1. Early Identification of Introductory Major's Biology Students for Inclusion in an Academic Support Program BETHANY V. BOWLING and E. DAVID THOMPSON Department of Biological Sciences, Northern Kentucky University At Northern Kentucky University our first semester Introductory Biology Course for science majors has an attrition rate of 35-40%. In an effort to improve student success in the course we sought to identify students early in the semester in need of academic support. Based on previous research we investigated the use of ACT scores in math (ACT-M) and scores on a test of scientific reasoning (SR; Lawson, A.E. 2000) as predictors of student success. Students scoring in the lowest third in either ACT-M or SR were randomly selected to be included in a university-wide peer mentoring program in which an upper-level student served as a small group leader for reviewing course material. Our results show that both SR (r=0.476, p.05), indicating the best model may be SR scores alone. While further investigation into the use of SR and ACT-M scores as predictors of success in the course is being conducted, our results suggest peer mentoring in its current form may not be an effective academic support strategy as there was no significant difference between students in the program and those with equally low predicting factors [t(47)=0.26, p>.05]. A number of studies over the last thirty years have considered various factors related to student success in introductory biology courses, including prior content knowledge, self-efficacy, cognitive style, developmental level, college entrance exam scores, grade point average (GPA), success in prior courses, reasoning ability, and course pedagogy. Anton Lawson, a well-known biology education researcher, has authored several papers on this topic. His research has continuously shown that scientific reasoning (SR) ability is the best predictor of student success in introductory biology courses (1980; 1997; 2007), not prior content knowledge as many instructors mistakenly believe. Additional research has been conducted on standardized college entrance exams, the ACT and SAT, as predictors of success in individual courses, since they are considered a predictor of success for the first year of college in general. These studies have indicated mixed results (Freeman et al., 2007; Johnson & Lawson, 2007). Previous research at NKU has suggested that ACT math scores (ACT-M) are a valid predictor of student success in our introductory biology courses. In regards to the present study, we identified the need to provide academic support for students in our introductory biology course due to the high attrition rate of around 40%. Traditionally, a university-wide sponsored peer mentoring program was offered to students seeking assistance. Since often students do not know of their performance level until after the first or even second exam, we sought to identify students early in the semester in need of academic support. Based on previous research in the literature and findings at our institution, we investigated the use of ACT-M and SR as predictors of student success. Background Objectives Methods Results Discussion 1.Determine the best factor for identifying students early in the semester in need of academic support 2.Analyze effectiveness of peer mentoring program With IRB approval, the SR test was administered to students in the first semester of the NKU Introductory Major’s Biology course series (BIO 150) on a voluntary basis. Prior ACT-M scores for the same students were also collected. Students were notified that results of the SR test and ACT-M scores would be used as the basis for our selection of students to take part in a peer mentoring program, conducted by the NKU Department of Biological Sciences and the Office of the Associate Provost for Student Success. Student volunteers scoring in the lowest third of the SR exam were selected. The same selection process was utilized for ACT-M scores. In all, 60 students were identified, 30 each based on SR and ACT-M scores. These students were divided at random into two groups, those who would receive peer mentoring and those who would not take part in the program. As such, we had a natural “control” for the study. Each peer mentoring group consisted of 5 students and one peer mentor. Peer mentors were upperclassmen selected because of proficiency in the subject matter, as evidenced by overall GPA and previous success in the same course. Each group was instructed to meet for one collaborative learning session each week at a time convenient for all members. The primary responsibility of the peer mentors was to provide structure to the collaborative learning session, and to ensure that problem- solving progressed at a reasonable pace. Additionally, peer mentors were required to “shadow” each student member of their group. This entailed meeting with each student every two weeks to assist with management of the academic schedule, to provide proactive mentor support, and to monitor academic progress. Overall BIO 150 lecture percentage was selected as the measure of student success in the course, with 70% as the selected value required for successful completion of the course. At the end of the semester, overall percentages were graphed as a function of SR or ACT-M and analyzed via correlation coefficients. Based on these data, a hierarchical multiple regression model was also conducted. After determining SR to be the best known predictor for our course, we developed a linear regression model to predict the grade that students were likely to receive based on SR scores [(course percentage) = 56.36 + (1.2)(SR), R 2 = 0.23]. Using this model, a score of 11 or better on the scientific reasoning test correlates to 65.9% or higher in BIO 150. The 33 students who had SR scores of 11 or less were compared to the remaining 113 students who finished the course and received SR scores of 12 or higher. To discern the efficacy of the peer mentoring program, the same 33 students who had SR scores of 11 or less were compared based on whether they received or did not receive peer mentoring. In our study, both SR (r=0.476, p<.001) and ACT-M (r=0.325, p<0.001) were significantly correlated with students’ grades in the course. The correlation for SR and course grade are similar to others in the literature. For instance, two of Lawson’s studies reported correlations between SR and course grade as r=0.43 (1998) and r=0.45 (2007). The correlation between ACT-M and course grade does differ from one study by Lawson which reported a correlation of the more general ACT/SAT scores with course grade as r=0.52. Our results are also a bit contradictory to previous studies indicating ACT-M as a predictor of success in our introductory biology courses. However, the previous studies conducted did not consider SR. Since both the predictors were significantly correlated with course grade, we conducted a hierarchical multiple regression model including both parameters. Interestingly, the model was found not to be statistically significant (r=.480, p>.05), suggesting it was not useful as a predictive tool. Based on our current data, it appears that SR alone may be the best predictor for student success in NKU’s introductory biology courses. We are continuing our research efforts in this area with slight modifications to the protocol for administering the SR test. Preliminary findings show we’ve had a greater participation rate by the students, thus increasing our sample size and providing a more representative sample of the population. This may contribute to a higher correlation coefficient between our predictor and course grade, providing a more accurate model. A comparison of final lecture scores based on SR results indicated a significant difference in the scores of those students above the predicted threshold (SR > 11) compared to students who scored below the threshold (p=0.005). This further suggests that scientific reasoning ability is a worthy predictor for student success in NKU’s introductory biology courses. However, students who scored below the threshold did not seem to benefit from the peer mentoring program. Students with at- risk SR scores who received peer mentoring performed no better than students with equivalent SR scores who did not receive peer mentoring (p>0.05). Previous versions of the peer mentoring program have offered a financial award for success in the course, which may have led to poor performance on the SR test in this study. To address this, we are exploring the possibility of developing a peer mentoring program in the future without financial awards. Parameter BSE of B β Step 1 Constant56.3553.800 SR 1.207.259.476 a Step 2 Constant53.0976.800 SR 1.099.320.434 ACT-M.213.368.073 Note: R 2 =.226 for step 1, Δ R =.003 for step 2. a. p <.001 Regression Analysis of Predictors of Student Success Note: SR (N=84) R 2 =.226, ACT-M (N=123) R 2 =.106. Student Success vs. Predicting Factors * Comparison of BIO 150 final lecture scores based on predicted success (SR>11). Note: The bars show arithmetic means with error bars indicating standard error of the mean, * denotes values that are significantly different (p = 0.005; t-test). Comparison of BIO 150 final lecture scores of students predicted to be at-risk at taking part in peer mentoring. Note: The bars show arithmetic means with error bars indicating standard error of the mean (p > 0.05; t-test).