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Profiling students' adaptation styles in Web-based learning
Computers & Education 36 (2001) 121±132
www.elsevier.com/locate/compedu
Pro®ling students' adaptation styles in Web-based learning Myung-Geun Lee * Department of Education, Yonsei University, Seoul, South Korea Received 3 April 2000; accepted 7 August 2000
Abstract Use of the Internet has brought about an alternative education form, the so-called Web-based instruction (WBI). It purports to sweep over current instructional problems and provide a revolutionary educational environment. One thing to be remembered along with the emergence of new educational technology such as the World Wide Web, however, is that diverse innovations throughout educational history did not last very long and resulted in bandwagons. In order not to be another historical bandwagon, the WBI needs to be studied in abundance and in depth. In this vein, this study analyzed WBI learners' adaptation styles and characteristics related with the styles by retrospectively assessing the perceptions of various aspects of WBI. Students participating in various courses at 11 universities nationwide in Korea which had been experimenting with WBI under the auspices of the Ministry of Education since 1998, were surveyed initially and the ®nal analysis consisted of 177 females and 157 males (n=334). The results indicated three ®ndings. First, retrospective post-assessment of learners' perceptions was a viable method for analyzing the adaptation styles in WBI. Second, WBI students were not learning uniformly so that there existed four distinct adaptation styles during WBI process. Third, any eorts to improve the quality of WBI need to consider these adaptation styles of students one way or another. These ®ndings are expected to be used for drawing useful strategies for implementing a more desirable WBI. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Distance education and telelearning; Post-secondary education; Pedagogical issues
1. Introduction The advent of broad access to the Internet has created a new medium for education and training. Use of the World Wide Web as an instructional tool, in particular, has brought about an alternative education form which purports to sweep over current instructional problems and * Tel.: +82-2-2123-3180; fax: +82-2-313-2158. E-mail address: [email protected] 0360-1315/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S0360-1315(00)00046-4
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provide a revolutionary educational environment (Brooks, 1997; Khan, 1997). The so-called Web-based instruction (WBI) in education and training ®elds, thus, is increasing at an exponential rate throughout the world. This is not an exception in Korea, where many higher education and corporate institutions are nowadays enjoying the Web-based education/training which is often called cyber education (Lee, 1999). Corporate and media institutions in this country, whether by aliation or alone, are also providing various Web-based courses to internal and/or external customers. The continuous growth of cyber education programs in Korea can be said to be mainly a way for the institutions to get management eciency from traditional cost-driven education. One thing that has to be remembered along with the emergence of new educational technology such as the WBI, however, is the fact that diverse innovations throughout the educational history did not last very long and resulted in mere bandwagons (Saettler, 1990). For example, the teaching machine and the programmed instruction movement in the 1950s began to decline within 10 years due to ineectiveness, theoretical grounds not supported in practice, etc. Although variations of programmed instruction such as the Keller Plan, IPI, PLAN in the 1960s and 1970s revived thereafter, these individualized instruction systems also waned by the mid-1970s. CAI and interactive video in the 1980s and the early 1990s as an alternative instructional technology are seemingly experiencing the same deterioration cycle. In order not to be another ``Teaching Machine'' in the history of education, the WBI needs to be studied in abundance and in depth. It is desirable that there has been a growing amount of researches being conducted on WBI in recent years throughout the world. For instance, research topics on WBI for the past 3 years (1996±1998) that were searched through ERIC (Educational Resources Information Center) are shown in Table 1. Overall, it seems conspicuous that the research topics vary from general inquiries such as comparison, and building and designing WBI to focused and diversi®ed ones such as learner perspectives and faculty development. A similar trend is also unfolding in education and corporate training ®elds in Korea. In-depth reading of research papers on WBI, however, reveals that in spite of diverse research subjects worldwide, their ultimate concerns tend to converge on the learner's achievement, especially Table 1 Research topics on WBI through ERIC during the recent 3 yearsa Topics
1997
1998
1999
Totals
Eectiveness (evaluation) Building and management Contents design State of aairs Learner perspectives (perception) Faculty development Interaction (group learning) Adoption process Strategies/factors
12 6 10 5 ± 2 ± 1 ±
12 10 6 1 5 2 2 ± 1
4 5 3 2 2 2 4 3 7
28 21 19 8 7 6 6 4 8
Totals
36
39
32
107
a The traditional distance education not based on the Web was not counted at the outset. WBI, Web-based instruction; ERIC, Educational Resources Information Centre.
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compared with traditional face-to-face instruction. Although being a natural phenomenon at a settlement stage of WBI, it still leaves room for consideration. The process of student learning in cyber space is more complex than conventional class learning for many reasons (Schuemer, 1993). Many assessments simply capture the eectiveness of WBI, failing to capture critical aspects such as learners' perceptual and metacognitive processes as they are occurring. Moreover, it may mislead the real nature of learning to compare the eectiveness without considering learning processes in an unfamiliar learning environment like WBI (see Ward & Newlands, 1998). Thus, the real eectiveness of WBI needs to be reserved until some more comprehensive approaches are fully implemented. In other words, research focus with regard to WBI needs to be directed to the process and nature of students' learning. In this vein, this study tried to analyze students' learning adaptation process in a new learning environment of WBI. More speci®cally, the purpose of the study is to identify learners' adaptation styles and characteristics related with the styles through surveying their perceptions of various aspects of Web-based learning. 2. Adaptation process in Web-based instruction Psychologically, a new environment requires a need to adapt and, accordingly, human behaviors tend to be responsive to dierent situational contexts (Schmeck & Geisler-Bernstein, 1989). This principle applies to instruction-learning contexts. That is, a new instruction-learning environment creates a need to adapt and learners are expected to deploy dierent learning styles according to their adaptation processes. It is, then, hypothesized that these dierent adaptation styles of learners may in¯uence their academic achievements. For instance, Ramsden (1988) investigated the in¯uence of new learning contexts comprised of teaching, assessment, and curriculum on students' deployment of learning styles. Expanding on this research, Lee and Lodewijks (1995) found that, by analyzing international students' perceptions of new learning environments and their personological characteristics, learning styles changed as a consequence of dierent learning environments and the change of their learning styles largely due to change of learning regulations was highly correlated to their grades. These researchers and related scholars (e.g. Schmeck, 1988) supposed that learners in a new learning environment developed a new perception and the change of learning styles arose from it, ultimately in¯uencing their academic achievements. In view of these research ®ndings, ways of evaluating the eectiveness of a new learning environment such as WBI, especially based upon post-implementation achievements compared with its counterpart face-to-face instruction are to be discontinued. This is because traditional means of assessment, relying mostly on measures taken upon completion of learning, fail to capture the critical processes such as perceptual and metacognitive ones as they are occurring (Rowe, Cooke, Hall & Halgren, 1996; Winne, Gupta & Nesbit, 1994). Thus, it is necessary to identify, among other things, how learners adapt to a new learning environment of WBI and which factors aect their adaptation process. In other words, predicated on pro®ling the dierent styles in learners' Web-based learning, not only can an individualized instruction satisfying their diverse needs be provided, but also, a more equitable evaluation on WBI can possibly be carried out. The adaptation process in WBI may be analyzed one way or another. First of all, it can be approached from an analysis of learning processes on the ¯y such as think-alouds, protocol
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analysis, and stimulated recall (see Wittrock, 1990). Being methodologically dicult and obtrusive to subjects in the course of participant observations and in-depth interview of subjects, these may still be useful in analyzing the processes. Or the so-called log ®le by which students' learning paths across Web-based learning materials are traced can be analyzed (Barab, Fajen, Kuligowich & Young, 1996; Cleave, Edelson & Beckwith, 1993; Lawless & Kulikowich, 1996; Misanchuk & Schwier, 1992). This is very eective in that, if the mechanism can be built into the computer system, it is not only unobtrusive to subjects but also the individual students' learning processes can be traced in detail. However, these analysis methods which are largely based on cognitive processes seem to be construed as being logically inappropriate. It is because all human behaviors including learning behavior cannot be separated from perceiving (Allen & Otto, 1996). Moreover, there is an inseparable relationship between the setting creating the need for adaptation and perceiving (Barker, 1968; Gibson, 1979). Studying the adaptation process of Web-based learners, therefore, needs to follow a model, which is based on comprehensive analysis of their perceptions. The point is how to conceptualize the perceptions which directly aect learners' adaptation process in Web-based learning. It has been largely noted that perception is partially the result of the environment and partially the result of the learner according to researches on the relationship between perception and learning (Lee & Lodewjiks, 1995; Schmeck, 1988). Taking this view into consideration, this paper presumes learning adaptation process in a new learning environment like WBI (see Fig. 1). That is, adaptation process in WBI is directly in¯uenced by perceptions which learners form during the learning process and these perceptions are a result of interactions between learners' personological things and learning context. The personological things in¯uencing the way learners perceive the learning environment are, for example, learners' motives, self-esteem, past experiences, etc. The learning context includes various aspects such as teacher, lecture, facilities, learning culture and the like. Thus, learning adaptation process in WBI is likely to be ®gured out by comprehensively
Fig. 1. Learning adaptation process in a new learning environment.
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investigating learners' perceptions on those variables. The results will be able to provide viable strategies matching up with dierent learning adaptation styles. 3. Method 3.1. Subjects The subjects in the present study consisted of 334 students of 11 Korean universities sampled on account of institutional types and operational styles from a total 65 institutions which had been experimenting with WBI since 1998 under the auspices of Ministry of Education. Of the respondents, 177 (53.0%) were female and 157 (47.0%) were male. Taking the population number into account, 1000 questionnaires were initially administered, but 334 were collected with most items adequately responded to for analysis (response rate: 33.4%), which in turn were ®nally analyzed. 3.2. Instrument In order to comprehensively investigate Web-based learners' perceptions, a questionnaire was developed and administered by the researcher. It consisted of four parts which contained as much information as possible including characteristics of Web-based learning environment as well as personal data of the learners. The ®rst part of the questionnaire asked largely about background information of the subjects. The second and third parts assessed the learners' perceptions of the Web-based learning environment, which were actually separated into two parts of a general instruction-learning environment and speci®c instruction-learning processes. The fourth part asked about the learners' perceptions of overall Web-based instruction. The structure and contents of the questionnaire used in this study are represented in Table 2. 3.3. Analysis procedures In an initial attempt to identify Web-based learners' adaptation styles, a cluster analysis on survey data was implemented, resulting in meaningless results due to the small number of Table 2 Structure and contents of questionnaire Parts
Variables
Items
Personal background
Sex, age, status, major, computer literacy, etc.
1±5
Instruction-learning environment
Learning participation, access tools, infrastructure problems
6±15
Instruction-learning process
Course type, amount of learning, diculty, learning materials, assignments, interaction, on-line database, methods of instruction, etc.
16±31
Overall WBI
Achievements, satisfaction, preference, motivation, other problems, etc.
32±37
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instances in most clusters. This may be because nominal variable items of the questionnaire had to be excluded from the analysis considering that continuous variables rather than nominal scale variables best ®t cluster analysis. In the present study, therefore, adaptation styles of WBI learners were retrospectively analyzed from their perceptions of the related variables after the conclusion of Web-based instruction. In other words, the learners' perceptions of their own achievements and their satisfaction with overall Web-based learning environment as encompassing personological things and learning context directly related with perceptions were combined to be analyzed for identifying the adaptation styles. Next, characteristics of each adaptation style were examined by analyzing the variables which were more related with each adaptation style than others. All data collected were processed using the SAS for Windows program. 4. Results 4.1. Overview of participants' characteristics as Web-based learners General characteristics of the subjects in the present study were drawn out from a descriptive statistical analysis on the variables concerning characteristics as WBI learners. As represented in Table 3, the distribution of majors of the 334 total participants is as follows: 158 humanities and social sciences (47.3%), 142 sciences and engineering (42.5%), and 34 arts, sports sciences and others (10.2%). Their computer literacy level can be said to be a little above the mean (2.85) on a 5 Likert scale. Responses of subjects to the Web-based instruction-learning environment indicated that the learning place was con®ned to the campus and home for almost all learners (95.1%) who, in turn, largely used LAN and MODEM as access tools. They also tended to interact more with peer students than with instructors and use an on-line database less than the mean (2.01 on 5 scale) during the instruction-learning process. Regarding overall Web-based instruction, subjects indicated that it was not better than traditional courses (2.04) in terms of academic achievements and their satisfaction with WBI was about mean (2.59). Nevertheless, they still revealed a high preference toward WBI in that about two-thirds of them (66.4%) reacted positively to the question ``Would you choose to take a Web-based course again in the future?''. 4.2. Adaptation styles of Web-based learners In order to identify adaptation styles of WBI learners, the interrelationship between their perception of academic achievements in WBI compared to traditional lecture courses and their satisfaction with overall WBI was ®rst analyzed. Of the 314 total subjects eligible for the analysis, 44 who did not respond to some of the questionnaire items in point were excluded and w2 test of the remaining 290 subjects was performed. As seen in Table 4, the interrelationship between WBI learners' perception of their own academic achievements and their satisfaction with overall WBI was statistically signi®cant (P=0.001). This means that the learners' perception of academic achievement in WBI compared to traditional courses possibly changes as their perception of degree of satisfaction with overall WBI varies. In other words, there exist WBI learners who perceive the degree of their academic achievement as being high or low and simultaneously the degree of satisfaction with overall WBI
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Table 3 Overview of subjects' characteristicsa Parts
Variables
Statistics
Personal background
Major
Human. & Social Sci. Sciences & Engi. Arts, Sports & Others Mean: 2.85
Computer literacy Instruction-learning environment
Instruction-learning process
Overall WBI
a
S.D.:
158 (47. 30%) 142 (42.50%) 3 4 (10. 20%) 1.05
Learning place
Campus Home Work Others
216 (66.70%) 92 (28.40%) 13 (4.00%) 3 (0.90%)
Access tools
LAN MODEM Leased line Others
111 (43.90%) 118 (46.64%) 14 (5.53%) 10 (3.95%)
Interaction with instructor
0 per week 1±2 per week 3±4 per week 5±6 per week Over 7 per week
36 (13.90%) 196 (75.70%) 27 (10.40%) 0 (0%) 0 (0%)
Interaction with peer
0 per week 1±2 per week 3±4 per week 5±6 per week Over 7 per week
73 (26.40%) 131 (47.50%) 46 (16.70%) 24 (8.70%) 2 (0.70%)
Use of on-line database
Mean: 2.01
S.D.:
0.54
Achievement
Mean: 2.01
S.D.:
0.84
Satisfaction
Mean: 2.59
S.D.:
0.97
Preference
Will take Won't take
200 (66.40%) 101 (33.60%)
Cases which did not respond to some items were excluded from the analysis.
as being high or low, which, in turn, reveals learners' adaptation styles in WBI process. Table 5 makes this point more clear, with meanings of each level for the degree of perception of academic achievement and satisfaction expressed respectively. As revealed here, four distinct adaptation styles are obvious from the bipolar levels of the degree of perception of academic achievement and satisfaction with WBI, except the intermediate level. That is, there exist ``model learners'' in WBI who perceive the degrees of academic achievement and satisfaction simultaneously as being high and, by contrast, ``maladaptive learners'' who perceive the degrees simultaneously as being low. On the other hand, there are ``disenchanted learners'' who perceive the degree of academic achievement as being high but, the degree of satisfaction as being low and, by contrast, ``fanatic learners'' who perceive the degree of satisfaction as being high even though perceiving the degree of academic achievement as being low. While the portion of learners who belong to these four styles are no more than about 40% of the total, what is important is the fact that four dierent adaptation styles of learners obviously exist in WBI.
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Table 4 Interrelationship between WBI learners' perception of academic achievement and satisfaction Frequency
Satisfaction
(Percent) (row percent) (col. percent)
1
2
3
50 (17.24) (75.26) (25.91)
12 (4.14) (18.18) (26.09)
4 (1.38)
2
105 (36.21) (66.88) (54.40)
28 (9.66) (17.83) (60.87)
24 (8.28) (15.29) (47.06)
3
38 (13.10) (56.72) (19.69)
6 (2.07) (8.96) (13.04)
23 (7.93) (34.33) (45.10)
Achievement
Totals
1
193
Totals
46
df
w2
4
20.58*
66
(7.84)
51
157
67
290
*P<0.001. Table 5 Adaptation styles of WBI learners Achievement
Satisfaction Low
Intermediate
High
Low
Maladaptive 50 (17.24%)
12 (4.14%)
Fanatic 4 (1.38%)
Intermediate
105 (36.21%)
28 (9.66%)
24 (8.28%)
High
Disenchanted 38 (13.10%)
6 (2.07%)
Model 23 (7.93%)
4.3. Analysis of characteristics of adaptation styles One-way ANOVA on related variables with subject groups by adaptation style, then, was tried to ®gure out characteristics which may determine the adaptation styles of WBI learners identi®ed earlier. The ``fanatic style'' learner group, however, had to be excluded from the analysis, not only because the number of cases in the cell for ANOVA was insucient, but because it was meaningless to do further characteristics analysis for literally the fanatically interested in WBI. Table 6 shows the results of one-way ANOVA for the remaining three adaptation style groups (model learners, maladaptive learners, and disenchanted learners). The ANOVA revealed that
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Table 6 ANOVA on related variables with subject groups by adaptation style Related variables
S.S.
df
M.S.
F
Computer literacy
0.52 101.15 1.10 15.11 0.79 42.65 5.81 57.76 1.74 160.69 6.22 87.66 7.16 272.70 14.62 5677.63 3.25 22.70 1.13 35.61 2.69 16.11 0.42 15.65 4.14 116.85
2 107 2 66 2 76 2 108 2 89 2 91 2 92 2 105 2 75 2 79 2 42 2 99 2 63
0.26 0.95 0.55 0.23 0.40 0.56 2.91 0.53 0.87 1.81 3.11 0.96 3.58 2.96 7.31 54.07 1.6 0.30 0.57 0.45 1.3 0.38 0.21 0.16 2.07 1.85
0.28
Number of learning participation Interaction with instructor Diculty of contents Rate of instructor's response Quality of instructor's response Interaction with instructor Learning amount per week Interaction with peer Providing of on-line database Use of on-line database Necessity of peer-providing materials Internet access fee
2.39 0.70 5.43** 0.48 3.23* 1.21 0.14 5.37** 1.25 3.51* 1.34 1.11
P<0.0.05. P<0.01.
especially four dependent variables of instruction-learning process area (diculty of contents, quality of instructor responses, eects of interaction with peer students, use of on-line database) were aected signi®cantly (P<0.01, P<0.05, P<0.01, P<0.05, respectively) by the three learner style groups among the total 13 related variables of three areas of personal background, instruction-learning environment, and instruction-learning process. In other words, WBI adaptation styles appeared to have characteristic dierences in terms of these four variables. To ®gure out if there is any spurious signi®cance and how these characteristic dierences exist among the adaptation styles, Bonferroni Test as a post hoc multiple comparison was implemented and displayed in Table 7. As seen here, it turned out that statistical signi®cance regarding the variable `use of on-line database' in the ANOVA table was spurious. Therefore, characteristic dierences among the adaptation styles were analyzed in terms of the remaining three variables. That is, the model learner style in WBI tended to consider learning contents rather easy and received more help from responses of instructors and interaction with peer students compared to disenchanted and maladaptive learner styles. The maladaptive learner style, in contrast, considered learning contents to be rather dicult, received far less help from
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Table 7 Analysis of characteristic dierences of WBI learner's adaptation styles (Bonferroni Test) Related variables
Group comparison
Dierence between means
Diculty of contents
Model±Disenchanted Disenchanted±Maladaptive Maladaptive±Model
3.22±3.42=ÿ0.20 3.42±3.78=ÿ0.36 3.78±3.22=0.56
Quality of instructor's response
Model±Disenchanted Disenchanted±Maladaptive Maladaptive±Model
3.72±3.22=0.50 3.22±3.05=0.17 3.05±3.72=ÿ0.67*
Interaction with peer
Model±Disenchanted
2.56±2.52=0.04 2.52±2.12=0.40* 2.12±2.56=ÿ0.44*
Maladaptive±Model *P<0.05.
responses of instructors and interaction with peer students than any other style. On the other hand, the disenchanted learner style had unique character of being not dierent from the other styles except that the style received some help from interaction with peer students. Overall, these results imply that any WBI system needs to provide instruction-learning strategies based upon the various adaptation styles and especially take care of the maladaptive and the disenchanted style learners one way or another. 5. Discussion This study investigated WBI learners' adaptation styles and characteristics related with the styles through surveying their perceptions of various aspects of the new learning environment. From the results of the study, several conclusions can be drawn. First, the research method which digressed from the obsessed trend being indulged in learners' achievements as the Web-based instruction reaches its settlement phase turned out to be a viable one. That is, ®guring out learners' adaptation styles retrospectively from analyzing their perceptions of learning context as Lee and Lodewijks (1995) and Ramsden (1988) analyzed learning styles from learners' perceptions generated various useful ®ndings. Directly in the ®eld of educational technology in regard to this research method, Jonassen's (1988) hypothesis that learners' perceived utility of hypermedia contents would aect their navigational choices, in particular, is supportive of its viability. Second, it was revealed that all students in WBI did not learn uniformly and, rather, four distinct adaptation styles of learners obviously existed in the instruction-learning process. Although the portion of learners, excluding in-between learners, who belong to these four styles are not that large, what is important is that four characteristic adaptation styles obviously exist during WBI. This ®nding is comparable to the previous study that learners' cognitive strategies in an open-ended learning system such as the World Wide Web may unfold in a variety of ways as being aected by self-reported knowledge and their perceptions (Hill & Hanna®n, 1997).
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Third, this study indicates that an eort to improve the quality of WBI should be begun not by treating all learners uniformly but by taking dissimilar adaptation styles of learners into account. For example, the maladaptive learner style among other styles identi®ed in the study is a desperate one in an instruction-learning situation. A WBI system, thus, needs to provide learning paths facilitating peer interactions or enough learning material including an on-line database to this style of learners by identifying them through an early detecting mechanism. Although the instructor's direct and kind feedback may be more important and even critical to this kind of learner (see Moore, 1993), it is to be thought as a general strategy when learners' adaptation styles are out of the question. Taken together, this study tried to provide basic knowledge from a new perspective which is needed to enhance the ecacy of WBI reaching its settlement phase. The study, also, is a small step in the expansion of research horizons in that it suggested a way to explore the diversity of research methodology to analyze adaptation process of Web-based learners through comprehensive examination of their perceptions rather than the existing method focusing largely on cognitive processes. However, a few things relevant to this study remain that must be studied in the future. First of all, replications of studies such as the present one might corroborate or modify at least some of the ®ndings here. Regarding the four adaptation styles, for instance, they could yield results to the extent that it becomes clear whether WBI learners should be encouraged to follow their natural approach or be encouraged to take a dierent approach depending on the situation. Another one is that the use of statistical methods such as cluster analysis with more sophisticated surveys could lead to more clear-cut and meaningful ®ndings. These ®ndings then will become a necessary grounding in establishing a desirable WBI system which provides individualized learning in a real sense as the ideal of educational technology envisioned through various educational innovations since the early twentieth century. References Allen, B. S., & Otto, R. G. (1996). Media as lived environments: the ecological psychology of educational technology. In D. Jonassen, The handbook of research for educational communications and technology (pp. 199±226). New York, NY: Macmillan. Barab, S. A., Fajen, B. R., Kuligowich, J. M., & Young, M. F. (1996). Assessing hypertext navigation through Path®nder: prospects and limitations. Journal of Educational Computing Research, 15(3), 175±195. Barker, R. G. (1968). Ecological psychology: concepts and methods for studying the environment of human behavior. Stanford, CA: Stanford University. Brooks, D. W. (1997). Web-teaching: a guide to designing interactive teaching for the World Wide Web. New York: Plenum. Cleave, J. B., Edelson, D., & Beckwith, R. (1993). A matter of style: an analysis of student interactions with a computer-based learning environment. Paper presented at the annual meeting of the American Educational Research Association, Atlanta, GA. Gibson, J. J. (1979). The ecological approach to visual perception. Boston, MA: Houghton Miin. Hill, J. R., & Hanna®n, M. J. (1997). Congnitive strategies and learning from the World Wide Web. Educational Technology Research and Development, 45(4), 37±64. Jonassen, D. H. (1988). Designing structured hypermedia and structuring access to hypermedia. Educational Technology, 28(11), 13±16. Khan, B. H. (1997). Web-based instruction. Englewood Clis, NJ: Educational Technology. Lawless, K. A., & Kulikowich, J. M. (1996). Understanding hypertext navigation through cluster analysis. Journal of Educational Computing Research, 14(4), 385±399.
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M.-G. Lee / Computers & Education 36 (2001) 121±132
Lee, K., & Lodewijks, H. G. L. C. (1995). The adaptation of international students' learning styles to dierent learning contexts. College Student Journal, 29, 319±332. Lee, M. G. (1999). A report on Korean cyber university model development for the twenty ®rst century. Seoul, Korea: Ministry of Education. Misanchuck, R. R., & Schwier, R. A. (1992). Representing interactive multimedia and hypermedia audit trails. Journal of Educational Multimedia and Hypermedia, 1(3), 355±372. Moore, M. G. (1993). Three types of interaction. In K. Harry, M. John, & D. Keegan, Distance education: new perspectives (pp. 19±24). London: Routledge. Ramsden, P. (1988). Context and strategy: situational in¯uences on learning. In R.R Schmeck, Learning and strategies and learning styles (pp. 159±184). New York: Plenum. Rowe, A. L., Cooke, N. J., Hall, E. P., & Halgren, T. L. (1996). Toward an on-line assessment methodology: building on the relationship between knowing and doing. Journal of Experimental Psychology: Applied, 2, 31±47. Saettler, P. (1990). The evolution of American educational technology. Englewood, CO: Libraries Unlimited. Schmeck, R. R. (1988). Learning strategies and learning styles. New York: Plenum. Schmeck, R. R., & Geisler-Bernstein, E. (1989). Individual dierences that aect the way students approach learning. Learning and Individual Dierences, 1(1), 85±124. Schuemer, R. (1993). Some psychological aspects of distance education. Hagen, Germany: Institute for Research into Distance Education. (ED 357 266) Ward, M., & Newlands, D. (1998). Use of the Web in undergraduate teaching. Computers & Education, 31, 171±184. Winne, P., Gupta, L., & Nesbit, J. (1994). Exploring individual dierences in studying strategies using graph theoretic statistics. The Alberta Journal of Educational Research, 40(2), 177±193. Wittrock, M. C. (1990). Generative processes of comprehension. Educational Psychologist, 24(4), 345±376.
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Pro®ling students' adaptation styles in Web-based learning Myung-Geun Lee * Department of Education, Yonsei University, Seoul, South Korea Received 3 April 2000; accepted 7 August 2000
Abstract Use of the Internet has brought about an alternative education form, the so-called Web-based instruction (WBI). It purports to sweep over current instructional problems and provide a revolutionary educational environment. One thing to be remembered along with the emergence of new educational technology such as the World Wide Web, however, is that diverse innovations throughout educational history did not last very long and resulted in bandwagons. In order not to be another historical bandwagon, the WBI needs to be studied in abundance and in depth. In this vein, this study analyzed WBI learners' adaptation styles and characteristics related with the styles by retrospectively assessing the perceptions of various aspects of WBI. Students participating in various courses at 11 universities nationwide in Korea which had been experimenting with WBI under the auspices of the Ministry of Education since 1998, were surveyed initially and the ®nal analysis consisted of 177 females and 157 males (n=334). The results indicated three ®ndings. First, retrospective post-assessment of learners' perceptions was a viable method for analyzing the adaptation styles in WBI. Second, WBI students were not learning uniformly so that there existed four distinct adaptation styles during WBI process. Third, any eorts to improve the quality of WBI need to consider these adaptation styles of students one way or another. These ®ndings are expected to be used for drawing useful strategies for implementing a more desirable WBI. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Distance education and telelearning; Post-secondary education; Pedagogical issues
1. Introduction The advent of broad access to the Internet has created a new medium for education and training. Use of the World Wide Web as an instructional tool, in particular, has brought about an alternative education form which purports to sweep over current instructional problems and * Tel.: +82-2-2123-3180; fax: +82-2-313-2158. E-mail address: [email protected] 0360-1315/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S0360-1315(00)00046-4
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provide a revolutionary educational environment (Brooks, 1997; Khan, 1997). The so-called Web-based instruction (WBI) in education and training ®elds, thus, is increasing at an exponential rate throughout the world. This is not an exception in Korea, where many higher education and corporate institutions are nowadays enjoying the Web-based education/training which is often called cyber education (Lee, 1999). Corporate and media institutions in this country, whether by aliation or alone, are also providing various Web-based courses to internal and/or external customers. The continuous growth of cyber education programs in Korea can be said to be mainly a way for the institutions to get management eciency from traditional cost-driven education. One thing that has to be remembered along with the emergence of new educational technology such as the WBI, however, is the fact that diverse innovations throughout the educational history did not last very long and resulted in mere bandwagons (Saettler, 1990). For example, the teaching machine and the programmed instruction movement in the 1950s began to decline within 10 years due to ineectiveness, theoretical grounds not supported in practice, etc. Although variations of programmed instruction such as the Keller Plan, IPI, PLAN in the 1960s and 1970s revived thereafter, these individualized instruction systems also waned by the mid-1970s. CAI and interactive video in the 1980s and the early 1990s as an alternative instructional technology are seemingly experiencing the same deterioration cycle. In order not to be another ``Teaching Machine'' in the history of education, the WBI needs to be studied in abundance and in depth. It is desirable that there has been a growing amount of researches being conducted on WBI in recent years throughout the world. For instance, research topics on WBI for the past 3 years (1996±1998) that were searched through ERIC (Educational Resources Information Center) are shown in Table 1. Overall, it seems conspicuous that the research topics vary from general inquiries such as comparison, and building and designing WBI to focused and diversi®ed ones such as learner perspectives and faculty development. A similar trend is also unfolding in education and corporate training ®elds in Korea. In-depth reading of research papers on WBI, however, reveals that in spite of diverse research subjects worldwide, their ultimate concerns tend to converge on the learner's achievement, especially Table 1 Research topics on WBI through ERIC during the recent 3 yearsa Topics
1997
1998
1999
Totals
Eectiveness (evaluation) Building and management Contents design State of aairs Learner perspectives (perception) Faculty development Interaction (group learning) Adoption process Strategies/factors
12 6 10 5 ± 2 ± 1 ±
12 10 6 1 5 2 2 ± 1
4 5 3 2 2 2 4 3 7
28 21 19 8 7 6 6 4 8
Totals
36
39
32
107
a The traditional distance education not based on the Web was not counted at the outset. WBI, Web-based instruction; ERIC, Educational Resources Information Centre.
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compared with traditional face-to-face instruction. Although being a natural phenomenon at a settlement stage of WBI, it still leaves room for consideration. The process of student learning in cyber space is more complex than conventional class learning for many reasons (Schuemer, 1993). Many assessments simply capture the eectiveness of WBI, failing to capture critical aspects such as learners' perceptual and metacognitive processes as they are occurring. Moreover, it may mislead the real nature of learning to compare the eectiveness without considering learning processes in an unfamiliar learning environment like WBI (see Ward & Newlands, 1998). Thus, the real eectiveness of WBI needs to be reserved until some more comprehensive approaches are fully implemented. In other words, research focus with regard to WBI needs to be directed to the process and nature of students' learning. In this vein, this study tried to analyze students' learning adaptation process in a new learning environment of WBI. More speci®cally, the purpose of the study is to identify learners' adaptation styles and characteristics related with the styles through surveying their perceptions of various aspects of Web-based learning. 2. Adaptation process in Web-based instruction Psychologically, a new environment requires a need to adapt and, accordingly, human behaviors tend to be responsive to dierent situational contexts (Schmeck & Geisler-Bernstein, 1989). This principle applies to instruction-learning contexts. That is, a new instruction-learning environment creates a need to adapt and learners are expected to deploy dierent learning styles according to their adaptation processes. It is, then, hypothesized that these dierent adaptation styles of learners may in¯uence their academic achievements. For instance, Ramsden (1988) investigated the in¯uence of new learning contexts comprised of teaching, assessment, and curriculum on students' deployment of learning styles. Expanding on this research, Lee and Lodewijks (1995) found that, by analyzing international students' perceptions of new learning environments and their personological characteristics, learning styles changed as a consequence of dierent learning environments and the change of their learning styles largely due to change of learning regulations was highly correlated to their grades. These researchers and related scholars (e.g. Schmeck, 1988) supposed that learners in a new learning environment developed a new perception and the change of learning styles arose from it, ultimately in¯uencing their academic achievements. In view of these research ®ndings, ways of evaluating the eectiveness of a new learning environment such as WBI, especially based upon post-implementation achievements compared with its counterpart face-to-face instruction are to be discontinued. This is because traditional means of assessment, relying mostly on measures taken upon completion of learning, fail to capture the critical processes such as perceptual and metacognitive ones as they are occurring (Rowe, Cooke, Hall & Halgren, 1996; Winne, Gupta & Nesbit, 1994). Thus, it is necessary to identify, among other things, how learners adapt to a new learning environment of WBI and which factors aect their adaptation process. In other words, predicated on pro®ling the dierent styles in learners' Web-based learning, not only can an individualized instruction satisfying their diverse needs be provided, but also, a more equitable evaluation on WBI can possibly be carried out. The adaptation process in WBI may be analyzed one way or another. First of all, it can be approached from an analysis of learning processes on the ¯y such as think-alouds, protocol
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analysis, and stimulated recall (see Wittrock, 1990). Being methodologically dicult and obtrusive to subjects in the course of participant observations and in-depth interview of subjects, these may still be useful in analyzing the processes. Or the so-called log ®le by which students' learning paths across Web-based learning materials are traced can be analyzed (Barab, Fajen, Kuligowich & Young, 1996; Cleave, Edelson & Beckwith, 1993; Lawless & Kulikowich, 1996; Misanchuk & Schwier, 1992). This is very eective in that, if the mechanism can be built into the computer system, it is not only unobtrusive to subjects but also the individual students' learning processes can be traced in detail. However, these analysis methods which are largely based on cognitive processes seem to be construed as being logically inappropriate. It is because all human behaviors including learning behavior cannot be separated from perceiving (Allen & Otto, 1996). Moreover, there is an inseparable relationship between the setting creating the need for adaptation and perceiving (Barker, 1968; Gibson, 1979). Studying the adaptation process of Web-based learners, therefore, needs to follow a model, which is based on comprehensive analysis of their perceptions. The point is how to conceptualize the perceptions which directly aect learners' adaptation process in Web-based learning. It has been largely noted that perception is partially the result of the environment and partially the result of the learner according to researches on the relationship between perception and learning (Lee & Lodewjiks, 1995; Schmeck, 1988). Taking this view into consideration, this paper presumes learning adaptation process in a new learning environment like WBI (see Fig. 1). That is, adaptation process in WBI is directly in¯uenced by perceptions which learners form during the learning process and these perceptions are a result of interactions between learners' personological things and learning context. The personological things in¯uencing the way learners perceive the learning environment are, for example, learners' motives, self-esteem, past experiences, etc. The learning context includes various aspects such as teacher, lecture, facilities, learning culture and the like. Thus, learning adaptation process in WBI is likely to be ®gured out by comprehensively
Fig. 1. Learning adaptation process in a new learning environment.
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investigating learners' perceptions on those variables. The results will be able to provide viable strategies matching up with dierent learning adaptation styles. 3. Method 3.1. Subjects The subjects in the present study consisted of 334 students of 11 Korean universities sampled on account of institutional types and operational styles from a total 65 institutions which had been experimenting with WBI since 1998 under the auspices of Ministry of Education. Of the respondents, 177 (53.0%) were female and 157 (47.0%) were male. Taking the population number into account, 1000 questionnaires were initially administered, but 334 were collected with most items adequately responded to for analysis (response rate: 33.4%), which in turn were ®nally analyzed. 3.2. Instrument In order to comprehensively investigate Web-based learners' perceptions, a questionnaire was developed and administered by the researcher. It consisted of four parts which contained as much information as possible including characteristics of Web-based learning environment as well as personal data of the learners. The ®rst part of the questionnaire asked largely about background information of the subjects. The second and third parts assessed the learners' perceptions of the Web-based learning environment, which were actually separated into two parts of a general instruction-learning environment and speci®c instruction-learning processes. The fourth part asked about the learners' perceptions of overall Web-based instruction. The structure and contents of the questionnaire used in this study are represented in Table 2. 3.3. Analysis procedures In an initial attempt to identify Web-based learners' adaptation styles, a cluster analysis on survey data was implemented, resulting in meaningless results due to the small number of Table 2 Structure and contents of questionnaire Parts
Variables
Items
Personal background
Sex, age, status, major, computer literacy, etc.
1±5
Instruction-learning environment
Learning participation, access tools, infrastructure problems
6±15
Instruction-learning process
Course type, amount of learning, diculty, learning materials, assignments, interaction, on-line database, methods of instruction, etc.
16±31
Overall WBI
Achievements, satisfaction, preference, motivation, other problems, etc.
32±37
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instances in most clusters. This may be because nominal variable items of the questionnaire had to be excluded from the analysis considering that continuous variables rather than nominal scale variables best ®t cluster analysis. In the present study, therefore, adaptation styles of WBI learners were retrospectively analyzed from their perceptions of the related variables after the conclusion of Web-based instruction. In other words, the learners' perceptions of their own achievements and their satisfaction with overall Web-based learning environment as encompassing personological things and learning context directly related with perceptions were combined to be analyzed for identifying the adaptation styles. Next, characteristics of each adaptation style were examined by analyzing the variables which were more related with each adaptation style than others. All data collected were processed using the SAS for Windows program. 4. Results 4.1. Overview of participants' characteristics as Web-based learners General characteristics of the subjects in the present study were drawn out from a descriptive statistical analysis on the variables concerning characteristics as WBI learners. As represented in Table 3, the distribution of majors of the 334 total participants is as follows: 158 humanities and social sciences (47.3%), 142 sciences and engineering (42.5%), and 34 arts, sports sciences and others (10.2%). Their computer literacy level can be said to be a little above the mean (2.85) on a 5 Likert scale. Responses of subjects to the Web-based instruction-learning environment indicated that the learning place was con®ned to the campus and home for almost all learners (95.1%) who, in turn, largely used LAN and MODEM as access tools. They also tended to interact more with peer students than with instructors and use an on-line database less than the mean (2.01 on 5 scale) during the instruction-learning process. Regarding overall Web-based instruction, subjects indicated that it was not better than traditional courses (2.04) in terms of academic achievements and their satisfaction with WBI was about mean (2.59). Nevertheless, they still revealed a high preference toward WBI in that about two-thirds of them (66.4%) reacted positively to the question ``Would you choose to take a Web-based course again in the future?''. 4.2. Adaptation styles of Web-based learners In order to identify adaptation styles of WBI learners, the interrelationship between their perception of academic achievements in WBI compared to traditional lecture courses and their satisfaction with overall WBI was ®rst analyzed. Of the 314 total subjects eligible for the analysis, 44 who did not respond to some of the questionnaire items in point were excluded and w2 test of the remaining 290 subjects was performed. As seen in Table 4, the interrelationship between WBI learners' perception of their own academic achievements and their satisfaction with overall WBI was statistically signi®cant (P=0.001). This means that the learners' perception of academic achievement in WBI compared to traditional courses possibly changes as their perception of degree of satisfaction with overall WBI varies. In other words, there exist WBI learners who perceive the degree of their academic achievement as being high or low and simultaneously the degree of satisfaction with overall WBI
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Table 3 Overview of subjects' characteristicsa Parts
Variables
Statistics
Personal background
Major
Human. & Social Sci. Sciences & Engi. Arts, Sports & Others Mean: 2.85
Computer literacy Instruction-learning environment
Instruction-learning process
Overall WBI
a
S.D.:
158 (47. 30%) 142 (42.50%) 3 4 (10. 20%) 1.05
Learning place
Campus Home Work Others
216 (66.70%) 92 (28.40%) 13 (4.00%) 3 (0.90%)
Access tools
LAN MODEM Leased line Others
111 (43.90%) 118 (46.64%) 14 (5.53%) 10 (3.95%)
Interaction with instructor
0 per week 1±2 per week 3±4 per week 5±6 per week Over 7 per week
36 (13.90%) 196 (75.70%) 27 (10.40%) 0 (0%) 0 (0%)
Interaction with peer
0 per week 1±2 per week 3±4 per week 5±6 per week Over 7 per week
73 (26.40%) 131 (47.50%) 46 (16.70%) 24 (8.70%) 2 (0.70%)
Use of on-line database
Mean: 2.01
S.D.:
0.54
Achievement
Mean: 2.01
S.D.:
0.84
Satisfaction
Mean: 2.59
S.D.:
0.97
Preference
Will take Won't take
200 (66.40%) 101 (33.60%)
Cases which did not respond to some items were excluded from the analysis.
as being high or low, which, in turn, reveals learners' adaptation styles in WBI process. Table 5 makes this point more clear, with meanings of each level for the degree of perception of academic achievement and satisfaction expressed respectively. As revealed here, four distinct adaptation styles are obvious from the bipolar levels of the degree of perception of academic achievement and satisfaction with WBI, except the intermediate level. That is, there exist ``model learners'' in WBI who perceive the degrees of academic achievement and satisfaction simultaneously as being high and, by contrast, ``maladaptive learners'' who perceive the degrees simultaneously as being low. On the other hand, there are ``disenchanted learners'' who perceive the degree of academic achievement as being high but, the degree of satisfaction as being low and, by contrast, ``fanatic learners'' who perceive the degree of satisfaction as being high even though perceiving the degree of academic achievement as being low. While the portion of learners who belong to these four styles are no more than about 40% of the total, what is important is the fact that four dierent adaptation styles of learners obviously exist in WBI.
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Table 4 Interrelationship between WBI learners' perception of academic achievement and satisfaction Frequency
Satisfaction
(Percent) (row percent) (col. percent)
1
2
3
50 (17.24) (75.26) (25.91)
12 (4.14) (18.18) (26.09)
4 (1.38)
2
105 (36.21) (66.88) (54.40)
28 (9.66) (17.83) (60.87)
24 (8.28) (15.29) (47.06)
3
38 (13.10) (56.72) (19.69)
6 (2.07) (8.96) (13.04)
23 (7.93) (34.33) (45.10)
Achievement
Totals
1
193
Totals
46
df
w2
4
20.58*
66
(7.84)
51
157
67
290
*P<0.001. Table 5 Adaptation styles of WBI learners Achievement
Satisfaction Low
Intermediate
High
Low
Maladaptive 50 (17.24%)
12 (4.14%)
Fanatic 4 (1.38%)
Intermediate
105 (36.21%)
28 (9.66%)
24 (8.28%)
High
Disenchanted 38 (13.10%)
6 (2.07%)
Model 23 (7.93%)
4.3. Analysis of characteristics of adaptation styles One-way ANOVA on related variables with subject groups by adaptation style, then, was tried to ®gure out characteristics which may determine the adaptation styles of WBI learners identi®ed earlier. The ``fanatic style'' learner group, however, had to be excluded from the analysis, not only because the number of cases in the cell for ANOVA was insucient, but because it was meaningless to do further characteristics analysis for literally the fanatically interested in WBI. Table 6 shows the results of one-way ANOVA for the remaining three adaptation style groups (model learners, maladaptive learners, and disenchanted learners). The ANOVA revealed that
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Table 6 ANOVA on related variables with subject groups by adaptation style Related variables
S.S.
df
M.S.
F
Computer literacy
0.52 101.15 1.10 15.11 0.79 42.65 5.81 57.76 1.74 160.69 6.22 87.66 7.16 272.70 14.62 5677.63 3.25 22.70 1.13 35.61 2.69 16.11 0.42 15.65 4.14 116.85
2 107 2 66 2 76 2 108 2 89 2 91 2 92 2 105 2 75 2 79 2 42 2 99 2 63
0.26 0.95 0.55 0.23 0.40 0.56 2.91 0.53 0.87 1.81 3.11 0.96 3.58 2.96 7.31 54.07 1.6 0.30 0.57 0.45 1.3 0.38 0.21 0.16 2.07 1.85
0.28
Number of learning participation Interaction with instructor Diculty of contents Rate of instructor's response Quality of instructor's response Interaction with instructor Learning amount per week Interaction with peer Providing of on-line database Use of on-line database Necessity of peer-providing materials Internet access fee
2.39 0.70 5.43** 0.48 3.23* 1.21 0.14 5.37** 1.25 3.51* 1.34 1.11
P<0.0.05. P<0.01.
especially four dependent variables of instruction-learning process area (diculty of contents, quality of instructor responses, eects of interaction with peer students, use of on-line database) were aected signi®cantly (P<0.01, P<0.05, P<0.01, P<0.05, respectively) by the three learner style groups among the total 13 related variables of three areas of personal background, instruction-learning environment, and instruction-learning process. In other words, WBI adaptation styles appeared to have characteristic dierences in terms of these four variables. To ®gure out if there is any spurious signi®cance and how these characteristic dierences exist among the adaptation styles, Bonferroni Test as a post hoc multiple comparison was implemented and displayed in Table 7. As seen here, it turned out that statistical signi®cance regarding the variable `use of on-line database' in the ANOVA table was spurious. Therefore, characteristic dierences among the adaptation styles were analyzed in terms of the remaining three variables. That is, the model learner style in WBI tended to consider learning contents rather easy and received more help from responses of instructors and interaction with peer students compared to disenchanted and maladaptive learner styles. The maladaptive learner style, in contrast, considered learning contents to be rather dicult, received far less help from
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Table 7 Analysis of characteristic dierences of WBI learner's adaptation styles (Bonferroni Test) Related variables
Group comparison
Dierence between means
Diculty of contents
Model±Disenchanted Disenchanted±Maladaptive Maladaptive±Model
3.22±3.42=ÿ0.20 3.42±3.78=ÿ0.36 3.78±3.22=0.56
Quality of instructor's response
Model±Disenchanted Disenchanted±Maladaptive Maladaptive±Model
3.72±3.22=0.50 3.22±3.05=0.17 3.05±3.72=ÿ0.67*
Interaction with peer
Model±Disenchanted
2.56±2.52=0.04 2.52±2.12=0.40* 2.12±2.56=ÿ0.44*
Maladaptive±Model *P<0.05.
responses of instructors and interaction with peer students than any other style. On the other hand, the disenchanted learner style had unique character of being not dierent from the other styles except that the style received some help from interaction with peer students. Overall, these results imply that any WBI system needs to provide instruction-learning strategies based upon the various adaptation styles and especially take care of the maladaptive and the disenchanted style learners one way or another. 5. Discussion This study investigated WBI learners' adaptation styles and characteristics related with the styles through surveying their perceptions of various aspects of the new learning environment. From the results of the study, several conclusions can be drawn. First, the research method which digressed from the obsessed trend being indulged in learners' achievements as the Web-based instruction reaches its settlement phase turned out to be a viable one. That is, ®guring out learners' adaptation styles retrospectively from analyzing their perceptions of learning context as Lee and Lodewijks (1995) and Ramsden (1988) analyzed learning styles from learners' perceptions generated various useful ®ndings. Directly in the ®eld of educational technology in regard to this research method, Jonassen's (1988) hypothesis that learners' perceived utility of hypermedia contents would aect their navigational choices, in particular, is supportive of its viability. Second, it was revealed that all students in WBI did not learn uniformly and, rather, four distinct adaptation styles of learners obviously existed in the instruction-learning process. Although the portion of learners, excluding in-between learners, who belong to these four styles are not that large, what is important is that four characteristic adaptation styles obviously exist during WBI. This ®nding is comparable to the previous study that learners' cognitive strategies in an open-ended learning system such as the World Wide Web may unfold in a variety of ways as being aected by self-reported knowledge and their perceptions (Hill & Hanna®n, 1997).
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Third, this study indicates that an eort to improve the quality of WBI should be begun not by treating all learners uniformly but by taking dissimilar adaptation styles of learners into account. For example, the maladaptive learner style among other styles identi®ed in the study is a desperate one in an instruction-learning situation. A WBI system, thus, needs to provide learning paths facilitating peer interactions or enough learning material including an on-line database to this style of learners by identifying them through an early detecting mechanism. Although the instructor's direct and kind feedback may be more important and even critical to this kind of learner (see Moore, 1993), it is to be thought as a general strategy when learners' adaptation styles are out of the question. Taken together, this study tried to provide basic knowledge from a new perspective which is needed to enhance the ecacy of WBI reaching its settlement phase. The study, also, is a small step in the expansion of research horizons in that it suggested a way to explore the diversity of research methodology to analyze adaptation process of Web-based learners through comprehensive examination of their perceptions rather than the existing method focusing largely on cognitive processes. However, a few things relevant to this study remain that must be studied in the future. First of all, replications of studies such as the present one might corroborate or modify at least some of the ®ndings here. Regarding the four adaptation styles, for instance, they could yield results to the extent that it becomes clear whether WBI learners should be encouraged to follow their natural approach or be encouraged to take a dierent approach depending on the situation. Another one is that the use of statistical methods such as cluster analysis with more sophisticated surveys could lead to more clear-cut and meaningful ®ndings. These ®ndings then will become a necessary grounding in establishing a desirable WBI system which provides individualized learning in a real sense as the ideal of educational technology envisioned through various educational innovations since the early twentieth century. References Allen, B. S., & Otto, R. G. (1996). Media as lived environments: the ecological psychology of educational technology. In D. Jonassen, The handbook of research for educational communications and technology (pp. 199±226). New York, NY: Macmillan. Barab, S. A., Fajen, B. R., Kuligowich, J. M., & Young, M. F. (1996). Assessing hypertext navigation through Path®nder: prospects and limitations. Journal of Educational Computing Research, 15(3), 175±195. Barker, R. G. (1968). Ecological psychology: concepts and methods for studying the environment of human behavior. Stanford, CA: Stanford University. Brooks, D. W. (1997). Web-teaching: a guide to designing interactive teaching for the World Wide Web. New York: Plenum. Cleave, J. B., Edelson, D., & Beckwith, R. (1993). A matter of style: an analysis of student interactions with a computer-based learning environment. Paper presented at the annual meeting of the American Educational Research Association, Atlanta, GA. Gibson, J. J. (1979). The ecological approach to visual perception. Boston, MA: Houghton Miin. Hill, J. R., & Hanna®n, M. J. (1997). Congnitive strategies and learning from the World Wide Web. Educational Technology Research and Development, 45(4), 37±64. Jonassen, D. H. (1988). Designing structured hypermedia and structuring access to hypermedia. Educational Technology, 28(11), 13±16. Khan, B. H. (1997). Web-based instruction. Englewood Clis, NJ: Educational Technology. Lawless, K. A., & Kulikowich, J. M. (1996). Understanding hypertext navigation through cluster analysis. Journal of Educational Computing Research, 14(4), 385±399.
132
M.-G. Lee / Computers & Education 36 (2001) 121±132
Lee, K., & Lodewijks, H. G. L. C. (1995). The adaptation of international students' learning styles to dierent learning contexts. College Student Journal, 29, 319±332. Lee, M. G. (1999). A report on Korean cyber university model development for the twenty ®rst century. Seoul, Korea: Ministry of Education. Misanchuck, R. R., & Schwier, R. A. (1992). Representing interactive multimedia and hypermedia audit trails. Journal of Educational Multimedia and Hypermedia, 1(3), 355±372. Moore, M. G. (1993). Three types of interaction. In K. Harry, M. John, & D. Keegan, Distance education: new perspectives (pp. 19±24). London: Routledge. Ramsden, P. (1988). Context and strategy: situational in¯uences on learning. In R.R Schmeck, Learning and strategies and learning styles (pp. 159±184). New York: Plenum. Rowe, A. L., Cooke, N. J., Hall, E. P., & Halgren, T. L. (1996). Toward an on-line assessment methodology: building on the relationship between knowing and doing. Journal of Experimental Psychology: Applied, 2, 31±47. Saettler, P. (1990). The evolution of American educational technology. Englewood, CO: Libraries Unlimited. Schmeck, R. R. (1988). Learning strategies and learning styles. New York: Plenum. Schmeck, R. R., & Geisler-Bernstein, E. (1989). Individual dierences that aect the way students approach learning. Learning and Individual Dierences, 1(1), 85±124. Schuemer, R. (1993). Some psychological aspects of distance education. Hagen, Germany: Institute for Research into Distance Education. (ED 357 266) Ward, M., & Newlands, D. (1998). Use of the Web in undergraduate teaching. Computers & Education, 31, 171±184. Winne, P., Gupta, L., & Nesbit, J. (1994). Exploring individual dierences in studying strategies using graph theoretic statistics. The Alberta Journal of Educational Research, 40(2), 177±193. Wittrock, M. C. (1990). Generative processes of comprehension. Educational Psychologist, 24(4), 345±376.