Critics of online distance education have alarmed the public recently with reports of severe drop rates and attrition in online classes. The notion that more students will drop out of online classes than traditional face-to-face classes enjoys the widespread acceptance usually reserved for scientific precepts (Parker, 1999; Carr, 2000). More importantly, though, many educators imply that the observed high drop rates should disqualify online education as a high-quality option to traditional education ("Distance Education," 2001).

Drop rates are among the characteristics that have routinely prompted distance education studies (Cookson, 1990; Dowdall, 1991; Parker, 1999). Drop rates for distance classes have been consistently higher than those of traditional classes and, according to some researchers, tend to suggest academic non-success (Diaz, 2000a; Phipps & Merisotis, 1999; Ridley & Sammour, 1996).

Though higher drop rates may accurately reflect a fundamental difference in outcomes between online and traditional educational environments, the mere fact of high drop rates is not necessarily indicative of academic non-success. This article suggests alternative views of the significance of high drop rates and recommends options for research and practice.

Differences Between Online and Traditional Students

The demographic differences between online and traditional students have been duly noted. Online students are generally older, have completed more college credit hours and more degree programs, and have a higher all-college prior GPA than their traditional counterparts (Diaz, 2000a; Gibson & Graff, 1992; Thompson, 1998).

In a study of 231 students in a college health education course, Diaz (2000a) found that online students were older, and more likely to have completed more college credit hours, than traditional students. Diaz noted that the profile of the online learner suggested a student with more life and academic experiences—attributes that made the student well suited to the independent, self-directed study associated with distance education. Diaz also found that successful online students exhibited a higher average GPA prior to enrollment in the online course (avg. GPA = 3.02) than unsuccessful students (avg. GPA = 2.25).

Learning styles present another important consideration for the study of drop rates. If optimal learning is dependent on learning styles, and these styles vary between online and traditional students, then teachers should consider altering their instructional methods as one means of preventing drops. Diaz (2000a) demonstrated that successful (i.e., course grade of "C" or better) online students were more strongly independent learners than were non-successful (i.e., course grade of "D," "F" or "W") online students, as evidenced by intercorrelation analysis. Successful students' independent styles of learning were significantly, negatively related (p < .01) to their collaborative and dependent learning styles. That is, their preference for independence was not tied to needs for external structure and guidance from their teacher or a need to collaborate with their classmates. This correlation did not exist in the non-successful students. Thus, a significant trait of the successful online student was a strong independent learning style. Diaz and Cartnal (1999) similarly demonstrated that online students possess stronger independent learning styles than their on-campus counterparts. According to the authors:

It is not surprising that students who prefer independent, self-paced instruction would self-select into an online class. It may be that the distance education format appealed to students with independent learning styles, and that independent learning preferences are well suited to the relative isolation of the distance learning environment (p. 134 [print]; "Discussion," ¶ 2 [online]).

Performance Differences

Online students often outperform traditional students when success is measured by the percentage of students that attain a grade of "C" or above, overall classroom performance (e.g., exam scores), or student satisfaction. Diaz (2000a), comparing the characteristics and success of online and traditional students, found that online students (N = 96) received twice as many "A" grades, while traditional students (N = 135) received twice as many "D" and "F" grades in a general health education class (Figure 1). The online students were also more satisfied with multiple aspects of their course as demonstrated by their responses to an 11-question satisfaction survey. While online students generally fared better in overall grades and grades on exams (Figure 2), they also dropped the course more frequently: a 13.5% drop rate for online students versus a 7.2% drop rate for traditional students. As Diaz noted, ". . . it seems very clear that students who enroll and persist in an online course will fare at least as well as their on-campus counterparts" (p. 95 [print]; p.95 [online]).

Ridley and Sammour (1996) examined student performance and satisfaction in courses delivered online. The problem addressed in their study was to determine how best to re-design degree programs and course offerings to improve institutional effectiveness, while at the same time providing a high-quality program with convenient student access. The authors used an Instruction Evaluation Survey (IES) to cull information from online students and from instructors who taught both online and traditional courses. Because students typically self-select into such classes, convenience sampling was used in the distribution of the survey. Performance data for online and traditional courses was obtained from the instructors. They noted a high rate of withdrawal from online classes over two semesters—30.0% and 25.0%, respectively. However, online students who persisted in the courses expressed satisfaction with their educational experience and were more likely to enroll in subsequent online courses. Based on results of the colleges' IES, online instructors rated student performance as "significantly higher" in online than in traditional classroom courses, in areas related to general skills development. The general conclusion, based on comparative data from four courses, was that "the online students' performance was quite comparable to, and in some cases excelled, that of their classroom counterparts" (p. 2).

Age may also be a significant contributor to the performance differences between traditional and distance students. Dille and Mezack (1991) and Souder (1994) have identified positive correlations between student age and success in telecourses and satellite delivery courses, respectively. Dille and Mezack suggest that older students are more successful because they are typically more mature and disciplined and may value their time and money more highly than younger students.

Making Sense of Drop Rates

If online students typically possess characteristics that research has linked with academic success (e.g., older age and more academic experience), why are they less successful in terms of persisting in a class for the full term? One possible answer is that we may have mistakenly defined "drop rate" as a characteristic synonymous with "academic non-success." However, I believe that many online students who drop a class may do so because it is the right thing to do. In other words, because of the requirements of school, work, and/or family life in general, students can benefit more from a class if they take it when they have enough time to apply themselves to the class work. Thus, by dropping the class, they may be making a mature, well-informed decision that is consistent with a learner with significant academic and life experience. This explanation would be consistent with their demographics while calling into question the idea that these students are academically unsuccessful or possess inferior academic abilities. In fact, a case could be made that many of the students who earn "D" and "F" grades would be better served by dropping a class. By doing so at the appropriate time, some might increase the likelihood of a successful academic career. For example, they would obviate the need to retake a course immediately, and dropping the class would not adversely affect their GPA, perhaps helping them to avoid academic probation.

Conclusions

How much should we worry about the high drop rates of online classes as opposed to students earning poor grades? Should we consider a drop necessarily as a sign of academic failure? Obviously, we should not neglect drop rates completely or avoid attempts to modulate the factors that lead to drops, but we should certainly not consider students who drop as "at-risk" students without further evidence to support such a belief. Further, we should not consider high drop rates as implicit evidence that online education is inferior to traditional education.

There could be many reasons why some students remain and others drop an online (or traditional) class. In fact, the reasons may have to do with such factors as student characteristics (i.e., demographics), the quality of the class or its instruction, the course's discipline, socioeconomic factors, disabilities, or apathy. Gibson (1998) reported three categories of factors that have emerged to explain and predict attrition in distance courses:

• Student factors: educational preparation, motivational and persistence attributes, student academic self-concept;
• Situational factors: family and employer support, changes in life circumstances; and
• Educational system factors: quality and difficulty of instructional materials, provision of tutorial support.

While institutions may have difficulty addressing student drops that occur because students have insufficient time to complete coursework or have insuperable financial commitments, they can certainly affect change on the level of student, teacher, and institutional preparedness. For example, teachers should give more attention to students' readiness prior to a distance class. Readiness surveys, computer skills surveys, and other questionnaires might help teachers pinpoint reasons for student success in an online environment. In this way, teachers might also determine student risk more accurately and, by initiating earlier interventions, prevent some cases of student drops. Online orientation courses and help desks can bring pivotal technical training and support to online students in hopes of reducing drops related to technical difficulties. Finally, pedagogy-based training (Diaz, 2000b; Diaz & Bontenbal, 2000) and technical in-services for online teachers can broaden the scope of their experience and their familiarity with technical teaching tools. In this manner, it may be possible to produce a cadre of teachers who are as comfortable and skilled online as they are in the traditional classroom.

Meanwhile, future research should consider many factors, including the role of teacher experience and/or instructional design and delivery quality in preventing attrition, the role of entry level student computer skills in preventing attrition, and the role of the online orientation process in preventing attrition. One important thread of research would be to attempt to learn more about why students drop courses. Do online students drop for the same reasons as traditional students, and are such drops reflective of non-success? One way to determine the reasons for student drops would be to use a drop survey like the one used at Cuesta Community College (Exhibit 1). This survey, which is given to all college students who withdraw from courses, not only inquires about the reasons for drops, but also solicits information regarding drops related to scheduling conflicts and drops related to access to student services. In time, the college hopes to code student drops and classify them for the purpose of preventing some types of attrition.

Ultimately, until we know more about why students drop courses, we should not assume that drops are synonymous with academic non-success, nor should we discredit online education as a viable alternative means of instructional delivery.

References

Carr, S. (2000, February 11). As distance education comes of age, the challenge is keeping the students. The Chronicle of Higher Education, 23, A1. Retrieved January 30, 2002, from http://chronicle.com/free/v46/i23/23a00101.htm

Cookson, P. (1990). Persistence in distance education. In M. G. Moore (Ed.), Contemporary issues in American distance education (pp. 192-203). Elmsford, New York: Pergamon Press.

Diaz, D. P. (2000a). Comparison of student characteristics, and evaluation of student success, in an online health education course. Unpublished doctoral dissertation, Nova Southeastern University, Fort Lauderdale, Florida. Retrieved January 26, 2002, from http://www.LTSeries.com/LTS/pdf_docs/dissertn.pdf

Diaz, D. P. (2000b). Technology training for educators: The pedagogical priority. Computer-Using Educators (CUE) Newsletter 22(2), 1, 25-27.

Diaz, D. P., & Bontenbal, K. F. (2000). Pedagogy-based technology training. In P. Hoffman & D. Lemke (Eds.), Teaching and learning in a network world (pp. 50-54). Amsterdam, Netherlands: IOS Press.

Diaz, D. P., & Cartnal, R. B. (1999). Students' learning styles in two classes: Online distance learning and equivalent on-campus. College Teaching 47(4), 130-135. Retrieved January 26, 2002, from http://www.LTSeries.com/LTS/html_docs/grslss.htm

Dille, B., & Mezack, M. (1991). Identifying predictors of high risk among community college telecourse students. The American Journal of Distance Education, 5(1), 24-35.

Distance education: The latest not-so-big thing. (2001, September). Perspective, 33, 5.

Dowdall, R. J. (1992). Learning style and the distant learner. Consortium project extending the concept and practice of classroom based research report (ERIC Document Reproduction Service No. ED 348 117). Sacramento, CA: California Community College Chancellor's Office.

Gibson, C. C. (1998). The distance learner's academic self-concept. In C. Gibson (Ed.), Distance learners in higher education: Institutional responses for quality outcomes (pp. 65-76). Madison, WI: Atwood.

Gibson, C. C., & Graff, A. O. (1992). Impact of adults' preferred learning styles and perception of barriers on completions of external baccalaureate degree programs. Journal of Distance Education, VII(1), 39-51.

Parker, A. (1999, December). A study of variables that predict dropout from distance education. International Journal of Educational Technology, 1(2). Retrieved February 5, 2002, from http://www.outreach.uiuc.edu/ijet/v1n2/parker/index.html

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

Ridley, D. R., & Sammour, H. Y. (1996). Viable alternative means of instructional delivery: Online courses as an alternative teaching method. College Student Journal, 30, 337-339.

Souder, W. E. (1994). The effectiveness of traditional vs. satellite delivery in three management of technology master's degree programs. The American Journal of Distance Education, 7(1), 37-53.

Thompson, M. M. (1998). Distance learners in higher education. In C. Gibson (Ed.), Distance learners in higher education: Institutional responses for quality outcomes (pp. 9-24). Madison, WI: Atwood.

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