First Monday

Web-based learning: Factors affecting students' satisfaction and learning experience by Kyung-Sun Kim and Joi L. Moore

This study investigates how students’ characteristics and behaviors affect their satisfaction and learning experience within Web–based courses. Eighty–two graduate students taking a Web–based course from a Midwest university participated in the study. Web–based questionnaires were used to collect data on student demographics as well as learning experiences and styles. Findings suggest that students’ interaction with classmates and their instructor may have an impact on their satisfaction with Web–based courses. In addition, students’ gender and their perceived level of course difficulty seem to be correlated with interaction.







The academic community, especially in higher education, has adopted and used the World Wide Web as one of the feasible delivery methods for learning activities. A report from the National Center for Education Statistics (Waits and Lewis, 2003) indicated that approximately 90 percent of public four–year institutions and about 50 percent of private institutions were offering, or planning to offer, distance education programs in 2000–2001. The number of institutions offering Web–based courses has been continually growing. To meet the increasing demands for distance education, educators have adopted advanced technologies, which often transform learning environments significantly. For example, the Web has been used for offering courses without geographical or temporal limits. Being less familiar with new technologies, however, students are more likely to face challenges and to be dissatisfied in such new learning environments. In fact, studies on Web–based courses have revealed that students taking Web–based courses tend to drop out or to get incompletes more often than those taking traditional face–to–face (FTF) courses (Easterling, 2001; Kember, 1989).

Findings from research on Web–based learning are often inconsistent, however. Some studies suggest that Web–based courses can be as effective as traditional FTF courses (Carter, 1996; Moore and Thompson, 1997). In general, students taking Web–based courses are reported to be satisfied with their learning experience (Wayland, et al., 1994; Wegner, et al., 1999). Other studies have yielded different findings. Students taking Web–based courses tended to be less satisfied due to problems and difficulties they encountered in terms of communication, interactions, and technologies (Hara and Kling, 1999). Apparently, there are many factors that contribute to student success and satisfaction in Web–based courses, such as the level of interaction and learning styles (Moore and Kearsley, 1996). Understanding how these factors influence the learning experiences of students will be important because it will lead to improvements in Web–based courses, making them more effective.

This study was conducted to better understand how students’ characteristics and behaviors impact their learning experience within Web–based courses. The goal of the study is to gain knowledge regarding how students learn within the Web environment so that we can design and deliver effective Web–based instructional activities.




The proliferation of Web–based courses has ignited numerous research studies exploring different variables that may influence the learning experience. Schrum and Hong (2002) suggest seven dimensions related to the students’ success in Web–based learning: (1) access to tools; (2) technology experience; (3) learning preferences; (4) study habits and skills; (5) student goals and purposes; (6) lifestyle factors; and, (7) personal traits and characteristics. In relation to access, it has been found that students in urban settings were more likely to express satisfaction with their learning experience because they have better access to technology (e.g., faster Internet connections) compared to students in non–urban settings (Irons, et al., 2002). Regarding technology experience, studies have revealed that computer experience or skills have little impact on the learning performance although they might affect the level of satisfaction (Fredericksen, et al., 2000; Swan, et al., 2000; Sturgill, et al., 1999). For other learner–related factors (e.g., age, gender, learning styles, strategies), research results are rather inconsistent (Fredericksen, et al., 2000; Karuppan, 2001; Swan, et al., 2000; Blum, 1999; Kearsley, 2000). Obviously, further research is needed, investigating the relationship between student characteristics, learning experience, and satisfaction in Web–based courses.

Learning styles and Web–based learning

The phrase "learning style" refers to learner preferences for different types of learning and instructional activities (Jonassen and Grabowski, 1993). It relates to the general tendency towards a particular learning approach for receiving, organizing, and processing information in order to make decisions and to form values (North Carolina Department of Public Instruction, 1999). According to Goldstein and Blackman, (1978), there exist a myriad of approaches to identify learning styles/preferences with each approach resulting in various types of learning styles.

Curry (1987) suggests that there is a relationship among learning styles, strategies, and outcomes. This also seems to be applicable in Web–based learning. Studies on Web–based learning suggest that learning styles are associated with students’ higher achievement, their preference and completion of Web courses, and also their interaction with course content (Douzenis, 1999; Terrell, 2002; Sabry and Baldwin, 2003).

Among different learning styles, field dependence and Kolb’s learning styles have been studied often in order to sort out their relationships with Web–based learning experience (Douzenis, 1999; Du, 2002; Shih and Gamon, 2002; Terrell, 2002). Other learning styles that might have an impact on Web–based learning (which have received less attention from researchers) include: (1) active vs. reflective; (2) sensing vs. intuitive; (3) visual vs. verbal; and, (4) sequential vs. global. According to Felder and Silverman (1988), active learners retain and understand information best by doing something "active" with it, such as discussing or applying the information or explaining it to others. Reflective learners, on the other hand, prefer to think about information quietly first and to work alone. Courses with theories and abstract concepts might be more favorable to reflective learners whereas courses requiring hands–on exercises and/or projects might be more satisfying to those with an active learning style. The sensing/intuitive learning style relates to the preference for information selection. Sensing learners would be good at learning facts and concepts, whereas intuitive learners would prefer discovering possibilities and relationships. Regarding visual vs. verbal learning styles, visual learners are more likely to process information by seeing images, such as pictures, diagrams, and graphs. On the other hand, verbal learners prefer to process information in words, by reading or listening. Relative to distance education, research has shown that visual learners prefer online collaboration while verbal learners prefer FTF (Becker and Dwyer, 1998). The sequential/global learning style describes preference for information processing strategies. Sequential learners tend to gain understanding in linear steps, with each step containing appropriate detail that reflects the relationship with other steps in the process, while global learners tend to learn and absorb materials almost randomly without seeing connections, and then suddenly understand the content. A recent study indicates that sequential/global learning style influences the level of interaction among students in Web–based courses (Sabry and Baldwin, 2003).

Interaction and Web–based learning

In reference to Vygotsky’s (1978) social development theory, social interaction is a critical cognitive developmental process (Pea, 1996). Learning is a social activity that involves interaction with the instructor and among students. Interaction is one of the central issues related to distance education today (Jackson, 1994). It encourages reflection and discussion, which makes it an important component of any learning experience (Harasim, 1990). When students perceive and regard interaction highly, they tend to be more satisfied with instruction in Web–based courses (Fulford and Zhang, 1993). Additionally, learners who always interact with an instructor and other students are more likely to be motivated and have a better learning experience (Garrison, 1990). A study found that students and professors recognize the importance of interaction in distance learning (Rodriguez, 1995). They believe that interaction contributes to enhanced communication, improved teaching, and increased level of students’ interest in content. It seems that perceived level of interaction is one of the key factors in enhancing a given learning experience.

Interactions in distance education settings can be classified in different ways. One of the most recognized ways of classifying interactions was suggested by Moore (1989). According to Moore, there are three types of interaction: (1) interaction between learner and content; (2) interaction between learner and instructor; and, (3) interaction between learner and learner. Another type of interaction — interaction between learner and interface — was suggested later by Hillman, et al. (1994). Learner–content interaction is between learners and the format of learning materials. The content and its format (e.g., text, audio, video, hyperlinks) can affect how the learner understands, perceives, or constructs the knowledge. For learner–instructor interaction, a teacher’s immediacy behavior, such as motivation, feedback, and dialog, can create a form of individualized instruction. As in most traditional courses, there is a positive relationship between teacher immediacy behavior and student satisfaction ratings with course and instructor in Web–based courses (Moore, et al., 1996). Learner–learner interaction is between learners and their classmates. Learners share information and ideas, but successful interaction may depend on age, experience, and level of autonomy. For example, younger learners do best with classmates in the same age group, but the age of classmates does not matter for adult and advanced learners (Moore and Kearsley, 1996). Finally, the learner–interface interaction is a variable that examines the relationship between the learners’ technology skill level and the technology’s capability of supporting easy retrieval of course content and communication between participants. Research suggests that frequent and constructive interaction between the instructor and students, and a dynamic discussion among students can affect the success of a Web–based course (Swan, et al., 2000).





Eighty–two graduate students who were taking at least one Web–based course from a public university participated in this study. Sixty–four (78 percent) of the participants were female and the rest were male students. The participants were from different age groups. Twenty–seven of the participants (33 percent) were in their 40s. The rest were: 31 percent, 18 percent and 18 percent in their 20s, 30s, and 50s, respectively. In general, the average age was relatively higher because all the participants were graduate students.

The data on technical skills revealed that most participants considered themselves as experienced users of computers (87 percent), e–mail (93 percent), and the Web (93 percent). Naturally, most of the participants felt comfortable using technology. In regards to their learning styles, 58 percent of the participants were sensing learners while the rest (42 percent) were intuitive ones. More than two–third of the participants (69 percent) were visual learners while 31 percent were verbal learners. Regarding the other learning styles (sequential vs. global; active vs. reflective), the participants were almost evenly split.

The participation was voluntary. Although some instructors offered extra credit for students who participated in the study, there was no noticeable difference in demographics between those who received extra credit and those who did not. However, this sample of convenience is a limitation of the study.

Web–based course management systems

WebCT and Blackboard were the Web–based course management tools used to deliver the courses examined in this study. Both tools have features that support delivery of content, and communication among students and the instructor through the Web. They provide access to course materials presented in various formats, and also permit synchronous as well as asynchronous communication among students and the instructor.


Near the end of semester, the researchers sent an e–mail message to students who were enrolled in Web–based courses, and encouraged them to complete two online questionnaires. First, the participants filled out the Index of Learning Styles questionnaire, developed by Richard M. Felder and Barbara A. Soloman of North Carolina State University (1991). Then, they were asked to complete a survey questionnaire, designed for collecting demographic, learning preference, and Web–based learning experience information. There were two open–ended questions asking for advice from the participants for other students, and also information regarding unexpected challenges the participants encountered while taking a Web–based course. Responses to the open–ended questions were analyzed qualitatively, which involved segmenting the information, developing coding categories, and then generating themes from the grouping of related categories. These steps are part of the systematic process for analyzing textual data as suggested by Tesch (1990).




Learner factors

Principal components analysis was performed on all learner characteristics and learning behavior variables in order to identify key factors. The analysis yielded three factors with eigenvalues greater than 1.00 (see Table 1). As an oblique solution revealed no relations among the extracted factors, the Varimax rotation method was used. As a result, three factors were identified. Factor 1, related to Technical Experience, includes Internet, Computer, E–mail, and Web experience variables. Factor 2, concerned with Learning Style, includes Sequential/Global (S–G), Sensing/Intuitive (S–I), Active/Reflective (A–R) learning styles, and Group/Individual learning preference. Factor 3, related to Interaction, includes Interaction with instructor, and Interaction with classmates. None of the identified factors are correlated.

After identifying three user factors, a multivariate regression was performed to find out the relationship between the factors and the participants’ satisfaction/preferences related to Web courses. Participants’ satisfaction with and recommendation of Web courses, and their preference on Web/FTF were used as criterion variables.


Table 1: Factor analysis on learner variables.

Factor 1
Factor 2
Factor 3
Internet experience
Computer experience
E–mail experience
Web experience
Sequential vs. global  
Sensing vs. intuitive  
Active vs. reflective  
Group vs. individual  
Interaction with instructor    
Interaction with students    


Regression analysis revealed that three factors were predictive of students’ satisfaction (31 percent), likelihood of recommending a Web course (29 percent), and Web/FTF preferences (23 percent); see Table 2.


Table 2: Multivariate regression with three factors (Technical experience; Learning style; Interaction).
a: R2 = .308; b: R2 = .290; c: R2 = .225.

Factor 1:
Technical experience
Factor 2:
Learning style
Factor 3:


Factors influencing students’ satisfaction with Web courses

Regression analysis revealed that Interaction (Factor 3) can significantly predict the students’ satisfaction with Web–based courses (F = 17.984, p < .01). Students who had more interaction with an instructor and other classmates tended to be more satisfied with their Web courses.

Factors influencing students’ recommendation of Web courses

With regard to the recommendation of Web–based courses, the Interaction factor could significantly predict whether students would recommend Web courses to others (F = 7.234, p < .01). That is, students who had more interaction with an instructor and classmates were more likely to recommend Web–based courses to others.

Factors influencing Web course preference

Interaction and Technical Experience factors could significantly predict preferred class type (Web vs. FTF). Students who had more interaction with an instructor and other classmates tended to prefer the Web–based courses to FTF (F = 6.209, p < .05). In addition, those with more technical experience were more likely to prefer Web courses (F = 9.963, p < .01).

Additional findings: Learners’ interaction

Correlation was used to identify variables related to the level of students’ interaction (see Table 3). The result revealed that female students tended to interact with other classmates more frequently than male students (r = -.222, p < .05). However, this finding should be interpreted with caution because the number of female participants in this study was unusually high (78 percent). It was also found that students who perceived that a given Web course was difficult tended to have more interaction with an instructor (r = -.249, p < .05).


Table 3: Correlation: Interactions with classmates and an instructor.
* p < .05.

Interaction with classmates
Interaction with instructor
Perceived difficulty


Unexpected challenges encountered and advice on learning strategies

Qualitative data, collected from open–ended questions, revealed the most unexpected challenges students encountered: a high level of interaction with classmates and an instructor. It seems that most students tend to anticipate more independent learning, and were quite surprised with the high level of interaction that occurred (and/or was required) in Web–based courses. Although they had to adjust their expectations in terms of the level of interaction, most students indicated that their interaction experiences were positive and helpful.

When students were asked about the advice they would give to other students taking Web courses, they tended to focus on four different areas: (1) managing time; (2) organizing course materials; (3) interacting with others to keep connected with class discussion; and, (4) interacting with others for clarification. Regarding time management, several students indicated that this factor is especially important in Web courses. As a student pointed out, students must be "self–disciplined and self–motivated in order to be successful in an online course." Related to the organization of course materials, several students felt that the amount of information presented in Web courses was quite overwhelming. They suggested printing all of the course content and sorting into binders, in order to organize information for easy access. This, of course, led to complaints of the costs associated with printing. With regard to interaction for class discussion, students expressed the need to check a given course site on a daily basis in order to keep up with new postings. Usually, the larger the student enrollment, the more postings that occur for students to follow. Several students suggested that they had to learn to "skim" through postings, and to selectively read postings in detail. As far as interaction for clarification is concerned, students pointed out that they usually asked more questions of a given instructor and their peers even when they felt initial apprehension. They suggested taking an active approach to help one another by sharing useful information, and offering feedback and clarification. Apparently, students need more effective and efficient interaction because of their physical separation from the instructor and other classmates and also limitations in communication.




Understanding what factors influence students’ satisfaction with a course is an important step toward the development of successful courses. In this study, it was found that students actively interacting with classmates and an instructor were more satisfied with their learning experience in Web–based courses, and were willing to recommend Web courses to others. Because of the physical separation imposed by the Web–based learning environment, a dialogue among students and a dialogue between students and the instructor are critically important to reduce any misunderstandings between students and an instructor (Moore and Kearsley, 1996). Those who interact and communicate well are more likely to have a clear understanding of each other and learning materials and become more involved in learning. Students will develop a sense of community with classmates and the instructor, which is linked to greater satisfaction with their academic program, and reduced feeling of isolation (Gibbs, 1995; Rovai, 2002). This explains why there is a strong connection between students’ interaction, and their satisfaction with and recommendation of a given course.

The level of interaction seems to be related to the gender of students. In this study, female students tended to be more actively involved in interaction than male students. Related research notes that female students have a tendency of placing emphasis on relationships and prefer to learn in an environment where cooperation is stressed rather than competition (Belenky, et al., 1986; MacKeracher, 1993). Blum’s (1999) research also suggests that there exists a relationship between gender and learning behavior. As in FTF learning environments, male students have a tendency to dominate discussion with abstract postings, and exhibit a separate or independent learning style, which reflects autonomy, certainty, control, and abstraction. Female students, on the other hand, provide more content and friendly comments, and prefer to learn in a connected manner by sharing and interacting with others. These findings concur with gender research (Belenky, et al., 1986; MacKeracher, 1993). Due to the low number of male students in this study (18 percent of participants), however, the findings from this study might not be generalized to other situations where male and female students are evenly represented. The higher level of interaction among female students observed in this study might have resulted from the fact that the majority of the students were female. As a result, they may have felt more comfortable interacting with other classmates. The relationship between gender and interaction should be investigated again in a larger study where both gender groups are well represented.

The strong connection between student interaction and satisfaction level found in this study is supported by other research (Fredericksen, et al., 2000; Swan, et al., 2000; Hong, 2002). When learners perceive a high level of interaction, they will be more satisfied with instruction (Fulford and Zhang, 1993). It seems obvious that instructors of Web–based courses should make an extra effort to encourage and facilitate student interaction with classmates and the instructor because more interaction improves students’ understanding of each other, and also the course materials. Strategies for improving interaction may include establishing online office hours and providing feedback for assignments in a timely manner.

Would the quality as well as the quantity of such interactions matter? Student comments regarding required class discussion and group work were among the common complaints of Web–based courses. Dissatisfaction was especially evident among students who expected Web courses to be "correspondence courses" wherein they would not be required to interact with classmates (Moore, et al., 2002; Howland and Moore, 2002). Further research regarding the nature of interaction and its impact on overall satisfaction with Web–based courses will help instructors create more effective learning activities.

In this study, learning styles did not seem to have any significant impact on the level of satisfaction. This coincides with Kearsley’s (2000) findings, which indicates no relationship between students’ learning styles and their satisfaction with Web–based courses. However, students’ learning behavior and their course completion rate seem to be influenced by learning styles (Karuppan, 2001; Sabry and Baldwin, 2003; Terrell and Dringus, 1999; Terrell, 2002). Further research is needed to study the relationship between different learning styles and learning performance and success in Web–based courses.




For successful learner outcomes in Web–based courses, both instructors and students must recognize that instructional and learning strategies must adapt to the characteristics of the online learning environment. It requires increased engagement among all participants, with students regulating their learning and instructors becoming active facilitators (Howland and Moore, 2002). The instructional design of Web–based courses should integrate the components of Brockett and Heimstra’s Personal Responsibility Orientation model (1991), which situates the student as the regulator of the learning process while the learning is facilitated by the instructor. Because not all students are self–regulators, there will always be some students who are not satisfied with a given course. This may result in low evaluation ratings for the course and instructor, students not choosing to enroll in Web–based courses, and students dropping a given course before the semester ends (Swift, et al., 1997).

For Web–based courses, students and instructors must modify their view of the learning environment by steadily moving away from passive, one–way interaction for learning to self–regulated and social learning activities. With this movement, educators are challenged to integrate innovative instructional activities that encourage student engagement and ownership of the learning process. This change may not be an easy conversion for most students because they may have to change learning strategies that were appropriate before distance education. In turn, educators will need to continue their endeavor in improving instructional strategies while students learn to take a more active role in their learning. End of article


About the authors

Kyung–Sun Kim is an Assistant Professor in the School of Library and Information Studies at the University of Wisconsin–Madison.
E–mail: kskim [at] slis [dot] wisc [dot] edu

Joi Moore is an Associate Professor at the School of Information Science and Learning Technologies, University of Missouri–Columbia.



D. Becker and M. Dwyer, 1998. "The impact of student verbal/visual learning style preference on implementing groupware in the classroom," Journal of Asynchronous Learning Networks, volume 2, number 2 (September), at, accessed 6 November 2005.

M.F. Belenky, B.M. Clinchy, N.R. Goldberger, and J.M. Tarule, 1986. Women’s ways of knowing: The development of self, voice, and mind. New York: Basic Books.

K.D. Blum, 1999. "Gender differences in asynchronous learning in higher education: Learning styles, participation barriers and communication patterns," Journal of Asynchronous Learning Networks, volume 3, number 1 (May), at, accessed 1 January 2004; this article is no longer available.

R.G. Brockett and R. Heimstra, 1991. Self–direction in learning: Perspectives in theory, research, and practice. London: Routledge.

V. Carter, 1996. "Do media influence learning? Revisiting the debate in the context of distance education," Open Learning, volume 11, number 1, pp. 31–40.

L. Curry, 1987. Integrating concepts of cognitive or learning style: A review with attention to psychometric standards. Ottawa: Canadian College of Health Service Executives.

C. Douzenis, 1999. "The relationship between cognitive style and achievement in a distance education course," paper presented at the Annual Meeting of the American Educational Research Association (Montreal, 1999).

Y. Du, 2002. "Effects of learning styles and class participation on students’ enjoyment level in distributed learning environments," paper presented at the Annual Conference of the Association for Library and Information Science Education (New Orleans, La., 15–18 January); also ERIC Document Reproduction Service No. ED463755.

D.N. Easterling, 2001. Distance learning performance/completion rates: Are there any predictors? Dayton, Oh.: Sinclair Community College, Office of Institutional Planning and Research.

R.M. Felder and L.K. Silverman, 1988. "Learning and teaching styles in engineering education," Engineering Education, volume 78, number 7, pp. 674–681.

R.M. Felder and B.A. Soloman, 1991. "Index of Learning Styles (ILS)," at, accessed 6 November 2005.

E. Fredericksen, A. Pickett, P. Shea, W. Pelz, and K. Swan, 2000. "Student satisfaction and perceived learning with online courses: Principles and examples from SUNY learning network," Journal of Asynchronous Learning Networks, volume 4, number 2 (September), at, accessed 6 November 2005.

C.P. Fulford and S. Zhang, 1993. "Perceptions of interaction: The critical predictor in distance education," American Journal of Distance Education, volume 7, number 3, pp. 8–21.

D.R. Garrison, 1990. "An analysis and evaluation of audio teleconferencing to facilitate education at distance," American Journal of Distance Education, volume 4, number 3, pp. 13–24.

J. Gibbs, 1995. Tribes: A new way of learning and being together. Sausalito, Calif.: Center Source Systems.

K.M. Goldstein and S. Blackman, 1978. Cognitive style: Five approaches and relevant research. New York: Wiley.

N. Hara and R. Kling, 1999. "Students’ frustrations with a Web–based distance education course," First Monday, volume 4, number 12 (December), at, accessed 6 November 2005.

L.M. Harasim, 1990. Online education: Perspectives on a new environment. New York: Praeger.

D.C. Hillman, D.J. Willis, and C.N. Gunawardena, 1994. "Learner–interface interaction in distance education: An extension of contemporary models and strategies for practitioners," American Journal of Distance Education, volume 8, number 2, pp. 31–42.

K.–S. Hong, 2002. "Relationship between students’ and instructional variables with satisfaction and learning from a Web–based course," Internet and Higher Education, volume 5, pp. 267–281.

J.L. Howland and J.L. Moore, 2002. "Student perceptions as distance learners in Internet–based courses," Distance Education, volume 23, number 2, pp. 183–195.

L.R. Irons, D.J. Jung, and R.O. Keel, 2002. "Interactivity in distance learning: the digital divide and student satisfaction," Educational Technology and Society, volume 5, number 3, pp. 175–188.

G.B. Jackson, 1994. "A conceptual model for planning agricultural distance education courses and programs," Proceedings of the 21st Annual National Agricultural Education Research Meeting. Dallas, Texas.

D.H. Jonassen and B.L.H. Grabowski, 1993. Handbook of individual differences, learning, and instruction. Hillsdale, N.J.: L. Erlbaum Associates.

C.M. Karuppan, 2001. "Web–based teaching materials: A user’s profile," Internet Research, volume 11, number 2, pp. 138–148.

G. Kearsley, 2000. Online education: Learning and teaching in cyberspace. Belmont, Calif.: Wadsworth Thomson Learning.

D. Kember, 1989. "A longitudinal–process model of drop–out from distance education," Journal of Higher Education, volume 60, number 3, pp. 278–301.

D. MacKeracher, 1993. "Women as learners," In: T. Barer–Stein and J.A. Draper (editors). The craft of teaching adults. Enlarged edition. Toronto: Culture Concepts, pp. 71–86.

A. Moore, J.T. Masterson, D.M. Christophel, and K.A. Shea, 1996. "College teacher immediacy and student ratings of instruction," Communication Education, volume 45, pp. 29–39.

J.L. Moore, K.-S. Kim, and L. Esser, 2002. "Aiming for effective student learning in Web–based courses: Insights from student experiences," In: M.A. Fitzgerald, M. Orey, and R.M. Branch (editors). Educational media and technology yearbook, volume 27. Englewood, Colo.: Libraries Unlimited, pp. 204–208.

M.G. Moore, 1989. "Three types of interaction," American Journal Distance Education, volume 3, number 2, pp. 1–6.

M.G. Moore and G. Kearsley, 1996. Distance education: A systems view. Belmont, Calif.: Wadsworth.

M.G. Moore and M.M. Thompson, 1997. The effects of distance learning. Research monographs, number 15. University Park, Pa.: American Center for the Study of Distance Education, Pennsylvania State University.

North Carolina Department of Public Instruction, 1999. Teaching for learning. Raleigh: North Carolina Department of Public Instruction.

R. Pea, 1996. "Seeing what we build together: Distributed multimedia learning environments for transformative communication," In: T.D. Koschmann (editor). CSCL: Theory and practice of an emerging paradigm. Mahwah, N.J.: L. Erlbaum Associates, pp 171–186.

D.E. Rodriguez, 1995. "Interaction in the ITESM’s distance education system," In: M.A. Koble (editor). Invitational research conference in distance education: Towards excellence in distance education: a research agenda, May 18–21, 1995: discussion papers. University Park, Pa.: American Center for the Study of Distance Education, Pennsylvania State University, pp. 244–252.

A.P. Rovai, 2002. "Sense of community, perceived cognitive learning, and persistence in asynchronous learning networks," Internet and Higher Education, volume 5, pp. 319–332.

K. Sabry and L. Baldwin, 2003. "Web–based learning interaction and learning styles," British Journal of Educational Technology, volume 34, number 4, pp. 443–454.

L. Schrum and S. Hong, 2002. "Dimensions and strategies for online success: Voices from experienced educators," Journal of Asynchronous Learning Environments, volume 6, number 1, pp. 57–67.

C.–C. Shih and J.A. Gamon, 2002. "Relationships among learning strategies, patterns, styles, and achievement in Web–based courses," Journal of Agricultural Education, volume 43, number 4, pp. 1–11.

A. Sturgill, W. Martin, and G. Gay, 1999. "Surviving technology: A study of student use of computer–mediated communication to support technology education," International Journal of Educational Telecommunications, volume 5, number 3, pp. 239–259.

K. Swan, P. Shea, E. Fredericksen, A. Pickett, W. Pelez, and G. Maher, 2000. "Building knowledge building communities: Consistencies, contact and communication in the virtual classroom," Journal of Educational Computing Research, volume 23, number 4, pp. 359–383.

C.O. Swift, J.W. Wilson, and J.P. Wayland, 1997. "Interactive distance education in business: Is the new technology right for you?" Journal of Education for Business, volume 73, number 2, pp. 85–89.

S.R. Terrell, 2002. "The effect of learning style on doctoral course completion in a Web–based learning environment," Internet and Higher Education, volume 5, pp. 345–352.

S.R. Terrell and L. Dringus, 1999. "An investigation of the effect of learning style on student success in an online learning environment," Journal of Educational Technology Systems, volume 28, number 3, pp. 231–238.

R. Tesch, 1990. Qualitative research: Analysis types and software tools. New York: Falmer Press.

L.S. Vygotsky, 1978. Mind in society: The development of higher psychological processes. Cambridge, Mass.: Harvard University Press.

T. Waits and L. Lewis, 2003. "Distance education at degree–granting postsecondary institutions: 2001–2002," at, accessed 6 November 2005.

J.P. Wayland, C.O. Swift, and J.W. Wilson, 1994. "Student attitudes toward distance learning," In: B.T. Engelland and A.J. Bush (editors). Marketing, advances in theory and thought: Proceedings of the annual meeting of the Southern Marketing Association, New Orleans, Louisiana, November 2–5, 1994. Evansville, Ind.: Southern Marketing Association, pp. 296–299.

S.B. Wegner, K.C. Holloway, and E.M. Garton, 1999. "The effects of Internet-based instruction on student learning," Journal of Asynchronous Learning Networks, volume 3, number 2, pp. 98–106.

Editorial history

Paper received 28 March 2005; accepted 3 October 2005.

Contents Index

Copyright ©2005, First Monday

Copyright ©2005, Kyung–Sun Kim and Joi L. Moore.

Web–based learning: Factors affecting students’ satisfaction and learning experience by Kyung–Sun Kim and Joi L. Moore
First Monday, volume 10, number 11 (November 2005),