Effect of Student Attitude to Course Format on Learning Performance: An Empirical Study in Web vs. Lecture Instruction.
Today the widespread availability and versatility of the Internet offer an exceptional opportunity to provide education anywhere, anytime at overall costs far below those of early distance education and traditional instruction modes. While correspondence studies, radio, television and video-conferencing were alternative formats to distance education courses during the last three decades, the Internet is taking the center stage today as the preferred format (Sopova, 1996; Porter, 1997). It is becoming increasingly important to study how the Web courses affect the overall learning outcome, as compared to traditional lecture methods. Administrators who propose that students learn equally in any format unintentionally employ the agricultural-botany paradigm that all students respond to a course as consistently as plants react to fertilizers (Willis, 1994). In real life, even when course format and content are the same, individual students learn differently. One reason for this outcome can be attributed to differences in background variables such as attitude to the course format, learning strategies, ethnicity and English as Second Language (Billings, 1989; Hoeksema, 1995; Ede et al., 1998; Rong and Preissle, 1998).
The objective of this paper is to examine how students differ in their preferences towards course formats. The two formats used in this study are Web and lecture. Further, the study also investigates how student attitudes towards these course formats affect their learning performance. The relationship between attitude to course format and the student's learning strategy is also studied. Finally, differences in attitudes to Web among various ethnic groups and ESL students are examined. The results have implications to both educators and administrators. It can help educators in designing courses that fit the individual's background, attitudes and learning methods they employ. Administrators can allocate resources and schedule courses in formats that maximize learning outcomes.
During the evolution of the distance education technologies, several areas have been researched. The major ones related to this study are, i) attitude, ii) learning strategies, and iii) ethnicity.
In a study on students' attitude to distance learning, Wilson (1990) found that students were initially skeptical about the effectiveness of television as delivery format. However, in the end they were willing to try out additional courses in the distance learning method. An unintended positive outcome of the satellite course was that the students felt they had gained a greater sense of personal responsibility and higher self-confidence as a result of the distance education experience.
Gee (1990) explored the impact of student's attitude to instructional settings on academic achievement. Learning outcome was found to be positively affected by student preferences to course formats. In a study involving graduate students, Smith and McNelis (1993) found that students with negative feelings towards distance education received lower grades. These students found the technology to be distracting in the beginning of the course. Even though by the end of the course, most of these students were more receptive to the concept of distance education as part of the future of instructional delivery, they still favored the conventional lecture mode.
Powers and Mitchell (1997) studied student perceptions and performance in a Web-based course. They found that many students perceived the Internet format to be more time consuming and continued to prefer the lecture format. Students felt there was lack of the classroom-community spirit. To compensate for this, some of the students engaged in email and listservs more often, used the Internet relay chat facilities to build a classroom community in the virtual space. The effect of the student attitudes on the learning performance was inconclusive.
Learning strategies refer to the activities by which learning is achieved. For example, reading aloud, copying notes, consulting peers, asking the instructor for clarification are all learning strategies. The use of learning strategies allows students to actively process information, thereby influencing their mastery of material and subsequent academic achievement (Pintrich, et al., 1993; Vermunt, 1992).
Hoeksema (1995) proposed two types of learning strategies: deep and surface. A deep learning strategy is directed at understanding the meaning of a task and to satisfy curiosity. A student using the deep strategy will put in longer study hours, make detailed notes from the text and class web site, do extra exercises and assignments with corresponding good results in performance (Vermunt, 1998). It may be considered the highest form of learning.
A surface learning strategy, on the other hand, is directed to memorizing facts, disjointed pieces of data, examples and illustrations (Hoeksema, 1995). A student using the surface strategy will have a reproducing orientation, memorizing pieces of information and more interested in getting good grades without having to fully master the material. In practice, many students using the surface strategy have been found to be successful because deep level learning are just not required to satisfy many examination requirements (Busato, et al., 1998; Vermunt, 1998).
Ethnicity and ESL
Ethnicity and English language proficiency also play a part in shaping students' attitude to Web courses and their performance (Sankaran and Bui, 1999). This is particularly true today in the U.S. where the class composition is taking on a multicultural facet with many students having limited levels of English proficiency. During the 1980's, Maryland, Florida, Virginia, Georgia, Texas, New Mexico, Nevada, California, and Alaska had all more than a 50% increase in their foreign population (Rong and Preissle, 1998). The percentage of students with Limited English Proficiency in the 5 to 17 years olds immigrant children stands nationally at 37.8% (US Bureau of Census, 1993). Studies have shown that recent immigrants and foreign students experience language barriers during their early years in the U.S. (Fischer, 1990; Stevens, 1994). Chizhik (1998) concluded that ESL students prefer face to face interaction to seek contextual and non-verbal cues.
A distinguishing characteristic of ESL students is the heterogeneity of their ethnicity and culture. They may bring along a variety of anxieties at having to prove themselves in a mainstream environment. They differ in academic self-concept, aspirations to higher education, family and peer influence (Kim et al., 1998). In a study by Hawkins and Paris (1997), it was found that minority and ESL students enter the university with fewer info-technology skills. Their PC ownership also lagged behind the national average (Muzzio, 1998).
In summary, understanding of student attitudes to course delivery formats and how they synergize with learning strategies to enhance overall learning outcome is important in distance education. Knowledge about the influence of ethnicity and English language proficiency on student attitudes and performance is also useful in today's multicultural classrooms. Currently, many universities employ a one-format fits all approach. This is not effective when students differ in their individual background characteristics.
The following hypotheses were tested:
H1: Students will have no preferences to one course delivery format over another (web/lecture).
H2: Student attitude to course format will have no influence on learning performance.
H3: There is no relationship between student attitudes towards course format and their learning strategies.
H4: There will be no difference in student attitudes towards the course format due to ethnicity.
H5: ESL students as will have a more positive attitude to lecture format as compared to non-ESL students.
The subjects for this study were students enrolled in an accelerated 4-week undergraduate business computer course. At the beginning of the course, the students chose either Web or lecture format. The instructor covered the same course content in both formats. The web format contained Powerpoint slides with condensed narration and a bulletin board called Hypernews. The instructor held office hours using the Internet Relay Chat and exchanged assignments and feedback via e-mail. The students were given a pre-test to measure their knowledge of course content. As a measure of performance, all students took the same final at the end of the course in a lecture hall. The maximum score attainable on the final was 75 points.
Attitude to Web and Learning Strategy
Survey A survey instrument was developed to quantify the attitude of each student towards Web-based instruction vs. traditional lecture. The instrument was developed over four iterations each time retaining only items that met the content validity requirement. Each item was a cafeteria style statement that described a preference to web-based course format that the student could find him/herself in agreement or disagreement. An interval scale of 1 to 5 was used with 1 representing strong, disagreement and 5 representing strong agreement with 3 being neutral.
The Attitude to Web subscale contained 13 items describing student preference to Web format, propensity to work independently as against in a classroom environment, access and willingness to use technology, current inventory of Internet skills, strength of belief in the effectiveness of web courses and their willingness to enroll in them. Some sample statements were: "Web-based education is a more efficient and time saving way of getting a degree","More courses at the college should be offered as a web course". To detect possible agreement bias, some statements were reverse-scored. The internal consistency was tested by computing Cronbach alpha coefficient which came out to be 0.83. The mean score of all the items was computed for each student and assigned as an Attitude to Web Score (AWS). The statements and rating scale were so designed that a high AWS represented a positive attitude to Web format and low AWS a positive attitude to lecture.
The Learning Strategy subscale contained 14 items to determine whether the student employed deep or surface method in learning. The Cronbach alpha coefficient was 0.71. Some sample statements were: "I practiced many more exercises in the book in addition to the assigned homework", "I am more likely to cram for exams at the last minute". As in the case of AWS, the mean score of all the items was computed for each student and assigned as a Learning Strategies Score (LSS).
H1: Effect of attitude on choice of course format
There were 116 students in the sample. Forty-six (39.7%) chose to take the course in the web format and 70 (60.3%) chose the lecture format. To test H1, the Attitude to Web Scores (AWS) were computed for the participants based on their responses to the survey questionnaire. The t-test was used to verify if the AWS in the web group and lecture group arose from independent samples. The results are shown in Table 1. It can be seen that the AWS was significantly higher for the Web group. Thus, the hypothesis that students will have no preferences to one course delivery format over another was rejected. If a choice were to be given, attitudes do play a role in the students exercising their option.
Table 1 Comparison of AWS for Web and Lecture Settings
N Mean t df p Attitude to Web 2.953 114 .004(*) Web Group 46 3.2 Lecture Group 70 2.8
(*) Significant at .01 level
H2: Effect of attitude on learning performance
As discussed in the methodology section, learning performance was measured using the final test scores. The mean test score for the web group was 43.4 and that of lecture group was 44.6. As can be seen from Table 2, the results of the t-test show that the difference was not significant. Thus, H2 which stated that there will be no difference in learning performance in spite of variations in attitudes to course format was supported.
Table 2 Comparison of Final Test Scores and Increment Test Scores in Web and Lecture Settings
N Mean t df p Final Test scores .712 114 .478 Web Group 46 43.4 Lecture Group 70 44.6 Incremental Group .149 114 .882 Web Group 46 12.8 Lecture Group 70 13.4
The amount of learning achieved by students in the course was computed by subtracting the pre-test score from the final score. Table 2 also shows that there was no significant difference in the incremental scores achieved by students in the Web or lecture format. This implied that both groups learned equally from the statistical point of view.
It was seen from the discussion of H1 that students had a tendency to match their attitude to the format of the course. Further, H2 suggested that there were no differences in the incremental learning in the Web or lecture format. However, one would want to know how the performance was affected if there was a mismatch between attitude and format. To gain additional insight into this area, the AWS range of 1 to 5 was divided into three equal parts, less than 2.3, 2.3 to 3.7, and greater than 3.7. Students with AWS of greater than 3.7 were considered Web-oriented, between 2.3 and 3.7 format-neutral, and those below 2.3 lecture-oriented. Their mean incremental test scores were then computed for each of these three categories. The results are shown in Figure 1.
[Figure 1 ILLUSTRATION OMITTED]
One can see from the above figure that the most gains were made when the attitudes of the students towards Web and lecture were matched to the course formats they chose to be in. Those who were Web-oriented and attended the Web format increased their scores by 15.3 points. Those who were lecture-oriented and chose the lecture format improved their performance by 18.7 points. Of the format-neutral, those who attended the web format gained 11.3 and the lecture format gained 12.9. When the attitudes and the formats were mismatched, students made the lowest gain. Thus, lecture-oriented students who took the Web format improved by only 8 points; the Web-oriented students who attended the lecture format improved by only 9.2 points. This brings out a point that any one course delivery format may not be the optimum method for all students and hence it would be beneficial to offer a course in multiple formats if possible.
H3: Relationship between attitude to Web and learning strategies
H3 was tested using the correlation analysis. Table 3 shows the correlation coefficients (Pearson r) between AWS and LSS in the Web and lecture groups.
Table 3 Relationship between AWS and LSS
LSS p AWS Web Group -.286 0.053 Lecture Group -.064 0.310
It can be seen that the Learning Strategy Scores were negatively related to attitude toward Web. It was significant at 0.10 level for the Web group. This implied that Web-oriented students employed surface learning strategy and that lecture-oriented students employed the deep learning strategy. This is what one would expect because the Web format, with no direct verbal interaction, lends itself better to present course materials in a more sequential manner. Important points in the material can be easier picked from the concise and organized Web materials. The Web format is very conducive to one using surface strategy that emphasizes memorization and reproducing ability.
H4 : Attitude to Web and ethnicity
ANOVA was used in testing H4. There were no statistical differences in AWS among the different ethnic groups at 0.05 level (F=2.234; p=0.071). Hence, H4 was supported. When the attitude scores were examined according to Web and lecture formats, all ethnic groups who attended the Web format had a higher AWS. The exception was Asians who attended the lectures despite their high AWS (Table 4). This could be due to family as well as cultural emphasis placed on education and attending classes in Eastern cultures.
Table 4 Mean AWS by Ethnicity
N AWS Asian 25 3.2 White 39 3.1 Hispanic 35 2.8 Middle Eastern 10 2.8 African American 7 2.4
H5 : Attitude to Web and ESL students
The mean AWS for all ESL students in the study was 2.9 and that of non-ESL students was 3.1. The difference was not statistically significant (t=0.890; p=0.376). H5 postulated that ESL students would prefer to attend lecture format and would hence tend to have a significantly low AWS compared to non-ESL students. Since this was not the case, H5 was rejected.
It can be seen from Table 5 that of the 65 ESL students, 27 (41.5%) chose the Web format and 38 (58.5%) chose the lecture. Those who took the Web format had a mean test score of 41.5 as against 45 for the lecture. A more compelling observation in this study is that ESL students in the Web format had an average of 4 years of residency in the U.S. as compared to 7 years in the lecture format. One would have expected that students who are recent immigrants would attend the lecture format in order to have more opportunities to interact with the instructor. An explanation could be that these students had better reading skills and were hesitant to be in the interactive lecture environment due to language and cultural barriers. They might have felt more comfortable to study by themselves in the Web format. This finding is contrary to the conclusion in Chizhik (1998) that ESL students prefer face to face interaction to seek contextual and non-verbal cues. This is an area for future research. Conclusion This study showed that when a course is offered in multiple formats, students tend to enroll in the one that is compatible with their attitude and learning strategies. Where students fail to make such match, learning outcomes are adversely affected. Students with Eastern cultural background placed a high emphasis on attending lectures. ESL students in their initial years of residency in the U.S. appeared to shy away from classroom interaction. The implication of this study for educators and administrators is that one-format fits all is not an effective course design. Every student learns differently. This study shows that Web is not the panacea for instruction; nor is the lecture or any other format. Thus, it is important to develop and match innovative course designs that support a student's individual way of learning. This can assure that our educational system brings out the best in each student.
Table 5 ESL Students Data Summary
Course Mean Mean Years US Format N AWS Test Score Resident Web 27 3.2 41.5 4 Lecture 38 2.7 45.0 7
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Siva R. Sankaran, College of Business Administration, California State University. Dalila Sankaran, Department of Nursing Science, Moorpark College. Tung X. Bui, College of Business Administration, University of Hawaii.
Correspondence concerning this article should be addressed to Dr. Siva R. Sankaran, College of Business Administration, California State University, 18111 Nordhoff Street Northridge, CA 91330-8372.
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|Author:||Bui, Tung X.|
|Publication:||Journal of Instructional Psychology|
|Article Type:||Statistical Data Included|
|Date:||Mar 1, 2000|
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