense, it exerts executive control over the process of learning and using knowledge. All three forms of knowledge are important in order for students to learn, but each requires different learning strategies (Derry, 1990; Marzano, et al., 1988).
Because declarative knowledge is in the form of multiple disconnected facts, it is thought to be stored in the long-term memory in loosely organized, tangled networks with semantically related ideas integrated more strongly to one another than unrelated ideas. Thus, it is thought important to help students organize their learning, identify relationships between new and prior knowledge, relate learning to the on-going activity, and think about how to connect and organize learning (Derry, 1990).
Procedural knowledge involves the operation on and the transformation of information. When well-learned, proceduralized skills can be accessed rapidly and used automatically with little conscious effort. Procedural knowledge is thought to be stored in the long-term memory in the form of condition-action rules: if certain conditions exist, then a specific action should be taken. Anderson (1983) suggests that in order to apply procedural knowledge students need to learn pattern-recognition skills and action-sequence procedures. Pattern recognition depends on the learned ability to selectively consider concepts and their defining attributes and rules, to perceive differences in patterns and respond differently, and to recognize when concepts can be generalized.
Action-sequence procedures are essential to the ability to carry out sequences of operations. Pattern recognition is thought to be activated in the working memory through the signaling of learned action sequences for a particular purpose.
The learning of action sequences is a slow process involving at least three phases. The first phase involves the learning and memorizing of a series of steps or rules as declarative knowledge. In the second phase the learner executes and practices so that performance becomes faster and more accurate as sequences are encoded in the memory so that there is less reliance on cues from declarative knowledge. The third phase involves collapsing performance sequences in order to reform larger and more efficient sequences (Anderson, 1983).
Declarative knowledge is acquired from listening to the teacher and memorizing. But declarative knowledge does not help to solve a problem unless it is activated and transformed into procedural knowledge. This is where conditional knowledge comes into play. Students must learn not only what is important, but also when and how to use knowledge (Bransford, Sherwood, Vye, & Rieser, 1986). While declarative and procedural knowledge contribute to the amount of information an individual can access, conditional knowledge contributes to its organization and accessibility (Tennyson, 1988).
When students can guide their own learning through the application of conditional knowledge, they are thought to be engaged in metacognition. Metacognition implies that one is aware of his or her thinking during the performance of a task and uses this awareness to control task performance (Derry, 1990; Marzano, et al.,1988). Idol and Jones (1990) suggest that metacognition involves both the knowledge and control of self as well as of process. Individuals must have persistence and a commitment to learning. They must take action in addition to being able to make use of the declarative, procedural, and conditional knowledge stored in their memories. Individuals must also assess what they are doing, deliberately select strategies to achieve certain objectives, check their progress, and make corrections. It is this emphasis on awareness and self-regulation that sets cognitive psychology off from behavioral psychology. Needless to say, the primary objective of instruction is to facilitate metacognition, that is, to help individuals to learn how to learn. The ability to engage in metacogntion sets expert performance off from the novice (Perkins & Salomon, 1989).
The Organization of Knowledge
Theorists also believe that the permanent memory organizes knowledge in three increasingly complex forms of information which help make up networks and facilitate the use of knowledge. Concepts represent categories of information organized by defining characteristics. Propositions relate to two or more concepts, and require the individual to integrate knowledge in order to rearrange information already stored in the permanent memory and to form new networks and structures as new information is introduced. Propositions can contain facts (declarative knowledge), include procedural knowledge, describe relationships, and make predictions. Schema, also called a "scripts" or "frames," are collections of concepts and propositions formed to represent knowledge structures associated with various information, events, or phenomena. Schema can be compared to mental blueprints. They also involve the formation of plans (Marzano, et al., 1988; Streibel, 1995; P. H. Winn, 1991).
Students make plans when they incorporate general schema into cognitive designs to accomplish a task. They make use of previous learning and new knowledge to formulate cognitive operations. Each successive operation results in an updated formulation of the general schema as new and previous learning are blended together and stored in the student's permanent and working memories (P. H. Winn, 1991). The ability to formulate plans through purposeful, self-regulated, corrective behavior contributes to metacognition.
Concepts, propositions, and schema achieve meaning in relation to one another and to the plans they help to formulate. By enabling individuals to focus on what is most important to learn, schema facilitate learning.
They help individuals to elaborate on information and experiences. They enhance orderly searches of memory, inferential reconstruction of gaps in memory, and assimilation of new information into existing memory structures (Marzano, et al., 1988; Winn, 1991). Plans enable individuals to use new knowledge by absorbing it into existing structures and making sense out of it. As more new information is assimilated into the existing memory networks, and as the individual engages in more problem solving situations, the ability to engage in abstract and generalizable thinking is enhanced (Sternberg, 1985). Metacognition results in learning how to learn.
Constructing the Learning Environment
What does all of this mean for the design of technical instruction? There are at least two major implications. First, the way content is conceived, used, and organized for instruction differs sharply from more conventional instruction. Second, the goal of instruction becomes to assist the individual in learning how to learn and in using knowledge, rather than to promote the recall of a fixed body of knowledge. Accordingly, the instructional context must be developed to promote self-learning through application and reflective thinking (Figure 2).
Instructional Uses of Content
Cognitive theorists have struggled with the instructional representation of knowledge. In its early forms, cognitive psychology appeared to minimize the central importance of subject matter in relation to the development of thinking skills. Recent theorists, however, recognize the crucial importance of
Figure 2 Applying constructionist concepts to technical learning
Help students to prepare for learning. Establish the importance of the topic and the connection to what has been previously learned. Help the student to use prior knowledge about the topic through such questions as What do I already know about the topic?, What do I need to learn?, How does what I will learn relate to what I already know?, What is important about what I will learn? Help students to organize learning. Show connections among new ideas and prior knowledge, and show how the new knowledge is organized and how parts are related. Call attention to key concepts and issues, and ask students to elaborate on what they are doing. Help students to control their own learning. Assist students in acquiring the capability to plan their own activity, reflect on their performance, monitor success or failure, alter responses, and take corrective action. Help students to become aware of factors that affect their own thinking, and to take control of their own thinking. Have students identify what they already know and what they need to know. Have students identify the assumptions that they are making, and engage students in self-questioning. Have students determine what cues to look for, and how to tell if a task is satisfactorily completed. Help students to attach meaning. Ask "why" questions; have students organize what they know into structures. Have students explain differences between their initial ideas and what actually happened, and to compare their explanations with formal explanations. Have students identify and compare their own problem-solving processes with those of an expert, or another student. Have students integrate their ideas into a body of prior knowledge. Encourage generalizations to other situations. Help students to use learning. Relate what they already know to the to-be-learned information; ask students to go beyond the immediate problem originally addressed to solve related problems. Move in a series of steps to problems that are different from the first problem. Include activities requiring students to use new ideas in multiple contexts. Use the same learning to develop new ideas; have students construct solutions to new problems by recalling concepts previously learned.
specific content. Both Glaser (1984) and Resnick (1989), for example, believe that all forms of learning and reasoning are knowledge driven. Marzano et al. (1988) argue that an understanding of formal content is essential for the formation of schema. However, from the cognitive perspective a primary value of specific content is its contribution to the formulation of the cognitive structures or networks necessary to attach meaning to learning (Idol, Jones, & Mayer, 1990; Streibel, 1995). "We learn most easily when we already know enough to have organizing schemes that we can use to interpret and elaborate upon new information," Resnick and Klopfer (1989, p. 5) observe. Learning is conceived as a process of meaning making, and to this end specific content is used in the fashioning of the concepts, propositions, and schema which contribute to meaning.
Specific knowledge has an important place within instruction, but it is best brought into play in the context of activities, that is, in the context in which knowledge is used in the real-world, rather than in abstract isolation. Contextualized knowledge is valued because it is knowledge that can be used. For this reason, instructional content is selected because it is considered essential to the technological activity itself, and not because it is part of a formal scheme of subject matter. The activity, rather than the abstract, formal discipline conditions what is taught.
Accordingly, in the case of technical instruction, use is made of interdisciplinary knowledge because technological activity itself is interdisciplinary in scope. Teaching is designed to promote integrative learning. Students are expected to integrate knowledge across subject domains into their existing store of concepts (cognitive structures) as knowledge is used.
Cognitive theorists distinguish between what they term "domain specific" and "non-domain specific" knowledge in order to clarify the differences between the instructional uses of knowledge. The former is the specific knowledge formally associated with a particular field of study, such as electronics, welding, geometry, bookkeeping, or chemistry, and the latter is the knowledge embedded in the cognitive strategies used by individuals to learn.
Domain specific knowledge is what is mainly taught through traditional subject-centered instruction patterned on the Tyler (1950) model. It is the "bits and pieces" of information, the facts, skills, procedures, and values imparted to the student (declarative and procedural knowledge). This is the content of instruction, and it is typically expressed in conventional instruction through specific behavioral objectives.
Non-domain specific knowledge, in contrast, relates to the thinking skills (cognitive strategies) learned by students as they acquire, employ, and reconstruct knowledge. It is generally expressed in the form of process functions associated with different cognitive operations (California Department of Education, 1990; Marzano et al., 1988; Parker & Rubin, 1966). When cognitive theorists speak of higher-order learning, they have in mind the construction of thinking skills, the non-specific knowledge in the form of propositions, concepts and schema that is activated as knowledge is put to use.
Non-domain specific knowledge is acquired as the domain specific knowledge is taught. At the same time, however, non-domain specific knowledge is essential to acquiring, remembering, and using domain specific knowledge. While the students are learning to diagnose a specific engine malfunction, for example, they are also learning the cognitive strategies relating to accumulating data, formulating questions, analyzing a problem, and testing assumptions-non-specific knowledge. While there is concern for the learning of specific content, there also is concern that students learn how specific content is generated, used, and reconstructed (Ennis, 1990; Perkins & Salomon, 1989).
The Organization of Instruction
In the case of more conventional instruction, the subject-matter organization often serves as a way to order and sequence instruction. This is because subject matter mastery is the dominating concern. In the case of instruction based on a cognitive perspective, however, the intent is to bring cognitive events into relation with the activity. The focus of instruction is on activity, not subject matter, and on the dynamic interaction between the learner and learning, rather than on pre-specified outcomes. For this reason, a clear organizing pattern cannot be derived from the content, as in the case of more conventional instruction.
One way to organize instruction is around the process functions associated with the use of knowledge. Figure 3 illustrates the relationship of process functions to activity, content, and instruction. Process functions, rather than the content divisions and subdivisions associated with formal knowledge, serve as the organizing centers of instruction. The crucial connection in the case of conventional instruction based on behaviorist concepts is between objectives and outcome measures, indicating the importance of subject matter mastery. In the case of cognitive-based instruction, however, the crucial nexus is between the process functions and the activity. It is through activity that meaning is achieved.
Figure 3 Process functions paradigm
Process functions can be formally defined as the random or ordered cognitive operations which are associated with knowledge and how it is generated and used (Parker & Rubin, 1966). Technical activity embodies such process functions as observing, formulating, comparing, ordering, analyzing, categorizing, and diagnosing. The technologist collecting and ordering information, the engineer identifying the dimensions of a problem, the doctor arriving at a decision, and the scientist formulating a critical question all depend on process functions (California Department of Education, 1990; Marzano et al., 1988; Parker & Rubin, 1966; P. H. Winn, 1991).
Individual process functions consist of a complex of interrelated intellectual operations. Even a seemingly simple process function, for example, observing, may include acquiring information through touching, looking, tasting, smelling, or listening. It may require students to look for patterns, classify, compare, contrast, and categorize. Making an inference may include such diverse functions as locating information, comparing and contrasting, and identifying relationships. The complexity of an individual process function also may vary depending on the characteristic of the related activity as well as its scope. Locating information, for example, may simply require using a catalogue to find a part number. At other times, locating information may involve a highly complex combination of skills needed for identifying, categorizing, and using information from a data base (Marzano et al., 1988).
Process functions should not be considered invariably sequenced, but rather are interwoven in complex ways as the individual formulates plans, revising and updating cognitive structures. The various process functions also may or may not operate simultaneously. At the same time, the application of specific process functions may trigger the application of additional process functions. "Cognition is both an effect caused by previous events and a cause of future events" (P. H. Winn, 1991, p. 200).
Marzano et al. (1988) suggest a framework for organizing instruction around core (non-domain specific) process skills. Focusing skills help the individual clarify meaning or a problem. Information-gathering skills are used to obtain either existing or new information, while remembering skills are useful strategies to assist in both long and short-term memorization. Through organizing skills structure is imposed on information and practices. Analyzing skills are higher order skills useful for pattern recognition, identifying relationships, and categorizing information. Generating skills help to construct meaning, and integrating skills help to establish connections. The final group of skills, evaluating, involve both establishing criteria and verifying in order to judge the value of ideas (Figure 4). The authors emphasize that the skills are used in combination, and there is no set order in which they are used. Their application contributes to metacognition.
Figure 4 Core thinking skills (from R. J. Marzano et al, p. 69)
Core Thinking Skills
Focusing Skills Analyzing Skills 1. Defining problems 11. Identifying attributes and components 2. Setting goals 12. Identifying relationships and patterns 13. Identifying main ideas Information Gathering Skills 14. Identifying errors 3. Observing 4. Formulating questions Generating Skills 15. Inferring Remembering Skills 16. Predicting 5. Encoding 17. Elaborating 6. Recalling Integrating Skills Organizing Skills 18. Summarizing 7. Comparing 19. Restructuring 8. Classifying 9. Ordering Evaluating Skills 10. Representing 20. Establishing criteria 21. Verifying
Parker and Rubin (1966) propose an interdisciplinary curriculum model with three operations: intake, manipulative, and applicative (Figure 5). The model appears to be particularly appropriate for use with technical activities. A number of instructional strategies can be used. What is important, according to the authors, is that the student actively engages in the manipulation of cognitive operations in a systematic way in order to clarify and assess the significance and consequence of learning. Parker and Rubin (1966) emphasize that knowledge is seldom applied by technicians, engineers, and scientists in the discrete packages through which it is traditionally organized and learned in the school. Their model provides a way to organize instruction to integrate and use knowledge from across fields of study.
Figure 5 Interdisciplinary process-centered instruction
(from J. C. Parker & L. J. Rubin, 1966, p. 63)
The authors see the manipulative stage during which the most "intense focus occurs" and knowledge is developed into a working cognitive tool, as the most important one.
The Instructional Context
Cognitive theory requires a change in assumptions about how to design the instructional context. It is not enough for the instructional designer to establish predetermined objectives and to develop a set of instructional strategies designed to bring about measurable changes in student performance. Cognitive theorists reject the notion that the learning environment can be pre-specified with enough certainty to result in integrative and higher-order learning (McLellan, 1993; Winn, 1991). In the case of simple, fixed knowledge in well-structured domains, the deterministic models of instruction popularized by behaviorists have functioned reasonably well. However, most learning outside of school, cognitive theorists argue, involves ill-defined problems that go beyond the application of fixed meaning. Thinking and problem-solving skills are required which draw on the ability of individuals to negotiate meaning within the constraints of the particular activity and their own experience. The aim of instruction, then, is to acculturate students into ways of knowing that are embedded in practice and social interaction, mirroring the way that knowledge is constructed and used in the real world (Brown, Collins, & Duguid, 1989).
Instruction based on a cognitive perspective is not highly structured, and it does not have the lists of predetermined objectives and accompanying teaching strategies that are so familiar to teachers versed in more traditional teaching concepts. Rather, the task of the instructional designer is to structure a dynamic and flexible learning environment that engages students in the interplay of knowledge with activity and context. Students engage in not only the organization of their own learning, but the selection of what they need to learn and when. The concept of metacognition implies strengthening the ability of students to take control over their own learning.
Following a cognitive perspective, the instructional context is designed to make use of contextualized activity. As mentioned before, cognitive theorists stress the importance of situated knowledge, also termed contextualized learning, in order to convey the idea that learning takes place best in a real-world context in which knowledge is put to use (Brown, Collins & Duguid, 1989; Collins, 1990; Resnick & Klopher, 1989). Students need the opportunity to perform the same tasks that experts do in a similar environmental context. Through the emulation of expert performance in similar conditions, the novice comes to terms with the construction and use of knowledge. Learning abstracted from its use in the work-world is considered incomplete (Collins, Brown, & Newman, 1989; Perkins & Salomon,1989).
Cognitive theorists also stress the importance of social learning. When students work together in ways that foster cooperative learning, an additional source of feedback is provided. Students share knowledge, help each other to grasp important concepts, and monitor each other's performance. Students often can help each other because as novices they fully grasp the relative difficulties involved with learning a particular skill. Students scaffold difficult concepts for each other, and they model successful behavior. Teachers make use of student contributions by eliciting explanations, encouraging discussion and debate, having individuals demonstrate effective ways to attack a problem, and establishing positive expectations. Learning cooperatively is considered a powerful motivator and also extends resources (Collins, Brown, & Newman,1989; Resnick & Klopfer, 1989; Roth, 1990).
The Teacher's Role
Teaching contrasts sharply from instruction patterned after more conventional practices. Direct instruction is minimized. The teacher is mainly concerned with facilitating the active engagement of students with learning. The teacher is a mediator, interceding between the student, the learning task, and the context; probing for understanding; helping to formulate questions; focusing attention; linking information; eliciting explanations; and guiding reflection. In the cognitive view, knowledge cannot be given directly to students but must be an outgrowth of their own efforts to observe, link, interpret, explain, and seek new understanding (Collins, 1990; Collins, Brown & Duguid, 1989; Resnick & Klopfer, 1989; Wood, 1995).
The term "cognitive apprenticeship" has become popular as a term to convey the active, contextualized character of learning. Cognitive apprenticeship implies that students are challenged by "real" tasks, the kinds of activities that experts engage in during work. Students use knowledge as it is used in the work world. As in the case of traditional apprenticeship, the intent is to guide novices from their initial state of knowing to expert performance. Modeling, coaching, and fading are used (Collins, 1990; Collins, Brown, & Newman, 1989; Wood, 1995).
Cognitive modeling involves the learner watching the teacher perform the task in the same way that the apprentice observes the master craftsman. The teacher elaborates on the particular strategies being used, while "thinking aloud" in order to demonstrate to the learner the reasoning involved. Cognitive modeling is more than just explaining, but also involves the teacher actually performing so that learners see the complete application of the strategy. It is important to elaborate on the errors or "wrong turns" made, on how errors are analyzed and detected, on how answers are posed and checked, on the connection with past learning, and on the new thinking involved.
Coaching involves guided help from the teacher as the student attempts to execute the task. Coaching occurs almost naturally as during modeling there is a gradual shift in focus from the teacher to students, and students assume a more active role. Typically, the teacher models while students observe, followed by student input and assistance over successive trials until students can work independently. The teacher provides elaboration and feedback, but there is a gradual fading of assistance until students can work on their own.
Essential to coaching is the concept of scaffolding. Instruction is structured so that students can build frameworks, or scaffold on which to construct new meaning. The teacher provides help in the form of cues, leading questions and summarization in order to help learners build connections to previous and new learning. Ausabel (1963) suggests that meaningful learning results when the new learning is related to concepts already in the individual's cognitive structure. Unless these relationships are built, learning is rote and easily forgotten.
Fading involves reducing guidance as students acquire a firmer grasp of the skill. The teacher limits hints, refinements, and feedback so that students can develop self-monitoring and correction skills through increasingly independent practice. The formation of schema is thought to be facilitated by having students apply skills in a variety of contexts (Roth, 1990).
The novice, then, observes, enacts, and practices, while at the same time learning to monitor performance, reflect, and make self corrections. The teacher encourages students to conceptualize material in new ways, exposes students to new and alternative ways of thinking, models performance, generates examples, provides specific cues and feedback, promotes reflection, and facilitates the use of knowledge.
Cognitive concepts of instructional design are still evolving. There are gaps both in theory and applications, as well as longstanding epistemological questions regarding content and its representation (Bednar, Cunningham, Duffy, & Perry, 1995; Held, 1996). Nevertheless, a framework has been developed which can guide the transformation of technical instruction, even if it is incomplete. The design of instruction based on cognitive theory shifts instructional emphasis from the passive learning of formally organized, specific content to the active acquisition and use of knowledge. Instructional interventions are designed to assist students to construct meaning, not to memorize information--hence, its usefulness in designing integrative and higher-order learning. Learning is thought to occur through a process of organizing memory into structures, or mental models, which facilitate the recall and use of information, and the construction of new structures. It is thought that instruction needs to be "contextualized," that is, situated in "real" settings which are rich in context and reflective of the real world. Instruction is designed to help students to develop learning strategies, to make inferences, to elaborate and reflect on the knowledge that is being presented, to generate relationships between what they know and what they are learning, and to monitor their own performance. Activity and application are considered to be the main ways in which learners build internal mental representations which conform to real uses of knowledge. All students may not learn the same information to the same degree, however, since learning is highly individualistic. According to cognitive theorists, individuals use their own experiences and cognitive structures to construct meaning (Idol & Jones, 1990; Resnick, 1989; Streibel, 1995; P. H. Winn, 1991).
In order to move toward a fuller application of cognitive concepts to technical instruction, major theoretical and practical work remains to be done. Although there are selective examples of the application of cognitive concepts to the design of technical instruction (Duncan, 1996; Engestrom, 1994; Foshay, 1995; Johnson, 1988,1989; Johnson, Flesher, Fere, & Jehng, 1992; Martin & Scribner, 1991; Soden, 1994), many of the instructional design and teaching implications of constructionist concepts have yet to be spelled out. Cognitive research at the classroom level tends to be highly specific to individual subject areas and learning situations, so it is difficult to generalize to technical education instruction. Research on teaching, in general, is very limited and tends to be prescriptive with only weak links to learning theory. With few exceptions, work relating to questions of gender, race, class, and the social construction of knowledge has not been translated into instructional prescriptions. This is particularly true in the case of technical instruction (Bednar et al., 1995; Bonner, 1988; Cobb, 1988; Prawat, 1992; Wood, 1995; Wood, Woloshyn, & Willoughby, 1995). Finally, we still know little about the instructional design variations that should be built into the different programming options within the larger family of technical programs. However, the field must continue to make strides in resolving conflicting issues, formulating operational concepts, advancing instructional design models, and in moving forward research. If, in technical education, we talk about the integration of academic instruction and the teaching of higher-order skills, we must also talk the language of cognitive theory, even if it remains incomplete.
Questions regarding what should be taught in school, the relative merits of different kinds of subject matter, and what constitutes the fundamentals of the curriculum have been endlessly debated in education. Over some hundred years ago, John Stuart Mills and Herbert Spencer engaged in the best known debate (Taba, 1962). They argued whether science should be introduced into the classical, liberal arts curriculum. The debate over curriculum content continues today, but hardly anyone questions the value of science or of technology for that matter. What is driving the questioning today is how instruction best can be presented. By today's terms, the debates of the past have been largely misfocused. To be sure, specific knowledge is important. The most important learning, however, may not be discrete knowledge, but rather it may be the active use of the intellectual process functions embedded in technical activity itself. Cognitive psychology asks "How can the most meaningful learning take place?" rather than "What is the most important content to learn?" It has shifted the debate from the teaching of specific content to how learning, itself, occurs, and how instruction best can be applied. There is strong support for the view that learning in our technological age is best conceived of as a process of knowledge construction, use, and reconstruction.
Educators concerned with technical instruction in all of its forms need to become versed in cognitive psychology and its instructional design implications. If technical instruction is going to be truly transformed so that it can address integrative and higher-level learning, it also is going to have to reconstruct its pedagogical foundation in order to encompass both subject matter as well as the intellectual processes and cognitive tools fundamental to knowledge use and thinking. Thoughtful educators everywhere are probing the transformational value of cognitive concepts for the design of instruction. If technical educators are going to become a part of what is probably the most revolutionary educational development in our time, their work, too, is going to have to be informed by a cognitive perspective which places primary emphasis on thinking and making meaning. This article has been an attempt to briefly outline some of the dimensions of constructionist concepts as they are applied to technical instruction.
Herschbach is an Associate Professor in the Department of Educational Policy, Planning, and Administration at the University of Maryland, College Park.
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