AECT Handbook of Research

Table of Contents

24: Learning with technology: Using computers as cognitive tools

24.1 Introduction
24.2 Computers as cognitive tools
24.3 Why cognitive tools?
24.4 Overview of the chapter
24.5 Computer programming languages as cognitive tools
24.6 Hypermedia/ Multimedia authoring systems as cognitive tools
24.7 Semantic networking as cognitive tools
24.8 Expert systems as cognitive tools
24.9 Databases as cognitive tools
24.10 Spreadsheets as cognitive tools
24.11 Conclusions
24.12 A final word
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24.2 Computers as cognitive tools

By contrast, this chapter represents a departure from the central emphasis in this handbook on media and technology as vehicles for educational communications. Instead, we focus on the applications of technologies, primarily computers, as cognitive tools. This chapter is about computer-based cognitive tools and learning environments that have been adapted or developed to function as intellectual partners to enable and facilitate critical thinking and higher-order learning. Examples of cognitive tools include (but are not necessarily limited to):

  • Databases
  • Spreadsheets
  • Semantic networks
  • Expert systems
  • Multimedia/hypermedia construction software
  • Computer-based conferencing
  • Collaborative knowledge construction environments
  • Computer programming languages
  • Microworlds

The cognitive tools perspective is distinctly different from traditional conceptions of instructional technologies. In cognitive tools, information is not encoded in predefined educational communications that are then used to transmit knowledge to students. With cognitive tools, the instructional design processes referred to above are eliminated. Instead of specialists such as instructional designers using technology to constrain students' learning processes through prescribed communications and interactions, the technologies are taken away from the specialists and given to learners to use as media for representing and expressing what they know. Learners themselves function as designers using technologies as tools for analyzing the world, accessing information, interpreting and organizing their personal knowledge, and representing what they know to others.

As important as it is to distinguish the "cognitive tools" perspective from the traditional educational media approach, it is also important to highlight differences between this conception of technology and earlier perspectives of using computers to support learning that have not been successful. Ever since Taylor (1980) presented his classic model of the roles of computers in education as "tutor, tool, and tutee," many educators and commercial entrepreneurs have predicted that computers would revolutionize education through one or more of these roles. In reality, none of these approaches has lived up to its promise.

In recent years, advocates of computer-based instruction and intelligent tutoring systems (ITS) who represent the computer-as-"tutor" perspective have begun to acknowledge the lack of impact they have had on mainstream education and training (cf. Lajoie & Derry, 1993; Shlechter, 1991). At least part of this failure stems from the overly restrictive perspective of students as perceivers or recipients of educational communications that characterizes the research in this field. Another factor contributing to the lack of success of ITS is that the technical difficulties inherent in building student models and facilitating humanlike communications have been greatly underestimated by proponents of the "tutor" model (see 19.5).

The computer-as-"tool" approach has also disappointed many of its proponents, although there have been some successes when tools have been embedded within innovative pedagogy such as a whole-language approach to literacy development (cf. Bruce & Rubin, 1993). In many cases, software tools such as word-processing, spreadsheet, database, and computer-aided design (CAD) programs have failed to improve teaching and learning significantly because they have been largely relegated to the service of a traditional "instructivist" pedagogy. Goodlad (1984) and others have described the teacher-directed, text- and workbook-dominated curriculum that has characterized educational practice for decades. Instead of being employed as cognitive tools to solve challenging problems, pursue personal learning goals, or accomplish authentic tasks, computer tools have often been regarded as objects for study themselves and subjected to the same deadly instructivist pedagogy that has stymied intellectual growth by most students in more traditional areas such as science, mathematics, and social studies. Consider, for example, computer-aided design (CAD) software, which has revolutionized professional practices and dramatically increased productivity in engineering, architecture, and other design fields. Industrial arts teachers (now called technology educators) have enthusiastically adopted CAD software into their classrooms and computers labs, but instead of engaging students in authentic tasks, they usually "teach" students the command sets for the software outside of any meaningful contexts. Not surprisingly, students end up failing to perceive the relevance and value of such programs within the design professions or their own lives. As pointed out by Salomon et al. (1991), "No important impact can be expected when the same old activity is carried out with a technology that makes it a bit faster or easier; the activity itself has to change" (p. 8).

The results of the "tutee" role for computers in education, despite the almost religious fervor with which it has been embraced in some circles (cf. Papert, 1980), have also been much less spectacular than promised (see According to the computer-as-"tutee" approach, students develop higher-order thinking skills and creativity by teaching the computer to perform tasks, e.g., draw a picture, through the use of "friendly" programming languages such as Logo (Papert, 1980) and microworlds such as Karel the Robot (Popyack, 1989). Studies aimed at investigating the effects of Logo (cf. Pea & Kurland, 1987) have failed to demonstrate the cognitive advantages promised by Papert and others. Defenders of the "tutee" approach would maintain that the implementations of Logo investigated in most studies were too brief and unfocused. To be sure, many applications of Logo and other microworlds described in the literature seem to lack the "mindful engagement" that Salomon and Globerson (1987) argue is necessary for learning. As shown in greater detail below, more intensive applications of Logo, wherein students are engaged in meaningful tasks over longer periods of time, have demonstrated more impressive cognitive effects (cf. Harel, 1991; Papert, 1993).

Updated August 3, 2001
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