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The impact of learning styles on student achievement in a web-based versus an equivalent face-to-face course.

This study investigated the relationship between students' learning styles and their achievement in two different learning environments: online instruction and traditional instruction. The results indicated that a) students in the traditional learning group had higher, but not statistically significant higher, levels of achievement than students in the online learning group, b) a student's learning style had no statistically significant effect on their course grades in any of the two instructional methods, and c) there was no significant interaction between the learning style and instructional method.


Since the advent of the Web, the faculty of mathematics and computer science at a large (> 20,000) Greek technological university is consciously reconceptualizing the design of its courses, using knowledge gained in successive phases of integration to expand access, to provide flexible learning environments, and to meet the ever expanding needs of graduate students. Through the years, Introduction to Programming using Java--COMP 120, a required core course for all computer science majors, has evolved from a traditional lecture based course to a fully interactive online course. During the course, students are introduced to object oriented programming concepts and techniques, through short classroom lectures and many laboratory tasks, and are engaged in extended learning activities and collaborative work using the online environment to test their knowledge and complete their projects.

The course web site on Moodle LMS supplements face-to-face classroom instruction by providing students with access to all lecture material, interactive textbook modules, laboratory modules, homework assignments and solutions, self-diagnostic quizzes and old tests, study guides, discussion boards, journal articles, and a wealth of other resources. Using the online environment students can choose to view a recorded lecture or interact with a narrated Flash-based presentation, or read a step-by-step description of every activity that took place in class and run an interactive simulation. Self-check exercises, focussing on specific language points, are designed as true/false, multiple choice or fill-in-the-blank, in order to engage students, either in the classroom or from any location with Internet access, in active learning by practicing the concepts they have learned in class and in the tutorials.

Since the course was launched in 1998, student attendance of COMP120 class lectures was optional while participation in the laboratory sessions was mandatory. This requirement was a serious barrier in the way of success for working students, very often resulting in failure or withdrawal from the course. In order to provide the levels of flexibility and convenience that appropriately address the diverse educational needs of the large numbers of students working part-time or full-time, a major redesign was undertaken in 2006 to update existing resources, create new online laboratory modules and test several technologies capable to imitate the interactive nature of COMP120 lab sessions.

After several successful experiments on the implementation and effectiveness of virtual pair programming and online synchronous instruction, the department reconsidered the closed form of laboratory sessions and decided to offer a fully online version of COMP 120. During the 2008 Fall semester, the 161 first year students who enrolled in COMP 120, could receive either online or traditional/face-to-face instruction. Students could work from home or from an on-campus computing lab, using screen sharing applications (such as Microsoft NetMeeting) or collaborative editors (such as Eclipse, with various plugins) for virtual pair programming, and a web-conference environment (Centra Live) that enabled them to participate in virtual, instructor-led, laboratory sessions.

Despite literature on the effectiveness of online instruction, little is known about the influence of learning styles in online learning. Little research also exists on how computer science students, with varying learning styles, learn in different learning environments. In this light, the purpose of this study was to compare students' achievement (on course grades) in two different learning environments--online instruction and traditional on campus instruction--based on their individual learning styles.

Online instruction and Learning styles

Online instruction provides a unique opportunity for learning materials, tasks and activities to fit individual learning styles and preferences, by allowing students to take control over the learning process, engage in social interaction and dialogue, develop multiple modes of representation and become more self aware (Oliver and McLoughlin, 1999). However, the flexibility allowed by online instruction has as side-effect that many students are pursuing online learning opportunities for the sake of convenience, without any real consideration of the appropriateness of this delivery mode for their individual learning styles. "Those students who may not have developed appropriate strategies for self-regulation may find that online education courses do not meet their needs and those students may subsequently drop the course; as a consequence, online courses have been associated with much higher rates of attrition than traditional face-to-face courses" (Summers, Waigandt and Whittaker, 2005).

Learning styles influences the way in which students learn and are characteristic cognitive, affective, and psychological behaviors that serve as relatively stable indicators of how learners perceive, interact with, and respond to the learning environment (Keefe, 1979). There are several models of learning styles that are currently being used to assess how students learn. "A learning style model classifies students according to where they fit on a number of scales pertaining to the ways they receive and process information" (Felder & Silverman, 1988). In this study, the Kolb's Learning Style Inventory (LSI) was used to measure the learning styles of students in the online and traditional environment.

According to Kolb (1984) individuals learn in four stages or modes: Concrete Experience (CE, e.g. laboratories, field work, observations), Reflective Observation (RO, e.g. journals, logs, brainstorming), Abstract Conceptualization (AC, e.g. papers, lectures, analogies), and Active Experimentation (AE, e.g. simulations, case study, homework). However, the process of constructing knowledge in different learning situations involves a creative combination among the four learning modes that is responsive to contextual demands. The combination of learning modes are used to establish four quadrants, reflecting four learning styles: Accommodators (favored CE and AE, i.e. feeling and doing), Divergers (favored CE and RO, i.e. feeling and watching), Assimilators (favored AC and RO, i.e thinking and watching), and Convergers (favored AC and AE, i.e. thinking and doing). The Kolb's LSI test, a statistically reliable and valid 12-item questionnaire, in which respondents attempted to describe their learning style, was given to the students one week after the start of the course.

"The literature on the connections of technology to teaching and learning styles is not well developed" (Grasha and Yangarber-Hicks, 2000). "Further research is necessary to understand how learning styles contribute to the experience of online education" (Valenta, Therriault, Dieter, and Mrtek, 2001).

Terrell and Dringus (2000) and Lippert, Radhakrishnam, Plank and Mitchell (2001) measured learning styles of online learning students with a high level of computer literacy, based on the Kolbs's LSI. Both studies showed that learning style had no effect on success in online learning but it determines preference for this delivery format. Students who fell into the Converger and Assimilator learning styles, felt more comfortable taking distance learning courses. Federico (2000) found that students with Assimilating and Accommodating learning styles demonstrated significantly more positive attitudes toward varied aspects of computer-based instruction than did students with Converging and Diverging learning styles. Wang, Wang, Wang, and Huang (2006) found that online students with a Divergent learning style performed the best, followed by other styles including Assimilator, Accommodator, and Converger.

Buerk, Malmstrom, and Peppers (2003) found significant differences in learning styles between online students (tended to have the Converger learning style) and their traditional counterparts (were more likely to have the Assimilator learning style), while Aragon, Johnson, and Shaik (2002), concluded that there are no significant differences in learning styles and learning performance between online and traditional graduate students. McNeal and Dwyer (1999), investigated the relationship between students' learning styles and methods of instruction, and concluded that no significant difference existed in learning between instruction that was designed in agreement with students' learning styles and instruction designed in disagreement with students' learning styles.

Research Questions

Given the sparseness and contradictory nature of evidence on the interaction between learning style and instructional method, the research questions for this study were 1) Is there a difference between the course grades of students based upon the instructional method?

2) Is there a difference between the course grades of students based upon their learning style?

3) Is there an interaction between learning style and instructional method based upon the course grades?


Participants in this study were 161 first year students, 77 of which were enrolled in the online section of the course, while the rest 84 were enrolled in the face-to-face section. Both sections were taught by the same instructor and all students submitted all assigned exams, homeworks and projects. This study was conducted utilizing a post-test only control group design which, according to Campbell and Stanley (1963), is a true experimental design that allows for the random assignment of individuals to treatments and treatments to groups. Two independent variables and one dependent variable were studied. The independent variables were the instructional method (with two levels: online and traditional instruction) and the learning style (with four levels: Accommodator, Divergent, Assimilator and Converger learner), while the dependent variable was the students' course grade (derived from a midterm exam, eight homeworks, two group projects and the final examination).


Descriptive statistics of the Kolb's LSI and students' course grades (grade could range between 0 and 100), for both delivery formats, are shown in Table 1. From those data is apparent that the predominant learning styles in the online group were the Assimilator (31%) and the Accommodator (27%), while in the traditional group most students fell into the Assimilator (31%) and Diverger (28%) learning styles. This result is in agreement with the findings of Federico (2000), who found that students with Assimilator and Accommodator learning style feel more comfortable taking online instruction, and Buerk et al (2003), who found that traditional students tend to be Assimilators. Students with the Divergent learning style performed the best in the online environment, in agreement with the findings of Wang et al (2006).

Based upon the results of students' preferred learning style and their course achievement, a 2 (method of instruction) x 4 (learning style) full factorial ANOVA (Table 2) was conducted to answer the three research questions of this study. Homogeneity of variance was assumed, with a Levine's statistic equal to 0.42.

The first research question sought to determine if there was a difference in student achievement, due to the instructional delivery method. Results revealed that students in the traditional group had higher (M = 68.79, SD = 20.90), but not significant higher performance than the online group (M = 67.44, SD = 18.87), based on their course grades, F(1, 153) = 0.319, p > 0.05. This finding is consistent with the results found by Terrell and Dringus (2000) and Lippert et al (2001), who suggest that there is no difference in students' performance between face-to-face and online instruction.

The second research question investigated whether students' learning style influenced their academic performance (course grade) differentially contingent upon whether they took the course in either an on face-to-face versus a fully online delivery format. The 2 x 4 ANOVA revealed no significant difference in students' course grades between the online and face-to-face group, F(3,153) = 0.797, p > 0.05. This result is in accordance with the findings of the research conducted by McNeal and Dwyer (1999).

The third research question aimed to find out if there was an interaction between instructional method and learning style, based upon the students' course grades. The 2 x 4 factorial analysis of variance in this case indicated no statistically significant interaction between the learning style and the method of instruction, based upon course grades, F(3,153) = 0.205, p > 0.05.

This result is in agreement with the findings of the research conducted by Aragon et al (2002), which suggest that there is no interaction between learning style and instructional method" students can be equally successful in face-to-face and online environments, no matter what their learning style.


Using a well-constructed course that offered an active learning environment, with highly interactive components such as exercises, lab activities, simulations and video, we compared two groups of students, which received either face-to-face or fully online instruction, to determine the impact of learning styles on students' outcomes. The results of this research showed there was no significant difference between these two learning environments in terms of the effects of the learning styles. That is, learners can be just as successful in the online environment as they can in the face-to-face environment, regardless of their learning style preferences.

One key feature of this research is the fact that students selected to participate in the online or the traditional group, based on their own needs, preferences and strengths (e.g. job schedules, family obligations, learning and studying preferences, familiarity with the technologies involved). The fact that so different learning styles such as Assimilator and Accommodator, were the majority of students in the online group, and the fact that Divergers performed the best, underline the need for the online component to include a wide variety of representations, activities and supports, and for more research, to determine whether and how student's learning preferences as activities and requirements in the online environment change.

Although our findings add weight to the findings of those researchers who showed that learning styles were non-significant predictors of learning in online instruction, we should always remember that learning is a continuous process grounded in experience. Being aware of our students' learning styles, we can design online modules and activities, or redesign sequences of events and interventions, to accommodate effectively their different academic strengths, weaknesses, skills, and interests.


Aragon, S. R., Johnson, S. D., & Shaik, N. (2002). The influence of learning style preferences on student success in online versus face-to-face environments. American Journal of Distance Education. 16, 227-244.

Buerk JP, Malmstrom T, Peppers E. (2003). Learning environments and learning styles: Non-traditional student enrollment and success in an Internet-based versus a lecture-based computer science course. Learning Environments Research, 6(2), 137-155.

Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Boston: Houghton Mifflin.

Federico, P. (2000). Learning styles and student attitudes toward various aspects of network-based instruction. Computers in Human Behavior, 16(4), 359-79.

Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674-681.

Grasha, A., & Yangarber-Hicks, N. (2000). Integrating teaching styles and learning styles with instructional technology. College Teaching, 48(1), 2-11,

Keefe, J. W. (1979). Learning style: An overview. In National association of secondary school principals (Ed.), Student learning styles: Diagnosing and prescribing programs (pp. 1-17). Reston, Virginia: National Association of Secondary School Principals.

Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Upper Saddle River, New Jersey: Prentice Hall.

Lippert, R., Radhakrishnam R., Plank, O., and Mitchell, C. (2001). Using different evaluation tools to assess a regional internet in service training. International Journal of Instructional Media, 28(3), 237-248.

McNeal, H., & Dwyer, D. (1999). Effect of learning style on consistent and inconsistently designed instruction. International Journal of Instructional Media, 26(3), 337-345.

Oliver, R. & McLoughlin, C. (1999). Curriculum and learning-resources issues arising from the use of web-based course support systems. International Journal of Educational Telecommunications, 5(4), 419-436.

Summers, J. J., Waigandt, A., and Whittaker, T. A. (2005). A comparison of student achievement and satisfaction in an online versus a traditional face-to-face statistics class. Innovative Higher Education, 29(3), 233-250.

Terrell, R., & Dringus, L. (2000). An investigation of the effect of learning style on student success in an online learning environment. Journal of Educational Technology Systems, 28(3), 231-238.

Valenta, A., Therriault, D., Dieter, M., & Mrtek, R. (2001). Identifying student attitudes and learning styles in distance education. Journal of Asynchronous Learning Networks. September 2001; 5(2).

Wang, K.H., Wang, T.H., Wang, W. L. and Huang, S. C. (2006). Learning styles and formative assessment strategy: enhancing student achievement in Web-based learning. Journal of Computer Assisted Learning, 22(3), 207-217.


Technological Educational Institute of Piraeus, Greece

Table 1
Descriptive Statistics
Dependent Variable:Grade

                Online               Traditional

                N    Mean     SD     N    Mean     SD

Converger       15   64.53   19.72   13   74.08   20.27
Diverger        17   70.53   17.84   24   68.29   20.46
Assimilator     24   64.46   18.48   26   68.42   22.81
Accommodator    21   70.43   19.98   21   66.52   20.24
Total           77   67.44   18.87   84   68.79   20.90

Table 2
Tests of Between-Subjects Effects
Dependent Variable:Grade

Source                   SS             df    Mean Square   F

Corrected Model          1243.179 (a)   7     177.597       .437
Intercept                714034.576     1     714034.576    1758.181
Instructional method     129.350        1     129.350       .319
Learning style           970.859        3     323.620       .797
Instructional method *
Learning style           250.017        3     83.339        .205
Error                    62136.535      153   406.121
Total                    810975.000     161
Corrected Total          63379.714      160

Source                   Sig.

Corrected Model          .877
Intercept                .000
Instructional method     .573
Learning style           .497
Instructional method *
Learning style           .893
Corrected Total

(a.) R Squared = .063 (Adjusted R Squared = .020)
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Author:Zacharis, Nick Z.
Publication:College Student Journal
Article Type:Report
Geographic Code:1USA
Date:Sep 1, 2010
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