Faculty Lend a Helping Hand to Student Success: Measuring Student-Faculty Interactions

1. Faculty Lend a Helping Hand to Student Success: Measuring Student-Faculty Interactions Amber D. Lambert, Ph.D. Louis M. Rocconi, Ph.D. Amy K. Ribera , Ph.D. Angie L. Miller, Ph.D. Yiran Dong Center for Postsecondary Research Indiana University

2. Outline • Literature Review • Current Study • Methods • Results • Discussion

3. Literature Review • Student-faculty interactions generally have a positive influence on educational outcomes, such as: • Cognitive growth and development of college students (Astin, 1993; Pascarella & Terenzini, 2005) • Retention (Lau, 2003; Pascarella & Terenzini, 1977) • Recent researchers have found that not all kinds of interaction have the same impact on student outcomes

4. Literature Review (cont.) • Teaching Clarity • Refers to teaching methods where “faculty demonstrate a level of transparency in their approach to instruction and goal setting in an effort to help students better understand expectations and comprehend subject matter” (BrckaLorenz, Ribera, Kinzie, & Cole, p. 2) • Positive relationship with various educational outcomes such as student achievement and satisfaction (Hativa, 1998; Pascarella & Terenzini, 2005; Chesebro & McCroskey, 2001). • Good Faculty Practices • Relationship between student and faculty that moves beyond the formal instruction that takes place during class (Crisp, 2009)

5. The Current Study • Main purpose: To explore how to measure student interactions with faculty in a concise way as part of a larger survey. • In particular, we are interested in: • whether the student-faculty interaction items load onto two distinct components, • if these two components have good measurement properties, • whether these components are good predictors of GPA and persistence into the second year.

6. Method: Participants • Data from the National Survey of Student Engagement (NSSE) 2011 pilot study (NSSE 2.0) • 1,006 first-year and 2,578 senior students attending 19 U.S. institutions • Institutions represented variety of regions, Carnegie classifications, and enrollment sizes • 34% males and 66% females • 79% with full-time enrollment status

7. Method: Measures • All relevant survey items from the 2011 NSSE 2.0 pilot administration were included in the EFA. • CFA analyses were done for those items that fell into the student-faculty interactions components. • For the predicative validity analyses, survey responses were also merged with institution-provided grade-point-average (GPA) and persistence outcome. • Controls: gender, ethnicity, parental education level, and prior academic ability (composite SAT and ACT scores)

8. Method: Analyses • A variable was created using a random number generator that put each student into one of two groups. • The first group consisting of half of the sample was used to conduct the exploratory factor analyses. • Direct oblimin rotation (oblique) used • The second half was used in the confirmatory factor analysis. • AMOS used to build the model • The entire sample was used for the predictive validity analyses. • Ordinary least squares (OLS) regression models for academic year GPA • Logistic regression models for persistence

9. Confirmatory Factor Analysis: Model-fit Results • Note: Strong model fit is reflected by GFI greater than .85, • CFI greater than .90, RMSEA less than .06, and PCLOSE greater than .05.

10. Path Model for Good Faculty Practices and Teaching Clarity Subscales

11. Items, CFA Factor Loadings, and Cronbach’s Alphas for First-Year and Senior Students

12. Predicting GPA

13. Predicting Persistence • Those in the middle 50% on Teaching Clarity have 79% greater odds of being retained than those in the bottom quartile of Teaching Clarity • Those in the top quartile of Teaching Clarity have 53% greater odds of being retained than those in the bottom quartile of Teaching Clarity • The average persistence rate difference between those in the top and bottom quartile in Good Faculty Practices was 7%

14. Limitations • NSSE is limited to those institutions that choose to participate • Not all of the possible predictors of GPA and persistence were available to us • Relied on self-reported perceptions of student experiences

15. Discussion • These items included in a larger survey serve as a good proxy for student-faculty interactions • Both the EFA and CFA suggest that these items make two strong scales for teaching clarity and good faculty practices that are also related to one another • As theorized from the literature, these measures for teaching clarity and good faculty practices also influence students’ GPA and persistence

16. Discussion (cont.) • These results would suggest that the more personal interactions found in the good faculty practices scale, such as got to know you and your background, that are much less likely to be found on course evaluations might be a more important predictor of student success • Measures like the short sets of items described previously could provide additional information on some aspects of classroom practices that are not being measured by course evaluations, especially in light of the problems with course evaluations and other assessments of instructor quality

17. Questions? Comments?

18. Contact Information • Amber D. Lambert – email: adlamber@indiana.edu • Louis M. Rocconi – email: lrocconi@indiana.edu • Amy K. Ribera – email: agarver@indiana.edu • Angie L. Miller – email: anglmill@indiana.edu • Yiran Dong – email: yidong@indiana.edu

19. References • Astin, A. (1993). What matters in college? Four critical years revisited. San Francisco: Jossey- Bass. • BrckaLorenz, A., Ribera, A., Kinzie, J., & Cole, E. (in press). Examining effective faculty practice: Teaching clarity and student engagement. To Improve the Academy, 31.      • Chesebro, J. L., & McCroskey, J. C. (2001). The relationship of teacher clarity and immediacy with student state receiver apprehension, affect and cognitive learning. Communication Education, 50(1), 59-68. • Crisp, G. (2009). Conceptualization and initial validation of the college student mentoring scale. Journal of College Student Development, 50(2), 177-191. • Hativa, N. (1998). Lack of clarity in university teaching: A case study. Higher Education, 36(3), 353-381. • Lau, L. K. (2003). Institutional factors affecting student retention. Education, 124(1), 126–136. • Pascarella, E. T., Terenzini, P. T. (1977). Patterns of student-faculty informal interaction beyond the classroom and voluntary freshman attrition. Journal of Higher Education, 48(5), 540-552. • Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students(Vol. 2): A third decade of research. San Francisco: Jossey-Bass.

20. Register Now for NSSE 2013 • (deadline Sept. 25) Introducing Updated NSSE • Retains NSSE’s focus on diagnostic & actionable information • New Engagement Indicators • Academic challenge • Deep approaches to learning • Collaborative learning • Quantitative reasoning • Experiences with faculty • Campus environment • Interactions with diversity • Modules • New & Updated Items • Comparisons to Prior-Year Results • FSSE & BCSSE Updates