Dr Boris Handal

University of Technology Sydney

Boris.Handal@uts.edu.au

Dr Anthony Herrington

University of Wollongong

tony_herrington@uow.edu.au

This paper critically examines the concepts of field dependent and field independent cognitive styles within the context of computer-based instruction. The literature suggests that hypermedia instructional environments are more likely to engage cognitively field independent learners. This active engagement may be the result of the non-structured and explorative nature of hypermedia environments, whereas field dependent learners prefer a more prescriptive and linear style of instruction. Following a review of both these learning style constructs and research studies associated with hypermedia environments, the paper provides a summary of implications together with potential avenues for future research.

Each student learns in a different way and individual differences in learning have been corroborated in many studies (cf., Yu-ping Hsiao, 1997). It has been argued that, given students learn in different ways, instruction should be designed in such a way that it can accommodate different learning styles (Raven, Cano, Garton, & Shelhamer, 1993). Several classifications of learning and/or cognitive styles have been proposed by authors such as Dunn and Dunn (1978), Felder (2000), Gardner (1993) and Kolb (1984). For the sake of brevity, the terms learning and cognitive styles will be used interchangeably to denote aptitudes, regular mental behaviours, traits, habits or mental tasks that an individual displays under problem-solving situations (Jonassen & Grabowski, 1993). The research literature in education offers an array of terms to distinguish the different ways in which individuals display these aptitudes (cf. McLoughlin, 1999) with the dimensions of field dependence (FD) and field independence (FI) being prominent.

Witkin and his associates (Witkin & Goodenough, 1979; Witkin, Moore, Goodenough, & Cox, 1977; Witkin, Oltman, Raskin, & Karp, 1971) developed the concept of field dependence and field independence to differentiate two distinct cognitive learning styles. According to these authors, the FI/FD dimensions are defined as ‘the extent to which a person perceives part of a field as discrete from the surrounding field as a whole, rather than embedded in the field; the extent to which a person perceives analytically’ (Witkin et al., 1977, p. 7). Over the years, other attributes have been described to characterise FI/FD learning styles. Summerville (1999) referred to field independence and field dependence dimensions as a global versus an articulated style that reflected the ‘degree to which an individual’s processing of information is affected by the contextual field’ (p. 3). FI learners have been referred to as ‘analytical, competitive, individualistic, task oriented, internally referent, intrinsically motivated, hypothesis testing, self-structuring, linear, detail oriented, and visually perceptive’ (Hall, 2000, p. 5) whereas FD learners have been referred to as ‘group-oriented, global sensitive to social interactions and criticism, extrinsically motivated, externally referential, not visually perceptive, non-verbal, and passive learners who prefer external information structures’ (Hall, 2000, p. 6). Governor (1998) added that FD learners are in more need of social input and external help in interpreting clues embedded in a particular learning task. Hu (1998) observed that FI learners are more analytic and rely less on external clues than their FD counterparts. FI learners, it appears, are more able to generate and structure their own knowledge rather than accepting knowledge reprocessed by others. Hall (2000) pointed out that the differences between FI and FD learners are more likely the result of ‘varying information processing skills such as selective attention, short-term memory encoding, and long-term recall at which field independent individuals are more accurate and efficient’ (p. 72).

Further development by Witkin’s team has led to the creation of the Group Embedded Figures Test (GEFT) to measure the FD/FI constructs and identify those learners that lean towards each category in their learning style. This test measures visual perceptiveness and requires the respondent to locate and differentiate simple geometrical figures that are embedded within a more complex visual field. Respondents scoring within one standard deviation above the mean are considered to be FI learners compared to their FD counterparts, whose scores are located one standard deviation below the mean. Students around the mean are considered to be field-mixed (FM). FI and FD scores measured by the GEFT are supposedly not correlated with intelligence or ability (Witkin & Goodenough, 1979; Witkin, Moore, Goodenough, & Cox, 1977; Witkin, Oltman, Raskin, & Karp, 1971). However, that claim is disputed by Sternberg (1997) and Tamaoka (1985) who argue that GEFT scores are related to intellectual ability since the test consists of correct and incorrect questions, and because those classified as FI learners are recognised as ‘better’ learners than their FD counterparts.

Several studies have indicated that FI learners perform better in traditional academic tasks than their FD counterparts. According to Simonson (1985), FD learners are more influenced by the social environments rather than by their own motivation. FD learners also appear to be more influenced by praise and criticism than FI learners. FI learners, in turn, are more proactive and usually have a strong self-concept. Yea-Ru Chuang (1999), contended that FI learners tend to solve problems through intuition and use of trial-and-error strategies, as opposed to FD learners, who perceive objects as a whole and look more for more uni-dimensional relationships. According to Miller (1997, p. 210) FD learners ‘prefer externally defined goals and organization’ while FI learners ‘can provide their own structure for learning activities’. The question then arises: How do FI/FD learners interact with computer based learning environments, in particular, hypermedia based environments?

In the past two decades computers have been increasingly used in education as a tool to foster learning. The introduction of computers in education has reformulated the role of the teacher and the learner, and the relationship between them and teaching. One of the major challenges in computer education is to refocus the view of computers as tools for learning rather than devices to learn about, that is, learning with computers rather than from or about them (Handal & Herrington, 2003; Jonassen & Reeves, 1996). Gibbons and Fairweather (1998) proposed that by using computers, teachers can expect students to interact with more complex materials. They also argued that computers allow teachers to act more as coaches and facilitators using a learner-centred style of teaching. There is no clear indication, however, as to whether computer-based environments can support diverse individual differences and learning styles. While a number of authors argue that educational software can accommodate those differences (Chinien & Boutin, 1992/1993; Chou & Lin, 1998; Liu & Reed, 1994; Whyte, Karolick, & Taylor, 1996), others claim the contrary (Burger, 1985; Post 1987; Rowland & Stuessy, 1988).

Ayersman and Von Minden (1995) propose two main and broad classifications of the use of computers in instruction: Computer-Assisted Instruction (CAI) and Hypermedia-Based Instruction (HBI). Tutorials, simulations, drill and practice, and games are examples of CAI. CAI is the most common form of educational software and it is characterised by programs that require students’ responses to stimuli by a program. Most of these interfaces induce repetitive responses in a linear sequence. It has been argued that CAI software does not build on student’ problem solving capabilities, as the software is predominantly based on routine exercises (Beynon & Mackay, 1993; Liu & Reed, 1994).

Hypermedia is a much more complex type of computer-based instruction which can manifest in a range of different forms. For example, many CD-ROM based programs are examples of hypermedia systems, as are many instructional materials delivered on the World Wide Web. In contrast to the linear approach of CAI, hypermedia materials are comprised of multiple nodes containing various media forms such as text, sound, graphics and movies either individually or combined. The structure of a hypermedia system enables users to move from one node to another at will, accessing information from nodes that are more associative and are delivered in a non-linear sequence, allowing the learner greater control and interactivity (Handal & Herrington, 2003). Because of the more sophisticated types of learning that the technology supports, and the technical advantages over CAI, it has been argued that HBI allows the learner to build more meaningful links and relationships among texts and information (Ayersman & von Minden, 1995). Moreover, it has been claimed that HBI encourages the learner to obtain a more coherent understanding, construct his or her own knowledge, and promote higher involvement in the acquisition of knowledge (Liu & Reed, 1994; Melara, 1996). There is some research suggesting that HBI is more effective than CAI in accommodating individual differences and improving academic achievement (Melara, 1996; Summerville, 1999; Weller, Repman, & Rooze, 1994). The next section explores in more detail the relationship between HBI and the FI/FD constructs.

The last decade or more has seen a great deal of research conducted in the area of field independent and field dependent learning and the interaction with hypermedia based instruction. Much of this research has sought to create guidelines for teachers and instructional designers on how to design effective and efficient learning environments for different types of learning styles. The following table provides a summary of this research and resultant implications.

In general, the findings outlined above appear to suggest that hypermedia learning environments, such as multimedia CD-ROMs and websites, provide an environment where FI learners have more opportunities to succeed. As Witkin et al. (1977) proposed, FD learners are less likely to establish a meaningful organization of ideas when the field lacks structure and where few clues are obtainable. The findings also suggest that FD learners benefit from graphic-based instruction in accordance with Hall’s (2000) suggestion than FD are less verbal and may require alternative and more visual forms of instruction. Differences across studies can be attributed to different researchers’ conceptualisations of operational variables, variety and use of hypermedia programs and the diversity of methodologies and research designs. On the basis of these studies it would be tempting to recommend that HBI environments should be used with FI learners and avoided with FD learners. Such a recommendation would fail to recognise that learning environments, learning styles and technology are not constant variables.

Traditional learning environments based on a linear sequencing of ‘bite-sized’ content arrived at through task analysis are being replaced, in both face to face and virtual classrooms, by tasks that are complex, authentic and ill defined (Herrington, Oliver, Herrington & Sparrow, 2000). It has been argued that such tasks can accommodate the diversity of learners’ backgrounds, abilities and learning styles (Kerka, 1995). Current learning theories also emphasise the importance of social interaction in the learning process (Bransford, Brown & Cocking 2000). As learning environments adopt these more recent theories of learning and instructional design, and as the technology continues to migrate from CD-ROM based multimedia to a greater online presence, then the increased opportunity for communication, collaboration and cooperation between learners and teachers on complex problem solving and investigations becomes apparent.

The possibility of learners’ styles changing over time has not been well researched, however, there appears to be some evidence that cognitive style may be a ‘flexible construct and malleable over the long term’ (Brown, 2003, p. 2). A potentially beneficial area for future research would be to investigate if and how both FI and FD learning styles change over time when they engage with HBI software that reflects current technology, learning theory and instructional design. Future studies would also benefit from research designs that did not seek to compare learning outcomes for different groups of learners, but instead investigated the qualitative interactions between cognitive styles, contexts, outcomes and learning environments that are facilitated by the affordances offered by computer-based technologies.

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