Balancing Marketing Education and Information Technology: Matching ...

APRIL JOURNAL ARTICLE 2004OF MARKETING EDUCATION

Balancing Marketing Education and Information Technology: Matching Needs or Needing a Better Match? Lynn Hunt, Lynne Eagle, and Philip J. Kitchen

The use of new information technology in marketing education has been widely, and often uncritically, accepted as both inevitable and beneficial with little in-depth analysis of this phenomenon, which is both a new mode of teaching (and learning) and a competency domain in its own right. This article examines both the potential advantages and dangers of information technology in the context of creating knowledge workers for the marketing industry. Research findings are presented to illustrate that students have distinctively different learning profiles and experiences, and these affect how students respond to traditional and new technological modes of teaching. The authors suggest that acceptance of new technologies in education by students will rely heavily on the ability of educational institutions to manage the change process.

Keywords: marketing education; education technology; information technology; learning styles; education management

The use of new information technology (IT) in teaching

marketing subjects has been hailed in the literature as both inevitable and beneficial (see, e.g., Lamont and Friedman 2001). Moreover, McCorkle, Alexander, and Reardon (2001) asserted that “it is widely accepted that future marketing graduates will increasingly be confronted with a work environment that is dependent on and interwoven with information technology” (p. 160). The use of IT in the classroom is thus seen as assisting in building students’ technical information literacy that will later prove beneficial in terms of employment. A further perceived benefit is that IT not only promotes more efficient teaching but also facilitates better student learning. Uncritical acceptance of this view is widespread in the literature. For example, Benbunan-Fich et al. (2001) made the case for integrating technology into the marketing curriculum both to develop IT skills in students and to supplement live teaching, but they did not address the contrary arguments. Even among

the critics (see Evans 2001), concerns regarding the lack of empirical evidence documenting the enhancement of academic achievement as a result of technology use are sidelined by discussion of what the role of technology in marketing education should be. The rush to endorse IT as the new savior of education has tended to neglect the impact of students in this change process. Proponents of integrating technology into teaching have assumed that students share their enthusiasm and will embrace the new technology. McCorkle, Alexander, and Reardon (2001) alluded to this issue but focus more on problems of acceptance by faculty. Any decision to move to a technology-based platform for teaching must give regard to both benefits and problems for students. We will review the literature on the perceived benefits and present evidence that these benefits do not appear to favorably influence students’ attitudes to technology-based teaching modes compared with more traditional teaching modes. The influence of student learning orientations on teaching mode preference is also explored. THEORETICAL BACKGROUND In the literature, there is a constant underlying assumption that IT does have (and will have) a positive impact. Indeed, it would be a brave person who suggested that the positives might have been substantially overstated. Our starting point, however, is that technology is not inherently good or bad; it is neutral. Advantages and disadvantages associated with IT are a consequence of the manner in which it is used and the un-

Lynn Hunt is a senior lecturer in the Department of Human Resource Management of the College of Business, Massey University. Lynne Eagle is an associate professor in the College of Business at Massey University, Palmerston North, New Zealand. Philip J. Kitchen is a professor in the Business School at the University of Hull, Hull, United Kingdom; e-mail: [email protected]. Journal of Marketing Education, Vol. 26 No. 1, April 2004 75-88 DOI: 10.1177/0273475303262350 © 2004 Sage Publications

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derstanding that the developers have of learning outcomes. To further the debate on the value of technology in education we ask two questions: (1) What are the perceived educational benefits of the new learning technologies? and (2) How ready and willing are students to take advantage of these benefits? EDUCATIONAL BENEFITS Potentially, students can derive from the new technology three major educational benefits. The first relates to the type of content and skills (that is subject matter expertise) that they can acquire in a technology-rich learning environment. The second concerns the new ways of learning that may be more effectively promoted through technology. These new learning processes foster greater autonomy and control by the student and are closely related to metacognitive skills, which are known to enhance the efficacy of the learning not just for the immediate content but also for future learning (Aleven and Koedinger 2002). A third perceived benefit of technology is social equity. It is suggested that IT is able to overcome diverse student needs caused by variables such as economics, gender, race, and location. Technology is seen as leveling the educational playing field. WHAT IS WORTH TEACHING? DOES TECHNOLOGY SUPPORT IT? The argument that the range of subject matter skills and knowledge taught will be extended by the use of technology has become inextricably linked to the ongoing debate about the best combination of discipline-specific and generic knowledge and skills a marketing graduate should possess (see, e.g., Berry 1993; Sterngold and Hurlbert 1998). Entrylevel marketing positions increasingly specify a degree as a prerequisite for entry consideration, and we accept that for the majority of students, study is a preparation for the world of work rather than for graduate-level tertiary studies. A marketing qualification cannot and does not represent just job training. Canzer (1997) stressed not only subject knowledge acquisition and theoretical concepts but also the ability to apply this knowledge to real marketing situations. McMullen (1998) suggested that graduates require both strong disciplinary knowledge and a number of key generic skills such as problem solving, managing information, effective communication, and exercising managerial judgment. Such generic skills are vital in helping graduates apply disciplinary skills and knowledge in the varied contextual circumstances and scenarios they will encounter in industry. McMullen’s (1998) views are supported by an earlier Australian Higher Education Council report (AHEC 1992) that advocates the following generic qualities in graduates: “critical thinking, intellectual curiosity, problem solving, logical and independent thought, effective communication and related skills in identifying, assessing and managing informa-

tion,” together with “ personal attributes such as intellectual rigor, creativity and imagination and values such as ethical practice, integrity and tolerance” (p. 21). The acquisition of a specific body of knowledge relevant to industry, coupled with the generic skills outlined above, are essential for marketing graduates to function in increasingly complex, competitive, and changing work environments. To these, Walker, Hanson, Nelson, and Fisher (1998) suggested the ability to be able to integrate knowledge acquired from a range of disciplinary areas, given the belief that flexibility and the ability to adapt and learn faster than rivals now constitutes a potential competitive advantage for firms. Adaptability to a rapidly changing environment is a theme espoused by several authors. Rabon and Evans (1998) suggested that all graduates need to be skilled in coordinating the intricacies of international business, with wider knowledge across all aspects of business. Winters (2001) noted that as trade barriers fall and quality standards rise, understanding cultural barriers becomes increasingly important—respect for the customer when communicating information is more important than ever before. Scott (1999) expands on this by introducing the concept of cultural fluency, the ability of graduands and staff to cross national and cultural boundaries and, in marketing communication specifically, to be able to match the intended marketer’s (sender) encoded marketing messages and the receiver’s (prospective customer) decoded meanings (Kitchen 1999, 2003). Winters (2001) suggested that cultural fluency will help managers and students select appropriate communication technologies and tools. King (1999) posited that diversity enriches the educational experience and promotes personal growth. While none of these authors address the role that technology might play in the development or dissemination of these skills, we contend that IT has increased the level and frequency of communication between countries and cultures, intensifying the need for cultural fluency. An Organization for Economic Cooperation and Development (OECD 2001) report stipulates that essential work competencies and technology are inextricably interwoven in today’s knowledge economy. The report defines important skills required by knowledge workers, such as communication, problem solving, ability to work in teams, and IT skills. Former U.S. Secretary of Labor Bob Reich suggests that the term knowledge worker refers to the abilities a person has acquired for problem identification, problem-solving, and strategic brokering competencies (Reich 1991). A defining characteristic of knowledge workers, apart from tertiary education, is the peripheral importance of facts to their skills profile because whatever data are required will be available to them at the touch of a computer key. The important skill these workers bring to their work is an ability to conceptualize problems and solutions. In Reich’s (1991) view, tertiary education should focus on the development of four basic skills: abstraction, systems

JOURNAL OF MARKETING EDUCATION

thinking, experimentation, and collaboration. These skills are central to the notion that knowledge workers are involved in the utilization and creation of knowledge. Herein lies the importance of IT literacy; the new information technologies provide the central linkage in knowledge creation. Although technology itself does not produce new knowledge, without the technological tools, that process slows considerably, opportunities for accessing and gathering information shrink, collaboration is restricted, and systems experience entropy. So, while technological advances have been instrumental in creating the knowledge economy, sustaining that economy relies on expanding technological literacy and related competencies. In this context, technology is in the process of dramatically changing what we need to teach or rather what students need to learn for successful assimilation and application in the world of work. DOES TECHNOLOGY PROMOTE NEW WAYS OF LEARNING? The second potential benefit focuses on marketing pedagogy—what role can information technology play in promoting more efficient and effective learning for all students? This implies at least two further related questions: (1) Are some learning orientations better than others? and (2) Are learning orientations associated with particular teaching modes? The term pedagogy derives from the Greek words paid, meaning child, and agogos, meaning leading, and is often used to describe the principles for teaching children. As long as we base our approaches to teaching and learning on principles designed for teaching children, it seems unlikely that we will facilitate student development of the critical skills needed by knowledge workers, even if and when they are [apparently] embedded in a technology-rich environment. In 1970, Knowles introduced the term andragogy to recognize that adults are not just “larger versions of children” (academic suspicions to the contrary) and require different approaches to learning and teaching than those traditionally used for children. While some have criticized his work as being ambiguous as to whether it is a theory about learning or a theory or model about teaching (Hartree 1984), others have pointed out the inconsistencies of a theory that juxtaposes two opposing therapeutic traditions, the humanist approach as argued by Rogers (1983) and a behaviourist approach as exemplified by Skinner (1978). Despite these criticisms, the central tenets of Knowles’s andragogy, 1. that adults are motivated by a desire to solve real-world problems, 2. have a need to be self-directing and independent, and 3. work collaboratively as part of a community and use their experience to interpret and understand new information,

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fit well with modern learning theories of constructivism and with many of the new competencies identified earlier as critical for the modern workplace. It seems to follow that students who pursue learning via an andragogical frame of reference would be rather more likely to develop those active, selfdirected, and problem-solving skills widely acknowledged to be essential for marketing professionals than those students who are taught in a pedagogical manner. How then might these characteristics of students be measured? LEARNING ORIENTATIONS For the past 35 years, researchers have tried to identify and measure the impact of the individual’s characteristics on the effectiveness of the learning process. It has been quite clear for some time that what the student does is more important to learning outcomes than what the lecturer does (Shuell 1986), but how might this contribution be quantified and understood? The results for the last three decades have been inconsistent and often disappointing. Research on these issues has followed three avenues. The first was aptitude-treatment interaction (ATI). This involved matching a student characteristic, such as anxiety, with a treatment designed to accommodate it. ATI was popular in the late 1960s and 1970s and continued to be periodically revisited into the 1990s (Snow 1992). Interest in learning styles began in the late 1960s and continues today. It is based on the notion that each person has a predisposition to go about learning in a particular way. These predispositions are defined differently by different groups. At one end of the spectrum, they are seen as being related to cognitive styles, but with a learning orientation. In this guise they are relatively stable characteristics, for example, visual-verbal learning styles (Riding and Rayner 1999). At the other end of the spectrum, a style is viewed as any preferred way of undertaking learning activity and may include such diverse elements as lighting levels, preference for working collaboratively, and motivation (Dunn 2000). The literature on learning styles is fragmentary and isolated in specific domains, and this has mitigated against a coherent and cohesive advance in the field (Bonham 1988). The third approach to individual differences began later, in the 1970s. It started almost simultaneously in three locations: Australia (Biggs 1976), England (Entwistle 1977), and Sweden (Marton and Saljo 1976a, 1976b). Unlike learning styles research, there was a high level of agreement and cohesion between the terminology, research, and results. Collectively, this area has come to be known as student approaches to learning (SAL). Briefly, the researchers found that students had three possible motives for studying; surface (interested only in getting the qualification), deep (intrinsically interested in the subject), and achieving (wanting to “do well”). Furthermore, these motives influenced the kind of strategies students use for learning. A surface motive was related to a

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surface strategy (memorizing), a deep motive triggered a deep strategy (understanding), and an achieving motive was associated with organizational strategies (achieving). A motive-strategy combination formed an approach. A number of studies have confirmed a relationship between the type of approach and the quality of learning outcome(s) (Hunt 1995; Sadler-Smith 1996; Trigwell and Prosser 1991). Many of the instruments developed from the three streams of research on individual differences were classified into an onion-like structure developed by Curry (1983). Inner layers represented measures of stable traitlike characteristics, while the outer layers comprised instruments measuring more flexible and modifiable characteristics. This study chose a “slice of the onion” approach, taking from each layer a selection of learning characteristics to create a more holistic picture of the student’s learning behavior. We have called this mix of aptitudes, styles, and approaches learning orientations. This raises the next question: what, if any, association is there between learning styles and students’ preferred modes of teaching? MODES OF TEACHING AND LEARNING ORIENTATIONS A recurring criticism of traditional modes of teaching is that they encourage student passivity. Saddler-Smith and Riding (1999) found a strong preference by most students for traditional modes of teaching. They determined these students to be teacher dependent (pedagogical), rather than selfdirected and motivated (andragogical), and thus more comfortable with a teacher-controlled learning environment. Akerlind and Trevitt (1995) also argued that traditional modes of teaching have supported a passive approach to learning. Furthermore, they stated that universities often introduce new technology explicitly to foster a more active, self-directed style of learning. Students learn from active engagement in the learning process, and from the earliest days of computer-based learning, technology has been promoted as being able to more effectively engage students in learning through regular, one-onone interactions and more recently, in online versions, providing students with an endless potential stream of new and varied information sources. In fact, searching and evaluating such information has come to be seen as a critical skill (MacDonald, Heap, and Mason 2001). However, technology can also be misused to denigrate the quality of learning. Bell (1998) lamented that easy access to the Web is reducing the more sophisticated online search skills that are needed by business students for searching serious, traditional online databases such as LEXIS-NEXIS, Dialog, and the Dow Jones Index. The Web produces masses of instant information: the only problem is that much of it is superficial and of little real value for study. According to Bell, this drawback had not dented the enthusiasm of his students

for taking the path of least resistance. Certainly an important part of a tertiary education is recognizing that all information is not equally valuable. “Facts” quoted in magazines are likely to be much less reliable than facts given in an academic journal. Understanding that any information or interpretation of information needs to be based on empirical evidence or logical deduction is important to student learning. The seductive quality of easy information may tempt students to lower their evaluative criteria and increase the attractiveness of information that has not been rigorously evaluated. If students are prepared to trade off quality for quantity, then education will be degraded rather than enhanced by technology, but even more critically, students will be inadequately prepared for their subsequent performance in the knowledge economy. The characteristics of technology that seem to offer the best opportunities for enhancing learning carry with them the potential for more spectacular failure than traditional delivery modes. Easy and rapid access to multiple sources of information and other resources offer students easy opportunities to cheat or take very superficial approaches to study. The challenge for education is to maximize technological opportunities that will enhance the development of skills for knowledge workers and raise the level of learning generally but avoid the pitfalls inherent in the nature of technology such as easy access to superficial or unreliable information. TECHNOLOGY AND STUDENT DIVERSITY While the teaching approach used by teachers may influence the type of learning approach students adopt, such decisions may also be influenced by the student’s experience, culture, or other predispositions. Given the diversity of students that now make up the student body in terms of ethnicity, age, gender, socioeconomic status, and physical ability, understanding how these differences are reflected in student learning is essential. In New Zealand, for example, one small campus has students from 80 different countries (Student Management System [SMS], 2002). This diversity is also reflected in Australian and U.S. student populations (King 1999; McKenzie and Schweitzer 2001). It is hoped that technology has been promoted by some governments as the solution that will remove inequalities and other differences between students and raise the standard of education among all sectors of the community by providing high-quality instruction inexpensively when, and where, needed. Such promotion is invariably accompanied by a decline in the unit of resource allocated by governments to fund student education. The fact is that student expansion and desire for tertiary education as a result of rising literacy in many areas of the world has not been accompanied by any real political gestures to accommodate staff and infrastructural requirements.


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MODES OF LEARNING/TEACHING Traditional Universities Distance Education Technology Mainstream Market 2.5%

Innovators

13.5%

Early Adopters

34%

Early Majority

34%

16%

Late Majority

Laggards

Innovators & Early Adopters

Late Majority & Laggards

Techno-savvy

Techno-phobic

FIGURE 1: Modes of Learning/Teaching and the Innovation-Adoption Model SOURCE: Adapted from Rogers’s (1983) “Adoption of Innovations” figure (reproduced in Kotler et al. (2001, p. 210) and incorporating McCorkle, Alexander, and Reardon’s (2001) technology diffusion commentary. Reprinted with permission.

If the altruistic aim of social equity is not driving the integration of technology, then what is? Observers such as Drucker (2000) argue that student demand for continuing education has been the trigger for growth in online delivery, which he estimates to be about 6% of U.S. GNP. Many online students are in full-time employment and do not want to spend their evenings fighting crowded highways to go to a traditional school. They want accessible and flexible ways of continuing their education. It is hard to avoid the conclusion that technology itself does not “cause” better learning or teaching; it merely provides powerful tools that enable good teachers to create better learning and teaching. The key is the manner in which they are used, which is why protagonists on both sides of the argument are able to cite examples to support both negative and positive arguments for using IT in education. Accepting that technology may have a beneficial role to play when used correctly, we now ask the question, How willing and able are students to take advantage of educational benefits? STUDENT ATTITUDES TO TECHNOLOGY IN EDUCATION The literature suggests that acceptance of technologybased teaching modes by students may depend on a range of factors such as managing the change process (Akerlind and Trevitt 1995), student characteristics including approaches to

learning (Shaw and Marlow 1999), and previous experience coupled with demographic and psychographic factors (Spennemann 1996). In fact, evidence in favor of IT being incorporated into business and management studies curricula indicates a greater enthusiasm by staff than by students (Hara and Kling 1999). Even here, it is a moot point as to whether this enthusiasm is generated by the center (i.e., administrators with the need to balance cash flows) or staff working at the coalface of educational delivery. McCorkle, Alexander, and Reardon (2001) suggested that differences in student learning willingness and ability can be viewed in terms of product adoption/diffusion theory. We have attempted to interpret this suggestion by overlaying their technology-focused labels on the standard marketing innovation-adoption model (see Figure 1), complete with the accepted percentages that fall into each category. While traditional and distance-learning modes are widely accepted, we suggest that here may be resistance to newer technologybased modes. McCorkle, Alexander, and Reardon (2001) did not analyze the implications of this model and made only a passing reference to the probability of teaching staff following a similar profile. As seen in Figure 1, traditional and distance learning modes are both well-accepted modes of student teaching and learning—almost in the market saturation stage. However, technology-based modes of teaching and learning are still almost in their infancy. To move from the early adopter stage

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to the early majority requires substantive opinion leader communication. Greater acceptance by the early majority may be hindered by the bitter feud between advocates and opponents of the integration debate. Both sides claim student attitudes support their argument. For example, Morss and Fleming (1998) found most students reported high levels of satisfaction with the online tools, did not feel that online learning placed an undue time burden on them, and strongly supported its continued use. Kendall (2001) reported similar levels of student satisfaction with the tools and experience of online learning. An ethnographic study by Wegerif (1998) also reported positive student experiences in an asynchronous learning network environment learning community. Opponents of technology-based teaching argue that students generally “want the genuine face-to-face education they paid for, not a cyber-counterfeit” (Noble 1998, p. 10). Studies from this side of the debate often find high levels of student dissatisfaction with technology, emphasizing the frustration of learning in a technology-based environment, and high levels of anxiety and confusion associated with ambiguous instructions (Burge 1994; Hara and Kling 2000; Wegerif 1998). A more balanced view is expressed by Akerlind and Trevitt (1995), who perceived a danger in using technology to rush students into more self-directed, autonomous approaches to learning without adequate preparation. Students are familiar and comfortable with more traditional passive learning modes and are largely unprepared for active, selfdirected learning, which may be thrust upon them in a new technology-based environment. This change potentially creates a conflict between students’ past experiences and their understanding of the learning process. Unless transitions to new situations are managed carefully, students will likely have difficulty adjusting to a learning environment that is not only technologically different but is based on new conceptions of learning and responsibilities. With this discussion in mind, let us now tackle some of the evident empirical issues involved here. RESEARCH METHOD In a study aimed at gauging student attitudes to teaching modes and learning orientations associated with these modes, we surveyed 1,279 students enrolled in the College of Business at a New Zealand university. They were recruited on a voluntary basis from a pool of students, using a stratified quota sampling technique to ensure a broad representation of the different student groups and year levels. The major demographic characteristics of the participants were as follows: 45% were male and 55% were female. The sample contained a broad spread of ages from under 20 to older than 40. Seventeen percent of the students were Asian, 7% were Polynesian, and 75% were Caucasian students.

These proportions within the sample reflect well the current student population. The sample also contained a balanced representation of the major student groups in relation to the level of award in which students were enrolled and their study mode (three internal campus sites and a distance learning group). OBJECTIVES The study was concerned with understanding how students rated technology-based teaching modes compared with more traditional modes of teaching, and the relationship between learning orientations and teaching mode preferences. Specifically, 1. Students’ preferences for different teaching modes. 2. Whether particular learning orientations influenced preferences for teaching modes.

Two measures were used. The first, Teaching Mode Preferences, consisted of a list of 19 different teaching modes that students were asked to rate on a 5-point scale, with 1 labeled low, 3 labeled medium, and 5 labeled high. Each item represented a mechanism for a structured interaction between the student and the material to be learned, that is, the items were instructional delivery methods. The items ranged from lectures, to tutorials, to student group work, to Web-based tests, and CD-ROMs. The second measure, Learning Orientations, was developed from a comprehensive review of current inventories concerned with learning styles and approaches to studying. Most inventories focused on one aspect of a student’s approach to learning and were felt to be too restrictive in understanding student learning behavior. To develop an instrument able to capture a range of characteristics that might make up a student’s orientation to learning, Curry’s (1983) onion model, which classifies learning inventories according to level of innateness, was used as a framework for selecting or developing items for the instrument. A “slice of the onion” was used to create a measure that sampled characteristics from each of the rings, providing a more holistic profile of the student’s approach. The Learning Orientations measure used Likert-type scales with anchors graduated from 5 (very much typical of me) to 1 (not at all typical of me). Both measures had adequate reliability when piloted. RESEARCH FINDINGS The two measures, Teaching Modes Preferences (19 items) and Learning Orientations (70 items), were subject to an exploratory principal components analysis (PCA). Orthogonal rotation with varimax was chosen for simplicity of reporting and because it was intended to use component scores for further analysis (Stevens 1986, 1996; Tabachnick


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TABLE 1 PRINCIPAL COMPONENTS ANALYSES OF PREFERENCES FOR TEACHING MODES Component

Loading Range

Technology based Personalized Student based Traditional

.48 to .85 .40 to .82 .75 to .81 .52 to .77

Eigenvalues 6.49 2.2 1.34 1.2

and Fidell 1989). Adequacy of the rotation was determined by the presence of a simple structure (Thurstone 1947). Finally, component scores were computed for each case using the regression method. The relationship between the two measures was tested in a multiple regression. TEACHING MODES The teaching mode scale produced four components accounting for 59% of the variance (see Table 1). The first component was labeled technology-based modes and included a range of technologies from e-mail, to videoconferencing, to Web-based courses. Component 2 comprised those modes that related to close personal interaction with instructing staff, such as one-on-one teaching. Student-based modes, such as student presentations and group work, made up the third component. The last component included traditional teaching modes such as lectures, study guides, and tutorials. The personalized mode had very modest reliability and would need further development if used again. Overall, students showed the greatest preference for those teaching modes with which they were most familiar, that is, traditional-based modes. This is consistent with the findings of Saddler-Smith and Riding (1999). Personalized and technology-based modes received much lower preference ratings, while student-based modes, with which students were also familiar, generally received very low ratings. LEARNING ORIENTATIONS Learning approaches describe a range of attitudes, dispositions, or styles that have been associated with learning outcomes (see, e.g., Biggs 1994; Blumenfeld et al. 1996). Students rated items that comprised the components on a 5-point Likert-type scale according to “how typical” they were of the student. Examples of items included the following: • The main benefit of a university education is that it will enable me to get more money. • I remember best what is written rather than what is spoken. • I generally put a lot of effort into trying to understand things that initially seem difficult. • I prefer listening to the lecturer more than reading the study guide.

Percentage of Variance

Reliability Coefficient

M

SD

34 11.6 7.07 6.07

.92 .59 .71 .72

2.82 2.95 2.58 3.76

0.96 0.97 1.05 0.72

• When I’m doing a piece of work, I try to bear in mind exactly what the particular lecturer seems to want. • The continual pressure of work—assignments, deadlines, and competition—often makes me tense and depressed. • I find a written version of the key points of a lecture much more useful that a diagram or oral summary. • I accept as correct things that I hear in lectures or read in books.

A total of 16 subscales emerged from a PCA of the learning approaches, accounting for 60.6% of the variance (see Table 2). To avoid overspecification, component loadings were set at .40, and to minimize errors in interpretation, components were described by considering loadings in descending order. A number of the components had marginal reliability. This appears to be because an expected component related to “dependent learning” split into several components, each made up of a relatively small number of items. While normal practice might be to discard such components, it was felt that the exploratory nature of the study warranted their inclusion for further discussion. The “most typical” characteristic of students is the “preference for structure.” Students want courses that are organized and well structured. They want clear guidelines and transparency in the assessment process. This theme is echoed in the “goal-focused” component. Students want to know from their lecturers very clearly and specifically what is required for them to pass the course. Students deem that success will come from focusing intently on cues and other information given by the lecturer as to what counts, what is important, what will be in the examination, and so on. Their ability to interpret or identify what the lecturer wants is regarded as an important strategy for success. The component “effort” also rated highly as typical of students. This component describes students who recognize the importance of discipline in their study habits and are willing to work hard to achieve the best possible results. An important element of this characteristic is the drive to “understand” the material. The motivation “ambition” is also an important defining characteristic of these students. Students with ambition are highly competitive and driven by a need to achieve academically in order to further their future professional careers.

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APRIL 2004 TABLE 2 PRINCIPAL COMPONENTS ANALYSES OF LEARNING ORIENTATIONS

Component

Loading Range

Eigenvalue

Percentage of Variance

Reliability Coefficient

M

SD

1. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

.58 to .86 .44 to .82 .65 to .81 .41 to .77 .63 to .85 .60 to .76 .46 to .69 .66 to .69 .51 to .74 .52 to .70 .46 to .73 .77 to .84 .57 to .62 .42 to .57 .77 to .81 .73 to .75

7.28 6.76 5.09 3.93 2.62 2.47 2.19 2.08 1.69 1.40 1.38 1.29 1.16 1.05 1.03 1.01

10.40 9.66 7.26 5.61 3.74 3.53 3.12 2.97 2.42 2.01 1.96 1.84 1.66 1.50 1.47 1.44

.91 .84 .87 .81 .85 .78 .78 .69 .73 .53 .51 .71 .55 .55 .65 .54

3.39 3.09 2.15 2.74 2.67 2.82 3.77 2.67 3.10 3.52 3.81 3.18 3.83 2.96 2.76 3.29

0.89 0.78 0.89 0.71 0.90 0.77 0.69 1.00 0.74 0.81 0.71 0.90 0.79 0.73 0.98 0.89

Positive attitude toward computers and information technology Anxiety Negative attitude toward computers Visual mode preference Collaborative learning mode Intrinsic motive Effort Extrinsic motive Listening mode preference Ambition Goal orientation Dependent learning Preference for structure Busyness Factual course preference Independent learning

NOTE: To conserve space, only the components and the range of loadings have been included.

A sizable group of students have a positive attitude to computers and IT in an educational context. They believe that technology can provide a better, more involved learning experience and improve motivation. In contrast to students described as having a “preference for structure,” a significant group of students identify themselves as “self-directed learners” who want the freedom to shape their own learning experiences. They are less interested in being told what to do and more concerned with determining for themselves how they go about engaging in the learning process. Students who rated high on “dependent learning” understand education to be about finding the “right answer.” This information is seen to be held by the lecturer, and students feel no inclination to challenge the opinions of their lecturers or to explore alternative views. Many students have an “auditory learning style.” They would rather listen to lecturers explaining new ideas than read about them in study guides or textbooks. Anxiety has long been recognized as a significant factor in hindering learning and in degrading performance during evaluation. Its position in the middle ranks of this list of characteristics suggests that it continues to blight the educational lives of students. Students high on this characteristic feel that they underperform during examinations and tests. Worry interferes with their ability to concentrate, and they often feel tense and anxious. A coping strategy frequently associated with anxiety is memorizing information. The component “busyness” is perhaps an obvious sign of the times. These students have such busy lifestyles they do not feel they have sufficient time to reflect on the new ideas

presented in class or in reading material. Once they leave the lecture theater, they “switch off” the lecture and move on to the next item on their agendas. The pressure of time is a constant companion. Relatively low on the order of characteristics is the component “intrinsic motivation,” a contrast to the higher rated “ambition” component that identified education as a means to an end. Students high on intrinsic motivation find their subjects interesting and absorbing and spend some of their spare time looking for additional information related to the topics they are studying. Some students have a preference for factual courses, that is, courses in which the answers are clear-cut, right or wrong. They are uncomfortable with courses based on argument and reasoning. Much lower down the order than “auditory learning” is another style component, “visual.” These students feel they learn more easily with diagrams and pictures than either written or spoken information. They will use diagrams themselves to summarize information when studying and more easily understand new information presented in a visual format. Most students do not see themselves as being “extrinsically motivated.” Those who are admit to being reluctant to be at the university. They do not enjoy their studies, and their primary purpose is to get a needed qualification. Relatively small numbers of students describe themselves as preferring to work collaboratively rather than alone. These students find working in a group stimulating and productive, and the idea of a combined grade appealing. They do not believe they will improve their grades by working alone.


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TABLE 3 STANDARD MULTIPLE REGRESSION OF LEARNING APPROACHES COMPONENTS ON PREFERENCE FOR TEACHING MODES Traditional Learning Characteristic Positive attitude to information technology (IT) Anxiety Negative attitude to IT Visual style Collaborative style Intrinsic motivation Effort Extrinsic motivation Listening style Ambition Strategic focus Dependent learning Need for structure Busyness Factual course preference Independent learning

R2 2 Adjusted R F F significance

Personalized

β

T Ratio

β

–.039 –.088* .004 –.005 .011 .132** .098* –.063 .115** .070* .110** .134** .092* -.007 –.026 –.082*

–1.14 –2.75 0.119 –0.175 0.370 3.94 2.69 –1.92 3.97 2.22 3.64 4.43 2.91 –.209 –0.89 –2.79

.032 .021 .054 .068* .086* .191** .092* –.040 .042 –.034 .066 –.047 .041 –.009 –.011 –.033

.144 .132 12.39 .0000

.102 .089 7.78 .0000

T Ratio 0.869 0.612 1.48 2.16 2.79 5.38 2.40 –1.146 1.55 –1.01 2.08 –1.46 1.20 –0.259 –0.339 –1.05

Technology Based β

T Ratio

.467** .017 –.011 .086* –.015 .131** .047 –.005 .017 –.018 –.032 .037 –.016 .060* .002 .048

14.81 0.573 –0.34 3.159 –0.554 4.24 1.39 –0.161 0.636 –0.625 –1.15 1.31 –0.552 1.97 0.068 1.76

.304 .294 30.18 .0000

Student Based β .028 –.088* –.018 .067* .511** .163** .012 .029 .027 .026 .006 –.012 .007 .042 –.042 .009

T Ratio 0.901 –3.020 –0.563 2.53 19.41 5.33 0.367 0.975 1.03 0.909 0.213 –0.418 0.228 1.43 –.1.56 .342

.322 .312 33.54 .0000

*p < .05. **p < .01.

Finally, relatively few students claimed to have a negative attitude to computers and IT. However, the group who did express this attitude felt frustrated and uncomfortable around computers and perceived them as difficult to use. INFLUENCES ON PREFERENCE FOR TEACHING MODES Standard multiple regression was performed to determine the influence of learning approaches on preference for teaching modes (see Table 3). Multiple regression was chosen as an appropriate measure for predicting the influence of several independent variables on a dependent variable. Standard multiple regression in which all of the independent variables were entered in a single block was used as there was no strong theoretical basis for ordering their entry into the equation (Tabachnick and Fidell 1989). The data were checked to ensure they met the assumptions for regression. Linearity and the influence of possible outliers were checked using the added variable plot (Chatterjee and Price 1991, p. 80). This looks for linearity of the relationship between the responses and any predictor after adjusting for all other independent variables in the model. Inspection of the plots showed linearity across the vast bulk of the range, with very slight curvature at the extreme edges in some instances. The data were examined for normality, which was met, and

the cases were independent (ensuring independence of the residuals). Multicollinearity was low (all the variables had a variance inflation factor below 2), indicating independence of the variables. Learning orientations accounted for between 9% and 32% of the variance. In each regression, intrinsic motivation was one of the strongest predictors of a high preference rating, suggesting that highly motivated students enjoyed a wide variety of teaching modes. For traditional modes of teaching, the strongest predictor was dependent learning, followed very closely by intrinsic motivation. A listening style was the third predictor, followed by goal focus, effort, and preference for structure, respectively. There was a negative correlation with independent learning and anxiety. The same negative correlation was found between anxiety and student-based modes of teaching, but not with the other two teaching modes. In the case of student-based teaching modes, it seems likely that the publicspeaking aspect of the mode may be a significant contributor to the anxiety, while in the traditional mode, the lack of control over the pace of learning may be a factor. The lowest contribution to the preference for a traditional mode was made by ambition. Overall, these student characteristics accounted for 13.2% of the variance. The largest predictor of technology-based modes, unsurprisingly, was a positive attitude to computers and IT. This

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was followed by intrinsic motivation in second place. A visual style was the third predictor followed by a small contribution from busyness. Given the importance of a positive attitude to computers and IT to predicting a preference for technology-based teaching, it was surprising that a negative attitude to computers did not emerge as a negative predictor. Altogether, 29.4% of the variance was accounted for. A collaborative learning style made a very strong predictor of student-based teaching modes, followed by intrinsic motivation. As mentioned previously, anxiety also had a negative correlation with this mode. These three variables accounted for 31.2% of the variance. Only 8.9% of the variance of a preference for personalized teaching modes was accounted for by student characteristics. The most important of these was intrinsic motivation. Smaller contributions were made by effort and a visual style. DISCUSSION OF TEACHING MODALITIES: IMPLICATIONS FOR EDUCATION PROVIDERS Students’ preference for traditional modes of teaching at a time when universities seek to introduce new technologybased modes on a large scale should give pause for thought. The strength of this attachment to the familiar appears likely to create resistance to new modes of learning. Of more concern is the association between dependent learning and a preference for traditional modes. Whether the traditional modes triggers dependent learning or dependent learners simply comfortable in this environment is difficult to establish. However, common sense suggests that at the very least, traditional teaching lends itself more easily to fostering dependency. This is likely to have the most impact on students who are inclined, for whatever reason, to a dependent style. Those students high on motivation, effort, and with a strong goal focus are more likely to rise above the intellectual numbing caused by much traditional teaching. If traditional modes do foster or sustain learning dependency, then this may support the case made by Davidman (1981) for weaning students off comfortable preferences to give them the opportunity to learn to cope with alternative modes. This view is also supported by Kirby (1988) and Pask (1988), who believe students must learn to be flexible. Similarly, Shipman and Shipman (1985) articulated the need for students to be proficient in multiple alternative modes. Certainly, the needs of the new knowledge workers characterized by “critical thinking, intellectual curiosity, problem solving, logical and independent thought” (AHEC 1992, p. 21) imply a need to reduce learning dependency. The new technology-based modes, while favored by students who had a positive attitude to computers and experience with them, were overall rated quite low. Two groups of students seem to be responsible for this result. The first group made scathing comments on the questionnaire regarding frequent and frustrating failures of the technology to perform

(see appendix). The second group was made up of those students who had very limited experience with computers and felt intimidated by them. One revealing comment claimed that the study itself was no more than a cynical device by the university to impose online learning on students. Student-based modes, overall, were the most disliked. This is partly explained by the relatively small number of students who have a collaborative learning style, many of whom were Asian or Polynesian. This finding is supported by other studies that found Caucasians had a lower preference for working collaboratively and cooperatively in a group than other ethnic groups. For example, Mexican Americans and African Americans were found to be more group oriented than white students (Remirez 1982, cited in Swanson 1995). These findings also agree with those of Anderson and Adams (1992), who concluded that women and non-Caucasian men had a higher preference for peer cooperation than Caucasian men. The implications of this for online learning should be of concern. Educational research has long identified interaction between students as a key variable in learning (Brookfield 1986; Slavin 1983). Much of the current interest in online learning has been driven by its potential to harvest the benefits of collaborative learning through the establishment of learning communities. Group work is thought to facilitate learning in a number of ways. Cohen (1984) found that working with others reduced uncertainty when faced with new, complex tasks and increased engagement with the task. Others have shown how the nature of the interaction between students provides alternative models of thinking and clarification of concepts as they are forced to defend or explain their own views (Sharan 1980; Slavin 1980; Webb 1980). If large groups of students feel uncomfortable with collaborative learning modes, this may provide a significant obstacle to implementing some of the most beneficial aspects of online learning. In addition to educational benefits, the ability to work collaboratively as part of a team is repeatedly identified as a critical skill for knowledge workers (e.g., OECD 2001; Reich 1991). Many of the important generic skills needed by knowledge workers (including marketing professionals), such as being able to work independently and collaboratively, to be lifelong learners (independent learning), possessing effective communication and Information and Communication Technology (ICT) skills, seem to benefit most from an integration of technology-based modes with student-based modes. Given the strong resistance by students to both of these modes, universities urgently need to consider strategies for changing attitudes. This should include managing the change toward more innovative teaching modes and ensuring the robust performance of the technology during learning. The strong preference most students have for a highly structured learning environment and a slightly lower tendency for dependent learning seems to hinder the develop-


ment of flexible, independent employees needed for the knowledge economy. While genetic criteria may account for some of the predisposition of these students, for example, the tendency to be anxious, other factors are also clearly at work. Traditional, passive modes of teaching encourage students to be dependent teacher pleasers rather than critical thinkers. Many universities have recognized the problem created by traditional practices and are not only changing teaching practice but are also introducing courses such as Critical Thinking to actively promote these skills. In addition, many of these students may have experienced negative learning environments that have diminished their motivation and drive to work hard; their self-concept; or have fostered unproductive, ineffective, learning strategies. Creative, innovative teaching and learning skills programs can turn these negatives around. Just as there is no “average” family with 2.4 children, there is no “average” marketing student or graduate. In traditional modes of teaching, each student is treated the same. Everyone gets to hear the same lecture, at the same rate, and complete the same assessment profile. One of the benefits of the new information technologies is that teaching can be designed to accommodate different learning orientations and even perhaps to foster individual creativity. Technology can enhance the teaching medium by adapting to the individual characteristics of the student. One of the keys to maximizing these advantages is identifying differences between students that can be accommodated, such as preference for a highly structured learning environment, or problems that can be modified, such as passive learning approaches. FUTURE RESEARCH This study has examined the role of technology as a delivery mechanism compared with traditional teacher-based delivery modes such as lectures. Clearly, technology is able to play a much more substantive role in the educative process, for example, as tools to be learned and used as part of the curriculum. A wide range of technology software applications, such as Excel, are routinely embedded in the teaching curriculum, and employers expect graduates to be proficient in common software packages. In addition, many traditional teaching materials such as textbooks habitually include CDROMs and Web sites to supplement or enhance the information being presented. We did not assess the impact of technology in these roles but recognize that they have made a substantial contribution to the character and substance of modern education. Further research might fruitfully incorporate these additional functions to obtain a more complete assessment of IT and education. CONCLUSION Technology has the capacity to be used for developing critical generic skills such as problem solving, managing

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information, and communication skills. The key here is the marriage of andragogical principles and creative use of technology. Pedagogy focuses on the teachers to orchestrate and control the flow of information. Andragogy requires environments to be organized so that students take more control and responsibility and are engaged in real-world tasks and problem solving. This is a paradigm shift from viewing learning as the accumulation of facts to a form of personal growth in which the student identifies meaning in information and learns to apply it to real-world problems and tasks. The latter model of learning is rooted in the reality of the business world and produces much better transfer of learning. It is important to point out that andragogical or constructivist principles do not need a technology-based environment to work, but technology is able to take these principles to a more sophisticated and efficient level than traditional classroom tools. However, key questions still remain: • How can the change from passive, traditional modes of learning and teaching to self-directed, autonomous modes, with or without technology, be most effectively managed? and • Can, and should, learning orientations be accommodated in individualized curricula design, or should the environment seek to change orientations to an idealized student profile?

It strikes us that the current trend of sheepherding— sponsored by government and adopted by academic deliverers (not necessarily willingly)—is unlikely to make way for the needed shepherding that is actually required. APPENDIX Questionnaire Items: Learning Orientations 1 The use of computers and information technology makes me feel more involved in my studies. 2 The main benefit of a university education is that it will enable me to get more money. 3 If conditions aren’t right for me to study, I generally manage to do something to change them. 4 I prefer using computers and information technology to traditional teaching and learning. 5 I remember best what is written rather than what is spoken. 6 I am at university because I feel I have to be rather than because I really want to be. 7 Computers and information technology help provide a better learning experience. 8 I get discouraged by low marks. 9 I prefer listening to the lecturer more than reading the study guide. 10 When I’m doing a piece of work, I try to bear in mind exactly what the particular lecturer seems to want. 11 I suppose I am more interested in the qualifications I’ll get than in the courses I’m taking. 12 One way or another I manage to get hold of the books I need for studying. 13 Lecturers sometimes give indications of what is likely to come up in the exams, so I look out for what may be hints. 14 Computers and information technology help me learn.

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15 I prefer to get new information in pictures, diagrams, graphs, or maps rather than written or spoken information. 16 The continual pressure of work—assignments, deadlines, and competition—often makes me tense and depressed. 17 I remember best what I hear rather than what I see. 18 I want top grades in my studies so that I will be able to select from among the best positions available when I graduate. 19 I find a written version of the key points of a lecture much more useful that a diagram or oral summary. 20 I generally put a lot of effort into trying to understand things that initially seem difficult. 21 The use of information technology increases my motivation for the course. 22 If I work on my own, I can be certain to get a better grade than if I had to work in a group. 23 I chose my present courses because I felt I had to, more so than because I was excited about the subject. 24 I prefer projects that let me decide what I want to do and how to do it. 25 I accept as correct things that I hear in lectures or read in books. 26 It is really important to me to do well in my studies. 27 Computers are not useful in teaching and learning. 28 I find working in a group more stimulating and productive. 29 I enjoy working with computers. 30 When studying, I prefer to summarize information as notes rather than diagrams. 31 I usually set out to understand thoroughly the meaning of what I am asked to read. 32 I prefer lecturers to tell me how they want the assignment done rather than letting me set my own objectives. 33 I find academic topics so interesting, I would like to continue with them after I finish this course. 34 When I’m tackling a new topic, I often ask myself questions about it of which the new information should answer. 35 I am very tense when I study. 36 Working with other people helps me in my studies. 37 I make simple charts, diagrams, or tables to summarize material in my courses. 38 I see myself as an ambitious person. 39 I like courses in which the answers are based on argument and reasoning rather than being just right or wrong. 40 Even when I am well prepared for an exam, I feel anxious. 41 I enjoy my studies so much I often become absorbed in an assignment. 42 I find I have to concentrate on memorizing a good deal of what we have to learn. 43 In a book with lots of pictures and charts, I prefer to focus on the written text. 44 Computers frustrate me. 45 Worrying about doing poorly interferes with my concentration in exams. 46 I often feel I have little control over how well I do at university. 47 Computers are difficult to use. 48 I prefer courses in which the answers are factually right or wrong. 49 I spend a good deal of my spare time finding out more about interesting topics that have been discussed in classes. 50 I understand better if someone explains it rather than reading about it. 51 I feel panicky when I take an important test or exam. 52 I have a lot of confidence when it comes to working with computers. 53 I understand diagrams better than written explanations. 54 I usually don’t have time to think about the implications of what I have read. 55 I find that studying academic topics can often be really exciting and gripping. 56 I prefer courses that specify in detail what I must do to pass the course.

57 Often I find I have read things without having a chance to really understand them. 58 I try to be disciplined in my study habits, so that I can do the very best I can. 59 I get so nervous and confused when taking an exam that I fail to answer questions to the best of my ability. 60 I aim to do just enough to pass. 61 I prefer courses that allow me a lot of freedom to choose which aspects of the course I want to focus on. 62 I prefer to work on my own rather than in a group. 63 I prefer listening to reading. 64 I am not very comfortable using computers. 65 I tend not to think about my study outside class. 66 The idea of group projects, with one grade for the entire group, appeals to me. 67 I remember best what I see in pictures or graphs rather than what I hear or read. 68 I feel apprehensive about using a computer. 69 I prefer to accept the lecturer’s ideas as being right. 70 I set out to get full marks for an assignment and try as hard as I can to achieve them. Teaching Modes 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Lectures Tutorials Group projects Student presentations Block courses One-on-one meetings with staff Printed study guides Telephone calls with staff E-mail E-mail groups / E-mail lists Web-based course administration Web-based course materials Web-based tests Other Web sites Web-based chat rooms Web-based bulletin boards CD-ROMs Online library services Video- or audioconferencing

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