About

Genevieve Lipp is an Assistant Professor of the Practice in Electrical and Computer Engineering and Mechanical Engineering and Materials Science at Duke University. She has recently taught courses in programming, dynamics, control systems, and robotics. Her primary areas of interest include integrating computing education into the engineering curriculum, mastery learning for programming, and enhancing engineering students' self-efficacy. Lipp earned a B.S.E. in mechanical engineering and a B.A. in German from Duke University, as well as an M.S. and Ph.D. in mechanical engineering with a focus on nonlinear dynamics from Duke University. She holds positions as the Director of Duke Engineering First Year Computing and as an Assistant Professor of the Practice in the Department of Mechanical Engineering and Materials Science, contributing to both departments through teaching and research activities.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Mathematics
• Pedagogy
• Engineering ethics
• Mechanical engineering
• Psychology
• Mathematics education

Selected publications

• WIP: Establishing Peer Observation of Engineering Teaching (POET) as a More Effective Means of Evaluating Teaching

  2025-08-21

  article
  PublisherDOI

• Board 147: Work-in-Progress: The Effect of Summarizing a Research Article on Students' Area of Robotics Interest

  2024

  Senior authorCorresponding
  • Artificial Intelligence
  • Computer Science
  • Artificial Intelligence

  Abstract Background The need for capable, ethical robotics engineers is growing with the industry valued at 32.32 billion in 2021 with anticipated growth of 12.1% from 2022 to 2030, and projected 17,900 mechanical engineering job openings each year. It is imperative that undergraduate and graduate programs prepare engineers for industry positions in robotics, and that they include and encourage diverse groups of students to enter the field. One way universities expose students to robotics disciplines is through research experiences, For example, the NSF REU Sites program where undergraduates are supported as they do research projects at a host institution. Research experiences allow students to develop skills of synthesizing information and thinking about the state-of-the-art for a field. Generally, engineering students choose research topics based on their previous experience, especially when they are intrinsically motivated. We believe more exposure to cutting-edge research enables students to see themselves working in and understand their options for a career in robotics. Additionally, it is hypothesized that this impact may be larger for diverse students with less exposure to working and research opportunities in robotics outside of class. While some research experiences require multiple months or semesters of student time commitment, we seek to understand whether a smaller intervention has an effect on robotics student preferences for specialized areas. Methods Participants in this study were from the only Introduction to Robotics course offered at Duke University (n=46). Participants ranged from third year undergraduates to first year graduate students. The intervention in this experiment is assigning a research paper in a particular topic in robotics. Anecdotal feedback from previous semesters has indicated that students enjoy having multiple homework assignments throughout the semester when they are asked to look up a paper related to a particular field covered in the robotics class, e.g. finding a paper in the field of manipulation and mobility after a manipulation lecture. The hypothesis of this work is that the topic that is assigned would affect the students relative interest in the subject and in pursuing a job or internship in the field after the course has ended. During the semester, students were asked to look up papers of the same subject for the first half of the semester. In the second half of the semester, students were grouped into the topics of either motion planning or control when being asked to look up papers in the subject. Students were asked three times during the semester (beginning, during midterm feedback, during final feedback) for their levels of interest in robotics areas (five-point Likert scale), specifically motion planning, control, human-robot interaction, and medical robotics. They were also asked if their interest in these topics leads them to pursue a post graduate job in this field, as many were in the job market for internships and full time positions. A demographic survey will also be distributed with the final feedback survey to gather data including education levels of the parents of participants and diversity characteristics. Expected results include having a measurable understanding of the effect of both giving research papers on their interest in a topic generally and for pursuing a potential career in the field. Anecdotal evidence suggests students enjoy the process of reading about aspects of robotics that are novel to the field, which we may not get to touch on in class, except through this exercise. Still, we cannot verify the full extent of how this intervention affects their interest relative to before the class. To assess this in the study, if there is sufficient parametric data, a paired t-test will be used to assess significance of the intervention. If the data are nonparametric, a single or paired sample sign test will be used to test the effect of the intervention and potential paired demographic effects of the intervention based on pre/post survey data. Conclusions Increasing the pipeline of diverse roboticists starts, in part, with impressionable experiences in introductory robotics classes. Assigning research paper reviews can help students understand the opportunities post-graduation in robotics and the challenges that are yet to be solved. The objective of this work is to measure the impact of a simple intervention that can be incorporated into homework assessment in many different styled classes in robotics.

  PublisherOA PDFDOI

• It’s Curling Night in New Orleans!

  2023

  • Computer Science
  • Computer Science
  • Engineering

  Abstract In a sophomore level dynamics class in Duke University’s Mechanical Engineering and Materials Science department, a tabletop sized ice rink was built to demonstrate collision mechanics of Curling stones. Cameras and image processing techniques were used to collect object position versus time data for single impact scenarios. The data was then analyzed by students to calculate the coefficient of restitution and velocities of each object after collision. Through this interactive, hands-on laboratory experiment, students were able to learn this foundational engineering concept.

  PublisherDOI

• Translation from Problem to Code in Seven Steps

  2019-05-03 · 11 citations

  article

  Students in introductory programming courses struggle with how to turn a problem statement into code. We introduce a teaching technique, "The Seven Steps," that provides structure and guidance on how to approach a problem. The first four steps focus on devising an algorithm in English, then the remaining steps are to translate that algorithm to code, test the algorithm, and debug failed test cases. This approach not only gives students a way to solve problems, but also ideas for what to do if they get stuck during the process. Furthermore, it provides a way for instructors to work examples in class that focus on the process of devising the code - instructors can show how to come up with the code, rather than just showing an example. We describe our experience with this technique in several introductory programming courses - both in the classroom and online.

  PublisherDOI

• A technique for translation from problem to code

  2018-06-20 · 1 citations

  article

  Students in introductory programming courses struggle with how to turn a problem statement into code. We introduce a technique, ``The Seven Steps,'' that provides structure and guidance on how to approach a problem. The first four steps focus on devising an algorithm in words, then the remaining steps are to translate that algorithm to code, test the algorithm, and debug failed test cases. This approach not only gives students a way to solve problems, but also ideas for what to do if they get stuck during the process. Furthermore, it provides a way for instructors to work examples in class that focus on the process of devising the code---instructors can show how to come up with the code, rather than just showing an example. We have used this technique in several introductory programming courses---both in the classroom and online. We describe this technique and results from its use in fall 2017 courses.

  PublisherDOI

• Massive Open Online Laboratories? Ongoing Work with Microelectronics Experiments Performed Outside of the Traditional Laboratory

  2016-07-06

  articleOpen accessSenior author

  Abstract With the advent of open source hardware and software, students are able to perform advanced microelectronic experiments outside of the laboratory setting using low-cost components and equipment. Offering experiments outside of a traditional lab accommodates distance-learning as well as large class sizes. Many authors have addressed comparisons of in-lab laboratories with ones completed virtually or remotely, however much less has been studied regarding the use of actual, physical laboratories by students at home or in their dormitory rooms outside of the traditional laboratory. To respond to and leverage technological advancements in portable test and measurement equipment, several student-accessible electronics hardware platforms were considered including the NI myDAQ, Arduino development board, TI LaunchPad, BitScope, Analog Discovery, and a Creative Soundblaster USB Audio System. Ultimately, an initial pilot study with 14 students was conducted in a summer microelectronics course using the NI myDAQ. This, along with high-quality instructional videos, allowed students to complete experiments on their own outside of the laboratory setting. In this study, two of the later course hardware exercises—multi-stage amplifier and op-amp amplifier—were offered to half of the class as labs to be completed outside of the traditional laboratory. Independent validation of the experiment using double-blindrandomized, multi-stage testing was performed and its effectiveness queried using in-person laboratory observations, instructor discussions, a post-laboratory survey, and student laboratory report assessment. Recommendations for implementing out-of-lab student experiments include anticipating hardware failure, encouraging student collaboration, and providing live TA assistance. As a result of the pilot work, additional laboratory offerings using these recommendations as well as explored alternative hardware solutions are being pursued.

  PublisherOA PDFDOI

• Analysis of stability differences in road and time trial bicycles with instantaneous and delayed control

  Sports Engineering · 2015-01-27 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Single-track vehicle dynamics and stability

  DukeSpace (Duke University) · 2014-01-01 · 3 citations

  dissertation1st authorCorresponding

  <p>This work is concerned with the dynamics and stability of nonlinear systems that roll in a single track, including holonomic and nonholonomic systems. First the classic case of Euler's disk is introduced as an example of a nonholnomic system in three dimensions, and the methodology for deriving equations of motion that is used throughout this work is demonstrated, including use of Lagrange's equations, accommodating constraints with both Lagrange multipliers and with Gauss's Principle. </p><p>Next, a disk in two dimensions with an eccentric center of mass is explored. The disk is assumed to roll on a cubic curve, creating the possibility of well-escape behavior, which is examined analytically and numerically, showing regions of multi-periodicity and chaos. This theoretical system is compared to an experiment designed</p><p>to demonstrate the same behavior.</p><p>The remainder of the present document is concerned with the stability of a bicycle, both on flat ground, and on a type of trainer known as "rollers." The equations of motion are derived using Lagrange's equations with nonholonomic constraints, then the equations are linearized about a constant forward velocity, and a straight path, yielding a two degree of freedom system for the roll and steer angles. Stability is then determined for a variety of different parameters, exploring the roll of bicycle geometry and rider position, along with the effect of adding a steering torque, taking the form of different control laws.</p><p>Finally, the system is adapted to that of a bicycle on rollers, and the related equations of motion are derived and linearized. Notable differences with the classic bicycle case are detailed, a new eigenvalue behavior is presented, and configurations for optimal drum spacing are recommended.</p>

  Publisher

• Effect of Rider Position on Bicycle Stability

  2013-11-15

  article1st authorCorresponding

  Bicycle stability has been of interest to dynamicists and athletes since before J. W. Whipple described the canonical model for bicycle motion in 1899. Since then, the subject has fascinated many who sought to find a simple way to describe the essence of stability for a hands free bicycle at a prescribed forward speed. Caster and gyroscopic effects have been shown to be helpful, but not necessary for there to exist a stable range of forward speeds. This work focuses on showing how using the eigenvalues of the linearized equations for roll and steer (with and without a steering torque) can illuminate the stabilizing and destabilizing effects of changing bicycle geometry and rider position. Of particular interest is the mathematical demonstration of the decreased stability a cyclist on a time trial bike experiences when in the aerodynamic position, as opposed to riding with hands on the brake hoods or bull horns.

  PublisherDOI

• Dynamics of an eccentric disk on a curved surface

  Physica D Nonlinear Phenomena · 2013-10-10 · 1 citations

  article1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Brian P. Mann

  7 shared

• Andrew D. Hilton

  Duke University

  2 shared

• Firas A. Khasawneh

  2 shared

• Dennis J. Tweten

  Washington University in St. Louis

  2 shared

• Susan H. Rodger

  Duke University

  2 shared

• Alexander B. Fulton

  Duke University

  1 shared

• Kip Coonley

  Duke University

  1 shared

• Justin Miles

  Duke University

  1 shared