Research
Note that my academic publications use my full name, Kristin Bartlett
Research Area 1: Equity in Engineering & Design Education
My dissertation research focused on validity and fairness in spatial skills assessment. This research was motivated by an overarching interest in diversity, equity, and inclusion in design and engineering. I became interested in spatial skills research when I heard the (inaccurate) suggestion that women’s lack of spatial ability was a contributor to their lower rates of participation in engineering and design. My research in this area takes a critical approach and draws on STS theory, gender theory, and visual perception research.
Selected publications:
Gender differences in spatial ability: A critical review
Kristin A. Bartlett and Jorge D. Camba, Educational Psychology Review, 2023
We argue that the construct of “spatial ability” itself has been co-constructed with gender, and thus has not been devised in a neutral way, but in a manner that is influenced by gender beliefs. Though spatial thinking is also required in feminized fields, past research has cast spatial ability as only necessary in masculinized STEM fields. Due to a prevailing belief that spatial ability was an inherently male ability, researchers “selectively bred” some spatial assessment instruments to maximize gender differences, rather than to precisely measure a spatial construct.
The role of a graphical interpretation factor in the assessment of spatial visualization: A critical analysis
Kristin A. Bartlett and Jorge D. Camba, Spatial Cognition and Computation, 2023
Common tests of spatial skills do not simply test one’s ability to mentally manipulate shapes. Instead, many popular assessments depend on a separate ability to comprehend two-dimensional graphical depictions of three-dimensional objects. This could be an example of construct-irrelevant variance which is a major threat to instrument validity.
Isometric projection as a threat to validity in the PSVT:R
Kristin A. Bartlett and Jorge D. Camba, ASEE Annual Conference & Exposition, 2022
We showed a subset of the shapes on the revised PSVT:R to a group of 111 engineering students enrolled in an introductory engineering graphics course and had them perform a sorting task to determine which shapes they viewed as real 3D shapes and which they did not. Our results showed that at least 19% of the answer bank shapes in the PSVT:R were not viewed as real 3D shapes by most participants, and at least 38% were not viewed as real 3D shapes by some participants. We conclude that the use of isometric views in the PSVT:R presents a threat to instrument validity if the test is to be considered to assess mental rotation ability.
An argument for visualization technologies in spatial skills assessment
Kristin A. Bartlett and Jorge D. Camba, HCII, 2022
We propose that emerging visualization technologies can and should be used to create updated, more accurate instruments for the assessment of 3D spatial thinking. We discuss the possible application of technologies such as advanced computer rendering, virtual and augmented reality, eye-tracking, adaptive testing, and randomized question banks. We argue that advanced visualization technologies can dramatically improve our ability to accurately assess spatial abilities in STEM educational contexts.
Is the PSVT:R suitable for evaluating spatial skill in design? A critique
Kristin A. Bartlett and Jorge D. Camba, DCC, 2022
Design educators and spatial researchers frequently use the Purdue Spatial Visualization Test: Rotations (PSVT:R) to assess students’ spatial abilities. Some researchers have claimed that the PSVT:R is the strongest measure of mental rotation ability, that relative to other tests, the PSVT:R most incorporates the “gestalt thinking process,” and that the gestalt thinking process is widely accepted as the key component of spatial ability. In this study, we present evidence that the claims surrounding the PSVT:R’s validity may not be accurate and represent a co-construction of gender and spatial ability. We suggest that the PSVT:R is not an ideal tool for assessment of spatial skill in design disciplines, and instruments that allow for open-ended responses are needed.
Research Area 2: Healthcare Technology Design & Development
I have professional and research experience developing interactive technologies for healthcare. I am an active member of the ZIBRIO Balance Research Institute where I have worked on translational research commercializing software and devices for fall prevention for older adults. My ongoing research interest in healthcare technology focuses on how designers of medical technology can avoid encoding bias in devices and tools.
Selected publications:
An RGB-D Sensor-Based Instrument for Sitting Balance Assessment
Kristin A. Bartlett and Jorge D. Camba, Multimedia Tools & Applications (in press, 2023)
This project was proposed by occupational therapists who were looking for a more accurate method to measure seated balance for their clients who aren’t able to stand. We developed a software system that uses the Kinect sensor to measure body movements during seated balance assessment exercises.
Designing a Novel Support System for Feeding Pumps - a Case Study in User Involvement in Medical Design
Kristin A. Bartlett, Masters Thesis, 2019
Medical devices are often designed without any user involvement, but many devices migrate from the hospital and end up in the home. I employed a participatory design process which included home users of feeding pumps in the design of a novel support system for feeding pumps. The resulting design was preferred by the users after in-home prototyping testing. This work was presented at the International Design Conference (IDC) 2019 in Chicago, IL.
Characterizing sensor accuracy requirements in an artificial intelligence-enabled medical device
Kristin A. Bartlett, Katharine E. Forth, and Stefan Madansingh, IPEM Translation, 2022
Artificial intelligence and machine learning applications are increasingly prevalent in the healthcare industry. In some cases, medical devices use sensor-collected data to feed into algorithms which generate scores or risk assessments that are used to inform patient care. The process of determining sensor accuracy requirements which will ensure that the algorithm generates reliable scores is not straightforward or well-defined. In this paper, we describe a simulation-based method to characterize sensor accuracy requirements for a device that uses a machine-learning algorithm to generate a postural stability score.
Validating a low-cost, consumer force platform as an accessible alternative for postural sway
Kristin A. Bartlett, Katharine E. Forth, Charles S. Layne, and Stefan Madansingh, Journal of Biomechanics, 2019
The purpose of the present study was to validate the center of pressure (COP) measurements in the Zibrio SmartScale. Simultaneous COP data was collected by a Zibrio SmartScale and a laboratory-grade force platform (LFP) under the dynamic motion of an inverted pendulum device intended to mimic the sway of a standing human. Results indicated that the Zibrio SmartScale can perform adequately as a lightweight and low-cost alternative method of COP measurement in comparison to a traditional LFP.