About

Professor Katie Ansell received her BS in physics and music from the University of Michigan in 2008 and completed her PhD at the University of Illinois at Urbana-Champaign in 2020. During her PhD, she developed a new curriculum for Physics 211 labs that fosters creativity, independent thinking, and practical experimental skills. This curriculum has expanded to include algebra- and calculus-based Mechanics and Electricity & Magnetism courses, and contributed to the growth of a Learning Assistant program and improved professional development for Teaching Assistants. She now oversees the pedagogy of introductory physics laboratories, supporting teaching teams in implementing an adaptive-expertise-focused curriculum. Her current work focuses on two main areas: improving instruction of algebra-based introductory physics through curriculum changes aligned with Universal Design for Learning principles and research in learning, and studying teamwork in introductory laboratories. Her research explores social and cognitive aspects of group coordination, using a complex systems approach to understand and support healthy teamwork among students. Her interests include understanding and supporting pre-health students in physics, group work and dynamics in classrooms, and student experiences in physics laboratories.

Research topics

• Computer Science
• Engineering
• Mathematics education
• Psychology
• Medical education
• Engineering management
• Management science

Selected publications

• Working effectively with your teaching team: Tips and Tricks from Laboratory and Design Course Instructors

  2025-08-21

  article1st authorCorresponding
  PublisherDOI

• Evaluating IBM’s Watson natural language processing artificial intelligence as a short-answer categorization tool for physics education research

  Physical Review Physics Education Research · 2024-03-22 · 4 citations

  articleOpen access

  Recent advances in publicly available natural language processors (NLP) may enhance the efficiency of analyzing student short-answer responses in physics education research (PER). We train a state-of-the-art NLP, IBM’s Watson, and test its agreement with human coders using two different studies that gathered text responses in which students explain their reasoning on physics-related questions. The first study analyzes 479 student responses to a lab data analysis question and categorizes them by main idea. The second study analyzes 732 student answers to identify the presence or absence of each of the two conceptual themes. When training Watson with approximately one-third to half of the samples, we find that samples labeled with high confidence scores have similar accuracy to human agreement; yet for lower confidence scores, humans outperform the NLP’s labeling accuracy. In addition to studying Watson’s overall accuracy, we use this analysis to better understand factors that impact how Watson categorizes. Using the data from the categorization study, we find that Watson’s algorithm does not appear to be impacted by the disproportionate representation of categories in the training set, and we examine mislabeled statements to identify vocabulary and phrasing that may increase the rate of false positives. Based on this work, we find that, with careful consideration of the research study design and an awareness of the NLP’s limitations, Watson may present a useful tool for large-scale PER studies or classroom analysis tools. Published by the American Physical Society 2024

  PublisherOA PDFDOI

• Survey of the Entrepreneurial Mindset of Students in Undergraduate Laboratory Courses

  2024-02-07

  articleOpen access

  Missouri, where she trained users on the focused ion beam (FIB), scanning electron microscope (SEM), and transmission electron microscope (TEM).

  PublisherOA PDFDOI

• Changes to a Circuits Lab Sequence to Encourage Reflection and Integration of Experiences Across Related Courses to Explore New Solution Spaces to an Engineering Problem

  2024-02-07 · 1 citations

  articleOpen access

  Abstract Engineering design requires the evaluation of trade-offs within a solution space to fit the constraints and demands of a specific application. An engineering curriculum provides its students a tailored series of courses to meet this goal. Course instructors anticipate students to regularly make connections to materials of past courses, assimilate the new information of the current course, and then explore expanded solution spaces. Disappointment arises when students fail to make these connections or often fail to recall fundamental concepts necessary to make informed decisions. In this paper we describe changes made to a junior level class to help students recall content from earlier courses on a particular topic in Electrical Engineering. This reflection better enables them to compare and contrast new material and even make connections with future course and industry solutions. Our initial survey indicates that student perception of these changes has been positive. Furthermore, a majority of the students responding to the survey suggest including similar exercises in lab modules on other topics.

  PublisherOA PDFDOI

• Development of a Community of Practice for Rethinking Best Practices in Post-COVID Experiential Learning

  2024-02-06 · 3 citations

  articleOpen access

  In the laboratory classroom, students have opportunities for design, problem solving, and exposure to real-world issues that are not usually present in traditional homework assignments.However, to operate effective laboratories, engineering departments and colleges must address challenges such as budget constraints, space limitations, class size, and limited teaching resources.The COVID-19 pandemic has only exacerbated these issues and added more with the need for online and remote learning experiences without sacrificing the benefits of experiential learning.Laboratory and design courses were significantly impacted by the sudden move to remote delivery during pandemic lockdowns.Instructors and departments made decisions for adapting each course based on specific needs.Throughout that time, instructors in lab and design courses identified both the successes and the continuing challenges to remote and hybrid delivery.When courses returned to in-person modalities, instructors considered what lessons learned can inform the future of experiential learning-based courses.This paper describes development of a Community of Practice (CoP) of lab and design course instructors to develop strategies and best practices across one engineering college as we enter a new era of teaching and learning, post-COVID.This paper describes formation of the lab and design CoP, practical operating details of the CoP, as well as lessons learned from delivery of workshops and meetings.In addition to providing a road map for instructors to form a similar working group at their institution, we will share knowledge gained, commonalities across course types, and a summary of answers to the questions that inspired the formation of this CoP.

  PublisherOA PDFDOI

• Getting Started Teaching an Undergraduate Engineering Laboratory

  2024

  • Computer Science
  • Computer Science
  • Engineering management

  A group of six faculty from laboratory and design courses at a large public university in the Midwest United States recently started a community of practice (CoP) for laboratory and design instructors.The goal of the CoP was to share resources and generate ideas for improving laboratory and design courses after the pandemic.We realized that many of us faced similar challenges during that time as we moved our courses to alternate formats and that we would have benefited from being able to share ideas and collectively brainstorm solutions.Since then, the CoP has grown to almost 40 members representing most of the departments in the college.We have hosted workshops, coffee chats, and other events to facilitate the exchange of ideas between members.Some of the popular topics have been facilitating teamwork; improving inclusivity and belonging; and training laboratory staff.These events have led to us curating resources in these areas.The purpose of this Tips and Tricks paper is to share these resources about teaching laboratory and design courses that we have collected within the CoP with the broader engineering education community, especially for instructors who have recently started teaching a course with a laboratory or large design project.

  PublisherOA PDFDOI

• Characterizing the complexities of experimental decision making in an introductory lab practical

  2017 Physics Education Research Conference Proceedings · 2023 · 1 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Management science

  This study characterizes the complexities of decision-making in an investigation-style practical in two case study groups with seven students in an introductory physics course. The students were trying to test a claim in a hypothetical situation about the effects of drag on the time it takes for a cotton ball to fall. Using two videos as case studies, the study focuses on the complexities of students' experimental decision-making centered around three metacognitive skills: 1) planning prior to investigation, 2) monitoring while doing the experimentation, and 3) evaluating the results. We characterize the complexities of experimental decision-making by creating visuals that show the occurrences of each code in each stage of decision-making. We concluded that the group with more complex approaches to experimental decision-making characterized by back and forth between activating multiple metacognitive skills were able to connect the topic to other physics concepts, have more effective conversations around discrepancies of data in multiple trials, and loop back the results of the experiments to the original claim. However, the other group showed a more linear effort lacking planning prior to investigation and evaluating the results. We argue the complexities of experimental decision-making can be an indicator of the presence or absence of some of the metacognitive skills in lab activities, but further studies need to confirm this finding before making a generalizable comment.

  PublisherOA PDFDOI

• Training Students to Establish and Maintain Positive Group Dynamics in Design and Design-Based Classes

  2021 IEEE Frontiers in Education Conference (FIE) · 2023 · 2 citations

  1st authorCorresponding
  • Computer Science
  • Mathematics education
  • Psychology

  Teaming of students in design-based engineering courses improves outcomes for projects and replicates the working environment of engineers. Yet the group-level and individual benefits of working in groups depend on healthy group function. In lab and design-based courses, where assignments are typically more open-ended and have high levels of interdependency, unhealthy group dynamics lead to issues such as inequitable division of labor and an undervaluing of certain students' thoughts and ideas. Context-based strategies like pre-assigned groups, task design, or peer grading mitigate these issues, but do not necessarily improve students' abilities to work together. We propose a learner-centered approach to group work in classes, in which instructors use context strategies while training students to establish, maintain, and repair group dynamics across the duration of a semester of instruction. Facilitated by a community of practice of laboratory and design instructors, common assignments to establish and support healthy group dynamics were administered in four design-focused courses at a large public university. Interventions included a beginning of semester team contract activity, a mid-semester reflection activity, and an end of semester self- and peer evaluation. This Work-in-Progress presents this Innovative Practice in its development and early assessment stages. Guiding principles and implementation across multiple course contexts are described, with suggestions as to how instructors may respond to information from these interventions. The aim of this work is for instructors to get a sense of how such a longitudinal, student-centered approach to team dynamics may be applied in their own teaching practice.

  PublisherDOI

• Using IBM�s Watson to automatically evaluate student short answer responses

  2022-09-21 · 1 citations

  articleOpen access

  Recent advancements in natural language processing (NLP) have generated interest in using computers to assist in the coding and analysis of students' short answer responses for PER or classroom applications. We train a state-of-the-art NLP, IBM's Watson, and test its agreement with humans in three varying experimental cases. By exploring these cases, we begin to understand how Watson behaves with ideal and more realistic data, across different levels of training, and across different types of categorization tasks. We find that Watson's self-reported confidence for categorizing samples is reasonably well-aligned with its accuracy, although this can be impacted by features of the data being analyzed. Based on these results, we discuss implications and suggest potential applications of this technology to education research.

  PublisherOA PDFDOI

• Navigating moments of uncertainty and socio-emotional risks in small-group work

  2021-10-01 · 2 citations

  articleOpen access

  Supporting students’ sense-making in discussions and labs is an important goal of college physics instruction. In this study, we explore how students navigate the socio-emotional risks of collaboration during moments of uncertainty while doing a lab activity. Attending to sense-making in the video recording of one group’s work, we contrasted two episodes where the quality of their collaboration played out differently. We found that one of the group members changed how they navigated the tension that arose within the group from episode 1 to 2, going from disengagement to forceful idea-sharing, which increased the risk of disagreement in the second episode and hindered the group’s sense-making progress. This case study shows how different approaches to socio-emotional risks could lead to different conceptual results of group work, contributing another example to a body of work showing how achieving our objectives for collaborative learning depends on careful attention to students’ epistemological, conceptual, and socio-emotional resources.

  PublisherOA PDFDOI

Frequent coauthors

• Rebecca Reck

  Harvard University

  40 shared

• Christopher Schmitz

  University of Illinois Urbana-Champaign

  40 shared

• Jessica R. TerBush

  University of Illinois Urbana-Champaign

  39 shared

• John S. Popovics

  University of Illinois Urbana-Champaign

  36 shared

• Holly Golecki

  University of Michigan–Ann Arbor

  10 shared

• C. Radhakrishnan

  Rensselaer Polytechnic Institute

  3 shared

• T. Stelzer

  2 shared

• M. Selen

  University of Illinois Urbana-Champaign

  2 shared

Education

• Ph.D, Physics

  University of Illinois Urbana-Champaign

  2020

Awards & honors

• Doug and Judy Davis Award for Excellence in Teaching Undergr…