Data Filename: CUES_Update_2013_2014 ReadMe File Name: ReadMe_ CUES_Update_2013_2014.txt ReadMe File Contents: Introductory Information: Data Filename: CUES_Update_2013_2014.pdf Data File Description: These data were collected as part of the Cornell University Engineering Success (CUES) Program. Data collected include student grades, test scores and retention. Data File Format: PDF Related Data Files: CUES_Focus_Groups_Summer_2014_Report.pdf Other Related Files: Spatial_Visualization_Test.pdf (Spatial Visualization Test) Principle Investigator / Data Owner: Alan T. Zehnder, Professor, Mechanical and Aerospace Engineering, Cornell University, Ithaca NY 14853, atz2@cornell.edu Associated Investigators: Sara X. Hernandez Contact Person: Director, Diversity Programs in Engineering, 146 Olin Hall, Cornell University, Ithaca NY 14853, dpeng@cornell.edu Data Collection Dates: 20130601-20150601 Data File Submission Date: 20150626 Data File Updated: None Keywords: engineering, spatial visualization, tutoring, mathematics, retention, underrepresented minority, first generation college, undergraduate Data File Contents: File provides a short summary of activities in the CUES Program, early retention indicators (persistence and grades), pre- and post-test scores on the Purdue Spatial Visualization Test, outcomes (grades) of students participating in tutoring and grade ranges of students participating in the Engineering Summer Math Institute. Project Information: Project Goals: The College of Engineering at Cornell University has a strong record of recruiting of underrepresented minority students. The goal of the Cornell University Engineering Success Program (CUES) is to increase the retention and graduation rate of underrepresented minority (URM) and first generation college (FGC) students from the current 66% to 84%, a level equal to the overall engineering student body. The CUES program includes three programs designed address known barriers to the success of highly qualified Cornell engineering students. These programs include: a spatial reasoning course; an engineering summer math institute; and enhanced tutoring program. Major Elements of CUES: Spatial Visualization (SV) Participants in the SV course were our cohort of pre-freshman summer students, also known locally as Ryan Scholars. Through the admissions process, all of these scholars have shown the potential to be successful as engineering students. However, they are selected for participation in the Pre-Freshmen Summer Program due to multiple background characteristics that place them at risk and that may hinder their persistence in engineering. They arrive on campus in the summer before their freshman year to acclimate to Cornell, to start taking their first calculus and computer science course, the SV course and to engage each other and the faculty and staff in engineering. The SV course was taught in Cornell’s 6 week summer session, meeting twice a week for 90 minutes. . The course material included the previously developed NSF ENGAGE curriculum covering topics such as rotations, reflections, flat-patterns, cutting planes, combining objects, and isometric/orthographic sketching. The course also included team projects. Each group was asked to create clear and accurate visuals using Cornell biomedical engineering faculty current engineering research data. These projects challenged the students’ ability to understand, manipulate, and communicate complex SV concepts. Mastery of these topics will help the students in future courses in engineering, chemistry and mathematics, especially multivariable calculus. Engineering Summer Math Institute (ESMI) A student’s poor performance in core math courses has been identified as a critical variable in the persistence of students in engineering. To address this barrier, ESMI provides students the ability to take a core math course (multivariable calculus, differential equations or linear algebra) and participate in research. Tuition, housing and a small stipend are provided to the students. Successful completion of the math course keeps students on track for affiliation and graduation. ESMI students were either placed in faculty research labs, or participated in a group project of a mathematical nature. The goal of project/research component of ESMI, is to connect the mathematics and other subjects learned in the classroom to engineering problems. Additionally all student participants attended Collaborative Learning Groups (CLGs) to augment their in- class work. CLGs are small-group, active learning sessions that provide students with a smaller, cooperative learning environment where they work together on concepts and sample problems to enhance understanding of course material. Enhanced Tutoring Program In the College of Engineering at Cornell, many majors require the successful completion of one or more specialized engineering science courses before a student is allowed to affiliate with that major. Students taking their first engineering courses in their majors are faced with needing for the first time, to translate and integrate knowledge. Despite the need, access to tutors becomes much more challenging when students begin to take these major specific courses since formal tutoring programs focus on introductory courses. The goal of the CUES enhanced tutoring program was to provide tutoring in distribution courses that are needed to affiliate with a major and also upper-level courses that are required to remain in god standing in a major. The program pre-identified courses based on data analysis of registration and grades in courses for the target populations in previous semesters. The success criterion for this intervention was the retention of target populations and the successful completion of the course by the tutees with a grade needed for affiliation. Courses for which tutoring was offered: Fall 2013: BEE 2600 CHEME 3130 CS 2800 ENGRD 2020 ENGRD 2110 ENGRD 2190 ENGRD 2200 ENGRD 2210 ENGRD 2300 ENGRD 2700 MAE 3780 Spring 2014: CEE 3510 CHEM 3900 CS 2800 ENGRD 2020 ENGRD 2110 ENGRD 2300 MAE 2030 PHYS 2213 Fall 2014: AEP 4230 BEE 3500 ENGRD 2020 ENGRD 2100 ENGRD 2110 ENGRD 2190 ENGRD 2210 ENGRD 2300 ENGRD 2700 ENRGD 2190 MSE 3010 Spring 2015: CS 3410 ENGRD 2110 ENGRD 2300 ENGRD 2700 ENGRD 2800 ENGRD 3200 MAE 2030 MAE 2120 Notes: 2000 = sophomore level, 3000 = junior, 4000 = senior AEP = Applied and Engineering Physics BEE = Biological and Environmental Engineering CHEME = Chemical and Biomolecular Engineering CEE = Civil and Environmental Engineering CHEM = Chemistry CS = Computer Science ECE = Electrical and Computer Engineering ENGRD = Engineering Distribution MAE = Mechanical and Aerospace Engineering MSE = Materials Science and Engineering PHYS = Physics Course Names: BEE 2600 Principles in Biological Engineering CHEM 3900 - Honors Physical Chemistry II CHEME 3130 - Chemical Engineering Thermodynamics CEE 3510 - Environmental Quality Engineering CS 2800 - Discrete Structures CS 3410 - Computer System Organization and Programming ENGRD 2020 - Statics and Mechanics of Solids ENGRD 2100 - Introduction to Circuits for Electrical and Computer Engineers ENGRD 2110 - Object-Oriented Programming and Data Structures ENGRD 2110 - Object-Oriented Programming and Data Structures ENGRD 2112 - Object-Oriented Design and Data Structures - Honors ENGRD 2190 - Mass and Energy Balances ENGRD 2210 – Thermodynamics ENGRD 2300 - Digital Logic and Computer Organization ENGRD 2700 - Basic Engineering Probability and Statistics ENGRD 3200 - Engineering Computation MAE 2030 - Dynamics MAE 2120 - Mechanical Properties and Selection of Engineering Materials MAE 3272 - Mechanical Property and Performance Laboratory MSE 3010 - Materials Chemistry PHYS 2213 - Physics II: Electromagnetism Sharing and Access Information: Licensing: This data is freely available for re-use. Please acknowledge the CUES: Cornell University Engineering Success Program, NSF Grant 1317501, Prof. Alan T. Zehnder and Sara X. Hernandez in any publications that use this data. Related Publications: Adebayo, OO, Evans, R, Farrar, EJ, McCray, T, Nathans-Kelly, T (2014). “Empowering early mastery of spatial-visualization skills in underrepresented minority engineering students.” Proceedings of the 2014 IEEE Frontiers in Education Conference. Madrid, Spain. Farrar, EJ, Adebayo, OO, McCray, TL, Nathans-Kelly, T, Evans, (2014). “Learning Spatial Visualization: Beyond Drills and into Early Mastery”. Proceedings of the 2014 European Society of Engineering Education (SEFI) Conference. Birmingham UK Recommended Citation: Alan T. Zehnder and Sara X. Hernandez, Data from: “CUES: Cornell University Engineering Success Program,” Identifier Funding Information: Collection of the data was funded by NSF grant 1317501