INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION. 17\3). 375-402 Copyright © 2004. Lawrence Erlbaum Associates, Inc.
Cognitive Modeling of Student Learning in Web-Based instructional Programs Sherry Y. Chen Robert D. Macredie Department of Information Systems and Computing, Brunei University
There has been tremendous growth in Web-based instruction over the past few years. Because the user group of Web-based instruction includes learners from heterogeneous backgrounds, individual differences become an important issue in the development of Web-based instructional programs. Among a variety of individual differences, cognitive style is a particularly important characteristic. This study aims to determine the relationships between learners' cognitive styles and their perceptions and attitudes toward the features of a Web-based instructional program. The results indicate that cognitive styles influence students' reactions to nonlinear interaction, independent learning, and navigation tools and the difficulties and problems that they encounter. The findings are applied to develop a learning model that can support the design of Web-based instructional programs.
1. INTRODUCTION
As the World Wide Web has grown to become a major channel for business communications, entertainment, and information exchange, it has also begun to be seen as a preferred technology to improve instruction in higher education (MacArthur & Lewis, 1996). Unlike other software with access confined to a rather homogeneous group of users. Web-based instructional programs are used by a population of learners who have far more heterogeneous backgrounds, in terms of their preferences, skills, and needs. The diversity in the user population results in a new challenge for instructional design. In response to this challenge, researchers need todirect more attention toward seeing how diverse populations are learning, accessing, and using Web-based instructional programs (Zoe & DiMartino, 2000). Therefore, empirical evaluation of learners' individual differences becomes paramount because such evaluation can provide concrete prescriptions for developing learner-centered programs that can be matched with the particular needs of each individual.
Requests for reprints should be sent to Sherry Y. Chen, Department of Information Systems and Computing, Brunei University, Uxbridge, Middlesex UB8 3PH, UK. E-mail:
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In this vein, the study reported in this article was designed to examine hovi' individual differences influence students' reactions to Web-based instructional programs. Among a variety of individual-differences approaches, this study focused on cognitive styles because they influence the effectiveness of teaching and learning (Miller, 1987). The research addressed a specific question: "What are the effects of students' cognitive styles on their perceptions and attitudes toward learning within a Web-based instructional program?" Answers to this question were sought by analyzing students' responses to the features provided by a Web-based program. On the basis of the findings, the authors offer suggestions to improve the development of Web-based instructional programs. This article begins by building a theoretical framework to present the relationships between cognitive styles and Web-based instructional programs, followed by the proposition of five hypotheses developed from an analysis of previous studies. It then progresses to discuss an empirical study of students' learning experiences in a Web-based instructional program. Subsequently, the findings of this empirical study are used to frame a design model, which illustrates how to integrate the preferences of each cognitive style into the development of Web-based instructional programs.
2. THEORETICAL RATIONALE AND HYPOTHESES 2.1. Web-Based Instruction
The values of Web-based instruction reflect its flexibilities. Traditional computer-based instructional programs present information in a linear fashion. Web-based instructional programs make use of hypermedia capabilities, which permit much more flexibility in the delivery of instruction by enabling users to select hypertext links (Federico, 2000). Such flexibility offers learners a rich exploration environment and encourages them to navigate by association. In this way, they can then construct their own individualized knowledge structure by cross-referencing related topics in their knowledge base. Therefore, learners are able to follow paths through the subject content produced by designers or to develop their own routes according to individually prescribed requirements (Large, 1996). The other flexibility that Web-based instruction provides is that learners can read course content through a computer network at any time and from different places (Chang et al., 1998), which is why many educators have tried to develop distance learning programs on the Web. These flexibilities provide learners with many opportunities to explore, discover, and learn according to their own individual needs. However, the freedom offered by Web-based instructional programs may come at a price because flexibility increases complexity (Ellis & Kumiawan, 2000). Ng and Gunstone (2002) indicated that although students had positive perceptions about self-based learning provided by Web-based instruction, the unstructured nature of the Web made some students need more time to search for information. Power and Roth (1999) reported that Web-based instruction is more dynamic and richer than other learning material but that it creates new challenges related to the
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effect on learners' comprehension. Andrew (2001) pointed out that use of the Web opens doors for students to explore a vast universal resource, but students still find it difficult to shed the hard copy habit. In addition, Y. Quintana (1996) found that although students gained the advantage of flexibility in time, pace, and distance with Web-based instruction, many of them felt isolated, suffered from a lack of motivation or lack of support, and found that the feedback provided was too limited, and consequently they dropped out of their courses. These studies provide evidence that not all types of students appreciate being given freedom in their learning processes. In particular, students who need more guidance through the learning process may meet an increased number of problems in using Web-based instructional programs. To address this limitation, an instructional program should be developed to support the unique needs of each individual learner (Carter, 2002). Only when learners' needs are identified can developers of programs effectively enhance functionality and increase learners' satisfaction (Ke, Kwakkelaarb, Taic, & Chenc, 2002). In other words, accommodation of the learner is the central issue in the design process. This is the reason why recent research has seen a shift toward emphasizing learner-centered design (Federico, 1999), which is theoretically motivated by sociocultural and constructivist theories of learning (Soloway et al, 1996). This design approach claims that the development of an instructional program should be based on the learner's point of view (Soloway, Guzdial, & Hay, 1994) and address the needs of learners (C. Quintana, Krajcik, & Soloway, 2000). Leaner-centered design is imperative, and understanding students' needs is of primary significance. In particular, it is critical to Web-based instructional programs because much more heterogeneity exists among students (Soloway & Pryor, 1996). A prominent issue is to recognize how the different needs of the various learners involved with a Web-based instructional program may influence their performance and satisfaction. To develop effective Web-based instructional programs, it is necessary to consider how to accommodate learners' differences. Therefore, individual differences arguably become an important consideration in the research area of Web-based instruction. A number of learner-centered studies have shown that individual differences have a strong impact on the use of instructional technology (Marchionini, 1995). An analysis of existing pedagogical studies also confirmed that the successful usage of instructional technology depends on the technology itself and the users' individual characteristics (H. Chou & Wang, 2000). For these reasons, research into individual differences on Wcb-based instruction has mushroomed in the past decade. The examined differences include cognitive style (Durfresne & Turcotte, 1997; Shih & Gamon, 1999), gender differences (Ford &: Miller, 1996; Leong & Al-Hawamdeh, 1999), system experience (Chen & Ford, 1998; Reed & Oughton, 1997), and domain knowledge (Lawless & Kulikowich, 1998). Among these differences, cognitive style has been identified as one of the most pertinent factors because it refers to a user's information-processing habits, representing an individual user's typical mode of perceiving, thinking, remembering, and problem solving (Messick, 1976). It has also been suggested that teachers should assess the cognitive styles of their students to design instructional strategies for optimal learning (Lee, 1992).
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2.2. Cognitive Styies
Cognitive style refers to an individual's preferred and habitual approach to organizing and representing information (Riding & Rayner, 1998). Within the area of cognitive styles, field dependence versus field independence has emerged as one of the most widely studied dimensions with the broadest application to problems of education (Messick, 1976; Witkin, Moore, Goodenough, & Cox, 1977) because it reflects how well a learner is able to restructure information based on the use of salient cues and field arrangement (Weller, Repman, & Rooze, 1994). Witkin et al. (1977) found that field dependent people have global perceptions, whereas field independent people are good at analytical thought. Previous research (e.g., Jonassen & Grabowski, 1993; Riding & Cheema, 1991) has noted conceptual links between field dependence-field independence and the holist-serialist classification proposed by Pask (1988). Field dependent individuals typically perceive objects as a whole and approach a task more holistically; field independent individuals focus on individual parts of the object and tend to be more serialistic in their approach to learning. Similar to field dependent individuals, holists process information in relatively global ways in that they tend to concentrate first on building an overall picture of the subject area, into which they subsequently fit procedural detail. Conversely, serialists have a learning pattern similar to that of field independent learners; they tend to maintain a local focus, concentrating on one thing at a time and on building up procedural understanding step by step. Evidence can be found in Ford and Chen (2001), who developed two Web sites with two different navigation paths: One used a depth-first path, and the other one used a breadth-first path. In the case of the depth-first path, each topic was presented in detail before the next topic (i.e., the serialistic condition), whereas the breadth-first path gave an overview of all material prior to introducing detail (i.e., the holistic condition). Field dependent users performed better following the breadth-first path, whereas field independent users did better following the depth-first path. In addition to the difference between global and analytical approaches, another distinction is that field dependent individuals rely more on external references; in contrast, field independent individuals rely more on internal references (Goodenough, 1976). This is reflected in the differences of their behaviors in the interpersonal domain (Witkin & Goodenough, 1981). Field dependent individuals pay more selective attention to social cues; they favor situations that bring them into contact with others and have the ability to get along with others. Conversely, field independent individuals tend to be more autonomous and show initiative, self-reliance, and the ability to think for themselves. The tendencies to rely primarily on external or internal references also affect their performance on cognitive restructuring tasks. Field dependent individuals are more influenced by format-structure, whereas field independent individuals are less affected by format-structure (Jonassen & Grabowski, 1993). Davis and Cochran (1989) explained this phenomenon as field dependent learners being more reliant on salient cues in learning. Conversely, field independent individuals tend to sample more cues inherent in the field and are able to extract the relevant cues necessary for the completion of a task.
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The other differences between field independent and field dependent individuals are that the former are likely to use active cognitive strategies, and the latter have a tendency to use passive strategies such as rehearsal (Frank & Keane, 1993). According to Witkin et al. (1977), field independent individuals adopt a hypothesis-testing role in learning; conversely, field dependent individuals adopt a spectator role in learning. Even (1982) noted that people who exhibit field independent styles are likely to benefit from a self-directed emphasis. Yet, field dependent learners also tend to prefer more structured learning environments, suggesting that field dependent learners may benefit from reorganization of the learning material to make the organizational structure more explicit (Chapelle &: Jamieson, 1986). The aforementioned differences in learner characteristics (global vs. analytical, external vs. internal, passive vs. active) may influence individuals' learning strategies in Web-based instruction. Several studies have shown that there is an interesting correlation between Reid dependence and learning behaviors in Web-based instructional programs. For example, Chen and Ford (1998) conducted a study in which they presented a Web-based instructional program with nonlinear formats to introduce students to artificial intelligence. They administered Riding's (1991) Cognitive Styles Analysis (CSA) to assess each participant's level of field dependence. The results indicated that field dependent students used the mairi menu more often than field independent students. Furthermore, Kim (2001) investigated the effects of cognitive style on Web searching experience. The Group Embedded Figures Test (GEFT) by Witkin, Oltman, Raskin, and Karp (1971) was administered to identify participants' cognitive styles. They found that field dependent novices tended to follow links prescribed by the Web page and to experience more disorientation problems. As a result, they suggested that field dependent individuals, especially when novices, might need special attention from interface designers and those who train Web users. Results from these studies suggest that different cognitive style groups use different learning strategies in Web-based instruction. These studies also indicate that further empirical work is needed to identify the learning preferences of different cognitive style groups, the results of which might help to guide the development and evaluation of Web-based instructional programs. This article presents such a study, which aims to examine how cognitive styles influence students' responses to the interface features of a Web-based instructional program, and subsequently develop support n:\echanisms for designers.
2.3. Developing a Rationale for the Hypotheses As discussed in Section 2.1, Web-based instruction programs make use of hypermedia techniques; in turn, the features of hypermedia learning may also influence learners' reactions to Web-based instruction, including nonlinear learning, learner control, and multiple tools. Chen and Macredie (2002) presented a comprehensive review of empirical studies concerning the effects of learners' cognitive styles from 1989 to 2001, particularly focused on Witkin's field dependence-field independence, on the effectiveness of hypermedia learning. On the basis of an analysis
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of the results of previous studies, they built a learning model that suggests that field dependent learners prefer guided navigation and field independent learners prefer free navigation, and that field dependent learners need more guidance and tend to use maps to process information globally, whereas field independent learners enjoy independent learning and tend to be analytical, locating specific information using an index. The model revealed that learners with different cognitive styles showed different reactions to nonlinear interaction and independent learning, and they favored using different navigation tools. Thus, this research ieads to three hypotheses: • Hypothesis 1: Cognitive styles will significantly influence learners' attitudes toward nonlinear interaction. • Hypothesis 2: Significant interactions will be found between learners' cognitive styles and their reactions to independent learning. • Hypothesis 3: Different cognitive style groups will favor different types of navigation tools provided by the Web-based instructional program. According to the available literature, cognitive styles may influence learners' requirements related to content presentation. Chen and Ford (1998) examined the relationships between students' cognitive styles and learning strategies (described in Section 2.2). In addition to the link between students' cognihve styles and the choice of navigation tools, the research also showed that students with different cognitive styles showed different requirements in relation to content presentation. Field independent students preferred content to be presented in a logical way, intermediate students regarded the presented content as too superficial, and field dependent students judged the content to be too detailed. Hence, one may hypothesize as follows: • Hypothesis 4: Learners with different cognitive styles will have different requirements in terms of content presentation within Web-based instruction. Previous studies have also indicated that cognitive styles might have significant effects on students' learning performance. Field independent students, who have a higher ability to engage in independent learning, perform better than field dependent students, who are less capable of learning independently (Boyce, 1999; Chuang, 1999; Umar, 1999). In this study, learning performance was measured by the perceived confidence that the learner has in his or her understanding of subject content. Thus, a final hypothesis might be proposed: • Hypothesis 5: Cognitive styles will have a significant effect on the confidence related to the understanding of the subject content within Web-based instruction. 3. METHODOLOGY DESIGN
The aforementioned five hypotheses were tested using quantitative measurement in which the data obtained from the closed questions of the questionnaire were sta-
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tistically analyzed to build up a picture of the mapping of the relationships between students' cognitive styles and their learning preferences. In addition, data from open-ended questions were used for qualitative evaluation to complement a more detailed analysis of the diversity in students' learning preferences according to their cognitive styles. The intention of using both quantitative measurement and qualitative evaluation was to overcome the individual weaknesses and biases of either approach used in isolation (Rossman & Wilson, 1985).
3.1. Participants
This study was conducted at Brunei University's Department of Information Systems and Computing. A total of 61 master's students participated in this study, representing 68*>1> of the population of the master's courses. All participants had the basic computing and Internet skills necessary to operate a Web-based instructional program. At the outset, they were inexperienced in the content domain of HTML authoring. Despite the fact that the participants volunteered to take part in the experiment, they were evenly distributed in terms of cognitive styles based on the CSA test results {see the Cognitive Styles Analysis section). In addition, the participants were an almost equal mix of male and female within each cognitive style group. Table 1 illustrates the distribution of the sample of this study.
3.2. Task Design
In this study, participants were asked to complete specific tasks by themselves. The task activities involved constructing a home page using Notepad, to measure the levels of understanding of HTML. Before the participants interacted with the Web-based instructional program, they each received a paper-based task sheet. On the basis of this task sheet, the participants were asked to locate specific information to complete the tasks successfully. Scoring consisted of summing the successfully completed items. The participants were allowed to decide the order in which they attempted the tasks on the sheet. One and a half hours were allocated for each participant to use the program and complete the task activities. Researchers designed the task to address 10 activities covering different areas, such as creating hypertext links, changing background colors, and producing tables. The aim was to have tasks be at a level of complexity that would maintain motivation in the participants {Scanlon, 2000). Furthermore, these 10 activities were aligned Table 1: The Distribution of the Sample Cognitive Style Field independent Intermediate Field dependent Total
Women
Men
Total
10 10 9 29
13 11 8 32
23 21 17 61
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with key areas of content within tbe Web-based instructional program, which meant that participants were forced to encounter all interface features. In this way, the authors were able to identify how members of different cognitive style groups perceived various interface features provided by the Web-based instructional program.
3.3. Research Instruments Research instruments work as a guide to make sure that the same information is obtained from different students. The research instruments used in this study included a Web-based instructional program to teach students how to use HTML, Riding's CSA to measure students' cognitive styles, and an exit questionnaire to identify students' perceptions and attitudes toward the Web-based instructional program. The following sections introduce and explain these three instruments.
Web-based instructional program. Students interacted with a Web-based instructional program entitled How to Use HTML. The teaching-learning approach in this program was based on the concept of self-organized learning. In other words, students were given freedom to choose their own navigational routes through the subject matter. Students could stud y topics and subtopics in any order. Three types of navigation control were available in this program as shown in Table 2. The program began by introducing its learning objectives and explaining the available navigation approaches; it was presented in 82 pages using text, tables, an index, and maps. The content was divided into three sections: Section 1, What is HTML?; Section 2, Working With HTML; and Section 3, Relations With SGML and WWW [World Wide Web]. Section 2, which covered 12 subtopics of HTML authoring, was the key element of the Web-based instructional program. Each subtopic was further split into five subject categories, consisting of (a) overview, (b) Table 2: Three Types of Navigation Control Contra!
Purpose
Sequence control To allow students to decide the sequence of subjects to be learned.
Content control
Display control
To allow students to control the selection of the contents they wish to learn. To allow students to choose one of several display options that cover the same concept.
Tool
Hierarchical maps: to show all topics and sub-topics in a hierarchical way. Alphabetical index: to list keywords in alphabetical order. Back-forward buttons: to see the page previously visited. Section buttons: to choose from three sections that hold the main content. Main menu: to present the main topics. Hypertext links: to connect relevant concepts. Display options: to include an overview, examples, detailed techniques, and so forth.
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detailed techniques, (c) examples, (d) related skills, and (e) references, so that analyses of students' preferred content presentation could be undertaken by examining the students' navigation paths and their replies to items in the questionnaire. Interface elements included the following: (a) a title bar located at the top of the screen showing the section name being viewed and the other available section buttons; (b) a control panel with the choices for menu, map, index, and quit buttons; and (c) the main body of the program, providing referenced links and subject categories for selection. Figure 1 shows the screen design of this program.
Cognitive Styles Analysis. The cognitive style dimension investigated in this study was the level of field dependence. A number of instruments have been developed to measure field dependence, including the GEFT by Witkin et al. (1971) and the CSAby Riding (1991). The GEFT derives scores for field independence by requiring participants to locate simple shapes embedded in more complex geometrical patterns. However, a criticistn of this approach is that levels of field dependence are inferred from poor field independence performance (Ford & Chen, 2001). The CSA differs from the GEFT in that it includes two subtests. The first presents items containing pairs of complex geometrical figures that the individual is required to judge as either the same or different. The second presents items each comprising a simple geometrical shape, such as a square or a triangle, and a complex geometrical figure, as in the GEFT, and the individual is asked to indicate whether or not the simple shape is contained in the complex one by pressing one of two marked response keys (Riding & Grimley, 1999). These two subtests seem to have different purposes. The first subtest is a task requiring field dependent capacity, whereas the second subtest requires the disembedding capacity associated with field independence. In this way, the CSA overcomes the GEFT limitation that affects the measures of field dependence and field independence, because field dependent competence is positively measured rather than being inferred from poor field independent capability (Ford & Chen, 2001). In addition, the CSA offers computerized administration and scoring. There-
FIGURE 1 Screen design of the Web instruction program.
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fore, the authors selected the CSA as the measurement instrument for field dependence in this study. The CSA measures what Riding and Sadler-Smith (1992) referred to as a Wholist/Analytic (WA) dimension, noting that this is equivalent to field dependence-independence. As Witkin et al. (1971) argued, a field independent individual is capable of a more analytical cognitive function than a field dependent individual, who uses a more glohal approach. Riding's (1991) recommendations are that scores below 1.03 denote field dependent individuals; scores of 1.36 and above denote fieid independent individuals; students scoring between 1.03 and 1.35 are classed as intermediate. In this study, categorizations were based on these recommendations. Table 3 presents the overall range of the WA scores in this study.
Exit questionnaire. A paper-based questionnaire was used to examine the research question "What are the effects of students' cognitive styles on their perceptions and attitudes toward learning within a Web-based instructional program?" This instrument was chosen because it has the potential to collect cognitive and affective data quickly and easily (Kinshuk, 1996). Another advantage of questionnaires is that the data may be both qualitative and quantitative, allowing them to play a part in both quantitative and qualitative studies (Su, 1991). Several well-known questionnaires are available in the field of human-computer interaction, such as Questionnaire User Interaction and Satisfaction, developed by the University of Maryland (1988), and Purdue Usability Testing Questionnaire, developed by Purdue University (1997). However, this study examined students' responses to a particular Web-based instructional program that provided multiple navigation tools and description formats, and the questionnaire was developed to test specific hypotheses related to cognitive styles. Therefore, we decided to design a questionnaire specifically for this study, instead of using an existing questionnaire. The questionnaire was divided into two parts. The first contained information regarding biographical data relating to the student and his or her experience of using computers, the Internet, and HTML. The second, which was the main focus, consisted of four open-ended questions and 47 closed statements to collect students' responses to the Web-based instructional program. It took students approximately 20 min to respond to all of the questions. The open-ended questions were used to explore students' experiences of doing the tasks, their opinions about the strengths and weaknesses of the Web-based instructional program, and the difficulties that they met. Students Table 3: The Overall Range of Wholist/Analytic Scores in This Study Cognitive Styles
Mean
Field independent (N = 23) Intermediate (N - 21) Field dependent (N = 17) Overall
1.56 1.15 0.82 1.21
SD
Minimum
Maximum
QM 001 0.13 0.32
1.36 1.03 0.61 0.61
1.85 1.35 1.00 1.85
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were free to describe their experiences and opinions in their own words, with relevant space provided, supplying a useful source of qualitative information. The closed statements were designed to test the hypotheses described in Section 2.3 by gathering a large amount of specific quantitative information about students' comprehension, preferences, and satisfaction or dissatisfaction with the Web-based instructional program. Five sections were included: Section A, level of understanding; Section B, content presentation; Section C, interaction styles; Section D, functionality and usability; and Section E, difficulties and problems. Table 4 illustrates the relationships between the hypotheses testing and the questionnaire design. Each closed statement could be classed as either in favor or not in favor of the program. The number of "favored" statements was almost equal to the "not-favored" statements (20 favored statements and 27 not-favored statements), in an attempt to reduce bias in the questionnaire. All statements used a 5-point Likert scale consisting of the response options strongly agree, agree, neutral, disagree, and strongly disagree. Students were required to indicate agreement or disagreement with each statement by placing a check mark at the response alternative that most closely reflected their opinion. Their perceptions and attitudes were measured by the following: 1. Positive Perceptions: the total score for all favored statements on the exit questiormaire with the same Likert scale score; 2. Negative Attitudes: the total score for all not-favored statements on tbe exit questionnaire with the same Likert scale score.
Table 4: The Relationships Between Hypotheses Testing and Questionnaire Design Foe MS of Hypothesis
Questionnaire
HI: Non-linear interaction
Section B Section E
H2: Independent learning
Section C
H3: Navigation tools
Section D
H4: Content presentation
Section B
H5: Confidence in understanding
Section A
Note. H = Hypothesis.
Examples of Closed Statcmciits
1 iike the fact that it allowed me to learn topics in any order. I was confused which options I wanted, because it provided too many choices. i like the fact that this tutorial allowed me to work at my own pace and direction. I would prefer to learn from human tutors than Irom this tutoriai. The map in this tutorial gives a meaningful framework of HTML. It is easy to find a route for a specific task with the index. The content of this tutorial is too superficial. I would like to have more detailed expianations. At the beginning, I was not sure how HTML worked, but I have gained a clear understanding by using this program. After using this program, I can easily use my knowledge to design home pages.
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3.4. Procedures
The experiment was conducted using a Web-based instructional program accessed through Microsoft's Internet Explorer They applied the following procedures: 1. The CSA was used to classify students' cognitive styles as field independent, intermediate, or field dependent. 2. Students were asked to interact with the instructional program on How to Use HTML, 3. Students were assigned a practical task, which involved constructing a Web page using HTML. 4. Students were asked to reflect on their opinions of the Web-based instructional program by completing a paper-based exit questionnaire. 3.5. Data Analyses
To investigate the students' views of the Web-based instructional program, the data collected from the closed statements on the questionnaire were coded for quantitative analysis. The independent variable was the participants' cognitive style. The dependent variable was the participants' responses given on the Likert scale {5 = strongly agree, 4 = agree, 3 ^ neutral, 2 = disagree, 1 ^ strongly disagree). The
Kruskal-Wallis test—a nonparametric statistical test equivalent to the one-way between-groups analysis of variance and suitable for comparing three or more independent groups of sampled data (Hatch & Lazaraton, 1991)—was applied to analyze participants' responses to the closed statements. A significance level of p < .05 was adopted for the study. Tables of frequency counts and percentages were produced for the students' responses to each question {see Section 4). Also analyzed were the qualitative data collected from the open-ended questions contained in the exit questionnaire. The students' responses were divided into three cognitive style groups, with the responses of each group coded under the following categories: (a) strengths of the Web-based instructional program, (b) weaknesses of the Web-based instructional program, (c) experience of doing the tasks, and (d) difficulties and problems met. Such qualitative approaches were used to illuminate the phenomena identified in the quantitative data.
4. DISCUSSION OF THE RESULTS
We applied the data obtained from the exit questionnaires to identify students' perceptions and attitudes toward the Web-based instructional program. As described in the Exit Questiormaire section, positive perceptions and negative attitudes were separately measured by the favored statements and the not-favored statements. The results indicated that field independent students had higher scores in the favored statements. In contrast, field dependent students obtained higher scores in the not-favored statements. In other words, field independent students signifi-
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cantly displayed tnore positive perceptions toward the Web-based instructiotial program, whereas field dependent students showed more negative attitudes to the program (see Table 5). In addition, the three cognitive style groups showed different perceptions and attitudes to the program features. Sections 4.1 to 4.5 present the quantitative results based on the hypotheses described in Section 2.3. Section 4.1 begins by discussing students' responses to the key feature of Web-based instruction, nonlinear interaction; it then progresses to consider the second related issue, independent learning {Section 4.2), where it is suggested that not all learners can accept independent learning. Section 4.3, which moves on to address another feature of Web-based instruction, multiple navigation tools, indicates that different cognitive style groups favor different tools for navigation. This is followed by a discussion of how cognitive styles influence learners' requirements for content presentation (Section 4.4). Subsequently, Section 4.5 examines whether cognitive styles influence learners' confidence in relation to understanding the subject content. This article then goes on to discuss qualitative results. Section 4.6 discusses whether learning by doing can enhance student learning in a Web-based instructional program, and finally the problems and difficulties that different cognitive style groups met are discussed in Section 4.7.
4.1. Nonlinear interaction Only a portion of the learners in this study favored nonlinear interaction, which is a key attribute of the Web-based instructional program. Field independent students appreciated the fact that this program allowed them to study topics in any order (see Table 6). However, field dependent students felt confused over which options they should choose (see Table 7). This may be because field independent individuals use more active approaches and are better at transferring concepts to new situations. Conversely, field dependent students are more comfortable in guided learning processes (C. Chou & Lin, 1997). These findings are consistent with Reiff's (1996) view that field independent individuals are self-structuring and use an internal frame of Table 5: Cognitive Style Field independent M SD Intermediate M SD Field dependent M SD
Students' Reactions and Their Cognitive Styles Positive Perceptions
Negative Attitudes
77.4118 12.9800
36.7368 6.9269
54.5263 10.6738
57.7222 10.1911
31.5263 6.6738
72.7222 11.1911
Note. Kruskal-Wallis results: For positive perceptions, N - 61; x^ - 11-32; p • .003. For negative attitudes, N = 61; x^ = 13.990; p = .001.
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Table 6: Views on Learning Topics in Any Order (like the fact tliat it allowed me to learn topics in any order. Intermediae
Field Independent
Field Dependent
Response Selected
n
%
n
/o
Strongly disagree Disagree Neutral Agree Strongly agree
2 3 6 10 2
8.7 13.0 26.0 43.5 8.7
1 6 6 8 0
4.8 28.6 28.6 38.1 0.0
Note.
n 1 8 4 2 2
cy
5.8 47.1 23.5 11.8 11.8
Significance: x^ (N - 61) = 8.54,;) - .014.
Table 7:
Views on the Range of Options Provided
/ was confused which options I wanted, because it provided too many choices. Intermediate
Field Independent
Field Dependent
Response Selected
n
%
n
/o
Strongly disagree Disagree Neutral Agree Strongly agree
2 10 6 5 0
8.7 43.5 26.1 21.7 0.0
0 8 6 7 0
0.0 38.1 28.6 33.3 0.0
Note.
n
%
0 1 3 9 4
0.0 5.9 17.6 53.0 23.5
Significance: x^ {N = 61) = 15.33, p = .001.
reference to structure problems and organize information; field dependent individuals rely more on external frames of reference and operate best where structures and analyses are already provided (Lyons-Lawrence, 1994). As the results in this section suggest, cognitive styles have significant effects on learners' attitudes toward nonlinear interaction. Therefore, Hypothesis 1 was supported.
4.2. Independent Learning In terms of Independent learning, field independent students appreciated the fact that the Web-based instructional program allowed them to work at their own pace (see Table 8). As suggested by Ford, Wood, and Walsh (1994), field independent learners tend to be more analytical, imposing their own structure on a situation, and to be relatively less passive in their behaviors. Conversely, field dependent students prefer to learn from human tutors rather than from a Web-based instructional program (see Table 9), implying that they seem better at learning material via human interaction. As indicated by Castaneda, Ramirez, and Herold (1972), field dependent learners have a greater social orientation than field independent learners
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Table 8: Views on Working at the Learner's Own Pace and Direction / like the fact that this tutorial allowed me to work at my own pace and direction. Field Independent Response Selected
n
o/ to
Strongly disagree Disagree Neutral Agree Strongly agree
1 5
4.3 21.7 21.7 43.5 %J
Note.
5 10 2
Intermediate
Field Dependent
n
/o
n
0 9 5 7 0
0.0 42.9 23.8 33.3 0.0
1 8 4 4 0
5.9 47.1 23.5 23.5 0.0
Significance: x^ (N - 61) - 7.50, p - .023.
Table 9: Views on the Learner's Preference for Human Tutors / would prefer to learn from human tutors tlian from this tutorial Field Independent Response Selected
Strongly disagree Disagree Neutral Agree Strongly agree Note.
Intermediate
n
%
n
2 11 6 4 0
8.7
47.8 26.1 17.4
0 10 6
0.0
5 0
Field Defiendent to
n
%
0.0
0
0.0
47.6 28.6 23.8 00
2
11.8 23.5 58.8
4 10 1
5.9
Significance: x^ (N = 61) = 23.01, p = .005.
and are more ready to accept other people's opinions or those positively related to interpersonal competencies. These findings also echo the view of Olstad, Juarez, Davenport, and Haury (1981), in that the construct of field dependence is associated with certain personality characteristics that may have important instructional and learning ramifications. These results suggest that learners with different cognitive styles showed different reactions to independent learning, so Hypothesis 2 was supported.
4.3. Navigation Toois The Web-based instructional program provided six types of navigation tools, including alphabetical index, back-forward buttons, a hierarchical map, hypertext links, a main menu, and section buttons. Students with different cognitive styles significantly favored three of these six tools (map, index, and hyperlinks). The following sections describe the findings related to each tool.
Hierarchicai map. Field dependent students thought that the hierarchical map provided them with a meaningful framework (see Table 10). This is consistent
Chen and Macredie
Table 10: Views on the Map of the Tutorial The map in this tutorial gives a meaningful frameivork of HTML. Field Independent Response Selected
n
Strongly disagree Disagree Neutral Agree Strongly agree
0 10 13 0 0
Note.
0.0 43.4 56.5 0.0 U.O
Intermediate
Field Dt'pendent
n
%
n
%
0 5 12 4 0
0.0 23.8 57.1 19.0 0.0
0 1 1 11 4
OX 5» 5.9 64.7 23.5
Significance: x^ (N - 61) = 6.04, p = 0.041.
with the findings of Ford and Chen's (2000) study. Field dependent students tend to use a global, spectator, and less analytic approach to learning (Witkin et al., 1977) and to require more structure and guidance compared with field independent students. Arguably, favoring the hierarchical map could be considered as reflecting a greater need for authoritative guidance.
Alphabetical index. Field independent students found it easy to select relevant information for the prachcal task using the alphabetical index (see Table 11). The alphabetical index provides learners with a means to locate particular information without going through a fixed sequence of information. A possible interpretation of this finding is that field independent students are strong in perceptual and conceptual tasks, actively segmenting information into relevant parts and analyzing the interrelationships among those parts (Goodenough, 1976). This is in accord with Witkin et al.'s (1977) findings that field independent individuals are more able to engage in learning requiring independent and analytical thought.
Hypertext iinks. Intermediate students found itusefultodiscover the relationships between different topics via hypertext links (see Table 12). In addition to this Table 11: Views on the Index of the Tutorial /(IS easy to find a route for a specific task with the index Field independent
Intermediate
Field Dependent
Response Selected
1!
%
n
%
Strongly disagree Disagree Neutral Agree Strongly agree
0 2 8 9 4
0.0 8.7 34.8 39.1 17.4
0 3 14 2 2
0.0 14.3 66.7 9.5 9.5
Note.
Significance: x^ (N - 61) = 9.42, p - .009.
ij
0 4 11 2 0
/u
0.0 23.5 64.7 11.8 0.0
Cognitive Modeling in Web-Based Instruction
391
Table 12: Views on the Links of the Tutorial / tiked to use the links because they can help me to discover the relationships betzveen different techniques. Field Independent
Intermediate
Field Dependent
Response Selected
n
%
n
%
n
%
Strongly disagree Disagree Neutral Agree Strongly agree
0 2 8 8 5
0.0 8.7 34.8
0 3 3 13 2
0.0 14.3 14.3 61.9 9.5
0 2 11 4 0
0,0 11,8 64,7 23.5 0,0
Note.
34.8 21,7
Significance :X^(N = 6i;1 = 7.12, p = . 0 2 8
quantitative data, the qualitative data from open-ended questions also indicated that 10 of 21 intermediate students thought that insufficient hypertext links were included in the program (see Section 4.7). That intermediate students favored using the hypertext links could arguably be seen as indicating high levels of engagement with the subject content, in that the hyperlinks represent interest in "follow-up" information relevant to the particular subject content being read at the time (Chen & Ford, 1998). The results presented in Section 4.3 suggest that different cognitive style groups favored using different types of navigation tools provided by the Web-based instructional program. Therefore, Hypothesis 3 was also supported.
4.4. Content Presentation
Results related to the content presentation of the program showed that field independent students thought the content was too superficial (see Table 13). They echoed this feeling in their responses to a further question where they said that they would like to have more detailed information (see Table 14). These results imply that they preferred to focus their attention on detail, suggesting that field independent learners preferred to takea serialist learning approach that concentrated primarily on procedural details when processing information in a learning context (Pask, 1976,1979). Table 13: Views on the Content of the Tutorial The content of this tutorial is too superficial. Field Independent Response Selected Strongly disagree Disagree Neutral Agree Strongly agree Note.
Intermediate
Field Dependent
n
%
n
%
n
%
0 6 4 13 0
0.0 26.1 17.4 56.5 0,0
0 11 5 5 0
0.0 42,4 23.8 23,8 0,0
0 10 3 2 2
0.0 58.8 17.6 11.8 11.8
Significance: y} (N = 61) = 27.44, p = ,001.
Chen and Macredie Table 14: Views on the Levels of Detailed Explanations I would like to have more detailed explanations. Field Independent
Intermediate
Field Dependent
Response Selected
n
%
n
%
n
%
Strongly disagree Disagree Neutral Agree Strongly agree
2 4 5 11 1
8.7 17.4 21.7 47.8 4,3
0 6 6 9 0
0,0 28.6 28.6 42,9 0.0
1 9 4 3 0
5.9 52.9 23.5 17.6 0.0
Note.
Significance: x^ (^ ^ 61) - 7.63, p ^ ,022.
Conversely, the field dependent students expressed opposite opinions in their answers to these questions, suggesting that they favored taking a holist approach that concentrates on building a conceptual overview (Wilson, 1998). The results presented in Section 4.4 suggest that field independent and field dependent learners show different requirements related to content presentation within the Web-based instructional program, offering support for Hypothesis 4. 4.5. Confidence in Understanding The aforementioned sections suggested that field independent and field dependent students showed different attitudes to the features of the Web-based instructional program and had different preferences with respect to the navigation tools and content development. However, they were similarly confident in understanding the subject content. After taking this program, 47.8% of field independent students and 52.9% of field dependent students felt that they had developed a clear understanding of HTML (see Table 15). In addition, 43.4% of field independent students and 47% of field dependent students thought that they had enough knowledge to design home pages (see Table 16). These results suggest that cogrutive style Table 15: Views on Confidence to Understanding HTML At the beginning, I was not sure how HTML worked, but I Imve gained a dear under
