Individual differences, computers, and instruction

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No. 3-4, 1995 in the USA.

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Individual

Differences, Computers, Instruction

and

David J. Ayersman Mary Washington

College

Avril von Minden Western Illinois University

A recent trend in education literature has been to generally accept that hypermedia can accommodate learning style differences because of the multimodal attributes that are involved. There is very little research, however, to support this claim. From the paucity of computer education and individual difference literature which does exist, the use of the computer as an instructional tool and its relationship to research on individual styles of learning appears to be an area holding great promise. It is hoped and anticipated that hypermedia will bridge the gap between individual differences and instruction. Hypermedia is believed to accommodate different learning styles and different entry levels of skill because of its flexibility and its potentially high level of learner control. Bridging the gap between instruction, computers, and learning styles may be possible by presenting materials to be learned in a way that encompasses individual differences in learning style. Ideally, this will involve providing such a rich design and presentation of instruction that all types of individual differences will be addressed. Unfortunately, there is not a great deal of research that examines this or provides support for this assumption. Too few researchers “are exploring the vast potential of the computer as a means to accommodate the Requests for reprints should be addressed to David J. Ayersman, Mary Washington Instructional Technology, Fredericksburg, VA 22401-5358. E-mail: [email protected] 371

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preferred learning styles of individual students” (Geisert, Dunn, & Sinatra, 1990, p. 297). From the earliest to the most recent applications of the computer in education, the need to match the learner with the instruction has persisted. Different types of computer-assisted instruction (CAI) have been developed to create learning environments that effectively address uniquely different segments of the learner population. Not all students have done equally well with CAI, nor have all students even benefited. Specific groups of students (i.e., those of low ability) have been the ones to benefit the most from CA1 (Cook, 1989). Unfortunately, the trend has been to present a standard form of instruction that has forced the student to adapt, and this approach has not been completely successful. However, it may be that the learner no longer needs to change to accommodate the instruction. When coupled with sound instructional design principles, current technology may enable learners of different backgrounds, abilities, and learning styles to enter a learning environment in which all of their individual needs can be addressed. This technology is called hypermedia. Multidimensional learning-style instruments can identify as many as 16 different types of learning styles (Briggs-Myers & McCaulley, 1992; Canfield, 1980; Canfield & Cantield, 1976). There are numerous other categories of individual differences that subsume additional types, styles, or preferences with an influence on learning. Rather than adapting the currently existing instruction to this diverse audience, the initial design of the instruction should incorporate these individual styles of learning. Providing conceptual and temporal overlap of information presented in different modalities can improve learning (Hannafin & Hooper, 1993). “If an instructional program is rich enough, it will provide sufficient diversity to meet the needs of all learners, and comprehensive instructional models that allow for such diversity have been developed” (Stice & Dunn, 1985). At present, the best method of achieving this goal is to develop instruction so that it encompasses many different styles of learning within its rich design. Hypermedia is one such model. To ask a teacher to adapt his/her instruction to such a variety of individual learning styles is not feasible in a classroom that is not technologically equipped. Although there are many effective teachers who are capable of teaching to a wide range of students, the teacher’s style of instruction, regretfully, cannot be expected to address such a diversity of student differences. Hypermedia can assist the instructor in this endeavor. This paper aims to provide a conceptual foundation for the development of hypermedia as an instructional tool for addressing individual differences. It is not the purpose of this paper to describe all of the many different instruments used to measure individual differences, nor to thoroughly explain each of the many types of individual differences. This type of information is readily available elsewhere (Jonassen & Grabowski, 1993) and is beyond the scope of this paper. Rather, the focus is to broadly examine the research involving individual differences and educational computing so as to assess the relationships that are present. This paper compiles and synthesizes findings in current literature in an effort to buttress the claim that hypermedia has the capacity to accommodate individual learning style differences. By so doing, it also provides a foundation on which research addressing these issues can continue to build.

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LITERATURE REVIEW Individual Differences and Instruction Many studies have shown aptitude treatment interactions which indicate the existence and significance of individual differences. Since media require the use of particular underlying skills for extracting information, individual differences in the aptitudes of the user interact with the media to affect learning (Ausburn & Ausburn, 1978; Cronbach & Snow, 1977; Olsen & Bruner, 1974; Gagnon, Neuman, M&night, & Fryling, 1986). It is generally recognized that learning is best facilitated if there is a close correspondence between the user’s internal representation and the media’s mode of representation. When the match is poor, additional translation is required; that is, externally coded messages often need to be transferred and translated into one’s preferred (or “task-required”) symbol system (Salomon, 1979). In current literature, the term “individual differences” encompasses many variables commonly referred to as cognitive styles or learning styles. Examining early sources reveals origins of the corpus of work on individual differences by Jung (1926), Lewin (1935), and Dewey (1938). The principles derived from this body of research depict the important role of experience in learning (Dewey, 1938), emphasize individual personality differences (Lewin, 1935), and classify distinctly different psychological types (Jung, 1926). These works emphasize the nature of learners as being unique because of causal variables such as psychological differences, prior experience, or personality differences. Our assumption is that all three of these variables are intricately interwoven to contribute to individual differences. Based on this research, Asch and Witkin (1948a, 1984b) began to focus on individual perceptual differences which they termed psychological differentiation. In the 1960s Witkin introduced the term cognitive style, which has more recently come to include both learning styles and learning modalities as concepts for describing the individual differences among learners. Often, learning style is inappropriately used as a metaphor to refer to the superordinate category of individual differences (Jonassen & Grabowski, 1993). Individual differences includes a broad range of variables (from perceptual preferences to personality types). Early instruments devised to measure these differences relied upon twodimensional formats. More recently, instruments have been designed that group these differences into as few as 4 (Kolb, 1976, 1985) or as many as 16 (Canfield, 1980; Myers, 1962) categories. Some of these differences are measured on the basis of self-report instruments whereas others are assessed by direct observations. A third type of instrument measures certain preferences or traits. The multiplicity of individual difference categories and the fact that many of these overlap on the basis of shared characteristics often make it difficult to distinguish among them. There are many definitions for types of individual differences. Witkin defined cognitive styles as our typical ways of processing information “regardless of whether the information has its primary source in the world outside or within us; and when in the world outside, regardless of whether the information is provided primarily by things or by persons and their activities” (Witkin, 1973, p. 5). Messick (1984) conceptualizes styles as “characteristic self-consistencies in information processing that develop in congenial ways around underlying personality trends” (p. 63). Learning styles are prevalent manners of

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approaching, obtaining, and processing information from one’s environment (Messick, 1976; Gordon, 1991). Despite the many alternative conceptions of individual differences, these conceptions appear to converge in the implication that individuals consistently exhibit stylistic preferences for the ways in which they organize stimuli and construct meanings for themselves out of their experiences (Bern, 1983; Gregorc, 1984; Messick, 1984). Cognitive style variables have also been suggested to exist within a single dichotomy (Schmeck, 1988) with “global-holistic/field dependent/right brained at one end of a possible continuum and focused-detailed/field independent/left brained at the other” (Ehrman, 1990, p. 10). Obviously, individual differences are not clearly distinguishable and groupings of them are not neatly identifiable. This fact has contributed to the lack of clear results from the individual differences research (Jonassen & Grabowski, 1993). The existence of so many differences among students creates the two problems of understanding and accommodating such diversity within the classroom. Jonassen and Grabowski (1993) provide a comprehensive explanation of the many individual differences and the instruments used to measure these differences. According to their framework, the general term “individual differences” encompasses cognitive controls, cognitive styles, learning styles, and personality types (Table 1). These four categories are used in this paper to group the results found within the literature. The study by Jonassen and Grabowski also examines differences in prior experience and intelligence. However, although related and admittedly important, this, in our opinion, causes their study to stray away from the more traditional interpretations of style differences. Although Jonassen and Grabowski describe research related to each of the individual difference classifications and styles, they do not provide information about studies that specifically relate individual differences to learning with computers. In this paper Jonassen and Grabowski’s framework - cognitive controls, cognitive styles, learning styles, and personality types - is utilized to categorize individual differences. The paper then examines computer research literature in which individual differences have been explored. Within each of these categories we have further described those subgroupings of individual differences that have been examined in the educational computing literature. Whereas these subgroupings (which are italicized on initial introduction) are discussed, subgroupings not examined in the computer education research are not expanded upon here for reasons of clarity and brevity. More detailed explanations of each are available (Jonassen & Grabowski, 1993). Cognitive Controls There are essentially six types of cognitive controls as explained by Jonassen and Grabowski: (a) Field Dependence/Independence (FD/I), (b) Cognitive Flexibility, (c) Impulsivity and Reflectivity, (d) Focal Attention, (e) Category Width, and (f) Automization. Cognitive controls “influence and control an individual’s perception of environmental stimuli” (Jonassen & Grabowski, 1993, p. 83). The most commonly researched individual difference has been FD/I. Jonassen and Grabowski equate FD/I with global versus articulated cognitive styles. Field Dependence/Zndependence. A description of the Group Embedded Figures Test (GEFT) characterizes the cognitive control of FD/I. Witkin developed the

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Table 1. Categories of Individual Differences (as Described by Jonassen 8 Grabowski, 1993) Used Within this Study Subcategory

Category Cognitive

Controls

Field Dependence/hdep8nd8nc8 Cognitive FIexibitity Impulsivity/Reflectivhy Focal Attention Category Width Automization

Cognitive Styles

Information Gathering Visu8/,fHaptic Visualizer~8rbalizer Leveling/Sharpening Information Organizing S8riatist/Hohkt Analytical/Relational

Learning Styles

Hill’s Cognitive Style Mapping Kolb’s Learning Styles Dunn and Dunn Learning Styles Grasha-Reichman Learning Styles Gregorc Learning Styles

Personality

Attentional and Engagement Styles Anxiety Tolerance for Unrealistic Experiences Ambiguity Tolerance Frustration Tolerance Expectancy and Incentive Styles Locus of Control Extroversion and httuversion Achievement Motivation Risk Taking versus Cautiousness

Types

Note. Not all individual differences have been examined in relation to computer-based instruction. From the available research we have described findings we feel relevant to the variables of individual differences and forms of computer-assisted instruction and hypermedia-assisted instruction. Those individual differences that were examined in this study are italicized.

GEFT to assess cognitive attributes. The GEFT involves having someone find simple graphical figures which are embedded within more complex backgrounds. The ability to do this determines one’s level of field independence. Essentially, results from the GEFT identify sources needed for processing and integrating incoming information. Field-dependent (FD) individuals rely more on external references; by contrast, field-independent (FI) people rely more on internal references. The FD person perceives objects as whole, whereas an FI person focuses on individual parts of the object. The determination of which of these styles is used for the integration of information establishes one’s particular preference. As with most individual differences, it is important to note that there is no relationship between cognitive style and intelligence - persons identified with any of the several different forms of cognitive style can be equally intelligent (Tamaoka, 1985). Cognitive Flexibility. As a cognitive control, cognitive flexibility versus cognitive constriction determines a person’s ability to ignore distractions from his/her environment while focusing on relevant information at hand. An individual high in flexibility would not be as easily distracted as someone classified as a cognitive constrictor. This cognitive control is often referred to as rigidity/flexibility

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(Jonassen & Grabowski, 1993). Flexible individuals are able to effectively ignore irrelevant information, whereas constricted individuals are less able to do so (Jonassen & Grabowski, 1993). Cognitive Styles There are five major types of cognitive styles which are further classified by two subheadings. Within the subgrouping of Information Gathering there are Visual/ Haptic, Visualizer/ Verbalizer, and Leveling/Sharpening styles. Within the subgroup of Information Organizing there are Serialist/Holist and Analytical/ Relational styles. Cognitive styles are recognized as describing learner traits, whereas cognitive controls influence and regulate perception. Styles are habits and controls are influences on these habits (Jonassen & Grabowski, 1993). Visual/Haptic. If learners habitually prefer to process information via visual means, they distinctly differ from those who habitually prefer to process information via tactile (haptic) means. Young children typically rely on haptic input; as they mature, they become more visual by preference (Jonassen & Grabowski, 1993). There are five qualities that comprise the visual preference: spatial relations, visual discrimination, figure-ground discrimination, visual closure, and object recognition (Jonassen & Grabowski, 1993). Visual/haptic perception is a factor of cognitive style that deals with a learner’s preference for using visual or kinesthetic sensory input and processing. Persons with a visual preference tend to show a greater ability to analyze and integrate visual information, mentally convert nonvisual information into visual, and show superior retention of mental images. Persons with a haptic preference tend to refuse to integrate details and partial impressions, internalize nonvisual information kinesthetically rather than convert them into visual information, and feel experiences subjectively. Haptic persons might be expected to perform better with interactive media, whereas those with visual preferences might perform better with observational media (Jonassen & Grabowski, 1993). Visualizer/Verbalizer. Just as learners may have visual or haptic preferences, they may also have verbal preferences. A visualizer would prefer to receive information via graphics, pictures, and images; whereas, a verbalizer would prefer to process information in the form of words, either written or spoken (Jonassen & Grabowski, 1993). The visual preference within this type of cognitive style is much simpler than the visual preference within the visual/haptic styles. It merely distinguishes between a preference for learning with words versus pictures (Jonassen & Grabowski, 1993). The differences among visualizers and verbalizers are often not as great as some other cognitive styles since many learners are equally comfortable using either modality (Jonassen & Grabowski, 1993). Although the Barbe and Milone (1980) instrument does not fit neatly into either the visual/haptic or the visualizer/verbalizer categories identified by Jonassen and Grabowski, it describes the learner as auditory, visual, kinesthetic, or a combination of these and, therefore, it has been included with this grouping. Serialist/Holist. This cognitive style also has a bipolar basis which depicts the learners preference for representing information. Holists are global by initially creating broad interpretations of their environment. Serialists are analytical by focusing on the details involved prior to making broad assumptions about their environment. Jonassen and Grabowski (1993) describe the serialist as combining

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information in a linear fashion, focusing on small chunks of information at a time and working from the bottom up, which infers that these learners prefer part-to-whole processing of information. They describe the holist as being able to focus on several aspects at the same time, having many goals, and working on topics that span varying levels of structure. It can be inferred from this that the holist prefers to process information in a whole-to-part sequence. Pask (1976) mentions that there are versatile learners who are flexible in alternating between either preference when this is most appropriate. Learning Styles There are five major types of learning styles: Hill’s Cognitive Style Mapping, Kolb’s Learning Styles, Dunn and Dunn Learning Styles, Grasha-Reichman Learning Styles, and Gregorc Learning Styles (Jonassen & Grabowski, 1993). The concept of learning styles differs slightly from cognitive styles in that it focuses more specifically on preferred methods of receiving information within a learning environment (Cranston & McCort, 1985). Jonassen and Grabowski (1993) distinguish between learning and cognitive styles by explaining that learning style instruments are typically self-report instruments, whereas cognitive style instruments require the learner to do some task which is then measured as a trait or preference. Kolb Learning Style. The Learning Style Inventory by Kolb (1985) identifies four separate learning styles: Diverger, Converger, Assimilator, and Accommodator. These four styles of learning are assessed via two dimensions (abstract/concrete and active/reflective) which describe four abilities required to be an effective learner. The explanation by Kolb is that we, as learners, are not expected to master each of the four to be successful learners, but that we are continually faced with decisions which force us to choose between these abilities and cause us to develop individual preferences as to which styles suit us best. We make this determination based on the situation and our place in the continuing process of moving from actor to observer and from specific to general involvement. This is not to be interpreted as implying that learning styles are subject to change. From a developmental point of view, the preference of learning style is seen as occurring throughout childhood, becoming relatively stable and permanent by adulthood. Scores on Kolb’s Learning Style Inventory identify the learner’s preferred style of receiving and organizing information as an emphasis on abstractness over concreteness and on action over reflection. Each of these four styles of learning has been associated with certain individual characteristics (Kolb, 1985; McCarthy, 1989). The diverger is particularly adapted to viewing a situation from multiple perspectives, has broad cultural interests, and excels in areas which require imagination and the generation of ideas through methods such as brainstorming. The converger relies on common sense, is better suited to the practical application of ideas, and is viewed as a pragmatist. The assimilator is portrayed as a thinker who specializes in inductive reasoning and the formulation of theories. The accommodator is more of a risk-taker, relies on intuitive trial and error approaches to problem solving, and is highly adaptive to new situations.

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Personality

Types

There are eight types of personality which are divided into two classes. Grouped under Attentional and Engagement Styles are Anxiety, Tolerance for Unrealistic Experiences, Ambiguity Tolerance, and Frustration Tolerance types. Within the class of Expectancy and Incentive Styles are Locus of Control, Extroversion and Introversion, Achievement Motivation, and Risk Taking versus Cautiousness personality types. Extroversion and Introversion. According to Jung, each of us possesses the mechanisms of extroversion and introversion. With extroverts, the “entire consciousness looks outward toward the world, because the important and decisive determination always comes to him from without. But it comes to him from without, only because that is where he expects it. . . . Interest and attention follow objective happenings and, primarily, those of the immediate environment” (Jung, 1926, p. 417). The predominance in the direction of mental energy (for extroverts, external; for introverts, internal) determines psychological type. Different personality types will characteristically form different opinions when observing the same object or situation. Jung also suggested that, theoretically, this psychic energy remains constant over the lifespan of the individual. The basis for the 16 types determined by the Myers-Briggs Type Indicator is the four separate preferences outlined in Jungian theory (Briggs-Myers & McCaulley, 1992).

Locus of control as a construct originated from Rotter’s social learning theory (Rotter, 1954) as a belief that reinforcement comes either from an external or an internal source. If reinforcement is believed to come from an external source (fate, luck, or chance) one does not, or does not see a need to accept responsibility for events, however, if reinforcement is seen to come from an internal source (self), responsibility of events is attributed to one’s own actions. For example, those with an internal locus of control would accept responsibility for their own successes or failures, whereas those with an external locus deflect this responsibility and ascribe control to external forces or events. Locus ofControl.

COMPUTERS AND INSTRUCTION Although there are many different ways that computers can be employed for effective instruction, we have chosen to focus on only two broad classifications. These two were chosen because they specifically focus on instructional uses of computers. The two classifications are Computer-Assisted Instruction (CAI) and Hypermedia-Assisted Instruction (HAI). Computer-Assisted

Instruction

CA1 consists of several types of software or, from a more global viewpoint, several categories of computer uses. Drill and practice, tutorials, simulations, and games are the four most common forms of CAI. Drill and practice programs are typically effective at increasing a learner’s rate of response through repeated exposures to stimulus, response, and feedback sequences of instruction. They also provide the necessary practice to hone newly acquired cognitive skills. Cognitive theorists have held little regard for this level of computer use, citing

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that it does not fully utilize the technology (Gravander, 1985; Jonassen, 1988). Tutorials and simulations can be quite elaborate or very simple, but they usually include more opportunities for learner-control, user-options, flexibility, and authentic learning environments. Tutorials are useful for presenting information and for guiding the student through the information (Alessi & Trollip, 1991). In simulations, students enter an environment that replicates some aspect of reality and interact with the information in a way that causes a specific outcome or result. Rather than learning by having the information presented to them, students learn via simulations and by actually experiencing the context of the real world. Early promises of CAI’s impact on education were students’ control of pace and the individualization of instruction. For years, however, CA1 has been too limiting and restrictive because of ineffective branching routines and the lack of an adequate theoretical focus (Cook, 1989). “CA1 traditionally assumed that the students differed only in their speed of learning, not in their intrinsic styles of learning” (Geisert et al., 1990, p. 297). The computer is uniquely capable of introducing learning materials through a student’s perceptual preference and then reinforcing that information through secondary and tertiary preferences; hence the term “multimodal instruction” and the hype over it (Geisert et al. 1990). Traditional CA1 has routed the learner through programmer-determined sequences of instruction that have not allowed sufficient latitude for the learner’s decisions. However, CA1 has proven effective for learning, with the most pronounced impact on certain segments of the learning population such as the learning disabled and low-ability students. Hypermedia-Assisted

Instruction

Hypermedia, which is a combination of multimedia and hypertext, is distinguishable from CA1 by the node-and-link structure inherent to the organization of information and the fact that it is also based on integrated media. That is, the distinction between CA1 and HA1 is the organization of the information (the types of linkages among concepts) and the fact that hypermedia involves the integration of multiple forms of media, whereas CA1 might involve fewer forms of media and fewer nonlinear information structures. Instruction delivered by way of CA1 may also be delivered using HAI, although hypermedia is typically a much richer environment (Heller, 1990). In hypertext, the individual nodes of information are connected via buttons to other related information nodes. The learner is able to branch from node to node while exploring concepts in more and more detail. The most common method of organizing information when authoring hypertext involves using hierarchically organized information and connecting it in the more easily understood nonlinear fashion using semantic relationships (Smeaton, 1991). Hypertext is defined as “a database that has active cross-references and allows the reader to jump to other parts of the database as desired” (Schneiderman & Kearsley, 1989, p. 3). When combined with multimedia, hypertext allows the user to jump via buttons not just to a text-based explanation of a concept, but also to spoken definitions and pictorial explanations or examples. HA1 provides a nonlinear method of establishing relationships that are more meaningful to the individual learner because of the context-based linkages that are used. Traditional forms of computer instruction have more often relied upon linear relationships between and among concepts which have constrained

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learners by restricting them to developing fewer and less meaningful relationships among concepts. The integrated use of sound, video, graphics, and text purportedly establishes the hypermedia learning environment as an extremely rich method of instruction, in which the learner is capable of formulating more numerous and more meaningful relationships. This multimodal approach to learning is thought to benefit students who have often been neglected by more traditional forms of instruction.

INDIVIDUAL DIFFERENCES, COMPUTERS, AND INSTRUCTION Despite the fact that hypermedia is expected to accommodate individual differences, Litchtield (1993) admits that research specifically addressing multimedia programs and learning styles is almost nonexistent. An examination of the individual differences literature reveals that there have been two emergent patterns for addressing individual differences. Adapting the instruction to meet the needs of differing students has been one course of action; having the learner adapt to the instruction has been the other. Both approaches require some form of compromise that detracts from the overall learning process. Whenever students are forced to expend adaptive energy in adjusting to the educational environment, they may have depleted energy reserves available for learning (Jenkins, 1988). Moreover, it is nearly impossible to adapt instruction to the learner when the typical public school teacher has 30 or more students to whom the instruction must be suited. A third option is to create more individualized instruction through the use of technology so that individual students may have their differences accommodated. Prior to hypermedia this was not readily possible as no other form of technology has been capable of nonlinearly linking information from multiple forms of media. Cognitive flexibility theory generally holds that there are multiple paths from one learning point to the next and that concepts can be broken down into multiple subconcepts (Spiro & Jehng, 1990). Hypermedia facilitates this flexibility because it allows topics to be explored in more than one way, by using numerous concepts and contexts to depict the information. These multiple paths are one solution to addressing learning style differences. Since students cannot be expected to traverse the same path from point A to B, educators need to ensure that more than a single path is available to them. This is not always a simple matter. Analogies, metaphors, examples, and many other methods are generally accepted as methods for providing these additional paths. In the realm of HAI, these multiple pathways are inherent to the nonlinear linking involved in the representation of the information. Providing instruction through multiple forms of media also allows learners to view a concept from multiple perspectives or to learn concepts by way of multiple paths and modalities. Similar to cognitive flexibility theory, Paivio (1986) describes a dual-code approach to instruction. Concept formation depends on which particular characteristics a person chooses as focal points as well as the individual’s method of storing and retrieving information. The dual-code theory of information processing emphasizes a multimodal approach to instruction that allows information to be received and then reinforced utilizing more than one of the five senses. Since students can have imaging and/or verbalizing preferences, this dual-coding approach appears doubly beneficial.

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Critical aspects of technology have been examined in the research. The use of color, audiovisual stimuli, and instruction-enhancing visuals have all been found to have a positive impact on learning. Not all studies examining these instructional attributes have chosen to group learners by style differences. Findings from reviews of the literature and those particular studies which focused on modality-based forms of instruction are discussed in the following section. Berry (1991), in a 1S-year review of research in which perceptual differences were examined, synthesized effects reported in the literature. He found that, although in some cases FI subjects benefit more from color materials, the majority of the research has shown no difference (e.g., Wieckowski, 1980). He also concluded that impulsive/reflective and leveling/sharpening research did not find significant interactions with color as a variable. Conversely, Berry found that hemispheric laterality research suggests a significant difference for processing color information that favors the right hemisphere. This review suggests that nonrealistic color processing may be a right hemisphere activity whereas color, and black and white processing might be oriented to the left hemisphere. Simonsen et al. (1985) examined live studies that dealt with the relationships among media, attitudes, persuasion, and learning styles. The specific learning style variables examined were field dependence (using the GEFT) and hemisphericity. Students whose scores on the GEFT were within one score of the average of all scores were excused, so as to more clearly distinguish between FI and FD individuals. The hemisphericity research explains that the leftbrained person is more logical, convergent, and analytical (Sperry, 1977) whereas the right-brained person is more holistic, intuitive, spatial, and divergent (Ornstein, 1977). The left hemisphere is attributed with language and processing information sequentially and temporally, whereas the right hemisphere is responsible for organizations of information across space. FD students were persuaded to have more positive attitudes toward the disabled when they used film rather than slides; for FI students, it did not seem to matter. Based on this research, it appears that some types of media may be more effective at changing attitudes for students with differing cognitive styles. The students who viewed the motion picture had more positive attitudes than students who viewed slides. Slides did not seem to have much effect for changing attitudes of FD students. Hativa and Reingold (1987) considered the importance of audiovisual stimuli on learning through microcomputer-based instruction. Students who learned with audiovisual (involving color, sound, and animation), instructional software performed better than those who learned with no-stimulus, instructional software (black/white, no sound, and no animations). Although this is evidence of the importance of stimulus materials used in both CA1 and HAI, this study did not group students according to individual learning preferences. No consideration was given to individual learning style differences which may have identified persons more and less suited to learning in each of the stimulus/nostimulus conditions. Barba and Armstrong (1992) examined two types of HAI, using eighth-grade Earth-science students. One type of instruction was augmented with a HyperCard tutorial and the other form of instruction consisted of the same HyperCard tutorial with the addition of interactive video. Although there was no significant difference in performance for the two forms of instruction, there

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was a significant difference for the low-verbal-ability group, which performed better with the design of instruction augmented with interactive video. Learning styles were not assessed as part of this study; however, it is important to note that the different sensory modalities addressed by the two forms of instruction did make a significant difference for the low-verbal-ability students. The visual reinforcement (interactive video) of the tutorial information was apparently sufficient to create significant differences in performance for those students who rely more on visual information to understand the concepts targeted by instruction. From this research we can draw two conclusions. First, not all learners are necessarily in need of the supplementary information available via visuals, color, and sound. Second, when learning is impacted by these instructional attributes, it improves learning for either a group of students or for all the students involved. Therefore, it seems logical that students might perform better in a CA1 environment when the design of instruction is appropriately matched to their particular learning style. Research specifically examining various individual differences in conjunction with CA1 and HAI is explored next. Cognitive Controls, Computers, and Instruction Computer-assisted instruction. Oddly enough, there is very little research that examines CA1 and cognitive controls. Atang (1984), in a study involving 85 undergraduate psychology students, examined the effect of color visuals on students’ achievement. The students were divided into three groups with one group using color visuals, one group using black and white, and the third group with no visuals. All received computer-based instruction in a programmed instructional format. The participants’ cognitive styles were assessed via the GEFT, resulting in 62 FI and 23 FD students. There were neither pretest nor posttest differences between the two cognitive style groups. There were no significant differences between the two groups using visuals (color and black/ white), and both groups had significantly higher posttest scores than the group with no visuals. The mean scores of the two visual-receiving experimental groups were not significantly different indicating that color was not a distinguishing factor. Although style differences were not found, the use of visuals did indicate superior performance for the two treatment groups. One conclusion was that students not using visuals required more time to process the information. Our interpretation of the lack of cognitive style differences is that the grouping of students did not depend upon their preferences (i.e., FD students might have benefited most from the visual stimulus materials and from using color). instruction. Liu and Reed (1994) utilized HA1 to instruct second language learning for graduate and undergraduate international students learning English. The HA1 consisted of a HyperCard program which incorporated interactive video and clips of the film Citizen Kane to instruct vocabulary and the appropriate use of the English language via real-life examples. The learners were exposed to parts of speech, sentence examples, context of language use, and definitions of the vocabulary through video, graphics, sound, and text. Participants were identified as FD, FI, or mixed using the GEFT. The results of this study revealed that, regardless of which style of learning the student was identified as having, the hypermedia instruction resulted in significant pre- to posttest gains in language performance. Hypermedia-assisted

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Wey and Waugh (1993) examined cognitive control attributes within a hypertext-based instructional lesson centering on Western Civilization. Their study involved 61 undergraduate volunteers who completed the GEFT. The participants were then assigned to one of two treatment conditions: text only or text and graphics. Findings revealed that the only significant performance differences between cognitive control groups were in the text-only group. The FI students performed significantly better than FD students assigned to the textonly treatment. There were no cognitive control group differences within the text and graphics treatment. One implication from this study is that text and graphics might be particularly beneficial for FD students. Interestingly, the cognitive control groups were also found to prefer significantly different information accessing strategies within both of the two treatments conditions. Weller, Repman, and Lan (1993) used the GEFT to categorize students by cognitive style (control) differences. The authors used a HyperCard-based form of hypermedia that they termed “The Computer Ethics Stack”. They found that the mean scores on a paper-and-pencil achievement test for FI students were significantly higher than those of FD students. The treatment consisted of one 50-minute class working with the computer during the students’ computer literacy period. Weller et al. also found that FD students answered fewer questions and accessed slightly more concept explanations within a computer software stack. The differences among learning patterns for the cognitive control groups support previous findings. In more recent research, Weller, Repman, Lan, and Rooze (this issue) examined “The Computer Ethics Stack” in conjunction with eighth-grade students. They found that FI students learned computer ethics more effectively than FD students based on a 25-minute treatment, regardless of whether advance organizers and/or structural organizers were used. They also found that the learning patterns of the students varied according to their FD/I groupings. Wang and Jonassen (1993) examined cognitive control variables in a hypertext learning environment in the content area of transfusion medicine. In the study, FI students were found to have spent significantly more time in the lab tests section and accessed more screens than the FD students. Findings also revealed that FI students spent less time per screen than the FD students, suggesting that FI students covered more of the program although they seemed to skim through it in the process. These outcomes further establish that students with differing cognitive control traits choose different methods for learning within a hypermedia environment. Perhaps the critical finding is that there was not a significant difference in achievement among the four cognitive control groups: FI, FD, cognitive constricted and cognitive flexible. This result implies that, although students go about learning in different fashions within a rich enough hypermedia environment, students of differing cognitive control characteristics can satisfy their learning preferences. This interpretation is tempered by small cell sizes and a few zero scores in some of the computations, resulting in inconclusive findings. Cognitive

Styles, Learning,

and Computers

Computer-assisted instruction. Gagnon et al. (1986) conducted a study involving 90 female subjects who were grouped into visual and haptic styles. The subjects were then put into one of three groups that received a specific treatment. The treatments were (a) participants played videogame Battlezone for 30 minutes,

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(b) participants watched a videotape of games played by their matched partner, and (c) participants watched a videotape of MASH. Aptitude by treatment interactions were found after the brief 30-minute treatment. Haptics performed significantly better when placed in the interactive condition, whereas those who scored low on the haptic perception performed better in the observational condition. The most important finding of this study was that individual differences were found to interact with ability to perform using the two types of media. Rowland and Stuessy (1988) conducted an exploratory study that found matching mode of CA1 (tutorial or simulation) to individual cognitive style (holist/global/whole-to-part and serialist/analytic/part-to-whole) produced significantly more effective learning with computers. The serialists were found to have difficulty learning with simulation types of software whereas the holists were found to have difficulty learning with tutorial types of software. When individuals were appropriately matched to mode of CAI, they performed significantly better than when mismatched. Holists favored simulation-type software, and serialists tended to perform better with tutorial-type software. The effect of cognitive style on mode of CA1 performance clearly revealed the benefit of appropriately matching these two variables in the learning environment. Martini (1986) investigated the effects on science achievement of seventhgrade students of matching and mismatching instructional methods according to modality preferences. All students performed significantly better when the form of instruction appropriately matched preferred sensory modality, and all students achieved better with CA1 than with auditory or visual forms of noncomputer instruction. The relationship between learning style and CA1 appears especially robust when the two variables are appropriately matched. Riding, Buckle, Thompson, and Hagger (1989) examined the relationship between modality-based preferences (verbal and imaginal) and performance using textual and pictorial materials presented through computer-based training. The verbalizers were found to perform much better with textual information, and imagers performed substantially better with pictorially presented information. The mode of information presentation was optimal when matched to modality preference. The authors point out that high-ability mathematics students with the imager style of learning were able to perform reasonably well with information presented in either pictorial or verbal form. This suggests that the high-ability student may simply be better able to combine multiple sensory modalities, whereas the low-ability student may not have yet mastered the verbal skills necessary to “pick-up” on these cues. The low-ability imagers tended to overrely on imaginal information, whereas the low-ability verbalizers overrelied on textual information. The styles of both low-ability groups appear to sacrifice one form of information gathering at the expense of the other. However, the high-ability students were better able to combine methods of information gathering through multiple sensory modalities. instruction. Overbaugh (this issue) examined the use of hypermedia to instruct pre-service teachers in classroom management techniques and practices. He used interactive video to portray actual classroom scenes depicting classroom management problems. These scenes were used in short clips, after which the student would respond to questions which then determined the nature of the next scene. The design of this instruction allowed the student an opportunity to explore the consequences of a classroom management decision in

Hypermedia-assisted

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a realistic, but safe, environment. Feedback was given to the student based on each decision throughout the course of the instruction. An important feature of the program was that the student was allowed to type in short answer/essay responses which were individually saved and reviewed prior to leaving the program. Following each response, the student was presented with a menu to choose the item that most closely resembled his/her response. Actual classroom management principles were not only viewed but also defined and evaluated for effectiveness. There were 148 possible conclusions which could be reached from the four scenarios presented by the instruction. Overbaugh used this HA1 instruction with a sample of pre-service undergraduate education majors. These participants were identified by learning modality using an inventory developed by Barbe and Milone (1980), which describes the learner as auditory, visual, kinesthetic, or a combination of these. Learners were categorized as either auditory or visual to determine possible differences in achievement using the interactive-video instructional treatment developed by Overbaugh. The HA1 was found to be equally effective for both the auditory and the visual learning modalities. That is, there were no significant differences between the two style groups on achievement. The HA1 was equally effective for students with either learning modality. Based on this study, it appears that hypermedia can encompass a diversity of students’ style preferences by incorporating user options and multimodal instructional features for learning. Learning Styles, Learning, and Computers Computer-assisted instruction. Cordell (199 l), using adult learners of varying abilities, examined the effect of learning styles and two forms of CA1 design on the outcomes of learning. Her study utilized Kolb’s 4MAT Learning Style Inventory with linear or branching CA1 designs. There were no significant differences in performance among the four learning style types, and there was no relationship found between learning style and instructional design. Although not significant, there tended to be better performance with branching CA1 by the assimilators and divergers, whereas the linear CA1 was favored by accommodators and convergers. Hypermedia-assisted

instruction. Ayersman (1994) utilized Kolb’s Learning Style

Inventory to examine learning in an HA1 environment using both pre-service and in-service teachers who were enrolled in a graduate-level Hypermedia in Education course. He examined possible differences in hypermedia knowledge among the four learning style groups before and after the 15-week course. There were no significant differences among the four learning style groups prior to the course, which he interpreted as indicating comparable levels of hypermedia inexperience. There was a significant increase in hypermedia knowledge for the collective group of students within the course following the hypermedia treatment. However, there were no significant learning style differences for hypermedia knowledge following the course, which suggests that the four learning style groups increased similarly in their hypermedia knowledge. Apparently, each of the four learning style groups was able to satisfy individual learning preferences due to the use of hypermedia for learning about hypermedia.

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Type

Computer-assisted instruction. The study mentioned previously in the visual/ haptic section of this paper (Gagnon et al., 1986) also examined locus of control. The authors proposed that locus of control might have an effect on how well an individual performs when using different media. Individuals with an internal locus of control may prefer higher levels of learner control (interactive media), whereas those with an external locus may prefer situations with less learner control (observational media). The 90 female subjects used were divided into three groups that (a) played the videogame Battlezone for 30 minutes, (b) watched a videotape of games played by their matched partner, or (c) watched a videotape of MASH. Aptitude by treatment interactions (ATIs) were found after the 30-minute treatment. Individuals with an external locus of control scored significantly better when placed in the observational format as opposed to the interactive environment. Subjects with an internal locus of control performed better in the interactive condition. Knupfer (1989) examined elementary school teachers’ psychological type and their use of instructional computing. She found that the Myers-Briggs Type Indicator revealed significant relationships between specific types (i.e., introversion/extroversion) and several dependent measures (i.e., the amount of training that teachers had taken through district-supported classes, feelings of adequacy of training, whether or not the principal encouraged computer use, factors which make it difficult to use instructional computing, and opinions about the quality of available software). Interestingly, learning environments that are stimulating enough for extroverts have been found too stimulating for introverts (Campbell & Hawley, 1983).

Hypermedia-assisted instruction. There was no available research that examined personality types and HAI. Even though there is abundant computer anxiety and achievement motivation literature, we interpret personality type as more closely associated with distinct personality styles that are relatively stable by maturity. Locus of control and extroversion/introversion were two such styles. In contrast, anxiety levels are prone to high degrees of fluctuation based largely on prior experience. All of the available research concerning personality types dealt with CA1 or non-computer-based instruction.

CONCLUSION

AND DISCUSSION

Why is there so little research examining the relationship of hypermedia and individual differences? Sheingold and Hadley (1990) found that, even in schools where the availability of computers is twice that of the national average, there was only about one teacher per school who integrated technology into his/her teaching. If so few teachers are using technology, then it follows logically that even fewer are researching it. Research which examines early computer applications of CA1 and explores more recent applications of HA1 have had essentially different outcomes. Although both applications of the computer are proposed as solutions for accommodating learner differences, only HA1 appears to allow learners to effectively follow their individual learning preferences. The distinction between CA1 and HA1 as they relate to individual differences is clear. With CAI, as in

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non-computer-based learning, the instruction must be modified or the learner must adapt in order for the learner and instruction to best suit each other. Conversely, “hypermedia-based learning environments allow the knowledge base to accommodate the learner rather than the learner accommodating the knowledge base” (Nelson & Palumbo, 1992, p. 288). This implies that multiple forms of instruction are present within a single HA1 lesson, meaning that the program must be richly developed. Aspects of hypermedia particularly suited to accommodating varying individual differences are the ability to deliver information in contextually meaningful sequences, at a variable pace controlled by the learner, and through multiple sensory modalities. FD/I and impulsivity/reflectivity cognitive styles are thought to differentially use pacing, which is a form of learner control. External pacing strategies may be needed for the impulsive styles, or they may avoid many options available to them (Canelos, Taylor, Dwyer, & Belland, 1988). Learner differences seem to have a profound impact on the method chosen for learning. When the preferred learning options are not available, students are prohibited from learning to their fullest potentials. When sufficient options are available, learners who need them can achieve at levels comparable to those who might not need the same options. It seems that research has not yet fully examined these concerns. The possibility of combining stimulus materials into a multiple sensory modality approach to computer education therefore merits further attention. The research to date has not yielded satisfactory results in the area of computer education and individual differences. In fact, finding that there is no significant difference in performance among learning style groups, and attributing this to the rich course design, may be somewhat misleading. Although significant findings would be expected to occur 5 out of 100 times by chance, the nonsignificant findings would constitute the more common outcome of 95 out of 100 times. Therefore, making assumptions based on these findings would seem to be extremely suspect. Using the lack of significant differences as proof of hypermedia’s ability to satisfy individual learner preferences is, therefore, using a weak burden of proof, although studies such as Liu and Reed (1994) have shown that students, regardless of learning style, have improved but this is based on selecting different information within the hypermedia environment. Future research should focus more on individual differences as they relate to HA1 and learning.

FUTURE RESEARCH

It has been shown that matching learning styles with CA1 produces the most significant improvements in student performance. From a broader perspective it would seem intuitively sound that matching instruction to learners’ styles affect positive learning outcomes. The problem to date has been the inability to individualize instruction for widely diverse student populations while ensuring that matching with one group of students is not done at the expense of another. Hypermedia appears to hold much promise and, in fact, has evidenced a limited degree of proof as to its effectiveness in addressing student diversity. Future research should explore additional learning style measures with students receiving instruction within hypermedia environments. A variety of content areas should be investigated to fully understand the broad applicability of HA1

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as it seeks to address varied needs of students with distinctly preferred ways of learning. Specifically addressing the ability of HA1 to accommodate individual differences as the focus of research, rather than inferring this from a lack of significant differences among style types at the posttreatment point, would be a much more reliable methodology for establishing hypermedia’s effectiveness. To date, the research has verified that individual differences do exist and that, when learner characteristics are appropriately matched with CAI, students perform better than when they are mismatched. From this research base has come the hope, and often the assumption, that HA1 will address these student differences. However, research is required to support this. Research on individual differences and their relationship to hypermedia-based instruction is in its infancy. With the large array of measurable differences, it may be quite some time before a convincing body of research is established. Until then, lack of corroboration from studies with such a focus should temper the assumption that hypermedia will effectively accommodate student differences and, thus, provide them with enhanced chances for academic success.

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