Computer - Assisted Instruction

Abstract

This article presents an overview of Computer-Assisted Instruction (CAI), with an emphasis on CAI theory and practice in public schools. CAI involves the use of computers to supplement or assist classroom instruction. Even though their capabilities were limited, the first public school CAI systems, operated on teletype machines to drill elementary school students in arithmetic, were shown effective in promoting skill development. As computer technologies advanced, the types of assistance that CAI systems could provide grew. CAI systems provide instructional assistance across all pre-kindergarten through college levels and across virtually all subject areas. One crucial issue in CAI theory and practice is comparative evaluation: how effective is CAI in comparison to other approaches it replaces during the class day? Various cumulative studies suggest neutral or mild to very significant positive benefits to CAI. Computer-Assisted Instruction's original theoretical roots can be traced to variations of Instructional System Design (ISD). Since the early 1990s, because of advances in technology and in theories of learning and cognition, CAI has taken more varied forms, and the kinds of learning that it has been used to promote have expanded significantly.

Overview

Computer-Assisted Instruction (CAI) involves the use of computers to supplement or assist classroom instruction. In its traditional form, CAI relies on software that presents information and guides a learner through a series of subject matter objectives, quizzing the student periodically and assessing progress to a mastery level. CAI does not supplant or fully replace the teacher in a classroom environment. The term CAI overlaps with the related term of Computer-Based Instruction (CBI). Though these terms have sometimes been used synonymously, CBI also refers to instruction that is organized more fully around the computer system as the primary source of instructional delivery. A system that combine either full or partial curriculum but also the related testing and records management for large groups of students (for example, a school) is referred to as an Integrated Learning System, or ILS. Some of the most prominent ILS's include the Plato System, Jostens Learning, and Pearson Education.

A primary advantage that CAI offers to the instructional process is that it permits students to proceed through curriculum objectives at a pace that they find comfortable. Classroom settings that move in lock-step at a pace determined by a teacher can either bore students by moving too slowly for them or lose students by moving on to new material before mastery has been acquired on that which has already been covered. CAI software, however, permits students to dwell on material or return to it until they have reached a mastery level. It allows the student to do so in a more private context than a classroom, where students who learn more slowly may be subjected to embarrassment or ridicule from classmates. It also permits students who learn more rapidly and who have mastered a set of objectives to progress without waiting to new skills and concepts that they have not yet acquired. Thus, CAI permits customization that is usually not possible to attain for all students in a classroom. This individualization is crucial to understanding why CAI is considered a powerful educational strategy.

CAI has several other advantages. In general, as a supplement rather than a replacement for teacher-based instruction, it provides a blended approach to learning and thus a more diverse educational experience for students. Virtually all large-scale studies that examine student attitudes find that CAI can motivate students and improve attitudes toward learning. Some studies have shown CAI improves school attendance. CAI can relieve a teacher of routine tasks that are associated with student practice, exercise, and drill, and do so at significantly lower cost than the teacher. Finally, one of the most important advantages involves what educational psychologists refer to as "locus of control," which is when students have a greater sense of control over their learning experience and how it is paced; they have less ambiguity about their performance because CAI systems furnish frequent feedback.

Early Frameworks of CAI. CAI has roots in several conceptual frameworks that were developed primarily in the 1950s through the 1980s. Three of the most important of these are Instructional System Design (ISD), pioneered most prominently by Glaser (1962), Mager (1975), and Dick and Carey (1990); hierarchical learning objectives and conditions for learning advanced by Gagne and colleagues at Florida State University (1977; 1992), and Mastery Learning, pioneered by Benjamin Bloom of the University of Chicago (Bloom, 1981). These frameworks collectively emphasize training and instruction that follow carefully structured sequences of subject material or tasks. This requires the instructional designer to break down the subject matter into a hierarchy of objectives. The student masters each objective in the sequence before moving on to the next objective. Frequent assessment of progress in each step towards mastery allows an evaluation both of the achievements of the student and the effectiveness of the instructional system. It also permits continuous feedback to the learner and positive reinforcement for successful completion of instructional tasks and for mastery of objectives.

This type of methodical, objective-based, and hierarchical approach to learning does not inherently require the use of any computer assistance at all, but it proved to be a good fit for early efforts to build software systems that could assist instruction.

Computers were actually teletype terminals connected to mainframe computers. The first published evaluations of CAI's effectiveness, in 1969, by Stanford researchers Patrick Suppes and Mona Morningstar, provided evidence of learning gains by grade school students in mathematics and in college level Russian (1969a; 1969b). Usage of CAI remained limited, however, through the 1970s in part because of expense and the physical difficulty of providing access to students to appropriate equipment. These and other factors limited the kinds of CAI software that was developed, which in turn limited the appeal of CAI. By the end of the 1970s and early 1980s, though, some studies had taken place to measure the effectiveness of CAI in areas such as physics instruction (Tennyson, 1981), in working with learning-disabled students (Isenberg, 1985; Schmidt, Weinstein, Niemic, & Walberg, 1985), and in comparing the effects of CAI in low socioeconomic status (SES) versus more affluent environments (Mevarech & Rich, 1985). These studies with early systems produced mixed results. The most promising results involved the finding that CAI was effective in structuring learning experiences for students from disadvantaged and low-SES environments.

Evaluations of Effectiveness. One of the most crucial issues in education evaluation has been whether CAI is a cost-effective component of classroom learning. Broadly speaking, three large issues are at stake for public school policy-makers in evaluating the use of CAI. These include the cost of hardware and software, the cost in training teachers, and the time that students devote to CAI that they would otherwise spend in other forms of instruction. The potential advantages of CAI fall into two principle categories: do students attain or exceed the learning objectives that would be available to them from other forms of instruction, and do students find more satisfaction in using CAI than in alternatives? Some researchers, such as Larry Cuban of Stanford University, have frequently challenged the use of computers and CAI as overly faddish (e.g., Cuban, 1993). These critics also complain that CAI can divert resources from more important learning experiences that schools should provide.

Metastudies of their effectiveness were undertaken. Some of the most important were by Kulik and colleagues, who repeatedly (1991; 1983; 1987; 1985) found statistically significant improvement trends in learning achievement for students in CAI. One later metastudy concluded, though, that the actual benefit attributable to CAI may more plausibly be attributed to the fact that CAI content may be higher quality materials than traditional alternatives (Fletcher-Flinn & Gravatt, 1995). Some metastudies focused on CAI with particular groups of students or subject areas. One early study suggested that learning disabled students did not benefit from CAI, but subsequent studies published since 2000 all point to significant gains for learning disabled students who participated in CAI in areas such as mathematics, reading comprehension, vocabulary, and spelling (Fuchs et al., 2006; Hall, Hughes, & Filbert, 2000; Jitendra, Edwards, Sacks, & Jacobson, 2004).

Metastudies continue to suggest that students from disadvantaged environments especially benefit from CAI. That is, CAI may contribute a structure that is especially effective with schools in different types of disadvantaged or low-SES settings (Mevarech & Rich, 1985; Swan & Guerrero, 1990). None of the prominent metastudies undertaken since 1980 have indicated that CAI has a significant negative impact on learning achievement, though several suggest that the impact was vaguely specified, minimal, or else attributable to other factors (Cousins & Ross, 1993). Additionally, some have suggested that CAI has a hidden "de-skilling" effect by focusing attention on basic skills that should be spent on more complex problem solving (Martin, 1999).

Several metastudies suggest statistically significant improvements attributable to the use of CAI (Cotton, 1991). Additionally, students often found CAI materials to be more intrinsically engaging and interesting than other instructional approaches (Anand & Ross, 1987; Cotton, 1991; Del Marie Rysavy & Sales, 1991). The US Department of Education supports research laboratories around the country to provide a foundation for all aspects of the administration of public school systems. This includes extensive research on educational processes in the classroom. In 1991, the Northwest Regional Educational Laboratory published a major synthesis of the benefits of CAI, outlining the rationale and known benefits for the approach. It also suggested that in terms of its benefits, CAI was most effective in "science and foreign languages, followed, in descending order of effectiveness, by activities in mathematics, reading, language arts, and English as a Second Language, with CAI activities in ESL found to be largely ineffective" (Cotton, 1991).

Evolution of CAI. The advent of microcomputers as a pervasive feature of schools in the 1980s, whether in separate computer labs or within classrooms, altered the accessibility and affordability of CAI. The RAND Corporation, for example, reported that the ratio of microcomputers to K–12 students grew from 1:125 in 1983 to 1:9 by 1995 (Glennan & Melmed, 1996). The increased penetration of computers in the classroom dramatically shifted the direction of CAI usage and development. Second, the notion of a purely personal computer gave way to networked systems in the late 1980s. This expanded to affordances of CAI for tracking student progress tied to a central server and for creating software that allowed students to work collaboratively. A third development, in the early to mid-1990s, involved the growth and availability of the Internet as a resource for CAI. The US Department of Education reports that in 1994, only 3 percent of instructional rooms (classrooms, libraries, computer labs) in public schools had Internet access. Internet access rose significantly, and by 2009, 93 percent of all instructional rooms had Internet access and the ratio of computers to students was 1:5.3 (Tabs, 2004; US Department of Education, National Center for Education Statistics, 2010). By 2018, 99 percent of students in the United States will have broadband, as part of the ConnectED program (Ross, 2015). Like the earlier explosion in availability of microcomputers, this change in access to technology significant altered the conditions for the kinds of CAI that could be made available to students.

The fourth development is that as technology advanced (including access to the Internet), so did the scope and type of instructional content that CAI could furnish. CAI was primarily text-based or used only primitive graphics until the early to mid-1990s. But the development of more sophisticated graphical systems or video permitted the transition from text-based CAI to software that could depict scientific ideas more vividly than traditional textbooks. This was especially important in highlighting important concepts in subjects such as microbiology, mathematics, chemistry, and earth science. It was also important in providing visual enhancements to literacy instruction and for students with learning disabilities.

A fifth development involved the ascent of artificial intelligence in educational technology. CAI software, traditionally limited to "straight-path" mastery learning (one objective followed by another in presentation) and to some branching approaches (by which students could pursue different sequences of objectives based on individual learning styles) could respond more flexibly to student responses and preferences. This development began to have influence in the late 1990s, and developed to include the use of what are called intelligent and perceptual interfaces that engage learners.

A sixth development has been the evolution of cognitive and learning sciences. Much more is known about the mechanisms by which humans learn (Bransford, Brown, & Cocking, 2000). This has given rise to competing notions or alternative viewpoints for designing CAI or other forms of educational technology.

Viewpoints

CAI was guided through much of its history by prevalent behavioral theories in instructional design that relied on a sequential, hierarchical and objective-based approach to education. This approach happened to fit the limitations in the forms of software available at the time. This approach generally proved appropriate for drill-oriented exercises or for helping students acquire and master information. In other words, CAI found an important niche in education. But cognitive and learning science, and deficiencies in behaviorism as a fully comprehensive theory of human learning, gave rise to approaches to education that could trace their origin to John Dewey, Jerome Bruner, and Jean Piaget. These and other researchers promoted the view that knowledge is not only acquired from external sources, but is also constructed as learners engage in problem-solving, especially in social contexts. The school of thought called Constructivism challenges what are seen as rigid assumptions about how learning occurs and challenges the assumption that learning should be defined as a hierarchy of content objectives that are sequentially mastered.

Constructivists consider knowledge formation to be more dependent on the learner and the learner's active experience with the environment. Under this interpretation, the role of the teacher is to guide students in their interactions but not necessarily prescribe all of the knowledge they will acquire. Additionally, constructivists argue that learning environments, such as those that rely on traditional CAI drill and exercise software, bolster procedural skills and declarative knowledge (i.e., facts and information) skills at the expense of complex reasoning and strategic knowledge. Constructivism is often associated with inquiry and discovery learning, and the related philosophy that students who discover principles or make connections on their own are more likely to retain them and to transfer those principles to new areas. They are also more likely to develop critical thinking and problem-solving skills. Even newer theories, such as "post-constructivism," promote systems thinking, modeling activities, and connectedness of knowledge forms, and will continue to define new approaches to CAI.

Future Frontiers for CAI. Even though controversies such as behaviorism versus constructivism characterize educational psychology and curriculum design, most agree that the future of CAI will involve both an emphasis on building skills in tasks that require drill and repetition and increasing attention to development of critical thinking and problem-solving competence. More specifically, what are some of the possible future trends for CAI? Programs like the Andes Physics Project from the University of Pittsburgh (VanLehn, Lynch, Schlze, & Shapiro, 2005) or Assistment from Carnegie-Mellon University (Mingyu & Heffernan, 2007) have made great advances in CAI, and other frontiers involve adaptation of virtual reality environments into the instructional process. These approaches rely heavily on the use of simulations to engage students. Important work in this area involves Chris Dede at Harvard University (Dede, 2003), Uri Wilensky at Northwestern University (Wilensky & Shapiro, 2003), or the Modeling Across the Curriculum Project at the Concord Consortium (Buckley et al., 2004). "Pedagogical agents," which include animated computer tools that can function realistically and empathetically with different personae such as a mentor, tutor, or guide while intelligently assisting in instruction are also significant, and much of this work, led by Amy Baylor (Baylor, 2000; Baylor & Kim, 2005) originated in the same Instructional Design Department at Florida State University that contributed so substantially to ISD theory. The adaptation of computer games into networked forms of CAI have been used. It is a relatively new field that is gaining attention and funding from both the US Department of Education and the National Science Foundation. Important examples involve work by Paul Gee and David Williamson Shaffer (Shaffer, Squire, Halverson, & Gee, 2005) at the University of Wisconsin and by Mary John O'Hair at the University of Oklahoma (O'Hair, 2005). These frontiers and others promise to deepen and broaden the role for CAI in classrooms of the future.

Terms & Concepts

Artificial Intelligence: A discipline that combines many fields, especially computer science and computer science, to produce software systems that emulate human ability to recognize, analyze and solve problems.

Behaviorism: A school of psychology that explains and predicts human behavior in terms of external stimuli such as reinforcement and punishment, and in terms of how the human responds to those stimuli. Behaviorism contrasts sharply with areas such as cognitive science or constructivism, where the focus of research and explanation is on mechanisms of thought and are more internally oriented.

Computer-Assisted Instruction (CAI): The use of computer technology to supplement or to assist in the delivery of instruction.

Computer-Based Instruction (CBI). A somewhat more expansive term than CAI. It typically includes CAI, but also refers to instruction that is based more fully around the computer system as the primary source of instructional delivery, in contrast to CAI, which refers to the computer as a supplement to teacher instruction.

Constructivism: An approach to learning that stresses cognitive activity more prominently than externally "delivered" instruction. Constructivism suggests that knowledge is constructed by the individual interacting with others and with the environment.

Instructional System Design (ISD): A set of guidelines based on theories of learning that instructional designers follow in developing curriculum or other forms of instructional programs.

Integrated Learning Systems (ILS): A comprehensive commercial system that includes instructional software and printed materials, CAI, and administration tools to track student and classroom progress.

Mastery learning: An approach to learning by which students must demonstrate proficiency in an educational objective or task before progressing to new objectives or tasks.

Metastudy: A research study that synthesizes the results of numerous prior studies on a particular subject, analyzing them for their strengths and weakness and seeking to identify trends or common findings they suggest.

Objective: A precise statement that specifies what a learner will know or tasks that a learner will be able to perform at the conclusion of training or other educational activity.

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Suggested Reading

Ari Gamage, T., & Chappell, P. (2013). Exploring the potential to use computer assisted language learning (CALL). University of Sydney Papers in TESOL, 8. 57–98. Retrieved December 20, 2013, from EBSCO Online Database Education Research Complete. http://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=91951814

Baroody, A. J., Eiland, M. D., Purpura, D. J., & Reid, E. E. (2013). Can computer-assisted discovery learning foster first graders’ fluency with the most basic addition combinations?. American Educational Research Journal, 50, 533–573. Retrieved December 20, 2013, from EBSCO Online Database Education Research Complete. http://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=87598679

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Shaffer, D. W. (2007). How computer games help children learn. New York, Palgrave Macmillan.

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