Skip to main content


Log in

A Multi-Level Assessment of the Impact of Orientation Programs on Student Learning

  • Published:
Research in Higher Education Aims and scope Submit manuscript


The purpose of this study was to investigate the influence of orientation programs on student academic and social learning. Moving beyond previous studies, we examined how participation in orientation programming affected student learning and how the impact of these programs on learning varied by organizational characteristics (i.e., institutional control, size of undergraduate enrollment, sponsoring division, and whether the institution has an office designated for managing orientation programs), student entry characteristics (i.e., gender, race, transfer status), and student experiences (i.e., perceived quality of orientation program in helping student transition and in meeting students’ expectations, positive experiences with orientation staff, and perceptions of orientation programs and their efficacy in helping students navigate resources and in providing useful campus-based information). Hierarchical linear analysis was used to analyze these cross-level effects. Results demonstrated that having a designated office for orientation programs on campus was important for narrowing the academic learning gap between new-first year and transfer students. Implications for researchers and practitioners were discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others


  1. The institutional-level variables posited for explaining the relationship between student-entry characteristics and outcomes are italicized.

  2. The database was constructed from two successive waves of data collection in two different, albeit successive years. Each institution was represented only once in the database. No institutions participated in both waves of the data collection effort.

  3. While it may not be necessary to use HLM for this analysis, it is also not inappropriate to do so. HLM still enables us to increase the precision of estimating effects within institutions and to test our a priori hypotheses regarding cross-level effects (see Raudenbush and Bryk 2002 for more detail regarding reasons for using hierarchical linear modeling in social science research).


  • Association of American Colleges and Universities. (2002). Greater expectations. Washington, DC.

  • Astin, A. W. (1993). What matters in college: Four critical years revisited. San Francisco: Jossey-Bass.

    Google Scholar 

  • Attinasi, L. C., Jr. (1989). Getting in: Mexican Americans’ perceptions of university attendance and the implications for freshman year persistence. Journal of Higher Education, 60(3), 247–277.

    Article  Google Scholar 

  • Banta, T. W., Lund, J. P., Black, K. E., & Oblander, F. W. (Eds.). (1996). Assessment in practice: Putting principles to work on college campuses. San Francisco: Jossey-Bass.

    Google Scholar 

  • Barefoot, B. O. (2005). Current institutional practices in the first college year. In M. L. Upcraft, J. N. Gardner, & B. O. Barefoot (Eds.), Challenging and supporting the first-year student; a handbook for improving the first year of college (pp. 47–63). San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • Berger, J. B., & Milem, J. F. (2000). Organizational behavior in higher education and student outcomes. In J. Smart (Ed.), Higher education: Handbook of theory and research (pp. 268–338). New York: Agathon Press.

    Google Scholar 

  • Bronfenbrenner, U. (1979). The ecology of human development. Cambridge, Mass.: Harvard University Press.

    Google Scholar 

  • Chemers, M. M., Hu, L., & Garcia, B. (2001). Academic self-efficacy and first-year college student performance and adjustment. Journal of Educational Psychology, 93(1), 55–65.

    Article  Google Scholar 

  • Engberg, M. E., & Mayhew, M. J. (2007). The influence of first-year success courses on student learning and democratic outcomes. Journal of College and Student Development, 48(3), 241–258.

    Article  Google Scholar 

  • Fidler, P. P. (1991). Relationship of freshman orientation seminars to sophomore return rates. Journal of the Freshman Year Experience, 3(1), 7–38.

    Google Scholar 

  • Fidler, P. P., & Hunter, M. S. (1989). How seminars enhance freshman success. In M. L. Upcraft & J. N. Gardner (Eds.), The Freshman year experience: Helping students survive and succeed in college (pp. 216–237). San Francisco: Jossey-Bass.

  • Fox, L., Zakely, J., Morris, R., & Jundt, M. (1993). Orientation as a catalyst: Effective retention through academic and social integration. In M. L. Upcraft, R. H. Mullendore, B. O. Barefoot, & D. S. Fidler (Eds.), Designing successful transitions: A guide for orienting students to college (pp. 49–59). Columbia, SC: National Resource Center for the Freshman Year Experience.

    Google Scholar 

  • Gardner, J. N., & Hansen, D. A. (1993). Perspectives on the future of orientation. In M. L. Upcraft (Ed.), Designing successful transitions: A guide for orienting students to college: The freshman year experience (pp. 183–194). Monograph Series Number 13. Columbia: University of South Carolina.

  • Gloria, A. M., Kurpius, S. E. R., Hamilton, K. D., & Wilson, M. S. (1999). African American students’ persistence at a predominantly white university: Influences of social support, university comfort, and self-beliefs. Journal of College Student Development, 40(3), 257–268.

    Google Scholar 

  • Graham, C., Baker, R. W., & Wapner, S. (1985). Prior interracial experience and Black student transition into predominantly White colleges. Journal of Personality and Social Psychology, 47, 1146–1154.

    Article  Google Scholar 

  • Herman, J. P., & Lewis, E. (2004). Transfer transition and orientation programs. In T. J. Kerr, M. C. King, & T. J. Grites (Eds.), Advising transfer students (pp. 57–64). Manhattan, KS: NACADA.

    Google Scholar 

  • Hughes, J. A., & Graham, S. W. (1992). Academic performance and background characteristics among community college transfer students. Community College Journal of Research and Practice, 16(1), 35–46.

    Article  Google Scholar 

  • Hurtado, S. (1996). Latino student transition to college: Assessing difficulties and factors in successful college adjustment. Research in Higher Education, 37(2), 135–157.

    Article  Google Scholar 

  • Hurtado, S., & Carter, D. F. (1997). Effects of college transition and perceptions of the campus racial climate on Latino students’ sense of belonging. Sociology of Education, 70(4), 324–345.

    Article  Google Scholar 

  • Hurtado, S., Carter, D. F., & Spuler, A. (1996). Latino student transition to college: Assessing difficulties and factors in successful college adjustment. Research in Higher Education, 37(2), 135–157.

    Article  Google Scholar 

  • Jacobs, B. C., Busby, R., & Leath, R. (1992). Assessing the orientation needs of transfer students. College Student Affairs Journal, 12(1), 91–98.

    Google Scholar 

  • Kalsner, L., & Pistole, M. C. (2003). College adjustment in a multiethnic sample: Attachment, separation-individuation, and ethnic identity. Journal of College Student Development, 44(1), 92–109.

    Article  Google Scholar 

  • Kenny, M. E., & Stryker, S. (1996). Social network characteristics and college adjustment among racially and ethnically diverse first-year students. Journal of College Student Development, 37(6), 649–658.

    Google Scholar 

  • Kerr, T. J., King, M. C., & Grites, T. J. (Eds.). (2004). Advising transfer students: Issues and strategies. NACADA Monograph Series No. 12. Manhattan, KS: NACADA.

  • Kinzie, J., Gonyea, R., Kuh, G., Umbach, P., Blaich, C., & Korkmaz, A. (2007). The relationship between gender and student engagement in college. Paper presented at the Annual Meeting of the Association for the Study of Higher Education.

  • Krallman, D., & Holcomb, T. (1997). First-year student expectations: Pre and post orientation. Paper presented at the Annual Meeting of the Association of Institutional Research.

  • Kuh, G. D. (1996). Guiding principles for creating seamless learning environments for undergraduates. Journal of College Student Development, 37, 135–148.

    Google Scholar 

  • Kuh, G., Schuh, J., Whitt, E., Andreas, R. E., Lyons, J. W., & Strange, C. C. (1991). Involving colleges: Successful approaches to fostering student learning and personal development outside the classroom. San Francisco: Jossey-Bass.

  • Mallinckrodt, B. (1988). Student retention, social support, and dropout intention: Comparison of black and white students. Journal of College and Student Development, 29(1), 60–64.

    Google Scholar 

  • Mayhew, M. J., Caldwell, R. C., & Hourigan, A. (2008). The influence of curricular-based interventions within first-year “success” courses on student alcohol expectancies and engagement in high-risk drinking behaviors. The NASPA Journal, 45(1), 49–72.

    Google Scholar 

  • Mayhew, M. J., Stipeck, C., & Dorow, A. (2007). The effects of orientation programming on academic and social learning with implications for transfers and students of color. Paper Presented at the Association for the Study of Higher Education, Kansas City, MO.

  • McDonald, S., & Vrana, S. (2007). Interracial social comfort and its relationship to adjustment to college. The Journal of Negro Education. Retrieved October 15, 2007, from

  • Milem, J. F., & Berger, J. B. (1997). A modified model of college student persistence: Exploring the relationship between Astin’s theory of involvement and Tinto’s theory of student departure. Journal of College Student Development, 38(4), 387–400.

    Google Scholar 

  • Mullendore, R. H., & Banahan, L. (2005). Designing orientation programs. In J. Gardner, L. Upcraft, & B. Barefoot (Eds.), Challenging and supporting the first-year student: A handbook for improving the first college year. San Francisco: Jossey-Bass.

    Google Scholar 

  • Nixon, P. N., & Martin, N. (1994). The effects of freshman orientation and locus of control on adjustment to college: A follow-up study. Social Behavior and Personality: an International Journal, 22(2), 201–208.

    Article  Google Scholar 

  • Palomba, C. A., & Banta, T. W. (1999). Assessment essentials: Planning, implementing, and improving assessment in higher education. Jossey-Bass: San Francisco.

    Google Scholar 

  • Pascarella, E. T. (1985). College environmental influences on learning and cognitive development: A critical review and synthesis. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 4, pp. 1–61). New York: Agathon.

    Google Scholar 

  • Pascarella, E. T., Terenzini, P. T., & Wolfe, L. M. (1986). Orientation to college and freshman year persistence/withdrawal decisions. The Journal of Higher Education, 57, 155–175.

    Article  Google Scholar 

  • Quintana, S., Vogel, M., & Ybarra, V. (1991). Meta-analyses of Latino students’ adjustment in higher education. Hispanic Journal of Behavioral Sciences, 13(2), 155–168.

    Article  Google Scholar 

  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Schlossberg, N. K. (1984). Counseling adults in transitions. New York: Springer Publishing Company.

  • Schuh, J. M., & Upcraft, M. L. (2001). Assessment practice in student affairs: An applications manual. San Francisco: Jossey-Bass.

  • Smedley, B. D. (1993). Minority-status stresses and the college adjustment of ethnic minority freshmen. Journal of Higher Education, 64(4), 434–452.

    Article  Google Scholar 

  • Smith, B. F., & Brackin, R. (1993). Components of a comprehensive orientation program. In M. L. Upcraft, R. H. Mullendore, B. O. Barefoot, & D. S. Fidler (Eds.), Designing successful transitions: A guide for orienting students to college (pp. 35–48). Columbia, SC: University of South Carolina.

  • Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: University of Chicago Press.

    Google Scholar 

  • Upcraft, M., & Farnsworth, W. (1984). Orientation programs and activities. New Directions for Student Services, 25, 27–38.

    Article  Google Scholar 

  • Upcraft, M. L., & Schuh, J. H. (1996). Assessment in student affairs: A guide for practitioners. San Francisco: JosseyBass.

    Google Scholar 

  • U.S. Department of Education, National Center for Education Statistics. (2001). Digest of Education Statistics 2000 (NCES 2001-034). Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement.

  • Weidman, J. (1989). Undergraduate socialization: A conceptual approach. In J. Smart (Ed.), Higher education: Handbook of theory and research (5th ed. pp. 289–322). New York: Agathon.

  • Williford, A. M., Chapman, L. C., & Kahrig, T. (2001). The university experience course: A longitudinal study of student performance, retention, and graduation. The Journal of College Student Retention, 2(4), 327–340.

    Article  Google Scholar 

  • Wilson, R. (2007, January 26). The new gender divide. The Chronicle of Higher Education, 53(21), A36.

    Google Scholar 

Download references


The authors gratefully acknowledge Drs. Heidi Grunwald and Laurie Behringer for their help in editing the paper.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Matthew J. Mayhew.



Fully Unconditional Models

For academic and social learning, the fully unconditional models can be expressed using a similar equation,

$$ Y_{ij} \, = \beta_{0j} + r_{ij}. $$

Y ij is the dependent variable (i.e., academic or social learning); β0j is the institution mean for institution j; and r ij is the deviation from the institution mean for students ij. Results of the fully unconditional model are used to estimate the proportion of variance that exists between and within colleges. In this case, the proportion of variance explained by institutional differences was approximately .053 for academic learning and .030 for social learning.

Level-1 Models

The Level-1 models can be represented as,

$$ \begin{gathered} Y_{ij} \, = \, \beta \, _{0j} + \, \beta \, _{ 1} \, \left( {Transfer*} \right) \, + \, \beta \, _{ 2} \, \left( {Male*} \right) \, + \, \beta \, _{ 2} \left( {Transgendered} \right) \, + \, \beta \, _{ 3} \, \left( {No \; reported \; gender} \right) \hfill \\ + \, \beta \, _{ 4} \, \left( {African \; American} \right) + \, \beta \, _{ 5} \left( {Asian \; American} \right) \, + \, \beta \, _{ 6} \, \left( {Hispanic \; American} \right) \; + \, \beta \, _{ 7} \, (Native \hfill \\ American) \, + \, \beta \, _{ 8} \, \left( {Mutiracial} \right) \, + \, \beta \, _{ 9} \, \left({Non{\text{-}}US \; Citizen} \right) \, + \, \beta \, _{ 10} \, \left( {Other} \right) \, + \, \beta \, _{ 1 1} \, (No \; reported \hfill \\ race/ethnicity) \, + \, \beta \, _{ 1 2} \, \left( {Academic\;expectation} \right) \, + \, \beta \, _{ 1 3} \left( {Academic \; Transition} \right) \, + \, \beta \, _{ 1 4} \hfill \\ \left( {Positive \; experiences \, with \, staff} \right) \, + \, \beta \, _{ 1 5} \, \left( {Navigating \; campus\;resources} \right) \, + \, \beta \, _{ 1 6} (Usefulness \hfill \\ of \; information) \, + \, r_{ij} , \hfill \\ \end{gathered} $$

where Y ij (i.e., academic or social learning) is a function of the average academic learning at an institution (β0j ) based on the effect of being a transfer student (β1), the effect of gender (β2–β3), the effect of race (β4–β11), the effect of perceived ac academic, social, and functional experiences (β14–β16), and error (r ij ).

*Although data structures for the Level-1 models for academic and social learning were similar, the process of centering variables was not. Based on preliminary results, for the academic learning model, variances for variables marked with an asterisk (i.e., transfer status and for indicator variables comparing men and women) were not constrained due to their likelihood of being influenced by Level-2 predictors. For the social learning model, variances for all Level-1 independent variables were constrained.

Level-2 Intercept-only Models

Level-2 intercept-only models for academic and social learning included the following representations:

$$ \begin{gathered} \beta_{0j} = \, \gamma_{00} + \gamma_{{01}} (\% \, Private) + \gamma_{{ 02 }} \left( {Average \; undergraduate \; enrollment} \right)+ \gamma_{{03 }} (\% {\text{ Housed\; in }} \hfill \\ {\text{Academic \;Affairs}}) + \gamma_{{04}} \left( {\%\;{\text{Housed \;in \;Student \;and \;Academic \;Affairs}}} \right) + \gamma_{{ 05 }} (\% {\text{ Housed\;in }} \hfill \\ {\text{Enrollment\; Management}}) + \gamma_{{ 06 }} \left( {\% {\text{\;Designated \;orientation \;office}}} \right) \, + \, u_{0 j,} \hfill \\ \end{gathered} $$

where the average academic or social learning at the institution (β0j ) was a function of institutional control (γ01), institutional size (γ02), the sponsoring division of orientation programming (γ03–γ05), whether or not the institution had a designated office for orientation programs (γ06), and deviations from the institutional average (γ00), plus error (u 0j ). This function is the same for the academic and social learning models; however, we added an additional equation to predict slopes in the academic learning model.

Level-2 Final Slope-as-Intercepts Model for Academic Learning

For academic learning, we were interested in explaining the academic learning gaps between transfer students and those originating at the institution. To do this, we included an additional model representation:

$$ \beta_{ 1j} = \, \gamma_{10} + \gamma_ {11} \left( {\%\;{\text{Designated orientation office}}} \right) \, + \, u_{ 1j,} $$

where (β1j ) indicated the relationship between transfer status and academic learning for each institution. This relationship (slope) is a function of whether or not the institution had a designated office for orientation programs (γ16), deviations from the institutional average (γ10), plus error (u ij ).

For ease with data interpretation, the reference group (i.e., institution average) for these data represents students enrolled in public institutions, with an average undergraduate enrollment, where orientation is housed in Student Affairs only, and that do not have a designated orientation office. This means coefficients representing γ11 should be interpreted as the incremental change that having a designated orientation office on campus contributes to explaining the relationship between academic learning and transfer status.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mayhew, M.J., Vanderlinden, K. & Kim, E.K. A Multi-Level Assessment of the Impact of Orientation Programs on Student Learning. Res High Educ 51, 320–345 (2010).

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: