[An excerpt from:]

Chad S. Carr

Pennsylvania State University

In S.P. Lajoie Computers as cognitive tools II: No more walls: Theory change, paradigm shifts and their influence on the use of computers for instructional purposes,

Mahwah, NJ: Lawrence Erlbaum Associates

Numerous reports decry the deficiencies in thinking skills of students at all educational levels. An important cause of these deficiencies (among many others), we argue, is the over-reliance of educators at all levels on a singular form of knowledge representation. Representing what learners know in only a single way engages only a limited set of cognitive skills. Engaging students in instructional activities and assessments that employ only a single formalism for representing their knowledge necessarily constrains their understanding of whatever they are studying. For example, in a physics class at Harvard, students who were competently solving physics problems that were represented mathematically (plug-and-chug) failed a test of conceptual understanding of the problems and their underlying principles (Panitz, 1998). Students had memorized and could apply the equations and problem-solving procedures without understanding the physics concepts they were representing mathematically. In another example, we recently analyzed the examinations that we conducted in core courses in a university business curriculum (e.g., management, marketing, finance) and found that 65% of the questions (all were multiple choice) in the course examinations assessed recall, memorization, or knowledge of what students were taught in lectures and read from the text; 25% were at the concept level; and 10% assessed higher-order thinking (e.g., rule, principle, inference, implication, etc.). Based on a series of examinations like these, business faculty are willing to certify (by virtue of a bachelor's degree) that graduates are competent to conduct business. Graduates' business competence, however, relies on their ability to recognize instances of the concepts that they memorized for those examinations in the real world and to know how to apply them in real-world practice, which requires understanding that was never examined or practiced in the large lecture courses. Business graduates, like most students in all levels of education, have deficient understanding of content because they were required to represent what they know in only one way (e.g., worksheets in K-12 classes, textbook problems in the sciences, definitional essays in the social sciences, and multiple-choice guessing everywhere) which engaged only a single set of cognitive skills.

There are numerous solutions to over-reliance on single formalisms for knowledge representation. Contemporary theories of learning recommend students who are constructive learners should be immersed in situated, problem-based learning environments that replicate real-world activity structures (Jonassen & Rohrer-Murphy, in press, Greeno, J., 1989). That solution, unfortunately, probably requires more of a paradigm shift in educational practice than most institutions are ready to accommodate. A more likely solution involves the use of multiple forms of knowledge representation using alternative active learning strategies and assessments. Every educator knows that learning is assessment-driven. So, an effective way to scaffold different kinds of learning is through alternative assessments (Lehrer, 1993). Since learners are motivated to exert intellectual effort to fulfill course task requirements, a solution to problems associated with over-reliance on single knowledge representation formalisms is to require learners to represent what they are learning in different ways. Computer technologies can facilitate that goal. In this chapter, we argue that an effective method (though not the only method) for supporting the representation of learner knowledge through multiple formalisms is to use computers as Mindtools (Jonassen, 1996, in press) to represent their knowledge. Mindtools are knowledge construction tools that learners learn with, not from . In this way, learners function as interpreters, organizers, and designers of their personal knowledge. Each Mindtool uses a different formalism for representing learners' knowledge, engaging a different set of critical cognitive skills.

Mindtools as Cognitive Tools

By using computers as Mindtools, we use technologies as knowledge construction tools that support, guide, and extend the thinking processes of their users (Derry, 1990). Mindtools provide structural, logical, causal, systemic, or visuo-spatial formalisms that scaffold different kinds of thinking and knowledge representation, that is, they manipulate the task (supplant the students’ performance by performing some part of the task or by adjusting the sequence or difficulty of the task) (Jonassen, 1998). Using computers as Mindtools enables learners to think in ways that they otherwise could not.

Mindtools are computer software applications, like databases, spreadsheets, semantic networking programs, expert systems, systems modeling tools, microworlds, hypermedia authoring tools, and computer conferencing, that enable learners to represent what they have learned and know using different representational formalisms. With some proficiency in using software applications, learners decide how to organize and represent their knowledge, rather than replicating or regurgitating teachers' interpretations. Using Mindtools to represent what they know necessarily engages learners in a variety of critical, creative, and complex thinking, such as evaluating, analyzing, connecting, elaborating, synthesizing, imagining, designing, problem solving, and decision making (Jonassen, 1996). When using computers as Mindtools, learners reflect on what they know and use those reflections to construct knowledge bases. They are teaching the computer, just as artificial intelligence researchers do when they build intelligent tutors.

An underlying issue of this volume and its first edition was the role of expert and student modeling in artificially intelligent tutoring systems. Derry and LaJoie (1993) argued that "the appropriate role for a computer system is not that of a teacher/expert, but rather, that of a mind-extending cognitive tool rather than a teaching agent." We agree, and suggest thatthe purpose of Mindtools is student modeling — by the student, not by the knowledge engineer for encoding in the system. Learners who use Mindtools to build knowledge bases are functioning as knowledge engineers, just as AI researchers do. Mindtools then represent a form of guerilla AI, with students wresting revolutionary control of the technology from the experts. We argue that knowledge engineering activates constructive learning strategies that are dormant in reproductive learning activities.

The remainder of the chapter will describe how different classes of Mindtools, semantic organization tools, dynamic modeling tools, information interpretation tools, knowledge construction tools, and conversation tools enable learners to represent what know in different ways, thereby engaging a range of cognitive activities.

Spreadsheets are computerized, numerical record keeping systems that were designed to replace paper-based, ledger accounting systems. Essentially, a spreadsheet is a matrix of empty cells with columns identified by letters and rows identified by numbers. Each cell is a placeholder for values, formulas relating values in other cells, or functions that mathematically or logically manipulate values in other cells. Functions are small programmed sequences that may, for instance, match values in cells with other cells, look up a variable in a table of values, or create an index of values to be compared with other cells.

Spreadsheets were originally developed and are most commonly used to support business decision making and accounting operations. They are especially useful for answering "what if" questions; for instance, what if interest rates increased by one percent? Changes made in one cell automatically recalculate all of the affected values in other cells (see fig 1).

fig 1 - sample financial spreadsheet

Spreadsheets also may be used as Mindtools for amplifying mental functioning. In the same way that they have qualitatively changed the accounting process, spreadsheets can change the educational process when working with quantitative information. Spreadsheets model the mathematical logic that is implied by calculations and so are useful for learning to solve algebra problems, for instance. Using spreadsheets helps students move from thinking in local cause-effect relations to general rule using in terms of the unknown and the mathematical relationships expressed in the problem (Sutherland & Rojano, 1994). Making the underlying mathematical reasoning obvious to learners by allowing them to manipulate them improves their understanding of the interrelationships and procedures. Spreadsheets are flexible Mindtools for representing (through charts and graphs, see fig 2), reflecting on, and speculating with quantitative information. They promote more open-ended investigations, problem-oriented activities, and active learning by students (Beare, 1992).

fig 2 - chart representing data

Cognitive Outcomes from Constructing Spreadsheets. Building spreadsheets requires abstract reasoning by the learner. Spreadsheets are rule-using tools that require that users become rule-makers (Vockell & van Deusen, 1989). They support problem solving activities, such as decision analysis. Perhaps more importantly, spreadsheets enable learners to consider implications of conditions or options and speculate and hypothesize about outcomes. Recently, spreadsheets have been used increasingly to model or simulate complex phenomena such as auditory encoding on the basilar membrane (Bremner & Denhem, 1992), visual information processing in the retina, lateral geniculate nucleous, and visual cortex (Halff, 1987), and neural networks. This form of dynamic systems modeling is the most powerful application of spreadsheets.

Important thinking skills that are engaged by modeling and speculating with spreadsheets include analyzing skills such as recognizing patterns, classifying, identifying assumptions, and finding sequences; connecting skills such as comparing/contrasting, logical thinking, deductive reasoning, and identifying causal relationships; a few creating thinking skills, and several complex thinking skills, especially in the designing and problem solving categories. Building and speculating with spreadsheets enables learners to represent their knowledge of topics they are studying as mathematical patterns and complex mathematical models of phenomena.

Database management systems are computerized filing systems designed to accelerate the storage and retrieval of information. Information is broken down into files that consist of matrices of records and fields. The records are instances, and the fields describe their characteristics. Databases use Boolean logic (using AND, OR, and NOT functions) to access relevant information from databases. The capabilities of high speed sorting and searching to answer queries about information in the database makes them essential for applications such as directories and catalogs.

Databases may also be used as tools for interpreting, analyzing, and organizing subject content by learners. Student-constructed databases using a Concept Development Strategy and an Interpretation of Data Strategy requires learners to select information to collect and to organize it into meaningful categories (Rooze, 1988-89). Student-constructed databases have been used to support history instruction (Knight & Timmons, 1986) and lessons on seashells (Goldberg, 1992), and as an inquiry tool to aid higher-level thinking in a fourth-grade American Indian studies course (Pon, 1984). Constructing database queries is a form of hypothesis testing (Katzeff, 1987). The database shown in Fig. 1 was developed by learners studying cells and their functions in a biology course. Although the intellectual benefits of building knowledge databases is obvious, more formal research on the efficacy of these activities is needed.

Fig. 1. Content database.

There are three basic activities involved in developing and using knowledge databases, each of which engages a different combination of cognitive processes. The simplest application is filling in an existing database by searching for information that fits into the data structure. For instance, in the database in Fig. 1, students could consult their textbooks to locate information about cell types, locations, shape, specialization, and tissue systems to include in the database. Constructing their own knowledge databases about cells would represent a more complex activity, in which students would develop the data structure (identify the fields), locate relevant information, insert it in appropriate fields and records. Finally, in order to apply their databases, students would search and sort the database to answer content queries about the content or to identify interrelationships and inferences from the content, such as "Do different shaped cells have specific functions?" Students can create such queries to test their own understanding of the database or to provide meaningful higher order activities for their peers.

Cognitive Outcomes from Building Knowledge Databases. Designing a database requires the learner to identify a content domain, sense an information need, and develop a data structure for accommodating the information to be included. Building databases involves analyzing, synthesizing, and evaluating information (Watson & Strudler, 1988-89). Database construction is an analytic process which engages important critical thinking skills such as evaluating, organizing, and connecting information; a few creative skills such as analogical reasoning and planning; and several complex thinking skills such designing a product, problem solving and decision making (Jonassen, 1996). Building knowledge databases enables learners to represent their knowledge of topics they are studying as organized tables of interrelated concepts using heterarchical knowledge structures and as queries for identifying specific subsets of information.