The purpose of this paper is to review
from an instructional-design (ID) point of view nine teaching
programs developed by cognitive psychologists over the last ten
years. Among these models, Collins' cognitive apprenticeship model
has the most explicit prescriptions for instructional design.
The paper analyzes the cognitive apprenticeship model, then uses
components of the model as an organizing framework for understanding
the remaining models. Differences in approach are noted between
traditional ID prescriptions and the cognitive teaching models.
Surprisingly, we were unable to identify common design strategies
common to all of the model programs. Key differences among programs
included: (1) problem solving versus skill orientation, (2) detailed
versus broad cognitive task analysis, (3) learner versus system
control, and (4) error-restricted versus error-driven instruction.
The paper concludes by arguing for the utility of continuing dialogue
between cognitive psychologists and instructional designers.
The field of instructional design (ID) emerged more than 30 years ago as psychologists and educators searched for effective means of planning and producing instructional systems (Merrill, Kowallis, & Wilson, 1981; Reiser, 1987). Since that time, instructional designers have became more clearly differentiated from instructional psychologists working within a cognitivist tradition (Glaser, 1982; Glaser & Bassok, 1989; Resnick, 1981). ID theorists tend to place priority on developing explicit prescriptions and models for designing instruction while instructional psychologists focus on understanding the learning processes in instructional settings.
Of course, the distinction between designers and psychologists is never clear-cut. Over the years, many ID theorists have explored learning processes, just as many psychologists have put considerable energy into the design and implementation of experimental instructional programs. However, because the two fields support different literature and theory bases, communication is often lacking. Cognitive psychologists have tended to overlook contributions from the ID literature, and likewise much design work of psychologists has gone unnoticed by ID theorists. (A happy exception to this trend is the pointed dialogue on constructivism in the May and September issues of Educational Technology magazine.)
Collins, Brown, and colleagues (e.g., Collins, 1991; Collins, Brown, & Newman, 1989) have developed an instructional model derived from the metaphor of the apprentice working under the master craftsperson in traditional societies, and from the way people seem to learn in everyday informal environments (Rogoff & Lave, 1984). They have called their model cognitive apprenticeships, and have identified a list of features found in "ideal" learning environments. Instructional strategies, according to the Collins-Brown model, would include modeling, coaching, scaffolding and fading, reflection, and exploration. Additional strategies are offered for representing content, for sequencing, and for maximizing benefits from social interaction.
Of course, many of Collins' recommended
strategies resemble strategies found in the ID literature (e.g.,
Reigeluth, 1983a). Clearly both fields could benefit from improved
communication concerning research findings and lessons learned
from practical tryout. With that goal in mind, the purpose of
this paper is to do an ID review of programs and strategies developed
by instructional psychologists, using the cognitive apprenticeship
model as a conceptual framework. Several teaching systems employing
cognitive apprenticeship ideals are described. The resultant review
should prove valuable in two ways: cognitive psychologists should
be able to make a better correspondence between their models and
current ID theory, hopefully seeing areas needing improvement,
and ID theorists also should be able to see correspondences and
differences, which may lead to revision or expansion of our
The Need for Cognitive Apprenticeships
The cognitive apprenticeship model rests on a somewhat romantic conception of the "ideal" apprenticeship as a method of becoming a master in a complex domain (Brown, Collins, & Duguid, 1989). In contrast to the classroom context, which tends to remove knowledge from its sphere of use, Collins and colleagues recommend establishing settings where worthwhile problems can be worked with and solved. The need for a problem-solving orientation to education is apparent from the difficulty schools are having in achieving substantial learning outcomes (Resnick, 1989).
Another way to think about the concept of apprenticeship is Gott's (1988a) notion of the "lost apprenticeship," a growing problem in industrial and military settings. She noted the effects of the increased complexity and automation of production systems. First, the need is growing for high levels of expertise in supervising and using automated work systems; correspondingly, the need for entry levels of expertise is declining. Workers on the job are more and more expected to be flexible problem solvers; human intervention is often most needed at points of breakdown or malfunction. At these points, the expert is called in. Experts, however narrow the domain, do more than apply canned job aids or troubleshooting algorithms; rather, they have internalized considerable knowledge which they can use to flexibly solve problems in real time (Gott, 1988b).
Gott's second observation relates to training
opportunities. Now, at a time when more problem-solving expertise
is needed due to the complexity of systems, fewer on-the-job training
opportunities exist for entry-level workers. There is often little
or no chance for beginning workers to acclimatize themselves to
the job, and workers very quickly are expected to perform like
seasoned professionals. True apprenticeship experiences are becoming
relatively rare. Gott calls this dilemma-more complex job requirements
with less time on the job to learn-the "lost" apprenticeship,
and argues for the critical need for cognitive apprenticeships
and simulation-type training to help workers develop greater problem-solving
A Brief Review of ID Models
It is assumed that readers will have some prior knowledge of ID models and theories; however, we offer a short overview to allow a clear contrast with certain cognitive approaches. ID models come in two generic varieties: procedural models for systems design (e.g., Andrews and Goodson, 1980) and conceptual models that incorporate specific instructional strategies for teaching defined content (Reigeluth, 1983a, 1987). The procedural models often are represented as flowcharts reflecting a series of project phases, progressing from needs and problems analyses to product implementation and maintenance. Procedural ID models depend less on learning theory and more on systems theory and project management methodologies (Branson & Grow, 1987). Of greater interest for our purposes are the instructional-strategy models. All such models are based on Robert Gagné's conditions-of-learning paradigm (Gagné, 1966), which in its time was a significant departure from the Skinnerian operant conditioning paradigm dominant among American psychologists. The conditions-of-learning paradigm posits that a graded hierarchy of learning outcomes exists, and for each desired outcome, a set of conditions exists that leads to learning. Instructional design is a matter of clarifying intended learning outcomes, then matching up appropriate instructional strategies. The designer writes behaviorally specific learning objectives, classifies those objectives according to a taxonomy of learning types, then arranges the instructional conditions to fit the current instructional prescriptions. In this way, designers can design instruction to successfully teach a rule, a psychomotor skill, an attitude, or piece of verbal information.
A related idea within the conditions-of-learning paradigm claims that sequencing of instruction should be based on a hierarchical progression from simple to complex learning outcomes. Gagné developed a technique of constructing learning hierarchies for analyzing skills: A skill is rationally decomposed into parts and sub-parts; then instruction is ordered from simple subskills to the complete skill. Elaboration theory uses content structure (concept, procedure, or principle) as the basis for organizing and sequencing instruction (Reigeluth, Merrill, Wilson, & Spiller, 1980). Both methods depend on task analysis to break down the goals of instruction, then on a method of sequencing proceeding from simple to gradually more complex and complete tasks.
Some of the teaching models being offered by cognitive researchers bear strong resemblance to traditional ID models. Larkin and Chabay (1989), for example, offer design guidelines for the teaching of science in the schools (pp. 160-163):
1. Develop a detailed description of the processes the learner needs to acquire.
2. Systematically address all knowledge included in the description of process.
3. Let most instruction occur through active work on tasks.
4. Give feedback on specific tasks as soon as possible after an error is made.
5. Once is not enough. Let students encounter each knowledge unit several times.
6. Limit demands on students' attention.
By any standard, these design guidelines are very close to the prescriptions found in component display theory, elaboration theory, and Gagné's instructional-design theory. The strong correspondence can be seen as good news for ID theories: Many current cognitive researchers seem to agree on some fundamentals of design that also form the backbone of ID models.
On the other hand, other cognitive teaching models emphasize design elements that traditional ID models historically have under-emphasized, such as learner-initiated inquiry and exploration, social "scaffolding," cooperative learning methods, and empathic feedback (Lepper & Chabay, 1988). Some cognitive teaching models explicitly differentiate themselves from traditional instructional development. Constructivist theorists, for example, have offered alternatives to the conditions-of-learning paradigm. Based on a neo-Piagetian framework, Case (Case, 1978; Case & Bereiter, 1985) has advocated an approach that focuses more explicitly on individual learners' misconceptions and knowledge structures. Bereiter (1991) develops a connectionist argument that suggests that human performance cannot be explained by explicit rules, but rather by a pattern matching process. Hence, the "family of instructional theories in which rules, definitions, logical operations, explicit procedures, and the like are treated as central (Reigeluth, 1983)...has produced an abundance of technology on an illusory psychological foundation" (p. 15).
Other theorists, influenced by Vygotsky's concept of a zone of proximal development, think of tasks differently from traditional ID. Newman, Griffin, and Cole (1989) characterize traditional design methods:
First, the tasks are ordered from simple or easy to complex or difficult. Second, early tasks make use of skills that are components of later tasks. Third, the learner typically masters each task before moving onto the next. This conception has little to say about teacher-child interaction since its premise is that tasks can be sufficiently broken down into component parts that any single step in the sequence can be achieved with a minimum of instruction. Teacherless computerized classrooms running "skill and drill" programs are coherent with this conception of change. (p. 153)
The authors contrast this task-analysis approach with a more teacher-based approach where simplicity is achieved by the social negotiation between teacher and learner:
The teacher and child start out doing the task together. At first, the teacher is doing most of the task and the child is playing some minor role. Gradually, the child is able to do more and more until finally he can do the task on his own. The teacher's actions in these supportive interactions have often been called "scaffolding,"... suggesting a temporary support that is removed when no longer necessary....There is a sequence involved ...but it is a sequence of different divisions of labor. The task-in the sense of the whole task as negotiated between the teacher and child-remains the same. (p. 153)
Several of the cognitive teaching models
reviewed below embody similar constructivist concepts. It is not
yet clear, however, precisely how such concepts would translate
themselves into guidelines for designing teaching materials. The
cognitive apprenticeship model discussed at length below includes
some well-worn design elements-such as modeling and fading-and
others that are relatively neglected by ID models-such as situated
learning, exploration, and the role of tacit knowledge.
In this section, the design elements of the cognitive apprenticeship model (based primarily on Collins, 1991) are reviewed and related to traditional ID concepts.
1. Content: Teach tacit, heuristic knowledge as well as textbook knowledge Collins et al. (1989) refer to four kinds of knowledge that differ somewhat from ID taxonomies:
Domain knowledge is the conceptual, factual, and procedural knowledge typically found in textbooks and other instructional materials. This knowledge is important, but often is insufficient to enable students to approach and solve problems independently.
Heuristic strategies are "tricks of the trade" or "rules of thumb" that often help narrow solution paths. Experts usually pick up heuristic knowledge indirectly through repeated problem-solving practice; slower learners usually fail to acquire this subtle knowledge and never develop competence. There is evidence to believe, however, that at least some heuristic knowledge can be made explicit and represented in a teachable form (Chi, Glaser, & Farr, 1988).
Control strategies are required for students to monitor and regulate their problem-solving activity. Control strategies have monitoring, diagnostic, and remedial components; this kind of knowledge is often termed metacognition (Paris & Winograd, 1990).
Learning strategies are strategies for learning; they may be domain, heuristic, or control strategies, aimed at learning. Inquiry teaching to some extent directly models expert learning strategies (Collins & Stevens, 1983).
ID taxonomies (Gagné, 1985; Merrill,
1983) pertain primarily to domain or textbook knowledge, although
the distinction is not explicit. Gagné's "cognitive
strategies" fits the last three strategies listed by Collins
et al. Merrill's "find a principle" or "find a
procedure" seems closest to those three strategies. Both
Gagné and Merrill are least specific about instruction
of this type of learning, although they both acknowledge its importance.
They recently have developed a framework for integrating learning
goals into more holistic structures (Gagné and Merrill,
2. Situated learning: Teach knowledge and skills in contexts that reflect the way the knowledge will be useful in real life. Brown, Collins, and Duguid (1989) argue for placing all instruction within "authentic" contexts that mirror real-life problem-solving situations. Collins (1991) is less forceful, moving away from real-life requirements and toward problem-solving situations: For teaching math skills, situated learning could encompass settings "ranging from running a bank or shopping in a grocery store to inventing new theorems or finding new proofs. That is, situated learning can incorporate situations from everyday life to the most theoretical endeavors" (Collins, 1991, p. 122).
Collins differentiates a situated-learning approach from common school approaches:
We teach knowledge in an abstract way in schools now, which leads to strategies, such as depending on the fact that everything in a particular chapter uses a single method, or storing information just long enough to retrieve it for a test. Instead of trying to teach abstract knowledge and how to apply it in contexts (as word problems do), we advocate teaching in multiple contexts and then trying to generate across those contexts. In this way, knowledge becomes both specific and general. (Collins, 1991, p. 123)
Collins cites several benefits for placing instruction within problem-solving contexts:
Learners learn to apply their knowledge under appropriate conditions.
Problem-solving situations foster invention and creativity (see also Perkins, 1990).
Learners come to see the implications of new knowledge. A most common problem inherent in classroom learning is the question of relevance: "How does this relate to my life and goals?" When knowledge is acquired in the context of solving a meaningful problem, the question of relevance is at least partly answered.
Knowledge is stored in ways that make it accessible when solving problems. People tend to retrieve knowledge more easily when they return to the setting of its acquisition. Knowledge learned while solving problems gets encoded in a way that can be accessed again in problem-solving situations.
Several of the models reviewed below, including some not cited by Collins et al. (1989), place instruction within a problem-solving context and at least some simulation of a real-life setting.
Taken in its extreme form that would require
"authentic," real-life contexts for all learning, the
notion of situated learning is somewhat vague and unrealistic.
Instruction always involves the dual goals of generalization and
differentiation (Gagné & Driscoll, 1989). In its more
modest form, however, the idea of context-based learning has considerable
appeal. Gagné and Merrill (1990) have pointed to the need
for better integration of learning goals through problem-solving
"transactions." The notion of situated learning, however
it is viewed, challenges the conditions-of-learning paradigm that
prescribes the breaking down of tasks to be taught out of context.
3. Modeling and explaining: Show how a process unfolds and tell reasons why it happens that way. Collins (1991) cites two kinds of modeling: modeling of processes observed in the world and modeling of expert performance, including covert cognitive processes. Computers can be used to aid in the modeling of these processes. Collins stresses the importance of integrating both the demonstration and the explanation during instruction. Learners need access to explanations as they observe details of the modeled performance. Computers are particularly good at modeling covert processes that otherwise would be difficult to observe. Collins suggests that truly modeling competent performance, including the false starts, dead ends, and backup strategies, can help learners more quickly adopt the tacit forms of knowledge alluded to above in the section on content. Teachers in this way are seen as "intelligent novices" (Bransford et al., 1988). By seeing both process modeling and accompanying explanations, students can develop "conditionalized" knowledge, that is, knowledge about when and where knowledge should be used to solve a variety of problems.
ID models presently incorporate modeling
and demonstration techniques. Tying explanations to modeled performances
is a useful idea, similar to Chi and Bassok's (1989) studies of
worked-out examples. Again, the emphasis is on making tacit strategies
more explicit by directly modeling mental heuristics.
4. Coaching: Observe students as they try to complete tasks and provide hints and helps when needed. Intelligent tutoring systems sometimes embody sophisticated coaching systems that model the learner's progress and provide hints and support as practice activities increase in difficulty. Burton and Brown (1979) developed a coach to help learners with "How the West Was Won," a game originally implemented on the PLATO system. Anderson, Boyle, and Reiser (1985) developed coaches for geometry and LISP programming. Clancey (1986) and White and Frederiksen (1986) include coaches in their respective programs on medical diagnosis and electric circuits, both discussed below.
Coaching strategies can be implemented in a variety of settings. Bransford and Vye (1989) identify several characteristics of effective coaches:
Coaches need to monitor learners' performance to prevent their getting too far off base, but leaving enough room to allow for a real sense of exploration and problem solving.
Coaches help learners reflect on their performance and compare it to others'.
Coaches use problem-solving exercises to assess learners' knowledge states. Misconceptions and buggy strategies can be identified in the context of solving problems; this is particularly true of computer-based learning environments (Larkin & Chabay, 1989).
Coaches use problem-solving exercises to create the "teachable moment." Posner, Strike, Hewson, and Gertzog (1982) present a 4-stage model for conceptual change: (1) students become dissatisfied with their misconceptions; (2) they come to a basic understanding of an alternative view; (3) the alternative view must appear plausible; and (4) they see the new view's value in a variety of new situations (see also Strike & Posner, in press).
Coaching probably involves the most "instructional
work" (cf. Bunderson & Inouye, 1987) of any of
the cognitive apprenticeship methods. Short of one-on-one tutoring,
coaching is likely to be partial and incomplete. Cooperative learning
and small-group learning methods can provide some coaching support
for individual performance. And computers can help tremendously
in monitoring learner performance and providing real-time helps;
yet presently coaching is only fully implemented in resource-intensive
intelligent tutoring systems. Much work is being done to model
the essentials of coaching functions on computer systems; we continue
to need resource-efficient methods for achieving the coaching
function. Still at issue is when and how coaching should be incorporated
into instruction, particularly as it relates to learner errors
and misconceptions. For example, should learners be permitted
to fail, perhaps even required to learn from failure, as in Clancey's
5. Articulation: Have students think about their actions and give reasons for their decisions and strategies, thus making their tacit knowledge more explicit. Think-aloud protocols are one example of articulation (Hayes & Flower, 1980; Smith & Wedman, 1988). Collins (1991) cites the benefits of added insight and the ability to compare knowledge across contexts. As learners' tacit knowledge is brought to light, that knowledge can be recruited to solve other problems.
John Anderson (1990) has suggested that
procedural knowledge-the kind that people can gain automaticity
in-is initially encoded as declarative or conceptual knowledge,
which later fades as the skill becomes proceduralized. Methods
for articulating tacit knowledge help to restore a conscious awareness
of those lost strategies, enabling more flexible performance.
Traditional ID models suggest practicing problem solving to learn
problem solving, but are surprisingly lacking in specific methods
to teach learners to think consciously about covert strategies.
6. Reflection: Have students look back over their efforts to complete a task and analyze their own performance. Reflection is like articulation, except it is pointed backwards to past tasks. Analyzing past performance efforts can also involve elements of strategic goal-setting and intentional learning (Bereiter & Scardamalia, 1989). Collins and Brown (1988) suggest four kinds or levels of reflection:
Imitation occurs when a batting coach demonstrates a proper swing, contrasting it with your swing;
Replay occurs when the coach videotapes your swing and plays it back, critiquing and comparing it to the swing of an expert;
Abstracted replay might occur by tracing an expert's movements of key body parts such as elbows, wrists, hips, and knees, and comparing those movement to your movements;
Spatial reification would take the tracings of body parts and plot them moving through space.
The latter forms of reflection seem to rely on technologies-video or computer- for convenient instantiation. Collins (1991) uses Anderson et al.'s Geometry Tutor as an example of reflective instruction; Clancey's (1986) GUIDON medical tutor requires reflective thinking because so much metacognitive responsibility is given to learners. Both are computer-based programs.
Articulation and reflection are both strategies
to help bring meaning to activities that might otherwise be more
"rote" and procedural. Reigeluth's (1983b) concern with
meaningful learning is indicative of the need; however, much of
traditional ID practice tends to undervalue the reflective aspects
of performance in favor of getting the procedure down right. The
risk of ignoring reflection is that learners may not learn to
discriminate in applying procedures; they may fail to recognize
conditions where using their knowledge would be appropriate, and
may fail to transfer knowledge to different tasks.
7. Exploration: Encourage students to try out different strategies and hypotheses and observe their effects. Collins (1991) claims that through exploration, students learn how to set achievable goals and to manage the pursuit of those goals. They learn to set and try out hypotheses, and to seek knowledge independently. Real-world exploration is always an attractive option; however, constraints of cost, time, and safety sometimes prohibit instruction in realistic settings. Simulations are one way to allow exploration; hypertext structures are another.
As Reigeluth (1983a) notes, discovery learning techniques are less efficient than direct instruction techniques for simple content acquisition. The choice must depend, however, on the goals of instruction: For near transfer tasks (cf. Salomon & Perkins, 1988), direct instruction may occasionally be warranted; for far transfer tasks, learners must learn not only the content but also how to solve unforeseen problems using the content; in such cases, instructional strategies allowing exploration and strategic behavior become essential McDaniel & Schlager, 1990).
Having thus represented a traditional ID
mode of thinking about exploration, we are still left unsatisfied.
Following a constructivist way of thinking, there is something
intrinsically valuable about situated problem-solving activity
that makes learning more effective than straight procedural practice.
This is an area that needs better articulation, including a rationale
for appropriate goals for exploratory and inquiry-based instruction,
and effective strategies for reaching those goals.
8. Sequence: Present instruction in an ordering from simple to complex, with increasing diversity, and global before local skills.
Increasing complexity. Collins et al. (1989) point to two methods for helping learners deal with increasing complexity. First, instruction should take steps to control the complexity of assigned tasks. They cite Lave's study of tailoring apprenticeships: apprentices first learn to sew drawers, which have straight lines, few pieces of material, and no special features like zippers or pockets. They progress to more complex garments over a period of time. The second method for controlling complexity is through scaffolding; for example, group or teacher support for individual problem solving.
Increasing diversity refers to the variety in examples and practice contexts.
Global before local skills refers to helping learners acquire a mental model of the problem space at very early stages of learning. Even though learners are not engaged in full problem solving, through modeling and helping on parts of the task (scaffolding), they can understand the goals of the activity and the way various strategies relate to the problem's solution. Once they have a clear "conceptual map" of the activity, they can proceed to developing specific skills.
The sequencing suggestions above bear a
strong resemblance to those of elaboration theory (Reigeluth &
Stein, 1983) and component display theory (Merrill, 1983). The
notion teaching of global before local skills is implicit in elaboration
theory; simple-to-complex sequencing is the foundation of elaboration
theory. The notion of increasing diversity is the near-equivalent
to the prescription to use "varied-example" and practice
activities in concept or rule learning. The cognitive apprenticeship
extends these notions beyond rule learning to problem-solving
contexts. At the same time, a number of research findings call
into question the simple-to-complex formula. For a more complete
discussion of sequencing issues, see Wilson & Cole (1992).
In the section below, we briefly review
several instructional systems developed by cognitive psychologists.
Some of these systems were cited by Collins and colleagues as
exemplifying cognitive apprenticeship features. Others were cited
by Glaser and Bassok (1989) as incorporating the best new knowledge
coming out of cognitive psychology. Still others were selected
because they embody noteworthy design concepts. After each program
is described individually, a comparison is made between the models
in an effort to identify common and not so common themes for discussion.
Anderson's Intelligent Tutors
John Anderson accounts for cognitive performance through the ACT* model of information processing (see, e.g., Anderson, 1990). According to this model, declarative knowledge is compiled into procedural skill through repeated practice performing a task. Whereas a novice must keep a procedure's steps in mind while performing, an expert turns several steps into an integrated routine, similar to the way subroutines work together in computer programming. Once knowledge is compiled from declarative to procedural knowledge, it may be performed with a minimum of allocated conscious attention.
Instruction based on the ACT* model would therefore emphasize guiding the learner through repeated practice opportunities to proceduralize the skills of the curriculum. Anderson and colleagues at Carnegie Mellon University have tested out this instructional approach through a series of intelligent tutoring systems aimed at teaching well-defined procedural skills, including programming in LISP (Anderson, Farrell, & Sauers, 1984), generating proofs for geometric theorems (Anderson, Boyle, & Yost, 1985), and solving algebraic equations (Lewis, Milson, & Anderson, 1988). Based on a cognitive analysis of the task, a model of the ideal student performance is developed for varying stages of competence, including assorted "buggy" procedures. As instruction proceeds, the system fits the learner's response pattern to its performance model, selects problems to minimize errors and optimize learning, and provides feedback and remediation accordingly. The technique of comparing learners' performance with a preexisting performance model is termed model tracing. The program does not make available the model's production rules to students directly; rather, the production rules trigger various instructional events, most notably intervention and feedback following incorrect performance.
Because new knowledge is best learned in the context of solving relevant problems, instruction is centered around problem-solving practice. Glaser and Bassok (1989) summarize the intelligent tutoring approach:
The CMU group advocates shortening preliminary
instructional texts, and restraining from elaborated explanations.
Texts should focus on procedural information, and students should
begin actual problem solving as soon as possible. Although textual
instructions should be carefully crafted to maximize correct encoding,
the inevitable misunderstandings should be corrected [immediately]
during problem solving. ( p. 638)
Clancey's Intelligent Tutoring Environments
Clancey's (1986) program GUIDON and its descendents use heuristic classification methods as the basis for an intelligent tutoring environment for medical diagnosis. The program differs from ACT* model prescriptions in several ways. First, it assumes that the learner has a basic understanding of terms, concepts and disease processes. Second, it assumes that learning is more efficient if the student determines what he/she needs to know next without being explicitly controlled by the system. Third, and most significantly, it is failure driven; that is, primary instruction occurs in the form of feedback to student errors.
GUIDON requires the student to make a diagnosis,
then to justify it with reasons. When the student's diagnosis
"fails," he/she must take steps to correct the reasoning
that led to it. Thus while the student develops expertise in medical
diagnosis in a realistic problem-solving context, he/she also
learns to detect and correct buggy procedures and misconceptions.
The program is nearly completely learner-controlled; at any point,
the student can choose to browse through the expert taxonomies
and tables, examine the expert's reasoning during problem solving,
ask questions, or request explanations. But ultimately the student
must generate the appropriate links in a solution graph for each
Qualitative Mental Models
White and Frederiksen's (1986) program to
teach troubleshooting in electrical circuits emphasizes the relationship
between qualitative models and causal explanations. White and
Frederiksen believe that mastery of qualitative reasoning should
precede quantitative reasoning. Their program builds on students'
intuitive understandings of the domain, carefully sequencing "real-world"
problems that require the student to construct increasingly complex
qualitative models of the domain. Although the program encourages
students to engage in diverse learning strategies (exploring,
requesting explanations, viewing tutorial demonstrations or problem
solving), it tries to minimize errors. Thus, like the ACT* model,
it does not directly address buggy algorithms and misconceptions.
Brown and Palincsar (1989; Palincsar &
Brown, 1984) have developed a cooperative learning system for
the teaching of reading, termed reciprocal teaching. The
teacher and learners assemble in groups of 2 to 7 and read a paragraph
together silently. A person assumes the "teacher" role
and formulates a question on the paragraph. This question is addressed
by the group, whose members are playing roles of producer
and critic simultaneously. The "teacher" advances
a summary, and makes a prediction or clarification, if any is
needed. The role of teacher then rotates, and the group proceeds
to the next paragraph in the text. Brown and colleagues have also
developed a method of assessment, called dynamic assessment,
based on successively increasing prompts on a realistic reading
task. The reciprocal teaching method uses a combination of modeling,
coaching, scaffolding, and fading to achieve impressive results,
with learners showing dramatic gains in comprehension, retention,
and far transfer over sustained periods.
Procedural Facilitations for Writing
Novices typically employ a knowledge-telling
strategy when they write: They think about their topic, then write
their thought down; think again, then write the next thought down,
and so on until they have exhausted their thoughts about the topic.
This strategy, of course, is in conflict with a more constructive,
planning approach in which writing pieces are composed in a more
coherent, intentional way. To encourage students to adopt more
sophisticated writing strategies, Scardamalia and Bereiter (1985)
have developed a set of writing prompts called procedural facilitations,
that are designed to reduce working-memory demands and provide
a structure for completing writing plans and revisions. Their
system includes a set of cue cards for different purposes of writing,
structured under five headings: new idea (e.g. "An
even better idea is..."; "An important point I haven't
considered yet is..."), improve ("I could make
my point clearer..."), elaborate ("An example
of this..."; "A good point on the other side of the
argument is..."), goals ("My purpose..."),
and putting it together ("I can tie this together
by..."). Each prompt is written on a notecard and drawn by
learners working in small groups. The teacher makes use of two
techniques, soloing and co-investigation. Soloing
gives learners the opportunity to try out new procedures by themselves,
then return to the group for critique and suggestions. Co-investigation
is a process of using think-aloud protocols that allow learner
and teacher to work together on writing activities. This allows
for more direct modeling and immediate direction. Bereiter and
Scardamalia (1987) have found up to tenfold gains in learning
indicators with nearly every learner improving his/her writing
through the intervention.
Schoenfeld's Math Teaching
Schoenfeld (1985) studied methods for teaching
math to college students. He developed a set of heuristics that
were helpful in solving math problems. His method introduces those
heuristics, as well as a set of control strategies and a productive
personal belief system about math, to students. Like the writing
and reading systems, Schoenfeld's system includes explicit modeling
of problem-solving strategies, and a series of structured exercises
affording learner practice in large and small groups, as well
as individually. He employs a tactic he calls "postmortem
analysis," retracing the solution of recent problems, abstracting
out the generalizable strategies and components. Unlike the writing
and reading systems, Schoenfeld carefully selects and sequences
practice cases to move learners into higher levels of skill. Another
interesting technique is the equivalent to "stump the teacher,"
with time at the beginning of each class period devoted to learner-generated
problems that the teacher is challenged to solve. Learners witnessing
occasional false starts and dead ends of the teacher's solution
can acquire a more appropriate belief structure about the nature
of expert math problem solving. Schoenfeld's positive research
findings support a growing body of math research suggesting the
importance of acquiring a conceptual or schema-based representation
of math problem solving.
John Bransford and colleagues at Vanderbilt
University have developed several instructional products using
video settings. Young Sherlock Holmes or Indiana Jones
may provide macrocontexts within which problems of various
kinds may be addressed. For example, when Indiana Jones quickly
replaces a bag of sand in place of the gold idol, the booby trap
is tricked into thinking the idol is still there. This scene opens
up questions of mass and density: If the idol were solid gold,
how big must a sand bag be to weigh the same,
and could Indy have escaped as he did carrying a solid-gold idol
of that size? Based on a single macrocontext, learners may approach
a variety of problems that draw on science, math, language, and
history. Bransford and colleagues have applied the name anchored
instruction to this approach of grounding instruction in information-rich
situations, and have reported favorable findings in field-based
and laboratory studies (The Cognition and Technology Group at
Vanderbilt, 1990). Recently, several videodisc-based macrocontexts
called the Jasper Series have been developed as a basis for research
and classroom instruction (The Cognition and Technology Group
at Vanderbilt, in press; Sherwood and The Cognition and Technology
Group at Vanderbilt, 1991; Van Haneghan et al., in press).
Cognitive Flexibility Hypertexts
Spiro and colleagues (Spiro & Jehng, 1990) have developed hypertext programs to address problems typically associated with acquiring knowledge in complex, ill-structured domains. The programs utilize videodiscs that construct multiple "texts" (audio/video mini-cases, about 90 seconds each) of a domain. The multiple representations in KANE (Exploring Thematic Structure in Citizen Kane) induce students to restructure their thinking as they grapple with the thematic issues which are presented through the mini-cases. The program codes various themes and automatically generates appropriately juxtaposed sequences when the learner selects a theme to explore. It relies on the learner to generate the relationships which are embodied in a set of mini-cases. Spiro's programs are best thought of as "rich" environments that allow sophisticated learners to pursue their learning goals in a flexible way. They do not typically include skill practice in a traditional sense, but instead rely on learner purposes and externally imposed assignments to give meaning to student browsing. There is an authoring shell, however, built into the system for teachers or students to use. Students, for example, may construct a series of mini-cases into a "visual essay" illustrating a theme not present on the Theme menu.
Like Clancey's intelligent tutoring environments, KANE assumes that students have basic knowledge of the content (in this instance, they must have seen the full movie in its natural sequence at least once).
The idea of mini-cases is central to Spiro's approach. Spiro and Jehng (1990) emphasize the differences between actual experiences and mini-cases:
a. the cases are immediately juxtaposed (hours and days do not pass between nonroutine cases);
b. the cases are thematically related (whereas there is no guarantee of instructive relatedness across naturally occurring adjacent cases);
c. the cases are stripped down to structurally significantly features, making it easier to extract dimensions of structural relatedness;
d. the cases are accompanied by expert commentary and guidance; and, finally,
e. because the cases are short, they are each easier to remember and more of them can be presented in a short amount of time, facilitating the recognition of relationships across cases. (p. 202; reformatted)
Spiro and Jehng emphasize that this instructional approach is difficult-it places great metacognitive demands on learners-but it addresses goals which are often overlooked in instruction precisely because they are difficult.
Instruction designed around mini-cases is
similar to Schank's use of stories in instruction (Schank &
Jona, 1991; Riesbeck & Schank, 1989). Schank's approach includes
videotaping and assembling a database of short "stories"
or cases from experts, then accessing and sequencing those stories
within an instructional framework according to expressed feelings,
perceptions, and needs of the learner. Case-based instruction
is a highly appealing design concept, and we look forward to reports
of working programs from Schank and colleagues.
Learning Through Design Activities: Computer Tools
Harel and Papert (1990) and Lippert (1988, 1990) have examined the effect of learning a content domain while simultaneously learning other domains, particularly software design.
Instructional Software Design Project.
Harel and Papert (1990) created a "total learning environment" in which fourth-graders simultaneously learned fractions and Logo programming; the students' goal was to design and develop a Logo program to teach something about fractions. "The 'experimental treatment' integrated the experimental children's learning of fractions and Logo with the designing and programming of instructional software" (Harel & Papert, p. 7), four hours a week during a 15-week semester. The students in the first control group spent the same amount of time learning Logo, but with a different purpose. The second control group programmed only once a week. Each group completed a two-month unit on fractions as part of the regular math curriculum.
The experimental treatment purposely cultivated a sense of meaningful activity, collegiality, and metacognitive awareness. The students had primary control over the task of designing and developing individual programs to "'explain something about fractions' to some intended audience" (Harel & Papert, p. 3). Initially Harel guided the students and teacher in a discussion of instructional design and educational software; she also provided models of designs, flowcharts and screens from her previous projects and shared notes and excerpts from programs developed by "real" designers and engineers. As needed, the teacher and Harel led discussions on issues related to understanding and teaching fractions, as well as on specific Logo programming skills.
On each day that the students worked on the project, they initially spent 5-7 minutes writing designs and plans in special notebooks and 45-55 minutes working on their computers, and then wrote about the problems they had encountered and changes they had made; occasionally they also included designs for the next session's activity. "The only two requirements were: (1) that they write in their Designer Notebooks before and after each working session; and (2) that they spend a specific amount of time at the computer each day" (Harel & Papert, p. 4) in order to fit the project into the schedule.
In many respects the students, teacher and researcher were like a community of scholars jointly in pursuit of knowledge, or a community of craft apprentices. At all times students had access to "experts" and to continual evaluation; they could compare their work with each others' work and to the work of experts (all representing various stages of expertise).
By the end of the study, not only did the experimental group significantly exceed both control groups in mastery of fractions, but on some items the students scored twice as high as sixth- to eighth-graders. The students were also more persistent than students in both control groups in trying to solve various Logo programming problems, and developed more metacognitive sophistication. They could find problems, develop plans, discard or revise inefficient plans, and control distractions and anxiety.
Harel and Papert hypothesize that no single factor alone contributed to the students' achievements. Instead they conclude that several factors interrelated in a holistic approach which may include but is not limited to:
the affective side of cognition and learning; the children's process of personal appropriation of knowledge; the children's use of LogoWriter; the children's constructivist involvement with the deep structure of fractions knowledge; the integrated-learning principle; the learning-by-teaching principle; and the power of design as a learning activity. (Harel & Papert, p. 30)
Lippert (1988, 1990) describes the way in which an expert system shell can be used as both an instructional and a learning strategy to "facilitate the acquisition of procedural knowledge and problem-solving skills in difficult topics" (1988, p. 22). Again, students learn by designing-developing an expert system individually or in groups, on their own or under the guidance of a teacher. According to Lippert, the strategy can be used with students as young as grade 6 and in any domain whose knowledge base can be expressed in productions.
Like the Harel and Papert approach, developing an expert system forces students to construct a meaningful representation of the domain. Most expert systems are systems which reduce a content domain to a set of IF-THEN rules. According to Lippert's scheme, the knowledge base is the key component and includes four parts: decisions which define the domain; questions which extract information (answers) from the user; rules that relate the answers to the decisions; and explanations (of questions or rules), which require the developer to understand the relationships among the various elements of the domain-the learner must understand "why" and "when," not merely "what." The developer constructs the knowledge base which the system then evaluates; if the system finds inconsistencies or redundancies, the developer must revise the knowledge base. In doing so, the learner must be reflective and articulate his or her implicit knowledge. Developing such a system helps students confront their misconceptions of the content.
In designing the system, students can experiment, trying out ideas and revising them as necessary until they are satisfied with their representation of the domain. They can initially restrict their system to one component of a domain and expand it to accommodate their expanding knowledge base. As in Harel and Papert's Logo environment, when students work in groups, they are exposed to and stimulated by different representations of the domain. And like the students in the Logo environment, students who design an expert system learn much incidental information and often become enthusiastic about learning the content.
Several issues remain unresolved with respect
to learning through design activities. Like many prototype programs,
factors contributing to the success of the programs are largely
embedded within the expertise of the researchers and teachers.
The critical factors in the instructional design need to be further
explicated; this will allow the approach to be more easily identified
and exported to different settings. A key issue is the question
of efficiency: Can learning through design make more efficient
use of time and resources, or does a design-oriented approach
require extra time and resources? Theoretically, learning through
design represents a fundamental alternative to traditional instructional
designs, and deserves, therefore, continued attention among ID
In reviewing the set of teaching models
described above, one is struck by the diversity of approaches
and features, and particularly by the key features not
common to all the model programs.
Problem-solving versus Skill Orientation
Overwhelmingly, the models approached instruction from a problem-solving orientation. The main exception is Anderson's LISP Tutor, which essentially teaches procedural skill, with little need for metacognitive reflection. Near transfer is the goal of Anderson's tutoring systems. "Problem solving" in this environment is something like working out problems at the end of a textbook chapter. We do not consider this kind of activity genuine problem solving in the full sense of the word. It is worthy of note that the LISP Tutor is the only program that does not include explicit guidance to encourage metacognitive thinking.
Spiro's KANE hypertext is another unusual problem-solving environment. Specific problems are not posed within the program; learners do not "practice" solving interpretation problems per se. Instead, learners are provided a rich environment to explore and study. Problem solving in this setting depends heavily on the predisposition and prior knowledge of the learner. Thus the explicit instructional guidance for problem solving is relatively weak, which would likely result in failure for the learner lacking in metacognitive and preexisting content knowledge. The rest of the programs include higher levels of guidance for problem solving and practice on procedural skills.
A problem-solving orientation toward instruction
is not implied by the conditions-of-learning paradigm; in fact,
the reverse seems to be true. In the words of an anonymous reviewer
of a previous draft of this article, "this is a content issue,
not a method issue." Traditional instructional designers
"would say that the needs analysis should determine whether
or not you have a problem-solving orientation or a skill orientation
(or both, or neither-perhaps an understanding orientation)."
Hence, from an ID point of view, problem-solving instruction is
only one of several kinds, each meeting different needs. A more
constructivist point of view, however, would suggest that virtually
all instruction somehow should be embedded within a problem-solving
context (e.g., Bransford & Vye, 1989; Wilson, 1989). The heavy
bias toward problem solving in the models reviewed seems to be
evidence of the viability of such an emphasis.
Detailed versus Broad Cognitive Task Analysis
Most of the models base instruction on a careful and detailed cognitive task analysis. These analyses go beyond behavioral observation; subtle and covert mental heuristics are sought and identified. Again, however, there is considerable variation among the teaching models. Intelligent tutors demand an exhaustive representation of the content; by contrast, the skills taught by group methods such as reciprocal teaching or the fractions software design project are not so well defined in advance.
There is no pretense among any of the projects' designers that the goals of instruction are completely captured by analysis. Rather, the instructional materials and interactions provide a setting wherein students can develop the subtle and often unidentified knowledge needed to succeed in the given task. The distinction between the identified learning goals and the knowledge actually acquired through learning activities is an important one that is often blurred in traditional ID theories. Given the right design, incomplete learning specifications can nonetheless lead toward complete learning outcomes. This attitude toward instructional outcomes helps deflect the criticism that ID is reductionistic because it pretends to capture and transmit expertise outside of the expert (cf. Dreyfus & Dreyfus, 1986; Bereiter, 1991).
Cognitive science has opened up an array
of tools for performing task/content analyses, including think-aloud
protocols, computer modeling, networks, maps, production systems,
and cognitive process analysis (Gardner, 1985; Nelson, 1989; Smith
& Wedman, 1988). Instructional designers can represent content
with greater precision and accuracy than before. Yet as important
as these analytic tools are, of greater importance to ID are theories
of knowledge and learning that are presently being developed within
cognitivist and philosophical traditions (e.g., Bereiter, 1991;
Lakoff, 1987; Paul, 1990). Faulconer and Williams (1990), for
example, outline a Heideggerian perspective that helps to resolve
the dualistic bias inherent in the current "objectivism"
versus "constructivism" debate (Duffy & Jonassen,
in press). Instructional designers can do more to be sensitive
to the variety of conceptions of knowledge and learning, and to
develop teaching models in line with those varying conceptions.
Authentic versus Academic Contexts
Brown, Collins, and Duguid (1989) define authentic activity as "the ordinary practices of the culture" (p. 34). We take this to mean that they have some bearing on real-life, everyday activities. There is a problem, however, in determining what counts as authentic activity. Apparently, the theorizing and inquiry activities of a nuclear physicist or historian qualify as authentic. For the term to have any precise referent, there needs to be a boundary drawn between authentic and inauthentic activity, and we are not sure where that boundary lies. Examining the set of teaching models, however, there surely seems to be some sort of authenticity continuum. Some programs relate more closely to real-life situations (e.g., the Jasper series), but most are more typically academic work (e.g., Anderson's LISP tutor). Some programs seem to fall in the middle (Spiro's KANE or Harel and Papert's fractions project). Clearly, the resemblance between the instructional setting and real-life settings is not a constant among the models.
ID theories have tended toward academic
contexts. For example, the Reigeluth volumes (1983a, 1987) contain
surprisingly little discussion of simulation as an instructional
strategy. The irony is that large numbers of ID practitioners
are heavily involved in developing "authentic" training
either in job sites or through simulations. Clearly more attention
needs to be given to prescriptions for designing simulations that
go beyond present models for direct instruction.
Learner versus System Control
There is also considerable variability on the learner control dimension. Some programs are tightly controlled (e.g., LISP Tutor) while several others are very flexible (e.g., GUIDON, KANE, fractions project). There seems to be a correspondence between the degree of learner control and the metacognitive goals of the instruction: Where metacognitive development is a goal, there is by necessity some flexibility allowing learners to assume responsibility over the "zone of proximal development" (Scardamalia & Bereiter, 1991). Where metacognitive skill is not a goal of instruction, learner control is less critical.
Yet even this generalization is not complete.
Schoenfeld's math program is very much oriented toward metacognitive
development, yet problem cases are carefully planned and sequenced
in advance by the instructor. Within the framework of the cases,
however, learners are able to negotiate control with the instructor.
The planned sequencing of cases may be accounted for by the well-definedness
of the content, which allows for a systematic progression from
simple to complex cases. The same can be said for White and Frederiksen's
progression of mental models. Learners may experiment and control
actions within levels, but movement from level to level is planned
in advance according to content parameters. With less defined
content (e.g., reciprocal teaching), sequence of cases is, at
least on the surface, much less important. Another view of reciprocal
teaching, however, is that the text being read is not the content;
rather, the content lies in the skills being modeled and practiced,
and the sequencing, under the informal control of the teacher,
does proceed from simple to complex.
Error-restricted versus Error-driven Instruction
GUIDON is the prime example of a program whose instructional presentations are triggered by the learner's errors. Presentations and explanations thus take the form of feedback to learner actions. In contrast, several programs seek to control errors through careful presentations and sequencing. Errors within the LISP Tutor or White and Frederiksen's troubleshooting tutor would be due to mismatches between the learner and the instructional plan; the learner can then be corrected and brought back on track. Most of the programs seem to fall in the middle on this feature; for example, errors in Schoenfeld's math program are expected, even modeled by the instructor, but the instruction is not completely keyed around diagnosis and response to student errors.
Feedback is an old topic of research, but
we know surprisingly little about it. It has long been recognized
that the best way to ensure learners' understanding is to have
them respond actively. At the same time, old maxims such as "provide
immediate knowledge of results" are being challenged (Salmoni,
Schmidt, & Walter, 1984; Corbett & Anderson, 1991; Lepper
& Chabay, 1988). There is some evidence that continuous detailed
feedback sometimes may actually serve to supplant the self-reflective
processes that a learner may otherwise apply to a problem. Error-driven
(i.e., feedback-heavy) instruction should be designed so as not
to interfere with such learning processes.
This paper has been concerned with the relationship between two related disciplines: cognitive psychology and instructional design. ID, the more applied discipline, faces the challenges of constantly re-inventing itself as new research in basic psychology sheds light on learning processes. It is no cause for concern that ID models need continual self-review; indeed, there would be cause for concern if the situation were otherwise. To provide some sense of context, however, we would like to call attention to some basic principles of cognitive apprenticeship articulated by the philosopher Erasmus more than 500 years ago. In a letter to a student friend, Erasmus offers some advice:
Your first endeavor should be to choose the most learned teacher you can find, for it is impossible that one who is himself no scholar should make a scholar of anyone else. As soon as you find him, make every effort to see that he acquires the feelings of a father towards you, and you in turn those of a son towards him . . . . Secondly, you should give him attention and be regular in your work for him, for the talents of students are sometimes ruined by violent effort, whereas regularity in work has lasting effect just because of its temperance and produces by daily practice a greater result than you would suppose . . . . A constant element of enjoyment must be mingled with our studies so that we think of learning as a game rather than a form of drudgery, for no activity can be continued for long if it does not to some extent afford pleasure to the participant.
Listen to your teacher's explanations not only attentively but eagerly. Do not be satisfied simply to follow his discourse with an alert mind; try now and then to anticipate the direction of his thought . . . Write down his most important utterances, for writing is the most faithful custodian of words. On the other hand, avoid trusting it too much . . . . The contests of minds in what we may call their wrestling ring are especially effective for exhibiting, stimulating, and enlarging the sinews of the human understanding. Do not be ashamed to ask questions if you are in doubt; or to be put right whenever you are wrong. (Erasmus, 1974, pp. 114-115)
At a time when terms like metacognition,
scaffolding , and cognitive apprenticeship are being
invented to describe the learning process, it is humbling to be
reminded of the insights of former paradigms. ID theorists may
chafe at the continuing need to revise their theories in light
of advances in psychological theory, but it is good for both fields
for the dialogue to continue. Indeed, the interaction between
basic psychology and applied instructional design can be expected
to continue for some time to come.
We would like to thank Jim Cole for calling
our attention to the works of Erasmus. We also thank suggestions
made by anonymous reviewers. Please send requests for reprints
to Brent Wilson, University of Colorado at Denver/Campus Box 106/P.
O. Box 173364/Denver CO 80217-3364.
Anderson, J. R. (1990). Cognitive psychology and its implications (3rd ed.). New York: Freeman.
Anderson, J. R., Boyle, C. F., & Yost, G. (1985). The Geometry Tutor. In Proceedings of the international joint conference on artificial intelligence, pp. 1-7. Los Altos CA: International Joint Conference on Artificial Intelligence.
Anderson, J. R., Farrell, R., & Sauers, R. (1984). Learning to program in LISP. Cognitive Science, 8, 87-129.
Andrews, D. H., & Goodson, L. A. (1980). A comparative analysis of models of instructional design. Journal of Instructional Development, 3(4), 2-16.
Bereiter, C. (1991, April). Implications of connectionism for thinking about rules. Educational Researcher, 10-16.
Bereiter, C., & Scardamalia, M. (1987). The psychology of written composition. Hillsdale, NJ: Erlbaum.
Bereiter, C., & Scardamalia, M. (1989). Intentional learning as a goal of instruction. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser, (pp. 361-392). Hillsdale NJ: Erlbaum.
Bransford, J. D., Goin, L. I., Hasselbring, T. S., Kinzer, C. K., Sherwood, R. D., & Williams, S. (1988). Learning with technology: Theoretical and empirical perspectives. Peabody Journal of Education, 64 (1), 5-26.
Bransford, J. D., Sherwood, R. D., Hasselbring, T. S., Kinzer, C.K., & Williams, S. M. (1990). Anchored instruction: Why we need it and how technology can help. In D. Nix & R. Spiro (Eds.), Cognition, education, and multimedia: Exploring ideas in high technology (pp. 115-141). Hillsdale NJ: Erlbaum.
Bransford, J. D., & Vye, N. J. (1989). A perspective on cognitive research and its implications for instruction. In L. B. Resnick & L. E. Klopfer (Eds.), Toward the thinking curriculum: Current cognitive research. Alexandria VA. 1989 ASCD Yearbook.
Branson, R. K., & Grow, G. (1987). Instructional systems development. In R. M. Gagné (Ed.), Instructional technology: Foundations. Hillsdale NJ: Erlbaum.
Brown, J. S., Collins, A., & Duguid, P. (1989, January-February). Situated cognition and the culture of learning. Educational Researcher, 32-42.
Brown, A., & Palincsar, A. S. (1989). Guided cooperative learning and individual knowledge acquisition. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in Honor of Robert Glaser (pp. 393-451). Hillsdale NJ: Erlbaum.
Bunderson, C. V., & Inouye, D. K. (1987). The evaluation of computer-aided educational delivery systems. In R. M. Gagné (Ed.), Instructional technology: Foundations. Hillsdale NJ: Erlbaum.
Burton, R. R., & Brown, J. S. (1979). An investigation of computer coaching for informal learning activities. International Journal of Man-Machine Studies, 11, 5-24.
Case, R. (1978). A developmentally based theory and technology of instruction. Review of Educational Research, 48, 439-463.
Case, R., & Bereiter, C. (1984). From behaviourism to cognitive behaviourism to cognitive development: Steps in the evolution of instructional design. Instructional Science, 13, 141-158.
Chi, M. T. H., & Bassok, M. (1989). Learning from examples via self-explanations. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in Honor of Robert Glaser (pp. 251-282). Hillsdale NJ: Erlbaum.
Chi, M. T. H., Glaser, R., & Farr, M. J. (1988). The nature of expertise. Hillsdale NJ: Erlbaum.
Clancey, W. J. (1986). From GUIDON to NEOMYCIN and HERACLES in twenty short lessons: ONR final report 1979-1985. AI Magazine, 7(3), 40-60.
Cognition and technology group at Vanderbilt. (1990, August-September). Anchored instruction and its relation to situated cognition. Educational Researcher, 2-10.
Cognition and technology group at Vanderbilt. (in press). Anchored instruction approach to cognitive skills acquisition and intelligent tutoring. In J. W. Regian and V. J. Shute (Eds.), Cognitive approaches to automated instruction. Hillsdale NJ: Erlbaum.
Collins, A. (1991). Cognitive apprenticeship and instructional technology. In L. Idol & B. F. Jones (Eds.), Educational values and cognitive instruction: Implications for reform. Hillsdale NJ: Erlbaum.
Collins, A., & Brown, J. S. (1988). The computer as a tool for learning through reflection. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems. New York: Springer.
Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and arithmetic. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 453-494). Hillsdale NJ: Erlbaum.
Collins, A., & Stevens, G. (1983). Inquiry teaching. In C. M. Reigeluth (Ed.), Instructional-design theories and models: An overview of their current status (pp. 247-278). Hillsdale NJ: Erlbaum.
Corbett, A. T., & Anderson, J. R. (1991, April). Feedback control and learning to program with the CMU Lisp Tutor. Paper presented at the meeting of the American Educational Research Association, Chicago.
Dreyfus, H., & Dreyfus, S. (1986). Mind over machine: The power of human intuition and expertise in the era of the computer. New York: The Free Press.
Duffy, T., & Jonassen, D. H. (in press). Instructional principles for constructivist learning environments. Hillsdale NJ: Erlbaum.
Eckert, P. (1990). Adolescent social categories-Information and science learning. In J. Gardner, J. G. Greeno, F. Reif, A. H. Schoenfeld, & A. A. diSessa, & E. Stage (Eds.), Toward a scientific practice of science education. Hillsdale NJ: Erlbaum.
Erasmus, D. (1974). The correspondence of Erasmus. Volume 1: Letters 1 to 141, 1484 to 1500. Translated by R. A. B. Mynors and D. F. S. Thomson. Toronto: University of Toronto Press.
Faulconer, J. E, & Williams, R. N. (1990). Reconsidering psychology. In J. E. Faulconer & R. N. Williams (Eds.), Reconsidering psychology: Perspectives from continental philosophy. Pittsburgh: Duquesne University Press.
Gagné, R. M. (1966). The conditions of learning (1st ed.). New York: Holt, Rinehart, & Winston.
Gagné, R. M., & Driscoll, M. P. (1989). Essentials of learning for instruction (2nd ed.). Englewood Cliffs NJ: Prentice-Hall.
Gagné, R. M., & Merrill, M. D. (1990). Integrative goals for instructional design. Educational Technology Research and Development, 38 (1), 23-30.
Gardner, M. K. (1985). Cognitive psychological approaches to instructional task analysis. In E. W. Gordon (Ed.), Review of Research in Education, 12, 157-195.
Glaser, R. (1982). Instructional psychology: Past, present, and future. American Psychologist, 37 (2), 292-305.
Glaser, R., & Bassok, M. (1989). Learning theory and the study of instruction. Annual Review of Psychology, 40, 631-666.
Gott, S. P. (1988a, April). The lost apprenticeship: A challenge for cognitive science. Paper presented at the meeting of the American Educational Research Association, New Orleans.
Gott, S. P. (1988b). Apprenticeship instruction for real-world tasks: The coordination of procedures, mental models, and strategies. In E. Z. Rothkopf (Ed.), Review of Research in Education, 15, 97-169.
Harel, I., & Papert, S. (1990). Software design as a learning environment. Interactive Learning Environments, 1, 1-32.
Hayes, J. R., & Flower, L. S. (1980). Identifying the organization of writing processes. In L. W. Gregg & E. R. Sternberg (Eds.), Cognitive processes in writing. Hillsdale NJ: Erlbaum.
Lakoff, G. (1987). Women, fire, and dangerous things: What categories reveal about the mind. Chicago: University of Chicago Press.
Larkin, J. H.& Chabay, R. W. (1989). Research on teaching scientific thinking: Implications for computer-based instruction. In L. B. Resnick & L. E. Klopfer (Eds.), Toward the thinking curriculum: Current cognitive research. Alexandria VA. 1989 Yearbook of the Association for Supervision and Curriculum Development.
Lepper, M. R., & Chabay, R. W. (1988). Socializing intelligent tutors: Bringing empathy to the computer tutor. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems (pp. 242-257). New York: Springer-Verlag.
Lippert, R. C. (1988, March), An expert system shell to teach problem solving. TechTrends, 22-26.
Lippert, R. (1990). Teaching problem solving in mathematics and science with expert systems. Journal of Artificial Intelligence in Education, 1(3), 27-40.
McDaniel, M. A., & Schlager, M. S. (1990). Discovery learning and transfer of problem-solving skills. Cognition and Instruction, 7(2), 129-159.
Merrill, M.D. (1983). Component display theory. In C. M. Reigeluth (Ed.), Instructional-design theories and models: An overview of their current status. (pp. 279-333). Hillsdale NJ: Erlbaum.
Merrill, M. D., Kowallis, T., & Wilson, B. G. (1981). Instructional design in transition. In F. Farley & N. Gordon (Eds.), Psychology and education: The state of the union. Chicago: McCutcheon.
Nelson, W. A. (1989). Artificial intelligence knowledge acquisition techniques for instructional development. Educational Technology Research and Development, 37(3), 81-94.
Newman, D., Griffin, P., & Cole, M. (1989). The construction zone: Working for cognitive change in school. Cambridge: Cambridge University Press.
Palincsar, A. M., & Brown. A. L. (1984). Reciprocal teaching of comprehension- fostering and monitoring activities. Cognition and Instruction, 1 (2), 117-175.
Paris, S. G., & Winograd, P. (1990). How metacognition can promote academic learning and instruction. In B. F. Jones & L. Idol (Eds.), Dimensions of thinking and cognitive instruction. Hillsdale NJ: Erlbaum.
Paul, R. W. (1990). Critical and reflective thinking: A philosophical perspective. In B. F. Jones & L. Idol (Eds.), Dimensions of thinking and cognitive instruction (pp. 445-494). Hillsdale NJ: Erlbaum.
Perkins, D. N. (1990). The nature and nurture of creativity. In In B. F. Jones & L. Idol (Eds.), Dimensions of thinking and cognitive instruction. Hillsdale NJ: Erlbaum.
Posner, G. K., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982) Accommodation of scientific conception: Toward a theory of conceptual change. Science Education, 66, 211-227.
Reigeluth, C. M. (Ed.). (1983a). Instructional-design theories and models: An overview of their current status. Hillsdale NJ: Erlbaum.
Reigeluth, C. M. (1983b). Meaningfulness and instruction: Relating what is being learned to what a student knows. Instructional Science, 12(3) 197-218.
Reigeluth, C. M. (Ed.). (1987). Instructional theories in action: Lessons illustrating selected theories and models. Hillsdale NJ: Erlbaum.
Reigeluth, C. M., Merrill, M. D., Wilson, B. G., & Spiller, R. T. (1980). The elaboration theory of instruction: A model for structuring instruction. Instructional Science, 9, 195-219.
Reigeluth, C. M., & Stein, R. (1983). Elaboration theory. In C. M. Reigeluth (Ed.), Instructional-design theories and models: An overview of their current status (pp. 335-381). Hillsdale NJ: Erlbaum.
Reiser, R. A. (1987). Instructional technology: A history. In R. M. Gagné (Ed.), Instructional technology: Foundations. Hillsdale NJ: Erlbaum.
Resnick, L. B. (1981). Instructional Psychology. Annual Review of Psychology, 40, 631-666.
Resnick, L. B. (1989, February). Learning in school and out. Educational Researcher, 13-20.
Riesbeck, C., & Schank, R. (1989). Inside case-based reasoning. Hillsdale NJ: Erlbaum.
Rogoff, B. (1990). Apprenticeship in thinking: Cognitive development in social context. New York: Oxford University Press.
Rogoff, B., & Lave, J. (Eds.). (1984). Everyday cognition: Its development in social context (pp. 95-116). Cambridge MA: Harvard University Press.
Salmoni, A. W., Schmidt, R. A., & Walter, C. B. (1984). Knowledge of results and motor learing: A review and critical reappraisal. Psychological Bulletin, 95(3), 355-386.
Salomon, G., Globerson, T., & Guterman, E. (1989). The computer as a zone of proximal development: Internalizing reading-related metacognitions from a reading partner. Journal of Educational Psychology, 81(4), 620-627.
Salomon, G., & Perkins, D. N. (1988). Rocky roads to transfer: Rethinking mechanisms of a neglected phenomenon. Educational Psychologist, 24(2), 113-142.
Scardamalia, M., & Bereiter, C. (1985). Fostering the development of self-regulation in children's knowledge processing. In S. F. Chipman, J. W. Segal, & R. Glaser (Eds.), Thinking and learning skills: Research and open questions (pp. 563-577). Hillsdale NJ: Erlbaum.
Scardamalia, M., & Bereiter, C. (1991). Higher levels of agency for children in knowledge building: A challenge for the design of new knowledge media. The Journal of the Learning Sciences, 1(1), 37-68.
Schank, R. C., & Jona, M. Y. (1991). Empowering the student: New perspectives on the design of teaching systems. The Journal of the Learning Sciences, 1(1), 7-35.
Schoenfeld (1985). Mathematical problem solving. New York: Academic Press.
Sherwood, R. D., & The Cognition and Technology Group at Vanderbilt. (1991, April). The development and preliminary evaluation of anchored instruction environments for developing mathematical and scientific thinking. Paper presented at the meeting of the National Association for Research in Science Teaching, Lake Geneva WI.
Smith, P. L., & Wedman, J. F. (1988). Read-think-aloud protocols: A new data-source for formative evaluation. Performance Improvement Quarterly, 1(2), 13-22.
Spiro, R. J., Coulson, R. L., Feltovich, P. J., & Anderson, D. K. (1988, October). Cognitive flexibility theory: Advanced knowledge acquisition in ill-structured domains (Technical Report No. 441). Champaign IL: University of Illinois at Urbana-Champaign, Center for the Study of Reading.
Spiro, R. J., & Jehng, J-C. (1990). Cognitive flexibility and hypertext: Theory and technology for the nonlinear and multidimensional traversal of complex subject matter. In D. Nix & R. J. Spiro (Eds.), Cognition, education, and multimedia: Exploring ideas in high technology (pp. 163-205). Hillsdale NJ: Erlbaum.
Strike, K. A., & Posner, G. J. (in press). A revisionist theory of conceptual change. To appear in R. Duschl & R. Hamilton (Eds.), Philosophy of science, cognitive science, and educational theory and practice. Albany NY: SUNY Press.
Van Haneghan, J. P., Barron, L., Young, M. F., Williams, S. M., Vye, N. J., & Bransford, J. D. (in press). The Jasper Series: An experiment with new ways to enhance mathematical thinking. In D. Halpern (Ed.), The development of thinking skills in the sciences and mathematics. Hillsdale NJ: Erlbaum.
Wertsch, J. V. (1989). Vygotsky and the social formation of mind. Cambridge MA: Harvard University Press.
White, B. Y., & Frederiksen, J. R. (1986). Progressions of quantitative models as a foundation for intelligent learning environments. Technical Report # 6277, Bolt, Beranek, & Newman.
Wilson, B. G. (1987). Computers and instructional design: Component display theory in transition. In M. R. Simonson & S. Zvacek (Eds.), Proceedings of selected research presentations (pp. 767 - 782). Washington, D. C.: Association for Educational Communications and Technology, Research and Theory Division.
Wilson, B. G. (1989). Cognitive psychology and instructional design. In S. Thiagarajan (Ed.), Proceedings of the National Society for Performance and Instruction, 317-333.
Wilson, B. G., & Jonassen, D. H. (1989). Hypertext and instructional design: Some preliminary guidelines. Performance Improvement Quarterly, 2(3), 34-49.
Wilson, B., & Cole, P. (1992). Returning the 'theory' to Elaboration Theory: Strategies for organizing instruction based on cognitive conceptions of learning. Paper presented at the meeting of the American Educational Research Association, San Francisco, manuscript in preparation.