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Factors for success in online and face-to-face instruction. (Online Instruction).

Abstract

This study compared academic achievement between undergraduate students in an introductory nutrition course online and students taking the same course in a large-class lecture format. Using a quasi-experimental research design, two instructional treatments (online, [N=35] and large-class lecture instruction [N=434]) were applied to two groups of students and their achievement was measured by a pretest and posttest of nutrition knowledge as well as final course grade. No significant differences in posttest scores (nutrition knowledge) were found. When data were partitioned for age, however, older students taking the online course had significantly higher final course grades than both their younger online and all large-class lecture counterparts. Online course participation was found to be related to student's pretest knowledge, posttest scores, and age.

Introduction

Many universities are striving to make the educational process more efficient (Guskin, 1994). The cost of educating students in an undergraduate program is rising faster than inflation. Fiscal resources of the academy are often stretched to their limit. Traditional solutions to effect efficiency have yielded teacher-centered instruction and increased class size (McKeachie, 1980). Until recently, many of these solutions have focused on faculty productivity.

Recently, researchers have begun to evaluate online teaching methods used in higher education that may make the educational process more effective for learners. Current research findings suggest that the benefits of online teaching methods include improved learning environments over traditional face-to-face teaching methods (Harasim, Hiltz, Teles, & Turoff, 1995). Using technology to distribute courses online appears to be an efficient approach that has the potential to improve learner productivity and provide a student-centered instructional platform.

Emerging teaching methods found in postsecondary education include distributed learning strategies, such as computer-mediated instruction through online course offerings (Bothum, 1998; Hiltz, 1990; Phipps & Merisotis, 1999). Universities are forging ahead with investments to develop technology platforms that will allow a growing number of faculty members to offer courses to students at a distance (Dede, 1997). To date, there is relative scarcity of original research to substantiate the success of learning outcomes in computer-mediated instructional environments for all students (Phipps & Merisotis, 1999). Moreover, critics of the more traditional method of teaching by lectures have argued that passive learning in large lecture halls is not as effective as an active learning environment (McKeachie, 1980).

Online Undergraduate Education

Generally, researchers have measured the effectiveness of online education by learner outcomes (scores or grades), participant attitudes toward the experience, and student satisfaction with the course delivery. Research methodologies used to conduct computer-mediated educational research have included descriptive, correlational, case study, and experimental research methods (Blocher, 1997; Clarke, 1999; Navarro & Shoemaker; 1999; Schulman & Simms, 1998; Schutte, 1997).

Research into online courses offered to students in higher education is gaining wider interest. Yet much of the published research has focused on students 25 years of age or older. Results from some of the research have indicated that older students do well in courses offered at a distance (Marsh & Wells, 1996; Schutte, 1997). For example, when graduate student achievement has been measured, students who received parallel online instruction significantly outperformed those receiving face-to-face lecture instruction (Dutton, Dutton, & Perry, 1999; Navarro & Shoemaker, 1999). Similarly, in classes using the case study method, groups of students who met to discuss cases using computer groupware outperformed those who met face to face (Schulman & Simms, 1998).

Furthermore, positive benefits of online instruction for older students have been supported regardless of institution. Gubernick and Ebeling (1997) reported that among graduate students in three competing public universities in Arizona, those taking courses online outscored students in face-to-face courses by 5% to 10% on standardized achievement tests. In agreement, a meta-analysis comparison of the results of six studies conducted at different business schools showed that online students consistently outperformed students receiving face-to-face instruction (Morrissey, 1998).

Conversely, Cheng, Lehman, and Armstrong (1991) found no significant differences in student achievement and attitudes toward subject matter of college students enrolled in traditional and computer conferencing classrooms. In their study students' ages were not identified. Likewise, Clarke (1999) found no differences in test scores of traditional-aged undergraduate students regardless of method of instruction.

Many factors mediate the success of online courses, e.g., age, gender, ethnicity, motivation, and cognitive strategies. Blocher (1997) examined the influence of such factors on students' interaction and engagement in computer-mediated instruction by measuring class communication through e-mail, listserv distribution, and bulletin boards in conjunction with traditional instruction. He found significant differences between how men and women communicate in an online versus a face-to-face instructional environment. Women communicating in a virtual environment tended to be less engaged than men in similar situations, and less than both men and women in face-to-face environments (Blocher, 1997). This finding has important implications, suggesting that men and women may differ in student learning.

Although there was no consensus on the relative advantages of online and face-to-face instruction, the literature surveyed generally indicated that student achievement in distance education courses is just as good as that of more traditional formats, regardless of student age (Russell, 1999). Moreover, students of all ages reported appreciating the flexibility and convenience of taking courses that are not time- and place-dependent (Schulman & Simms, 1998). The one common finding in the identified research was that younger undergraduate students generally prefer traditional face-to-face instruction to computer-mediated instruction (Clarke, 1999; Dutton et al., 1999; Schlosser & Anderson, 1994). To date, little has been reported comparing the effect of student age on achievement in online instruction and traditional face-to-face instruction.

The purposes of this study were to compare course achievement between students receiving computer-mediated instruction and large-class lecture instruction, and to determine if some students were better served by computer-mediated instruction. Specifically, the researchers wanted to determine which student factors led to student success in computer-mediated instruction versus traditional large-class lecture sections.

Methods

Design of the Study

A quasi-experimental design was used. Students enrolled in an undergraduate nutrition science class were divided between a traditional large-class lecture section and an online computer-mediated section. This study was guided by the following research questions:

Q1: Do students taking an introductory nutrition education course in an online environment have significantly higher student achievement scores than those students who receive the instruction in a large-class lecture environment?

Q2: Is student achievement related to their level of engagement with the computer-mediated instruction?

Q3: Are there differences in student achievement in older students when compared to their younger counterparts in either online or face-to-face instruction?

Setting and Sample

This study was conducted in the spring of 1999 at a large public university in New England. Students participating in this research were enrolled in "Nutrition for a Healthy Lifestyle" (Nutrition 130), which is a general education elective for non-nutrition majors that fulfilled the university's general education requirement for a science course elective. The course is offered every semester, with an average enrollment of 450 students.

The sample was drawn from students registered in the introductory nutrition class. At the first class meeting, one of the researchers made a presentation to invite students to take the course online. Thirty-eight students volunteered to take the online course. At the end of the first week of class, the researchers, the online course instructor, and online course administrators conducted two in-person training sessions for the online students to train them to use the conferencing and quiz software and to acquaint them with each other.

Of the 469 undergraduate students who participated in the research project, 307 (65.5%) participants were female and 161 (34.5%) were males. Three hundred seventy-nine of the students (81%) were white, 23 (5%) were African American, 37 (8%) were of Asian decent, and 28 (6%) were Hispanic. Student age ranged from 18 to 48 years with a mean age of 23.6. Students were labeled "non-traditional students if they were older than 24 years, a common definition in the literature (Harris & Brooks, 1998). Thirty-three students (7%) were non-traditional-aged students and 436 (93%) were traditional-aged students.

Of 494 students enrolled in the lecture class, 434 (87.3%) that completed the pretest and posttest were included in the sample. In the online section, three students out of 38 failed to complete the course work and were given an incomplete grade. Thirty-five students (92.1%) who completed the online course and took the pretest and posttest were included in the sample.

Procedure

Two instructional treatments were used in this research, a large lecture format with small group discussions and an online course with a computer conferencing software package and e-mail to facilitate instructor-student and student-student infraction and discussion. In each course format (lecture and online), students used the same textbook, covering the same topics. The researchers had decided to use two different instructors for each format to reduce the effect of instructor bias. In both formats, students were administered a pretest, the instructional treatment, and a posttest. Both instructional treatments (lecture and online) required students to read the text, complete assignments, and take three in-person exams. The research team and the course instructors collaboratively created the three exams to include the same information and content in both treatments.

To measure student achievement, the research team developed a 28-item multiple-choice instrument consisting of nutrition knowledge that both instructors expected students to know at the end of their course. The instrument was administered to students at the start of the semester (pretest) and was included in a cumulative exam taken by the students at the end of the course (posttest). Student achievement was measured by the difference between their pretest scores and posttest scores.

Students taking the course online were required to post the results of course activities and participate in the course through computer conferencing software. The number of student postings was used to measured online student engagement.

Students in the traditional lecture course attended class two times a week for 75 minutes each. They were also required to attend a small discussion group, which met for 50 minutes, seven times during the semester. The course grade was based on two multiple-choice short-answer exams during the semester and one cumulative multiple-choice final exam at the end of the semester. Additional points were awarded for attendance at the discussion groups, completion of three individual nutrition assignments, and completion of one group assignment.

In the online format, students were given a series of activities to complete weekly and were required to post their results and respond to classmates using the WebBoard computer conferencing software by O'Reilly & Associates (1999). The features of this program allowed students to interact through threaded discussions and small group collaboration. Additionally, the course website provided students access to online nutrition resources through a web page library.

Further online course requirements included online practice quizzes using the University of Massachusetts "OWL" system (which allows for interactive questions and feedback), two face-to-face multiple-choice short-answer exams, completion of a group project conducted via the WebBoard, and a cumulative face-to-face final exam.

Independent t-test, ANOVA and ANCOVA were employed to compare differences among treatment groups. Significant differences were further analyzed through the Sheffe test.

Results

Student Achievement

Table 1 compares the means and standard deviations (SD) of achievement scores for students who participated in the course via a lecture-small group format versus students taking the course online. Pretest nutrition knowledge scores were significantly higher in online students than in their traditional class peers. Change in nutrition knowledge between pretest and posttest was statistically significant in both treatment groups, with nutrition knowledge in the large-class lecture group improving by 70.5% at the posttest over the pretest scores. Comparably, scores in the online group increased by 55.7% at the posttest. However, the difference in achievement from the pretest to the posttest between the large-class lecture and the computer-mediated group was not statistically significant. Although there were no significant differences in knowledge scores, the online section did achieve a statistically significant higher final course grade average (82.9 versus 78.2), even after adjusting for the higher online pretest scores. See issue's website <http://rapidintellect.com/AEQweb/win01.htm>.

Student Engagement

Student engagement in the online course was tracked by two measures, number of times students logged onto the course website and the number of postings. Students were required to post assignment results using the computer-conferencing software. Table 2 shows the means and SD of age and course achievement for highly involved (> 30 postings) and less involved (<30 postings) online students. Students who posted more frequently were significantly older than those who posted less than 30 messages. Students who were more engaged with the course (n=22) scored significantly higher on the pretest, with 13.7 correct responses compared to 11.4 for the students who posted less than 30 messages (n=14). Those students who posted more than 30 messages scored on average three more correct answers than students who posted less than 30 messages (16.9 correct versus 20.4). However, when adjusted for pretest score, posttest scores were not significantly different between high and low posting students (p=.113). Thus, the level of online student engagement did not appear to be related to their achievement of nutrition knowledge. See issue's website <http://rapidintellect.com/AEQweb/win01.htm>.

Student Age

Students taking the course in either instructional method were partitioned into two groups: traditional-aged (18-24 years old) and non-traditional-aged (>24 years old) students. Table 3 presents the mean scores and SD for student achievement of the two age groups in both instructional methods, as measured by pretest, posttest, percentage of change between pretest and posttest, and final course grade. The older online students scored higher at pretest than their lecture counterparts and the younger online students, with a tendency to score above the other groups at posttest as well. When adjusted for pretest score, there were no significant differences between age or treatment groups. The older students taking the course online, however, finished the course with significantly higher final course grades than the traditional-aged online students, traditional-aged large-class lecture group and non-traditional-aged large-class lecture group, even when adjusted for higher pretest scores. See issue's website <http://rapidintellect.com/AEQweb/win01.htm>.

Discussion

Students participating in an online introductory nutrition course scored higher in knowledge at pretest than students participating in the traditional lecture course. Overall knowledge gains from pretest to posttest, however, were the same between the two instructional treatments. Online students did have slightly higher overall course grades than students in the lecture format, but this may reflect the differences in the components that comprise the final grade between the two courses. Although the two course formats were equivalent regarding learning objectives, textbook, and instructional activities, the nature of the two course formats prevented identical assignments. For example, the three individual assignments and one group assignment of the traditional course were not identical to the weekly online assignments and postings, thus course grades may have differed. Another rationale for the higher overall course grades of the online versus the lecture format may be that these online participants were self-selected from the lecture format and had higher prior knowledge and perhaps greater experiences in nutrition and may have thus preferred to forego the necessity to attend classes and review material for which they already had familiarity. However, posttest scores did not differ between the 2 treatment groups. Overall, this study supports the findings of Schulman and Sims (1998), which found that student achievement for undergraduate students was similar between online and lecture formats.

The hypothesis that students more engaged in the course through posting messages would learn more than those less engaged deserves further research. In this study, students who posted frequently (> 30 times) scored higher at pretest and posttest compared to students posting 30 times or less. When adjusted for pretest scores, there was no difference in posttest scores of highly engaged verses less engaged online students. Students who posted more frequently were on average 8 years older than those who posted less frequently. While the characteristics of students who post messages frequently have been reported (Blocher, 1997), we are not aware of other studies investigating the relationship between frequency of posting messages and learning. Further research is needed to explore the factors that lead to more frequent posting (such as age, attitudes, confidence in their knowledge, or motivation), and measurable learning outcomes that relate to higher course engagement.

In looking at academic achievement of older and younger students in both the lecture and online formats, only the older students in the online course had higher nutrition knowledge at pretest than older students in the lecture course, or younger students in either course style. Posttest nutrition knowledge scores were not significantly different among older and younger students in traditional or online courses. However, the older students in the online course had a significantly higher overall course grade than their peers in the lecture course or the younger students. These findings confirm the results of other studies (Dutton et al., 1999; Navarro & Shoemaker, 1999; Schutte, 1997), which show positive course outcomes for older students participating in computer-mediated instruction. Additional studies are needed to identify factors contributing to achievement among older students in online courses and to incorporate them in future educational interventions.

Conclusions

The findings reported here confirm much of the earlier research conducted on large-class lecture and computer-mediated instructional environments. When outcome was measured by test scores, no significant differences were found between the instructional methods (Cheng et al, 1991; Clarke, 1999). When achievement was measured through final course grades, which included assignments that required higher-order synthesis of content, online students significantly outperformed traditional lecture students (Gubernick & Ebeling, 1997). Regardless of achievement measurement, older online students outperformed younger students in either instructional treatment as well as older students in the large-class lecture treatment. Older students receiving the traditional lecture treatment did not do as well as the other three groups. Although not statistically-different, older students in the lecture treatment scored on average 1/2 a letter grade lower than the two traditional-aged (younger) groups and 2 letter grades lower than the non-traditional-aged (older) online students. Future research in this area should focus on explaining why traditional-aged undergraduate students do not perform as well in online course instruction as older, non-traditional-aged students. The results found in this study indicate that students received benefits from both lecture and online instruction. Understanding the factors that contribute to student achievement would lead to course designs that better meet the needs of all learners.

References

Blocher, M. (1997). Self-regulation of strategies and motivation to enhance interaction and social presence in computer-mediated communication. Dissertation Abstracts International, 58-03A, 0823.

Cheng, H., Lehman, J., & Armstrong, P. (1991). Comparison of performance and attitude in traditional and computer conferencing classes. The American Journal of Distance Education, 5(3), 51-64.

Clarke, D. (1999). Getting results with distance education. The American Journal of Distance Education, 12(1), 38-51.

Cohen, N.L., Beffa-Negrini, P., Cluff, C., Laus, M.J., Volpe, S.L., Dun, A.T., & Sternheim, M.M. (1999). Nutrition science online: Professional development of secondary school teachers using the Internet. Journal of Family and Consumer Sciences Education, 17(1), 25-33.

Dede, C. (1997). Distributed learning: How new technologies promise a richer educational experience. Connections: The New England Board of Higher Education, vol. 1, 12-16.

Dutton, J., Dutton, M., & Perry, J. (1999). Do online students perform as well as traditional students? Manuscript submitted for publication.

Gubernick, L., & Ebeling, A. (1997). I got my degree through e-mail. Forbes, 159, 84-92.

Guskin, A.E. (1994). Reducing student costs and enhancing student learning. Change, 7, 23-29.

Harasim, L., Hiltz, S.R., Teles, L., & Turoff, M. (1995). Learning Networks: A field guide to teaching and learning online. Cambridge, MA: The MIT Press.

Harris, M.B. & Brooks, L.J. (1998). Challenges for older students in higher education. Journal of Research and Development in Education 31(4), 226-235.

Marsh, L.C., & Wells, K.L. (1996). Key aspects of a computerized statistics course. Journal of Computing in Higher Education, 8(2), 72-93.

McKeachie, W.J. (1980). Class size, large classes, and multiple sections. Academe, 66, 24-27.

Morrissey, C. A. (1998). The impact of the Interact on management education: What the research shows. Available: http://www.horizon.unc.edu/TS/cases/1998-06.asp

Navarro, P., & Shoemaker, J. (1999). The power of cyber learning: An empirical test. Journal of Computing in Higher Education, 11(1), 29-54.

O'Reilly & Associates (1999). University of Massachusetts: Amherst, MA

Phipps, R., & Merisotis, J. (1999). What's the difference: A review of contemporary research on the effectiveness of distance learning in higher education. The Institute of Higher Education Policy.

Russell, T.L. (1999). The no significant difference phenomenon. Chapel Hill: Office of Instructional Telecommunications, North Carolina State University.

Schlosser, C.A., & Anderson, M.L. (1994). Distance education: Review of the literature. Washington, DC: Association for Educational Communications and Technology.

Schutte, J.G. (1997). Virtual teaching in higher education. Northridge, CA: The California State University-Northridge, Available: www.csun.edu/sociology/virexp.htm.

Schulman, A.H., & Sims, R.L. (1998). Learning in an online format versus an in-class format: An experimental study. T.H.E. Journal, 26(11), 54-56.

Trinkle, D.A. (1999). Distance education: A means to an end, no more, no less. The Chonicle of Higher Education, 45(48), A15.

Brian Miller is a Lecturer in the Department of Hotel, Restaurant, and Travel Administration. His Doctorate of Education is from the University of Massachusetts. Nancy L. Cohen, Ph.D.,R.D. is Professor and Head in the Department of Nutrition, and State Nutrition and Food Specialist with the University of Massachusetts Extension. Patricia Beffa-Negrini, Ph.D., is an Adjunct faculty member at the University of Massachusetts Department of Nutrition. Specialist in Online Projects.
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Author:Beffa-Negrini, Patricia
Publication:Academic Exchange Quarterly
Geographic Code:1USA
Date:Dec 22, 2001
Words:3587
Previous Article:Editorial.
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