Integration of Embedded Lead Tutors

1. Integration of Embedded Lead Tutors Abstract In a collaboration between the Pirate Tutoring Center and several faculty members on campus, we have implemented an embedded lead tutor model similar to that from Muhlenberg College. We seek to investigate how students utilize lead tutor services and the impact of the embedded lead tutor model on student performance.  Specifically, we analyzed the size of tutored populations, the class performance on the final exam, and tutoring trends by grade category in Dr. Carter’s Chemistry 1130 class from Fall 2011 to Spring 2013. Results and Discussion Size of Tutored Population: A significant increase in the size of the tutored population is observed for the courses with embedded tutors. This increase is even greater for Spring 2013, which had embedded tutors in the prerequisite class the previous semester. Class Performance on Final Exam: A significant increase in class performance on the final exam is observed for the courses with embedded tutors. This increase is even greater for Spring 2013, which had embedded tutors in the prerequisite class the previous semester. However, these improvements are seen in both the tutored and non-tutored populations of students, and therefore may reflect another influence. The final exam content has remained largely unchanged across all four semesters. Tutoring Trends by Grade Category: In Fall 2011, when there was not an embedded tutor, there were similar averages of tutoring contacts per student in each grade category. In Spring 2012 and Fall 2012, when there was an embedded tutor in CHEM 1130 but not for the prerequisite course (CHEM 1120) in the previous semester, there was a significant increase in the size of the tutored population. However, in both semesters, the average number of contacts per student for the A students was significantly lower than the averages for B or C students. In Spring 2013, however, when an embedded tutor was available both in this course and the prerequisite course the previous semester, the A students had the largest average of tutoring contacts per student. This data suggest that, prior to Spring 2013, the best students were not seeking tutoring as often as other students. The observed changes in passing rate and course GPA are consistent with this explanation. Corroboration with beginning-of-semester GPA, high school GPA, and SAT scores is needed to strengthen this observation. • Embedded Lead Tutor Model • The Embedded Lead Tutor Model combines components of supplemental instruction and peer tutoring to support the academic success of students in specific courses that are thresholds to progression toward a major. Courses will be identified based on enrollment, academic department support, and impact on persistence toward major completion. We will focus on data associated with Chemistry 1130. • Embedded Lead Tutors Responsibilities: • Meet with faculty mentor each week • Hold four hours of tutoring appointments each week • Lead one course concept workshop each • Attend class 2 hours each week (2 times) • Provide walk in tutoring one evening each week (3 hours)    • Supervise and lead volunteer peer tutors during walk in hours • Record and post workshops and test reviews weekly on Tegrity • Spend one hour on workshop preparation each week • Attend CRLA training sessions including ADED 3500 first semester tutoring • Present tutoring materials utilizing UDL and PTC technology • Peer Tutors Responsibilities: • Provide walk in tutoring one evening each week (3 hours)   • Attend PTC tutor orientation and level I training sessions • Review course materials with Lead Tutor in preparation for tutoring sessions • Course Faculty Responsibilities: • Assist in the identification and recruitment of volunteer tutors • Meet weekly with the Lead Tutor and assist in workshop planning • Utilize the Starfish early alert system and identify students in academic difficulty • Grant access to Blackboard and other course systems for the Lead Tutor (if applicable) • Share course syllabus and exam schedules with PTC staff • Encourage and refer students to tutoring services • This information has been developed into a resource handbook to facilitate the faculty mentor’s effectiveness in implementing the Embedded Tutor Model. This handbook will be available to faculty beginning Summer 2013. • Future Directions • Perform statistical analysis to address the following research questions: • Is there a relationship between the number of tutoring contacts and student course grades? Is there a relationship between the number of tutoring contacts and end-of-semester GPA? • Do these relationships vary with beginning-of-semester GPA, high school GPA, or SAT score? Do these relationships vary with the type of tutoring contact (tutor-led workshop, drop-in tutoring, or study skills session)? • Use surveys and interviews to address the following research questions: • What do students and tutors perceive as the value of tutoring in general and the lead tutor model in particular? • What factors influence whether or not a student attends tutoring sessions? Faculty Learning Community Members Andrea Carter – Chemistry Teaching Instructor David Bjorkman – Chemistry Teaching Instructor Elizabeth Coghill – Director of the Pirate Tutoring Center Robin Grochowski – Learning Specialist Sheryll Wood – English Teaching Instructor Data Collection and Analysis The database of PTC attendance records were compared with Dr. Carter’s CHEM 1130 course grades for Fall 2011 – Spring 2013. These data were analyzed to determine the average number of tutoring contacts per student, the passing rate, course GPA, and final exam average for tutored versus non-tutored populations. Pirate Tutoring Center Lead Tutors Evan Arthur, Ruby Behairy, Jason Atkinson, Michael Shea, Alexandra Simkus, Rachel Johnson, Katie Stephens, Julianna Womble, Cori Wright