1 20th Annual Conference on Distance Teaching and Learning click here -> At-Risk Factors for the Community College Web-Based Student Herbert. E. Muse, Jr., Ph.D. Associate Director, Distance Learning Montgomery College With the growth of distance learning it is imperative to find out what traits, if any, separate successful online learners from those who do not succeed. The research is copious in areas of retention and persistence in higher education credit courses and programs. It is less comprehensive in these areas regarding courses in which learners interface with their instructors and content on the Internet. This paper addresses this issue by reporting a quasi-replication of Osborn (2001). In this research factor analysis reduced thirty-six items into seven factors for further analysis. These seven factors were combined with seven background factors for employment in a discriminant function analysis. Results of the quantitative analysis suggest GPA, Study Environment, Age, time since Last College Class, and Background Preparation were significant factors in discriminating between successful and non-successful community college Web-based students. Furthermore, a qualitative analysis of why students dropped their Webbased courses indicated that students who were frustrated or overwhelmed in their first week of coursework, and who could not load, access, or find all necessary learning materials, would drop the course while they still had a chance to do so. The basis of this work was originally published as The Web-based community college student: An examination of factors that lead to success and risk. The Internet and Higher Education 6(3), 241-261. 2003 Elsevier Inc. All rights reserved. Introduction The growth of distance learning, principally online learning has been phenomenal in the past five to ten years. In the United States, 1680 institutions offered over 54,000 online courses in 2001 (Simonson, Smaldino, Albright & Zvacek, 2002). On the down side, little is known about why students succeed or fail in this environment. Empirical data describing who may be successful in Web-based learning environments are scarce, especially at the community college level. Another reason for concern is based on the fact that institutions of higher learning facilitate numerous activities that help students along their learning paths. For traditional programs, these interventions (counseling, training, course design) are enmeshed in the day-to-day activities of the school. But at many colleges and universities online courses and programs were launched without full consideration or implementation of services and support that may give the student a greater chance of success. For Webbased courses, it is not clear how to support the student or how to create courses that play to the strengths of the online learner. Another area of concern is the issue of drop out. When a student drops out of a class, all stakeholders suffer. The student realizes a sense of failure. In many cases, the instructor realizes a loss too. The institution suffers financial loss, especially if the student cannot find another class, or worse yet, stops attending the school altogether. Parents, institutions of financial aid, and others also stand to lose when a student drops a course or program. These concerns suggested the need for research into why students at the community college succeed; specifically what traits separated the successful learners from those who do not succeed.
2 20th Annual Conference on Distance Teaching and Learning click here -> Background During the last decade of the 20th century the growth of public networks, chief among them the Internet, gave rise to a form of distance learning that mixed a number of technologies into one platform. Educators were among the first end-users of these public networks. Computer conferencing (sophisticated bulletin boards), email, chat, file transfer protocol (ftp), telnet (logging on to a remote terminal from your own), the World Wide Web, and later streaming audio and video, all merged into a single platform. The emergence of the World Wide Web as an application to provide online learning spurred even greater development of the Internet as a medium of teaching and learning (Connick, 1999; McVay, 2001; Web- Based Education Commission, 2000). An area of education that has continually plagued instructors and academic administrators is that of persistence and dropout. Closely related to dropout is attrition, which is the loss of student enrollment (from the institutional point of view). The foundations of risk in a learning endeavor have been explained for the most part by studies of persistence and dropout. For many years researchers have explored reasons for dropout in higher education. Numerous studies concentrated on drop out in distance learning classes, from correspondence studies to videoconferencing, telecourses, email, and finally computer conferencing (Giles). To date, little has been uncovered about the traits of a Web-based learner. To complicate matters there have been numerous approaches to research in these areas. Some have studied drop out and persistence of the distance learner using measurements of single variables (Boshier, 1973). The work of many, including Powell, Conway and Ross (1990), looked at multiple variables to describe the traits of the distance learner as they relate to dropout and persistence. The different approaches have caused what Phipps and Merisotis (1999) have described as "gaps" in the research and obstacles to a greater understanding of the problems. Research on Persistence and Success of the Online Learner The author attempted to fill some of these gaps and was influenced by some who looked at the patterns of persistence in online learners. The works of Parker (1999), Giles (1999) and Osborn (2001) were three of these. Parker examined locus of control, gender, number of distance education courses completed, age, financial assistance, and number of hours employed using correlation and discriminant analysis. Giles (1999) produced another work that focused on early versions of the online student. Giles states that the variables of 1) timely submission of assignments; 2) the recommendation of one student to another to take an online class; and 3) acting, one of the three categories of Atman s Goal Orientation Index (Atman, 1987), are significant predictors of persistence/dropout in 98 to 100 % of the cases of students using computer conferencing. The study of Osborn involved Web-based and videoconferencing students at a mid-size university. The research of Osborn with Web-based students is the precursor to the work described here. Osborn was influenced by Tinto (1975), Billings (1988), Kennedy and Powell (1976), and Kember (1995). She developed an instrument to measure traits of Web-based students (Osborn, 2000). Using factor analysis Osborn was able to reduce the number of factors for further study to six: computer confidence, locus of control, study environment, enrollment encouragement, tenacity, and motivation. Some of these same traits were investigated in this research.
3 20th Annual Conference on Distance Teaching and Learning click here -> Research Methods This study was guided by 4 research questions: 1. In terms of computer confidence, enrollment encouragement, need for support, preparation, computer skills, tenacity, study habits, Web skills, motivation, study environment, background confidence, and external locus of control, which of these factors will be used to compute a student s ability to successfully complete a Web-based class? 2. Using a survey, does a weighted combination of the critical factors indicate which students are at risk for failing to successfully complete the Web-based class? 3. Do age, gender, GPA, number of hours currently worked, years since last college course, number of previous distance learning courses taken, educational level, and number of credits in the current semester significantly affect successful completion of Web-based classes? 4. What reasons are reported most often for student dropout in Web-based classes? Data Analysis The population for this study was 1028 non-duplicated Web-based students at Montgomery College, Maryland in the fall 2002 semester. The online questionnaire elicited over 350 responses, or 34% of the population. Through reductions in the data set, over 26% of the responses were used for analysis. The author employed the instrument developed by Osborn (28 items) and added 8 items from an online readiness instrument related to computer and web skills of the online learner (Kronheim, S., Pugh, M., & Spear, M. H. (2001). Factor analysis suggested that 10 questions be dropped from further analysis. Seven components (factors) were retained for further analysis: Computer Skills, Study Environment, External Locus of Control, Computer Confidence, Web Skills, Motivation, and Background Preparation. Along with 7 background variables, these were used for computing a discriminant analysis function. The result of the discriminant function analysis showed that Grade Point Average, Study Environment, Age Group, Last College course, and Background Preparation were significant in distinguishing between successful and non-successful online students at the community college. The data indicated that the function itself was significant as a means of measurement. The answer to research question four regarding reasons given for drop out is that students could not get things to work and thus fell behind early in the course. Things were often the course management software, specialized software, e.g., language compilers, aspects of their computer or communication systems, certain assignments, plug-ins, learning packs (books on disk), and any other logistical issue that made them feel frustrated and worried about falling behind. Most of the responses to this question were related to technology. The researcher did not ask them to specify what would not work, but many of the open-ended responses included these types of issues. Conclusions The inferences are that adults who are academically integrated, as measured by previous grades and education level, and who believe they are ready to take courses in this mode, are likely to succeed in the Web-based learning environment, and that having a stable study environment is also important for success. Another inference of the study was that online students should quickly obtain and feel comfortable using the course management software and the many learning resources before beginning the coursework in earnest.
4 20th Annual Conference on Distance Teaching and Learning click here -> References Atman, K. S. (1987). The role of conation (striving) in distance education enterprise. The American Journal of Distance Education, 1(1), 14-24. Billings, D. M. (Ed.). (1988). A conceptual model of correspondence course completion. The American Journal of Distance Education. 2(2), 23-35. Boshier, R. (1973). Educational participation and dropout: A theoretical model. Adult Education, 23(4), 255-282. Commonwealth of Learning. (2001). The Changing Faces of Virtual Education. Vancouver, Canada. Connick, G. P. (1999). The Distance Learner's Guide. Upper Saddle River, N.J.: Prentice Hall. Giles, I. (1999). An examination of persistence and dropout in the online computer-conferenced classroom. Unpublished dissertation, Virginia Polytechnic Institute and State University, Blacksburg. Kember, D. (1995). Open Learning Courses for Adults. Englewood Cliffs, N.J.: Educational Technology Publications. Kennedy, D., & Powell, R. (1976). Student progress and withdrawal in the Open University. Teaching at a Distance, 7(November), 61-75. Kronheim, S., Pugh, M., & Spear, M. H. (2001). Readiness for Online Studies. College Park, MD: University of Maryland University College. McVay, M. (2001, November/December). Effective student preparation for online learning. The Technology Source, 6p. Osborn, V. (2000). The Distributed Learning Survey. Denton, TX: University of North Texas. Osborn, V. (2001). Identifying at-risk students in videoconferencing and web-based distance education. The American Journal of Distance Education, 15(1), 41-54 Parker, A. (1999). A study of variables that predict dropout from distance education. International Journal of Educational Technology, 1(2), 9p. Phipps, R., & Merisotis, J. (1999, May/June 1999). What's the difference? Outcomes of distance vs. traditional classroom-based learning. Change, 13-17. Powell, R., Conway, C., & Ross, L. (1990). Effects of student predisposing characteristics on student success. Journal of Distance Education, V(1), 5-19. Simonson, M., Smaldino, S., Albright, M., & Zvacek, S. (2002). Teaching and Learning at a Distance. (2 nd ed.). Upper Saddle River, N.J.: Prentice Hall. Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89-125.
5 20th Annual Conference on Distance Teaching and Learning click here -> Web-Based Education Commission (2000). The power of the Internet for learning: Moving from promise to practice. Washington, D.C.: U.S. Department of Education. Biographical Sketch Herbert E. Muse, Jr., Ph.D., is Associate Director of Distance Learning at Montgomery College, MD. Dr. Muse does considerable collaborative work in the MarylandOnline consortium, and is a member and former President, President-Elect, and Past President of the Maryland Distance Learning Association, a chapter of the United States Distance Learning Association. He is President-Elect of the College of the Air Distance Education Consortium. Dr. Muse teaches as an adjunct instructor of Educational Technology at Loyola College of Maryland in the Graduate School, and at University of Maryland University College in the Information Systems Management program at the undergraduate level. Address: The Office of Distance Learning Montgomery College 20200 Observation Drive Germantown, MD 20876 E-mail: Buddy.Muse@montgomerycollege.edu Phone: 301.444.6005 Fax: 301.444.6004