The Tinto Model As a Guide to Adult Education Retention Policy
David E. Towles
Coordinator of Research & Assessment
Liberty University School of Lifelong Learning
Associate Vice President of Recruiting and Admissions
In an environment of great competition for students and resources, retention plays an important role in policy development. The literature provides a typical starting point for such considerations, often with the Tinto model (Tinto, 1975, 1987) as a primary source of orientation even for policy makers within the institutions where adult learning and distance education predominate (Bean, 1982; (Kember, 1989). Nonetheless, the Tinto model does contain certain characteristics that may cause practitioners to question its role in guiding retention policy in adult education programs. For one thing, it was derived largely from studies of full-time residential students who recently graduated from high school. This is in contrast with the case of adult learners--many of whom live off campus, attend part time and perhaps even have children of their own who recently graduated from high school (Kember, 1989).
Tinto's Leaving College (1987) provides several examples of instances where he shows this orientation. He states, for example, "Though employment is generally associated with lower rates of college persistence, full time work is clearly more harmful than part time" (p.67). Since adults are most likely to be working full time while attending, this statement suggests a definite residential orientation. He likewise notes that "students who stay at home expose themselves to a number of potential risks," and his stress on departing from former communities seems to ignore adult distance education where students, by definition, "stay at home" (p.96). Granted, Tinto recognizes the "external forces" (p.87) that complicate retention at the distance learning institution. Nonetheless, his emphasis on factors internal to the institution as primary determinants of retention most clearly reflects his orientation toward the residential setting:
Though external events may be important for some students, for most their impact upon institutional departure is seen as secondary to that of one's experiences within the college. (p.125)
Due perhaps to this focus on traditional students, adult education practitioners may view the Tinto model as placing excessive emphasis on social interaction with too little stress on the incoming characteristics of the students themselves. A strong goal orientation is a characteristic that often distinguishes adult students from those in residential programs (Thomas, 1990) as described by Tinto. Family orientation marks yet another point of distinction between the two groups, with adults showing greater concern for their families (Kember, 1989). Pascarella, Duby, and Iverson (1983) also note that the Tinto model fails to account sufficiently for the background characteristics that commuting students may bring to the program. Suffice to say that the Tinto model might use some modification to reflect more clearly retention patterns in adult and distance learning programs.
While at least one study (Pascarella, Duby, & Iverson, 1983) substantiates the belief that, for nonresident students, Tinto places too much emphasis on social integration, findings from their study also support Tinto's stress on academic integration within the nonresident college or university. At the Liberty University School of Lifelong Learning (LUSLLL), this focus on academic integration marked the starting point for a study completed during the spring of 1993 to measure the effects of student/teacher interaction on retention within this adult distance learning program serving students across the nation and around the world. The Liberty University School of Lifelong Learning is the video-based distance education arm of Liberty University in Lynchburg, Virginia. Its reliance on mail, telephone, and other technologies to support video instruction allows for a great deal of flexibility for adult students seeking graduate and undergraduate degrees without having to relocate or change jobs. Yet while course survey results and alumni survey data over the past three years show strong satisfaction with the program, concern has developed recently about ways of improving retention rates.
Guiding this study was the literature suggesting that faculty play an important role in distance education (Burnham, 1988; Crane, 1985; Hezel & Dirr, 1990). Accordingly, faculty contact is seen as highly valued by students in distance learning formats; in fact, students rate it as even more important than interaction with other students (Hezel & Dirr, 1990). Students' sense of social and academic isolation has been associated with nonpersistence in distance education (Garrison, 1987). Perceptions among decision makers in the institution found support in the literature suggesting that first-time students are often surprised at the amount of work required in distance learning courses (Crane, 1985) and that students in college-credit distance learning courses may consider these courses as comparable to on-campus courses in content and challenge (Crane, 1985).
A Study Measuring the Effects of Student/Teacher Interaction
In the spring of 1993, LUSLLL implemented a study examining the effects of student/teacher interaction on academic integration and hence retention (Tinto, 1975). Earlier assessment studies had reinforced the belief that factors outside the institution were primary determinants of retention. At LUSLLL, a survey of dropouts, for example, revealed that 60% of them desired to re-enroll but were unable to do so due to job and family responsibilities (Towles & Ellis, 1992). These findings meshed well with Kember's view disputing Tinto's idea (1987) that influences inside the institution were primary determinants of retention.
Outside influences notwithstanding, the decision was made to measure the effects of a retention program centering on activities within the institution. Due to time constraints, the study featured course completion as the first step in a longitudinal, multifaceted approach to program retention at LUSLLL. The purpose of the study was to evaluate the effect of a program of telephone-based, faculty-initiated student contact on the course completion rate of students taking general education courses through LUSLLL.
The study centered around 120 students taking the following general education courses during the fall of 1992: first-year Biology, second-year Government, second-year History, and first-year Music. The treatment group consisted of 15 students per course who had received faculty-initiated contact. The control group consisted of 15 students per class who, for various reasons, had not received such faculty phone calls. Care was taken to ensure that students receiving phone calls did not differ substantially from those in the control group. For example, students had a grade point average of 2.7 while those in the control group had a 2.6; those in the treatment group matched the control group with an average age of 39. And of the 85 subjects transferring credit into the LUSLLL program, 45 (38%) were in the treatment group while 40 (33%) were in the control group--again, differences in percentage proved statistically significant.
Following Tinto's 1987 suggestion, care was taken to include in the sample only the students who failed to complete due to factors other than academic dismissal. This study was intended to serve as a first step toward a systematic and longitudinal approach to determining both the appropriate level of attrition to expect and the proper types of attrition that may result from phenomena such as stopout and the accomplishment of personal goals that may include course completion but not graduation. It centered around the question of what influence faculty-initiated interaction might have on retention.
Some Effects of Increased Interaction
Interestingly, findings from this study failed to support completely claims by Tinto (1975) and Pascarella, Duby, and Iverson (1983) that student-faculty interaction contributes positively to retention. Among the 120 students comprising the sample of this study, for example, no significant difference in overall completion rates was discovered between students receiving faculty-initiated calls and those not receiving them. But, as shown in Table 1, 17% of non-called students failed to complete--as compared to 9% of students called by faculty who failed to complete.
Nonetheless, the study did reveal significant differences in completion rates among students in different academic classes. As shown in Figure 1, only 57% of freshmen in the study completed, while all of the seniors completed.
A study of students receiving phone calls revealed no significant differences among classes. However, the examination of completion rates for students not receiving calls revealed significant differences in completion rates. Only 41% of freshmen not called completed their courses, less than half the percentage of non-called juniors (85%) and seniors (100%). As shown in Figure 2, differences in completion rates for called and non-called sophomores, juniors, and seniors were minimal, whereas the completion rate for non-called freshmen was 40% while the completion rate for called freshmen was 80%, a meaningful difference.
Implications for Policy Making
So what is the relationship between the Tinto model, studies extending its domain to include adult education, and the present study? Ironically, Tinto's notion of "fit" may cast some light on this question. Granted, his concept relates primarily to the fit between the institutional environment and the characteristics and attitudes that the student brings into that environment. However, this study may illustrate a relationship between research and institutional assessment because this use of Tinto's model may support, to an extent, the concern that decision makers have for choosing models that come the closest to "fitting" their individual institutions.
|Students Completing Courses||49
|Students Not Completing Courses||11
based on sample of 120 students in 4 courses
Source: student records
Figure 1. Course Completion Rates by Academic Classification
As shown in this study, the Tinto model--even in its application to distance learning (Kember, 1989)--still did not "fit" the institution well enough to guide policy making decisions. Simply following the Tinto model straight off the shelf, policy makers might suggest something of a shotgun approach in which all students would be contacted, no matter what their academic classification. Moreover, some decision makers might have sought to determine differences in retention among students' different ages, while our experience shows that, at an average 39 years of age, our students are well beyond the age of maturity and thus unlikely to show retention differences based on age.
Not only might such an approach waste resources on phone calls to students who don't need them; considerable anecdotal evidence at LUSLLL suggests that some students consider unsolicited faculty calls to be more of a hindrance than a help. This conclusion finds support from those who indicate that adult learners generally perform best in a learning environment where they enjoy a great deal of independence (Merriam & Caffarella, 1991).
Findings from a later study (Towles, 1994) indicate that our students are likely either never to begin the course after receiving it or stop working on the course after completing the first examination. Thus, while the current study offers a clearer picture of who should receive increased faculty interaction, the later study offers insight into when those calls should occur.
These studies represent important steps in a systematic and longitudinal assessment of student/teacher interaction that will inform policy making considerations in this area as it performs its task of selecting the most appropriate research models, using their most salient constructs to guide assessment, and then using assessment to then fit those models to the particular circumstances of the institution. The expected outcome of such an assessment process is heightened efficiency in the use of important student resources directed toward student retention.
Figure 2. Course Completion Rates by Classification
Bean, J.P. (1982). Conceptual models of student attrition: How theory can help the institutional researcher. In E.T. Pascarella, (Ed.), Studying student attrition, New directions in institutional research, No. 36. San Francisco: Jossey-Bass.
Burnham, B.R. (1988). An examination of perceptions and motivations of faculty participating in a distance education project. Paper presented at the Teaching at a Distance Conference (Madison, WI August). (ERIC ED 304 133)
Crane, V. (1985). Student uses of Annenberg/CPB tele-courses in the Fall of 1984. Corporation for Public Broadcasting. (ERIC ED 264 822)
Garrison, D.R. (1987). Researching dropout in distance education. Distance Education, 89, 95-101.
Hezel, R.T., & Dirr, P.J. (l990). Understanding distance education: Identifying barriers to college attendance. Washington, DC: Annenberg/CPB Project. (ERIC ED 340 335)
Kember, D. (1989). A longitudinal-process model of dropout from distance education. Journal of Higher Education, 60, 278-301.
Merriam, S.B., & Caffarella, R.S. (1991). Learning in adulthood. San Francisco: Jossey-Bass.
Pascarella, E., Duby, P., & Iverson, B. (1983). A test and reconceptualization of a theoretical model of college withdrawal in a commuter institution setting. Sociology of Education, 56, 88-100.
Thomas, A.M. (1991). Beyond education: A new perspective on society's management of learning. San Francisco: Jossey-Bass.
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45, 89 - 127.
Tinto, V . (1987). Leaving college: Rethinking the causes and cures of student attrition. Chicago: University of Chicago Press.
Towles, D.E., & Ellis, J.R. (1992). Dropout survey results. Unpublished report. Lynchburg, VA: School of Lifelong Learning.
Towles, D.E. (1994). Dropout survey results. Unpublished report. Lynchburg, VA: School of Lifelong learning.