by James W. Pellegrino
University of Illinois at Chicago
IMPLICATIONS FOR CURRICULUM, INSTRUCTION, AND ASSESSMENT
There are multiple benefits of focusing on issues of how people learn with regard to matters of curriculum, instruction, and assessment. At the level of curriculum, knowledge of how people learn will help teachers and the educational system move beyond either–or dichotomies regarding the curriculum that have plagued the field of education. One such issue is whether the curriculum should emphasize “the basics” or teach thinking and problem-solving skills. Both are necessary. Students’ abilities to acquire organized sets of facts and
skills are actually enhanced when they are connected to meaningful problem-solving activities, and when students are helped to understand why, when, and how those facts and skills are relevant. And attempts to teach thinking skills without a strong base of factual knowledge do not promote problem-solving ability or support transfer to new situations.
Pearson Education (FTPress.com)
Focusing on how people learn also helps bring order to a seeming chaos of instructional choices. Consider the many possible teaching strategies that are debated in education circles and the media. They include lecture-based teaching, text-based teaching, inquiry-based teaching, technology-enhanced teaching, teaching organized around individuals versus cooperative groups, and so forth. Are some of these teaching techniques better than others? Is lecturing a poor way to teach, as many seem to claim? Is cooperative learning good? Does technology-enhanced teaching help achievement or hurt it?
Research and theory on how people learn suggests that these are the wrong questions. Asking which teaching technique is best is analogous to asking which tool is best—a hammer, a screwdriver, a plane, or pliers. In teaching, as in carpentry, the selection of tools depends on the task at hand and the materials one is working with. Books and lectures can be wonderfully efficient modes of transmitting new information for learning. They can excite the imagination, and hone students’ critical faculties. But one would choose other kinds of activities to elicit from students their preconceptions and level of understanding, or to help them see the power of using metacognitive strategies to monitor their learning. Hands-on activities and experiments can be a powerful way to ground emergent knowledge, but they do not alone evoke the underlying conceptual understandings that aid generalization. There is no universal best teaching practice.
If, instead, the point of departure is a core set of learning principles, then the selection of teaching strategies, mediated, of course, by subject matter, age and grade level, and desired outcome, can be purposeful. The many possibilities then become a rich set of opportunities from which a teacher constructs an instructional program rather than a chaos of competing alternatives.
Perhaps no area stands to gain more from knowledge of how people learn than the area of assessment, a persistent concern in the educational process. Assessing educational outcomes is not as straightforward as measuring height or weight; the attributes to be measured are mental representations and processes that are not outwardly visible. Thus, an assessment is a tool designed to observe students’ behavior and produce data that can be used to draw reasonable inferences about what students know. Another recent National Academy of Sciences report, Knowing What Students Know (Pellegrino et al., 2001), emphasizes that the targets of inference should be determined by cognitive models of learning that describe how people represent knowledge and develop competence in the domain of interest. The cognitive models suggest the most important aspects of student achievement about which one would want to draw inferences and provide clues about the types of assessment tasks that will elicit evidence to support those inferences.
The process of collecting evidence to support inferences about what students know represents a chain of reasoning from evidence about student learning that characterizes all assessments, from classroom quizzes and standardized achievement tests to computerized tutoring programs to the conversation a student has with his or her teacher as they work through an experiment. The process of reasoning from evidence can be portrayed as a triad of three interconnected elements known as the assessment triangle. The vertices of the assessment triangle represent the three key elements under-lying any assessment: a model of student cognition and learning in the domain; a set of beliefs about the kinds of observations that will provide evidence of students’ competencies; and an interpretation process for making sense of the evidence. These three elements may be explicit or implicit, but an assessment cannot be designed and implemented without some consideration of each. The three are represented as vertices of a triangle because each is connected to and dependent on the other two. A major tenet of Knowing What Students Know (Pellegrino et al., 2001) is that for an assessment to be effective, the three elements must be in synchrony. The assessment triangle provides a useful framework for analyzing the underpinnings of current assessments to determine how well they accomplish the goals we have in mind, as well as for designing future assessments.
The cognition corner of the triangle refers to a theory or set of beliefs about how students represent knowledge and develop competence in a subject domain (e.g., fractions). In any particular assessment application, a theory of learning in the domain is needed to identify the set of knowledge and skills that is important to measure for the task at hand, whether that be characterizing the competencies students have acquired thus far or guiding instruction to further increase learning. A central premise is that the cognitive theory should represent the most scientifically credible understanding of typical ways in which learners represent knowledge and develop expertise in a domain. These findings should derive from cognitive and educational research about how people learn, as well as the experience of expert teachers. Use of the term cognition is not meant to imply that the theory must necessarily come from a single cognitive research perspective. Theories and data on student learning and understanding can take different forms and encompass several levels and types of knowledge representation that include social and contextual components.
Depending on the purpose for an assessment, one might distinguish from one to hundreds of aspects of student competence to be sampled. These targets of inference for a given assessment will be a subset of the larger theory of how people learn the subject matter. Targets for assessment could be expressed in terms of numbers, categories, or some mix; they might be conceived as persisting over long periods of time or apt to change at the next problem step. They might concern tendencies in behaviour, conceptions of phenomena, available strategies, or levels of development.
IMPLICATIONS FOR THE DESIGN OF LEARNING ENVIRONMENTS
How do we take the knowledge about how people learn, as well as the implications for curriculum, instruction, and assessment, and use it productively to design effective learning environments? What role is there for information and communication technologies in this process? These questions do not have simple answers and at least one implication is that to achieve the higher level thinking and learning outcomes we want for our students, we will need to build learning environments that more carefully and consistently implement design principles that foster an effective integration of curriculum, instruction, and assessment. Furthermore, all three elements must be driven by theories, models, and empirical data on domain-specific learning. There is of course a tension in the juxtaposition of general principles of instructional design with matters of domain-specific knowledge and understanding. This tension cannot be avoided and requires that broader principles must always be tailored to specific cases of subject matter learning. Furthermore, it is likely to be the case that learning environments sensitive to such matters will be more complex than those designed and implemented in the past. Some of that complexity will be enabled and/or supported by information and communication technologies.
To address the design challenges alluded to previously, we need to ask what the findings from contemporary research on cognitive and social issues in learning and assessment, such as those described earlier and in the How People Learn (Bransford et al., 1999; Donovan et al., 1999) and Knowing What Students Know (Pellegrino et al., 2001) reports, suggest about general characteristics of powerful learning environments. Four such characteristics have been identified which in turn overlap with four major design principles for instruction that are critically important for achieving the types of learning with understanding that are espoused in contemporary educational standards. The four characteristics of powerful learning environments are as follows:
Consistent with the ideas about the multiple and interacting elements of a powerful learning environment, all driven by concerns about how people learn, are four principles for the design of instruction within such a contextual perspective:
We consider each principle in turn and briefly describe how technology can support its realization. A more complete discussion of these ideas and the variety of technology-based tools available to support these design principles can be found in Goldman et al. (1999, 2002). The present focus is on specific technology tools and applications rather than technology in general or its general or specific effects on student learning outcomes. For those interested in learning outcomes, a variety of analyses have appeared in recent years of the impact of technology on instruction, including discussions and evidence of when technology applications appear to be most effective in producing student learning gains (see, e.g., Cognition and Technology Group at Vanderbilt [CTGV], 1996; Kulik, 1994; Schacter & Fagnano, 1999; Wenglinsky, 1998).
Instruction Is Organized Around the Solution of Meaningful Problems
When students acquire new information in the process of solving meaningful problems, they are more likely to see its potential usefulness than when they are asked to memorize isolated facts. Meaningful problems also help students overcome the “inert knowledge” problem, defined by Whitehead (1929) as knowledge previously learned but not remembered in situations where it would be potentially useful. Seeing the relevance of information to everyday problems helps students understand when and how the information may be useful.
When students see the usefulness of information, they are motivated to learn (McCombs, 1991, 1994). Research on the relationship between interest and learning indicates that personal interest in a topic or domain positively impacts academic learning in that domain (Alexander, Kulikowich, & Jetton, 1994). New approaches to motivation emphasize motivational enhancement through authentic tasks that students perceive as real work for real audiences. This emphasis contrasts with earlier emphases on elaborate extrinsic reinforcements for correct responding (for discussion, see Collins, 1996).
Problem solving is at the core of inquiry- or project-based learning. Students will work on problems that are interesting and personally meaningful (Brown & Campione, 1994; CTGV, 1997; Hmelo & Williams, 1998; Resnick & Klopfer, 1989). Several contemporary educational reform efforts use dilemmas, puzzles, and paradoxes to “hook” or stimulate learners’ interests in the topic of study (Brown & Campione, 1994, 1996; CTGV, 1997; Goldman et al., 1996; Lamon et al., 1996; Scardamalia, Bereiter, & Lamon, 1994; Secules et al., 1997; Sherwood et al., 1995).
One major challenge for inquiry-based learning environments is developing problems that are rich and complex enough to engage students in the kinds of sustained inquiry that will allow them to deeply understand important new concepts. Bringing complex problems into the classroom is an important function of technology. Unlike problems that occur in the real world, problems that are created with graphics, video, and animation can be explored again and again. These multimedia formats capture children’s interest and provide information in the form of sound and moving images that is not available in text-based problems and stories. Multimedia formats are more easily understood and allow the learner to concentrate on high-level processes such as identifying problem-solving goals or making important inferences (Sharp et al., 1995).
Although technology-based problem environments come in many forms, an important characteristic is that they are under the learner’s control: Stories on interactive videodisc, CD-ROM, or DVD can be reviewed many times and specific frames or pictures can be frozen and studied. Problems presented via the World Wide Web or in hypermedia allow students to search easily for the parts that interest them most. Exploratory environments called microworlds or simulations allow students to carry out actions, immediately observe the results, and attempt to discover the rules that govern the system’s behavior. No matter what form of technology is involved, the student is primarily responsible for deciding how to investigate the problem, and the technology creates an environment in which flexible exploration is possible.
The cumulative work on The Adventures of Jasper Woodbury Problem Solving Series (CTGV, 1994, 1997, 2000) is one example of an attempt to develop meaningful problems and use instructional design principles based on cognitive theory. The Jasper series consists of 12 interactive video environments that invite students to solve authentic challenges, each of which requires them to understand and use important concepts in mathematics. For example, in the adventure known as Rescue at Boone’s Meadow which focuses on distance–rate–time relations, Larry is teaching Emily to fly an ultralight airplane. During the lessons, he helps Emily learn about the basic principles of flight and the specific details of the ultralight she is flying, such as its speed, fuel consumption, fuel capacity, and how much weight it can carry. Not long after Emily’s first solo flight, her friend Jasper goes fishing in a remote area called Boone’s Meadow. Hearing a gunshot, he discovers a wounded bald eagle and radios Emily for help in getting the eagle to a veterinarian. Emily consults a map to determine the closest roads to Boone’s Meadow, then calls Larry to find out about the weather and see if his ultralight is available. Students are challenged to use all the information in the video to determine the fastest way to rescue the eagle.
After viewing the video, students review the story and discuss the setting, characters, and any unfamiliar concepts and vocabulary introduced in the video. After they have a clear understanding of the problem situation, small groups of students work together to break the problem into subgoals, scan the video for information, and set up the calculations necessary to solve each part of the problem. Once they have a solution, they compare it with those that other groups generate and try to choose the optimum plan. Like most real-world problems, Jasper problems involve multiple correct solutions. Determining the optimum solution involves weighing factors such as safety and reliability, as well as making the necessary calculations. The Jasper series focuses on providing opportunities for problem solving and problem finding. It is not intended to replace the entire mathematics curriculum.
Instruction Provides Scaffolds for Achieving Meaningful Learning
In the previous section, we briefly described the benefits of giving students the opportunity and responsibility of exploring complex problems on their own. This is clearly a way to support the implementation of knowledge-centered elements in a learning environment. The mere presence of these opportunities, however, does not lead to learning with understanding, nor will they enhance a learner-centered approach. Because of the complexity of the problems and the inexperience of the students, scaffolds must be provided to help students carry out the parts of the task that they cannot yet manage on their own. Cognitive scaffolding assumes that individuals learn through interactions with more knowledgeable others, just as children learn through adult–child interactions (Bakhtin, 1935/1981; Bruner, 1983; Vygotsky, 1962). Adults model good thinking, provide hints, and prompt children who cannot “get it” on their own. Children eventually adopt the patterns of thinking reflected by the adults (Brown, Bransford, Ferrara, & Campione, 1983; Wood, Bruner, & Ross, 1976). Cognitive scaffolding can be realized in a number of ways. Collins, Brown, and Newman (1989) suggested modeling and coaching by experts, and providing guides and reminders about the procedures and steps that are important for the task.
Technologies can also be used to scaffold the solution of complex problems and projects by providing resources such as visualization tools, reference materials, and hints. Multimedia databases on CD-ROM, videodisc, or the World Wide Web provide important resources for students who are doing research. Technology-based reference materials provide several advantages over those in book format. Most important, they allow the presentation of information in audio or video format. In many cases, students can see an actual event and create their own analysis rather than reading someone else’s description. Electronic references are easy to search and provide information quickly while students are in the midst of problem solving. For example, definitions of words and their pronunciations are readily available while a student is reading or writing a story. Hints and demonstrations can be effortlessly accessed when a student is stuck while setting up a math problem. The knowledge that is acquired in these “just in time” situations is highly valued and easily remembered, because learners understand why it is useful to them.
Technology can help learners visualize processes and relations that are normally invisible or difficult to understand. For example, students might use spreadsheets to create a graph that demonstrates a trend or shows if one result is out of line with the rest. These graphs are useful in initial interpretations of numerical data and also valuable for reporting it to others. Graphs, maps, and other graphic representations can be created by students or automatically generated by simulation programs to depict the changes brought about by student actions.
Instruction Provides Opportunities for Practice With Feedback, Revision, and Reflection
Feedback, revision, and reflection are aspects of metacognition that are critical to developing the ability to regulate one’s own learning. Many years ago, Dewey (1933) noted the importance of reflecting on one’s ideas, weighing one’s ideas against data and predictions against obtained outcomes. In the context of teaching, Schon (1983, 1988) emphasized the importance of reflection in creating new mental models. Content-area experts exhibit strong self-monitoring skills that enable them to regulate their learning goals and activities. Self-regulated learners take feedback from their performance and adjust their learning in response to it. Self-monitoring depends on deep understanding in the domain because it requires an awareness of one’s own thinking, sufficient knowledge to evaluate that thinking and provide feedback to oneself, and knowledge of how to make necessary revisions. In other words, learners cannot effectively monitor what they know and make use of the feedback effectively (in revision) unless they have deep understanding in the domain. The idea that monitoring is highly knowledge dependent creates a dilemma for novices. How can they regulate their own learning without the necessary knowledge to do so? Thus, the development of expertise requires scaffolds for monitoring and self-regulation skills so that deep understanding and reflective learning can develop hand-in-hand.
Analyses of expert performance indicate that the development of expertise takes lots of practice over a long period of time (e.g., Bereiter & Scardamalia, 1993; Glaser & Chi, 1988). Cycles of feedback, reflection, and opportunities for revision provide students with opportunities to practice using the skills and concepts they are trying to master. Cognitive theories of skill acquisition place importance on practice because it leads to fluency and a reduction in the amount of processing resources needed to execute the skill (e.g., Anderson, 1983; Schneider, Dumais, & Shiffrin, 1984; Schneider & Shiffrin, 1977). Practice with feedback produces better learning than practice alone. Thorndike (1913) provided a simple but elegant illustration of the importance of practice with feedback for learning. He spent hundreds of hours trying to draw a line that was exactly 4 inches long. He did not improve—until he took off his blindfold. Only when he could see how close each attempt had come to the goal was Thorndike able to improve. Unless learners get feedback on their practice efforts, they will not know how to adjust their performance to improve.
An early and major use of technology was providing opportunities for extended practice of basic skills. It is important to distinguish between two stages of basic skill development: acquisition and fluency. Acquisition refers to the initial learning of a skill, and fluency refers to being able to access this skill in a quick and effortless manner (such as math facts). If basic skills are not developed to a fluent level then the learning process is incomplete and the student will not be able to function well in the real world.
Although there is no question that the nature of a drill-and-practice application makes it ideal for providing endless practice in almost any curricular area, the use of drill-and-practice is inappropriate when a student is in an acquisition phase of learning. As the name implies, computer-based drill-and-practice is designed to reinforce previously learned information rather than provide direct instruction on new skills. If technology is to be used during the acquisition phase of a new skill or concept, the tutorial is more appropriate than drill-and-practice. A technology-based tutorial differs from a drill-and-practice application in that a tutorial attempts to play the role of a teacher and provide direct instruction on a new skill or concept. The tutorial presents the student with new or previously unlearned material in an individualized manner, providing frequent corrective feedback and reinforcement.
It is important to remember that tutorials and drill-and-practice software came into existence at a time when teacher-led lecture and recitation was widely accepted and that these applications frequently mirror this instructional approach. For some, the mere mention of this type of software evokes a negative response; however, when students encounter difficulties in the process of solving meaningful problems, the opportunity for individualized instruction and practice can be very valuable. This is especially true when the curriculum provides students a chance to apply what they have learned by revising their solutions to the problem that caused them difficulty.
Fortunately, there are now multiple examples that support a wide range of formative assessment practices in the classroom. They include exciting new technology-based methods such as the Diagnoser software for physics and mathematics (Hunt & Minstrell, 1994), Latent Semantic Analysis for scoring essays (e.g., Landauer, Foltz, & Laham, 1998), the IMMEX system for providing feedback on problem solving (Hurst, Casillas, & Stevens, 1998), as well as the Curriculum Based Measurement system (Fuchs, Fuchs, Hamlett, & Stecker, 1991) and Knock Knock environments (CTGV, 1998) for feedback on literacy skills to young children. Such software can also be used to encourage the kind of self-assessment skills that are frequently seen in expert performance.
The Social Arrangements of Instruction Promote Collaboration and Distributed Expertise, as Well as Independent Learning
The view of cognition as socially shared rather than individually owned is an important shift in the orientation of cognitive theories of learning. It reflects the idea that thinking is a product of several heads in interaction with one another (Bereiter, 1990; Hutchins, 1991). In the theoretical context of “cognition as socially shared,” researchers have proposed having learners work in small groups on complex problems as a way to deal with complexity. Working together facilitates problem solving and capitalizes on distributed expertise (Barron, 1991; Brown & Campione, 1994, 1996; CTGV, 1992a, 1992b, 1992c, 1993a, 1993b, 1994, 1997; Pea, 1993; Salomon, 1993; Yackel, Cobb, & Wood, 1991). Collaborative environments also make excellent venues for making thinking visible, generating and receiving feedback, and revising (Barron et al., 1995; CTGV, 1994; Hatano & Inagaki, 1991; Vye et al., 1997, 1998).
A number of technologies support collaboration by providing venues for discussion and communication among learners. Through the use of computer networks, many schools today are connecting their computers to other computers often thousands of miles away. By networking computers within a room, building, or larger geographic area, students can send and receive information to and from other teachers or students not in their physical location. By networking computers, teachers and students are freed from the constraints of location and time. For example, students can log on to a network at any time that is convenient to send or receive information from any location attached to their network. Also, given the heavy dependence on text in most networked systems, students have a reason to use text to read, write, and construct thoughts and ideas for others to read and respond to. In addition, a vast amount of information is available through the Internet.
A vast array of communications services are rapidly becoming available to schools. For example, two-way video and two-way audio systems are now being used to allow students and teachers at remote sites to see and hear each other. In this way, face-to-face interactions can take place over great distances in real time. Communal databases and discussion groups make thinking visible and provide students with opportunities to give and receive feedback, often with more reflection, because the comments are written rather than spoken. Networked and Web-based communications technologies such as e-mail, List Serves, and more sophisticated knowledge-building software such as Knowledge Forum (Scardamalia & Bereiter, 1994) can also help students form a community around important ideas. Such technology helps capture ideas that otherwise can be ephemeral, and it supports communication that is asynchronous as well as synchronous.
FINAL COMMENTS: MAKING IT ALL WORK
Although the design principles mentioned earlier can be described individually, it is important to recognize that they need to work together. Thus, it is worth noting that various methods to incorporate knowledge-centered, learner-centered, assessment-centered, and community-centered elements in the overall instructional design have been explored in working with the Jasper Adventures and a related set of science materials known as the Scientists In Action series. For more complete descriptions of this body of work and data, see CTGV (1992a, 1992b, 1992c, 1993a, 1993b, 1994, 1997, 2000); Goldman, Zech, Biswas, et al. (1999); Pellegrino et al. (1991); Vye et al. (1997, 1998); and Zech et al. (1994, 1998).
With regard to learner-centered and assessment-centered issues, efforts were made to provide frequent and appropriate opportunities for formative assessment (Barron et al., 1995, 1998; CTGV, 1994, 1997, 2000). These include assessment of student-generated products at various points along the way to problem solution such as blueprints or business plans, and assessment facilitated by comparing intermediate solutions with those generated by others around the country who are working on similar problem-based and project-based curricula. In these examples, assessment is both teacher and student generated, and it is followed by opportunities to revise the product that has been assessed. The revision process is quite important for students and seems to lead to changes in students’ perspectives of the nature of adult work as well as conceptual growth.
Advantage was also taken of the fact that different ways of organizing classrooms can also have strong effects on the degree to which everyone participates, learns from one another, and makes progress in the cycles of work (e.g., Brown & Campione, 1996; Collins, Hawkins, & Carver, 1991). It has proven beneficial to have students work collaboratively in groups, but to also establish norms of individual accountability. One way to do this is to set up a requirement that each person in a group has to reach a threshold of achievement before moving on to collaborate on a more challenging project, for example, to be able to explain how pollution affects dissolved oxygen and hence life in the river, or to create a blueprint that a builder could use to build some structure. Under these conditions, the group works together to help everyone succeed. The revision process is designed to ensure that all students ultimately attain a level of understanding and mastery that establishes a precondition for moving from the problem-based to project-based activity.
The larger model that emerged is known as SMART, which stands for Scientific and Mathematical Arenas for Refining Thinking (Schwartz, Brophy, Lin, & Bransford, 1999; Schwartz, Lin, Brophy, & Bransford, 1999). The SMART model incorporates a number of design features to support all four features of an effective learning environment. For example, a variety of scaffolds and other technology-based learning tools were developed to deepen the possibilities for student learning. They included (a) Smart Lab, a virtual community for students in which they are exposed to contrasting solutions to problems in the context of being able to assess the adequacy of each; (b) Toolbox, various visual representations that can be used as tools for problem solving; and (c) Kids-on-Line, which features students making presentations. By using actors who make presentations based on real students’ work, we were able to seed the presentations with typical errors. This design feature allows students to engage in critical analysis of the arguments and see same-age peers explaining their work in sophisticated ways.
In the context of developing the SMART model, it has proven useful to provide students and teachers with access to information about how people outside their classroom have thought about the same problem that they are facing (CTGV, 1994, 1997, 2000). Such access can help students be more objective about their own view and realize that even with the same information other people may come to different conclusions or solutions. In addition, discussion of these differences of opinion can support the development of shared standards for reasoning. This can have a powerful effect on understanding the need for revising one’s ideas and as a motivator for engaging in such a process. Internet and Web-based environments now provide excellent mechanisms to incorporate these processes within the over-all SMART model. Some of these mechanisms include interactive websites with database components that are dynamically updated as students from multiple classrooms respond to items or probes on the website.
In summary, it is only through the process of designing complex learning environments such as the Jasper Adventures and the accompanying SMART model and implementing them in multiple classrooms that we can develop a truly rich and useful understanding of the complexities of connecting learning theory, instructional design, and technology. Such attempts to work in Pasteur’s Quadrant (Stokes, 1997) put theory into practice, provide the feedback we need about what works and why, and thus provide the basis for much richer and evolving theories of learning and instructional design.
University of Illinois at Chicago
IMPLICATIONS FOR CURRICULUM, INSTRUCTION, AND ASSESSMENT
There are multiple benefits of focusing on issues of how people learn with regard to matters of curriculum, instruction, and assessment. At the level of curriculum, knowledge of how people learn will help teachers and the educational system move beyond either–or dichotomies regarding the curriculum that have plagued the field of education. One such issue is whether the curriculum should emphasize “the basics” or teach thinking and problem-solving skills. Both are necessary. Students’ abilities to acquire organized sets of facts and
skills are actually enhanced when they are connected to meaningful problem-solving activities, and when students are helped to understand why, when, and how those facts and skills are relevant. And attempts to teach thinking skills without a strong base of factual knowledge do not promote problem-solving ability or support transfer to new situations.
Pearson Education (FTPress.com)
Focusing on how people learn also helps bring order to a seeming chaos of instructional choices. Consider the many possible teaching strategies that are debated in education circles and the media. They include lecture-based teaching, text-based teaching, inquiry-based teaching, technology-enhanced teaching, teaching organized around individuals versus cooperative groups, and so forth. Are some of these teaching techniques better than others? Is lecturing a poor way to teach, as many seem to claim? Is cooperative learning good? Does technology-enhanced teaching help achievement or hurt it?
Research and theory on how people learn suggests that these are the wrong questions. Asking which teaching technique is best is analogous to asking which tool is best—a hammer, a screwdriver, a plane, or pliers. In teaching, as in carpentry, the selection of tools depends on the task at hand and the materials one is working with. Books and lectures can be wonderfully efficient modes of transmitting new information for learning. They can excite the imagination, and hone students’ critical faculties. But one would choose other kinds of activities to elicit from students their preconceptions and level of understanding, or to help them see the power of using metacognitive strategies to monitor their learning. Hands-on activities and experiments can be a powerful way to ground emergent knowledge, but they do not alone evoke the underlying conceptual understandings that aid generalization. There is no universal best teaching practice.
If, instead, the point of departure is a core set of learning principles, then the selection of teaching strategies, mediated, of course, by subject matter, age and grade level, and desired outcome, can be purposeful. The many possibilities then become a rich set of opportunities from which a teacher constructs an instructional program rather than a chaos of competing alternatives.
Perhaps no area stands to gain more from knowledge of how people learn than the area of assessment, a persistent concern in the educational process. Assessing educational outcomes is not as straightforward as measuring height or weight; the attributes to be measured are mental representations and processes that are not outwardly visible. Thus, an assessment is a tool designed to observe students’ behavior and produce data that can be used to draw reasonable inferences about what students know. Another recent National Academy of Sciences report, Knowing What Students Know (Pellegrino et al., 2001), emphasizes that the targets of inference should be determined by cognitive models of learning that describe how people represent knowledge and develop competence in the domain of interest. The cognitive models suggest the most important aspects of student achievement about which one would want to draw inferences and provide clues about the types of assessment tasks that will elicit evidence to support those inferences.
The process of collecting evidence to support inferences about what students know represents a chain of reasoning from evidence about student learning that characterizes all assessments, from classroom quizzes and standardized achievement tests to computerized tutoring programs to the conversation a student has with his or her teacher as they work through an experiment. The process of reasoning from evidence can be portrayed as a triad of three interconnected elements known as the assessment triangle. The vertices of the assessment triangle represent the three key elements under-lying any assessment: a model of student cognition and learning in the domain; a set of beliefs about the kinds of observations that will provide evidence of students’ competencies; and an interpretation process for making sense of the evidence. These three elements may be explicit or implicit, but an assessment cannot be designed and implemented without some consideration of each. The three are represented as vertices of a triangle because each is connected to and dependent on the other two. A major tenet of Knowing What Students Know (Pellegrino et al., 2001) is that for an assessment to be effective, the three elements must be in synchrony. The assessment triangle provides a useful framework for analyzing the underpinnings of current assessments to determine how well they accomplish the goals we have in mind, as well as for designing future assessments.
The cognition corner of the triangle refers to a theory or set of beliefs about how students represent knowledge and develop competence in a subject domain (e.g., fractions). In any particular assessment application, a theory of learning in the domain is needed to identify the set of knowledge and skills that is important to measure for the task at hand, whether that be characterizing the competencies students have acquired thus far or guiding instruction to further increase learning. A central premise is that the cognitive theory should represent the most scientifically credible understanding of typical ways in which learners represent knowledge and develop expertise in a domain. These findings should derive from cognitive and educational research about how people learn, as well as the experience of expert teachers. Use of the term cognition is not meant to imply that the theory must necessarily come from a single cognitive research perspective. Theories and data on student learning and understanding can take different forms and encompass several levels and types of knowledge representation that include social and contextual components.
Depending on the purpose for an assessment, one might distinguish from one to hundreds of aspects of student competence to be sampled. These targets of inference for a given assessment will be a subset of the larger theory of how people learn the subject matter. Targets for assessment could be expressed in terms of numbers, categories, or some mix; they might be conceived as persisting over long periods of time or apt to change at the next problem step. They might concern tendencies in behaviour, conceptions of phenomena, available strategies, or levels of development.
IMPLICATIONS FOR THE DESIGN OF LEARNING ENVIRONMENTS
How do we take the knowledge about how people learn, as well as the implications for curriculum, instruction, and assessment, and use it productively to design effective learning environments? What role is there for information and communication technologies in this process? These questions do not have simple answers and at least one implication is that to achieve the higher level thinking and learning outcomes we want for our students, we will need to build learning environments that more carefully and consistently implement design principles that foster an effective integration of curriculum, instruction, and assessment. Furthermore, all three elements must be driven by theories, models, and empirical data on domain-specific learning. There is of course a tension in the juxtaposition of general principles of instructional design with matters of domain-specific knowledge and understanding. This tension cannot be avoided and requires that broader principles must always be tailored to specific cases of subject matter learning. Furthermore, it is likely to be the case that learning environments sensitive to such matters will be more complex than those designed and implemented in the past. Some of that complexity will be enabled and/or supported by information and communication technologies.
To address the design challenges alluded to previously, we need to ask what the findings from contemporary research on cognitive and social issues in learning and assessment, such as those described earlier and in the How People Learn (Bransford et al., 1999; Donovan et al., 1999) and Knowing What Students Know (Pellegrino et al., 2001) reports, suggest about general characteristics of powerful learning environments. Four such characteristics have been identified which in turn overlap with four major design principles for instruction that are critically important for achieving the types of learning with understanding that are espoused in contemporary educational standards. The four characteristics of powerful learning environments are as follows:
- Effective learning environments are knowledge centered. Attention is given to what is taught (central subject matter concepts), why it is taught (to support “learning with understanding” rather than merely remembering), and what competence or mastery looks like.
- Effective learning environments are learner centered. Educators must pay close attention to the knowledge, skills, and attitudes that learners bring into the classroom. This incorporates preconceptions regarding subject matter, and it also includes a broader understanding of the learner. Teachers in learner-centered environments pay careful attention to what students know as well as what they don’t know, and they continually work to build on students’ strengths.
- Effective learning environments are assessment centered. Especially important are efforts to make students’ thinking visible through the use of frequent formative assessment. This permits the teacher to grasp the students’ preconceptions, understand where students are on the “developmental corridor” from informal to formal thinking, and design instruction accordingly. They help both teachers and students monitor progress.
- Effective learning environments are community centered. This includes the development of norms for the classroom and school, as well as connections to the outside world, that support core learning values. Teachers must be enabled and encouraged to establish a community of learners among themselves. These communities can build a sense of comfort with questioning rather than knowing the answers and can develop a model of creating new ideas that builds on the contributions of individual members.
Consistent with the ideas about the multiple and interacting elements of a powerful learning environment, all driven by concerns about how people learn, are four principles for the design of instruction within such a contextual perspective:
- To establish knowledge-centered elements of a learning environment, instruction is organized around meaningful problems with appropriate goals.
- To support a learner-centered focus, instruction must provide scaffolds for solving meaningful problems and supporting learning with understanding.
- To support assessment-centered activities, instruction provides opportunities for practice with feedback, revision, and reflection.
- To create community in a learning environment, the social arrangements of instruction must promote collaboration and distributed expertise, as well as independent learning.
We consider each principle in turn and briefly describe how technology can support its realization. A more complete discussion of these ideas and the variety of technology-based tools available to support these design principles can be found in Goldman et al. (1999, 2002). The present focus is on specific technology tools and applications rather than technology in general or its general or specific effects on student learning outcomes. For those interested in learning outcomes, a variety of analyses have appeared in recent years of the impact of technology on instruction, including discussions and evidence of when technology applications appear to be most effective in producing student learning gains (see, e.g., Cognition and Technology Group at Vanderbilt [CTGV], 1996; Kulik, 1994; Schacter & Fagnano, 1999; Wenglinsky, 1998).
Instruction Is Organized Around the Solution of Meaningful Problems
When students acquire new information in the process of solving meaningful problems, they are more likely to see its potential usefulness than when they are asked to memorize isolated facts. Meaningful problems also help students overcome the “inert knowledge” problem, defined by Whitehead (1929) as knowledge previously learned but not remembered in situations where it would be potentially useful. Seeing the relevance of information to everyday problems helps students understand when and how the information may be useful.
When students see the usefulness of information, they are motivated to learn (McCombs, 1991, 1994). Research on the relationship between interest and learning indicates that personal interest in a topic or domain positively impacts academic learning in that domain (Alexander, Kulikowich, & Jetton, 1994). New approaches to motivation emphasize motivational enhancement through authentic tasks that students perceive as real work for real audiences. This emphasis contrasts with earlier emphases on elaborate extrinsic reinforcements for correct responding (for discussion, see Collins, 1996).
Problem solving is at the core of inquiry- or project-based learning. Students will work on problems that are interesting and personally meaningful (Brown & Campione, 1994; CTGV, 1997; Hmelo & Williams, 1998; Resnick & Klopfer, 1989). Several contemporary educational reform efforts use dilemmas, puzzles, and paradoxes to “hook” or stimulate learners’ interests in the topic of study (Brown & Campione, 1994, 1996; CTGV, 1997; Goldman et al., 1996; Lamon et al., 1996; Scardamalia, Bereiter, & Lamon, 1994; Secules et al., 1997; Sherwood et al., 1995).
One major challenge for inquiry-based learning environments is developing problems that are rich and complex enough to engage students in the kinds of sustained inquiry that will allow them to deeply understand important new concepts. Bringing complex problems into the classroom is an important function of technology. Unlike problems that occur in the real world, problems that are created with graphics, video, and animation can be explored again and again. These multimedia formats capture children’s interest and provide information in the form of sound and moving images that is not available in text-based problems and stories. Multimedia formats are more easily understood and allow the learner to concentrate on high-level processes such as identifying problem-solving goals or making important inferences (Sharp et al., 1995).
Although technology-based problem environments come in many forms, an important characteristic is that they are under the learner’s control: Stories on interactive videodisc, CD-ROM, or DVD can be reviewed many times and specific frames or pictures can be frozen and studied. Problems presented via the World Wide Web or in hypermedia allow students to search easily for the parts that interest them most. Exploratory environments called microworlds or simulations allow students to carry out actions, immediately observe the results, and attempt to discover the rules that govern the system’s behavior. No matter what form of technology is involved, the student is primarily responsible for deciding how to investigate the problem, and the technology creates an environment in which flexible exploration is possible.
The cumulative work on The Adventures of Jasper Woodbury Problem Solving Series (CTGV, 1994, 1997, 2000) is one example of an attempt to develop meaningful problems and use instructional design principles based on cognitive theory. The Jasper series consists of 12 interactive video environments that invite students to solve authentic challenges, each of which requires them to understand and use important concepts in mathematics. For example, in the adventure known as Rescue at Boone’s Meadow which focuses on distance–rate–time relations, Larry is teaching Emily to fly an ultralight airplane. During the lessons, he helps Emily learn about the basic principles of flight and the specific details of the ultralight she is flying, such as its speed, fuel consumption, fuel capacity, and how much weight it can carry. Not long after Emily’s first solo flight, her friend Jasper goes fishing in a remote area called Boone’s Meadow. Hearing a gunshot, he discovers a wounded bald eagle and radios Emily for help in getting the eagle to a veterinarian. Emily consults a map to determine the closest roads to Boone’s Meadow, then calls Larry to find out about the weather and see if his ultralight is available. Students are challenged to use all the information in the video to determine the fastest way to rescue the eagle.
After viewing the video, students review the story and discuss the setting, characters, and any unfamiliar concepts and vocabulary introduced in the video. After they have a clear understanding of the problem situation, small groups of students work together to break the problem into subgoals, scan the video for information, and set up the calculations necessary to solve each part of the problem. Once they have a solution, they compare it with those that other groups generate and try to choose the optimum plan. Like most real-world problems, Jasper problems involve multiple correct solutions. Determining the optimum solution involves weighing factors such as safety and reliability, as well as making the necessary calculations. The Jasper series focuses on providing opportunities for problem solving and problem finding. It is not intended to replace the entire mathematics curriculum.
Instruction Provides Scaffolds for Achieving Meaningful Learning
In the previous section, we briefly described the benefits of giving students the opportunity and responsibility of exploring complex problems on their own. This is clearly a way to support the implementation of knowledge-centered elements in a learning environment. The mere presence of these opportunities, however, does not lead to learning with understanding, nor will they enhance a learner-centered approach. Because of the complexity of the problems and the inexperience of the students, scaffolds must be provided to help students carry out the parts of the task that they cannot yet manage on their own. Cognitive scaffolding assumes that individuals learn through interactions with more knowledgeable others, just as children learn through adult–child interactions (Bakhtin, 1935/1981; Bruner, 1983; Vygotsky, 1962). Adults model good thinking, provide hints, and prompt children who cannot “get it” on their own. Children eventually adopt the patterns of thinking reflected by the adults (Brown, Bransford, Ferrara, & Campione, 1983; Wood, Bruner, & Ross, 1976). Cognitive scaffolding can be realized in a number of ways. Collins, Brown, and Newman (1989) suggested modeling and coaching by experts, and providing guides and reminders about the procedures and steps that are important for the task.
Technologies can also be used to scaffold the solution of complex problems and projects by providing resources such as visualization tools, reference materials, and hints. Multimedia databases on CD-ROM, videodisc, or the World Wide Web provide important resources for students who are doing research. Technology-based reference materials provide several advantages over those in book format. Most important, they allow the presentation of information in audio or video format. In many cases, students can see an actual event and create their own analysis rather than reading someone else’s description. Electronic references are easy to search and provide information quickly while students are in the midst of problem solving. For example, definitions of words and their pronunciations are readily available while a student is reading or writing a story. Hints and demonstrations can be effortlessly accessed when a student is stuck while setting up a math problem. The knowledge that is acquired in these “just in time” situations is highly valued and easily remembered, because learners understand why it is useful to them.
Technology can help learners visualize processes and relations that are normally invisible or difficult to understand. For example, students might use spreadsheets to create a graph that demonstrates a trend or shows if one result is out of line with the rest. These graphs are useful in initial interpretations of numerical data and also valuable for reporting it to others. Graphs, maps, and other graphic representations can be created by students or automatically generated by simulation programs to depict the changes brought about by student actions.
Instruction Provides Opportunities for Practice With Feedback, Revision, and Reflection
Feedback, revision, and reflection are aspects of metacognition that are critical to developing the ability to regulate one’s own learning. Many years ago, Dewey (1933) noted the importance of reflecting on one’s ideas, weighing one’s ideas against data and predictions against obtained outcomes. In the context of teaching, Schon (1983, 1988) emphasized the importance of reflection in creating new mental models. Content-area experts exhibit strong self-monitoring skills that enable them to regulate their learning goals and activities. Self-regulated learners take feedback from their performance and adjust their learning in response to it. Self-monitoring depends on deep understanding in the domain because it requires an awareness of one’s own thinking, sufficient knowledge to evaluate that thinking and provide feedback to oneself, and knowledge of how to make necessary revisions. In other words, learners cannot effectively monitor what they know and make use of the feedback effectively (in revision) unless they have deep understanding in the domain. The idea that monitoring is highly knowledge dependent creates a dilemma for novices. How can they regulate their own learning without the necessary knowledge to do so? Thus, the development of expertise requires scaffolds for monitoring and self-regulation skills so that deep understanding and reflective learning can develop hand-in-hand.
Analyses of expert performance indicate that the development of expertise takes lots of practice over a long period of time (e.g., Bereiter & Scardamalia, 1993; Glaser & Chi, 1988). Cycles of feedback, reflection, and opportunities for revision provide students with opportunities to practice using the skills and concepts they are trying to master. Cognitive theories of skill acquisition place importance on practice because it leads to fluency and a reduction in the amount of processing resources needed to execute the skill (e.g., Anderson, 1983; Schneider, Dumais, & Shiffrin, 1984; Schneider & Shiffrin, 1977). Practice with feedback produces better learning than practice alone. Thorndike (1913) provided a simple but elegant illustration of the importance of practice with feedback for learning. He spent hundreds of hours trying to draw a line that was exactly 4 inches long. He did not improve—until he took off his blindfold. Only when he could see how close each attempt had come to the goal was Thorndike able to improve. Unless learners get feedback on their practice efforts, they will not know how to adjust their performance to improve.
An early and major use of technology was providing opportunities for extended practice of basic skills. It is important to distinguish between two stages of basic skill development: acquisition and fluency. Acquisition refers to the initial learning of a skill, and fluency refers to being able to access this skill in a quick and effortless manner (such as math facts). If basic skills are not developed to a fluent level then the learning process is incomplete and the student will not be able to function well in the real world.
Although there is no question that the nature of a drill-and-practice application makes it ideal for providing endless practice in almost any curricular area, the use of drill-and-practice is inappropriate when a student is in an acquisition phase of learning. As the name implies, computer-based drill-and-practice is designed to reinforce previously learned information rather than provide direct instruction on new skills. If technology is to be used during the acquisition phase of a new skill or concept, the tutorial is more appropriate than drill-and-practice. A technology-based tutorial differs from a drill-and-practice application in that a tutorial attempts to play the role of a teacher and provide direct instruction on a new skill or concept. The tutorial presents the student with new or previously unlearned material in an individualized manner, providing frequent corrective feedback and reinforcement.
It is important to remember that tutorials and drill-and-practice software came into existence at a time when teacher-led lecture and recitation was widely accepted and that these applications frequently mirror this instructional approach. For some, the mere mention of this type of software evokes a negative response; however, when students encounter difficulties in the process of solving meaningful problems, the opportunity for individualized instruction and practice can be very valuable. This is especially true when the curriculum provides students a chance to apply what they have learned by revising their solutions to the problem that caused them difficulty.
Fortunately, there are now multiple examples that support a wide range of formative assessment practices in the classroom. They include exciting new technology-based methods such as the Diagnoser software for physics and mathematics (Hunt & Minstrell, 1994), Latent Semantic Analysis for scoring essays (e.g., Landauer, Foltz, & Laham, 1998), the IMMEX system for providing feedback on problem solving (Hurst, Casillas, & Stevens, 1998), as well as the Curriculum Based Measurement system (Fuchs, Fuchs, Hamlett, & Stecker, 1991) and Knock Knock environments (CTGV, 1998) for feedback on literacy skills to young children. Such software can also be used to encourage the kind of self-assessment skills that are frequently seen in expert performance.
The Social Arrangements of Instruction Promote Collaboration and Distributed Expertise, as Well as Independent Learning
The view of cognition as socially shared rather than individually owned is an important shift in the orientation of cognitive theories of learning. It reflects the idea that thinking is a product of several heads in interaction with one another (Bereiter, 1990; Hutchins, 1991). In the theoretical context of “cognition as socially shared,” researchers have proposed having learners work in small groups on complex problems as a way to deal with complexity. Working together facilitates problem solving and capitalizes on distributed expertise (Barron, 1991; Brown & Campione, 1994, 1996; CTGV, 1992a, 1992b, 1992c, 1993a, 1993b, 1994, 1997; Pea, 1993; Salomon, 1993; Yackel, Cobb, & Wood, 1991). Collaborative environments also make excellent venues for making thinking visible, generating and receiving feedback, and revising (Barron et al., 1995; CTGV, 1994; Hatano & Inagaki, 1991; Vye et al., 1997, 1998).
A number of technologies support collaboration by providing venues for discussion and communication among learners. Through the use of computer networks, many schools today are connecting their computers to other computers often thousands of miles away. By networking computers within a room, building, or larger geographic area, students can send and receive information to and from other teachers or students not in their physical location. By networking computers, teachers and students are freed from the constraints of location and time. For example, students can log on to a network at any time that is convenient to send or receive information from any location attached to their network. Also, given the heavy dependence on text in most networked systems, students have a reason to use text to read, write, and construct thoughts and ideas for others to read and respond to. In addition, a vast amount of information is available through the Internet.
A vast array of communications services are rapidly becoming available to schools. For example, two-way video and two-way audio systems are now being used to allow students and teachers at remote sites to see and hear each other. In this way, face-to-face interactions can take place over great distances in real time. Communal databases and discussion groups make thinking visible and provide students with opportunities to give and receive feedback, often with more reflection, because the comments are written rather than spoken. Networked and Web-based communications technologies such as e-mail, List Serves, and more sophisticated knowledge-building software such as Knowledge Forum (Scardamalia & Bereiter, 1994) can also help students form a community around important ideas. Such technology helps capture ideas that otherwise can be ephemeral, and it supports communication that is asynchronous as well as synchronous.
FINAL COMMENTS: MAKING IT ALL WORK
Although the design principles mentioned earlier can be described individually, it is important to recognize that they need to work together. Thus, it is worth noting that various methods to incorporate knowledge-centered, learner-centered, assessment-centered, and community-centered elements in the overall instructional design have been explored in working with the Jasper Adventures and a related set of science materials known as the Scientists In Action series. For more complete descriptions of this body of work and data, see CTGV (1992a, 1992b, 1992c, 1993a, 1993b, 1994, 1997, 2000); Goldman, Zech, Biswas, et al. (1999); Pellegrino et al. (1991); Vye et al. (1997, 1998); and Zech et al. (1994, 1998).
With regard to learner-centered and assessment-centered issues, efforts were made to provide frequent and appropriate opportunities for formative assessment (Barron et al., 1995, 1998; CTGV, 1994, 1997, 2000). These include assessment of student-generated products at various points along the way to problem solution such as blueprints or business plans, and assessment facilitated by comparing intermediate solutions with those generated by others around the country who are working on similar problem-based and project-based curricula. In these examples, assessment is both teacher and student generated, and it is followed by opportunities to revise the product that has been assessed. The revision process is quite important for students and seems to lead to changes in students’ perspectives of the nature of adult work as well as conceptual growth.
Advantage was also taken of the fact that different ways of organizing classrooms can also have strong effects on the degree to which everyone participates, learns from one another, and makes progress in the cycles of work (e.g., Brown & Campione, 1996; Collins, Hawkins, & Carver, 1991). It has proven beneficial to have students work collaboratively in groups, but to also establish norms of individual accountability. One way to do this is to set up a requirement that each person in a group has to reach a threshold of achievement before moving on to collaborate on a more challenging project, for example, to be able to explain how pollution affects dissolved oxygen and hence life in the river, or to create a blueprint that a builder could use to build some structure. Under these conditions, the group works together to help everyone succeed. The revision process is designed to ensure that all students ultimately attain a level of understanding and mastery that establishes a precondition for moving from the problem-based to project-based activity.
The larger model that emerged is known as SMART, which stands for Scientific and Mathematical Arenas for Refining Thinking (Schwartz, Brophy, Lin, & Bransford, 1999; Schwartz, Lin, Brophy, & Bransford, 1999). The SMART model incorporates a number of design features to support all four features of an effective learning environment. For example, a variety of scaffolds and other technology-based learning tools were developed to deepen the possibilities for student learning. They included (a) Smart Lab, a virtual community for students in which they are exposed to contrasting solutions to problems in the context of being able to assess the adequacy of each; (b) Toolbox, various visual representations that can be used as tools for problem solving; and (c) Kids-on-Line, which features students making presentations. By using actors who make presentations based on real students’ work, we were able to seed the presentations with typical errors. This design feature allows students to engage in critical analysis of the arguments and see same-age peers explaining their work in sophisticated ways.
In the context of developing the SMART model, it has proven useful to provide students and teachers with access to information about how people outside their classroom have thought about the same problem that they are facing (CTGV, 1994, 1997, 2000). Such access can help students be more objective about their own view and realize that even with the same information other people may come to different conclusions or solutions. In addition, discussion of these differences of opinion can support the development of shared standards for reasoning. This can have a powerful effect on understanding the need for revising one’s ideas and as a motivator for engaging in such a process. Internet and Web-based environments now provide excellent mechanisms to incorporate these processes within the over-all SMART model. Some of these mechanisms include interactive websites with database components that are dynamically updated as students from multiple classrooms respond to items or probes on the website.
In summary, it is only through the process of designing complex learning environments such as the Jasper Adventures and the accompanying SMART model and implementing them in multiple classrooms that we can develop a truly rich and useful understanding of the complexities of connecting learning theory, instructional design, and technology. Such attempts to work in Pasteur’s Quadrant (Stokes, 1997) put theory into practice, provide the feedback we need about what works and why, and thus provide the basis for much richer and evolving theories of learning and instructional design.
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