Group roles in unstructured labs show inequitable gender divide

Abstract

Instructional labs are being transformed to better reflect authentic scientific practice, often by removing aspects of pedagogical structure to support student agency and decision making. We explored how these changes impact men’s and women’s participation in group work associated with labs through clustering methods on the quantified behavior of students. We compared the group roles students take on in two different types of instructional settings: (i) highly structured traditional labs, and (ii) less structured inquiry-based labs. Students working in groups in the inquiry-based (less structured) labs assumed different roles within their groups, however men and women systematically took on different roles and men behaved differently when in single- versus mixed-gender groups. We found no such systematic differences in role division among male and female students in the traditional (highly structured) labs. Students in the inquiry-based labs were not overtly assigned these roles, indicating that the inequitable division of roles was not a result of explicit assignment. Our results highlight the importance of structuring equitable group dynamics in educational settings, as a gendered division of roles can emerge without active intervention. As the culture in physics evolves to remove systematic gender biases in the field, instructors in educational settings must not only remove explicitly biased aspects of curricula but also take active steps to ensure that potentially discriminatory aspects are not inadvertently reinforced.

Full text

Group roles in unstructured labs show inequitable gender divide
Katherine N. Quinn ,1,2 Michelle M. Kelley,3Kathryn L. McGill,4Emily M. Smith,5
Zachary Whipps ,6and N. G. Holmes 3
1Center for the Physics of Biological Function, Princeton University, Princeton, New Jersey 08540, USA
2Initiative for the Theoretical Sciences, CUNY Graduate Center, New York, New York 10016, USA
3Laboratory of Atomic and Solid State Physics, Department of Physics, Cornell University,
Ithaca, New York 14853, USA
4Department of Physics, University of Florida, Gainesville, Florida 32611, USA
5Department of Physics, Colorado School of Mines, Golden, Colorado 80401, USA
6Department of Physics, Cornell University, Ithaca, New York 14853, USA
(Received 20 December 2019; accepted 28 April 2020; published 26 May 2020)
Instructional labs are being transformed to better reflect authentic scientific practice, often by removing
aspects of pedagogical structure to support student agency and decision making. We explored how these
changes impact men’s and women’s participation in group work associated with labs through clustering
methods on the quantified behavior of students. We compared the group roles students take on in two
different types of instructional settings: (i) highly structured traditional labs, and (ii) less structured inquiry-
based labs. Students working in groups in the inquiry-based (less structured) labs assumed different roles
within their groups, however men and women systematically took on different roles and men behaved
differently when in single- versus mixed-gender groups. We found no such systematic differences in role
division among male and female students in the traditional (highly structured) labs. Students in the inquiry-
based labs were not overtly assigned these roles, indicating that the inequitable division of roles was not a
result of explicit assignment. Our results highlight the importance of structuring equitable group dynamics
in educational settings, as a gendered division of roles can emerge without active intervention. As the
culture in physics evolves to remove systematic gender biases in the field, instructors in educational settings
must not only remove explicitly biased aspects of curricula but also take active steps to ensure that
potentially discriminatory aspects are not inadvertently reinforced.
DOI: 10.1103/PhysRevPhysEducRes.16.010129
I. INTRODUCTION
The demographic composition of physicists is not repre-
sentative of the general population, with men overrepre-
sented not only in number but also in high-ranking positions
within the physics community [1]. In exploring the under-
lying mechanisms for this, there has been a large focus in
education research on gaps in performance between men and
women on concept inventories and course grades [2,3].
While informative, this approach provides an incomplete
picture [3,4]; importantly, student persistence in physics
can often be independent of their physics test scores [5].
New strides in science education research now include
investigating more metrics such as sociocultural factors [6,7],
self-efficacy [8,9], sense of belonging [10], and identity
formation [9,11,12]. Moreover, participation in the physics
community through the roles people take on within the
community can heavily shape one’s identity as a physicist
[13]. Within any community, members assume different roles
as they take on different responsibilities, perform certain
functions, and are perceived in specific ways by themselves
and by the group [14]. Understanding what roles develop
throughout students’physics education is critical, as the field
of physics is associated with masculinity, suggesting that a
gendered division of roles may greatly influence the modern
practice of physics. [15,16].
Students have little direct experience with the field,
however [17], and their perceptions of the field and their
physics identities are developed through their immersion in
physics courses. Many courses (including labs) involve
significant group work, which can leverage the fact that
strong peer relationships can benefit students’development
of their science identities [18–21]. As with group work in
other aspects of physics courses (such as cooperative
problem solving, tutorial, or in-class lecture activities),
lab activities require coordination of group members as
they collect and interpret a common dataset. Lab activities
are distinct from other learning environments in that there
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are multiple distinct activities that must be carried out, so
division of labor, and thus assigning distinct roles, is much
more common.
In this study, we explored patterns in the behaviors
students exhibit in the context of physics labs. In doing so,
we aim to better understand the group roles that emerge in
these spaces. Labs provide an environment where students
interact with peers and engage in physics experiments in
ways that can influence their perception of physics and of
themselves as physicists [22]. Furthermore, labs are chang-
ing nationally in response to calls to provide students with
more authentic science experiences [23]. Understanding
how students behave and interact with each other in
different lab environments, and the roles students assume
in these settings, can inform educators and researchers
when designing new pedagogy to better address inequities.
Identity formation is a complicated, multidimensional
process that includes gender, race, physical ability, socio-
economic status, sexual orientation, and religion, among
many, many other factors. The formative process includes
individual agency as well as broader cultural and societal
factors [24]: the impact of the broader culture outside of the
physics classroom strongly influences one’s identity for-
mation (such as a culturally perceived notion of physics as a
masculine field [25]). Importantly, how one develops a
sense of identity impacts the set of available roles one may
take on in a particular context, and strongly determines
persistence in a particular field [26]. In this study, we
analyzed the quantified behavior profiles (discussed further
in Sec. II) as a way of probing the roles students take on in
physics labs to understand some of the ways in which these
roles can be equitably or inequitably divided.
We define an equitable division of roles as one in which
all members are equally likely to assume each role, i.e.,
every role is available to every member. Note that this is
different from equal or identical roles, in which every
student performs the same function and thus would behave
similarly from each other. An inequitable division of roles
is one in which not every role is available to every member.
For example, certain members are expected to assume, or
prevented from taking on, certain roles. At the individual
level, roles that are divided among group members may not
be indicative of inequitable division. However, if students
systematically behave differently in groups, then broader
statistical analyses will reveal these overarching inequities.
For instance, roles may be gendered, in the sense that there
is an inequitable gender divide, with men and women
taking on systematically different roles [27,28].
Prior research has found that group work often involves
inequitable participation between men and women. For
example, female students participated less in group dis-
cussion when they were outnumbered by male students
[29,30] and responded disproportionately less than male
students to instructor-posed questions in lecture [31].In
physics lab courses, students have described the available
roles themselves as being either masculine or feminine [22]
and women have been found to engage less frequently with
hands-on equipment [32,33] or with computers [34] when
working in mixed-gender pairs. In contrast, contradicting
results were found when comparing the performance of
male majority, female majority, and mixed groups on
engineering design tasks across two different courses [35].
Individual students’behaviors can be used to probe the
roles they take on in physics labs, and are likely a result of
their personal identity (gender or otherwise), the particular
instructional context, and the broader physics culture
[26,27,34,36–38]. Understanding students’experiences
in labs through the behaviors and roles they take on both
highlights existing gender disparities as well as informs
future research on students’persistence in science. We
specifically sought to understand the impact of different
instructional lab environments on student roles and how
these roles are divided between men and women. One way
to make labs more authentic is to make them discovery-
based and inquiry-driven, removing structure from the lab.
How does removing pedagogical structure in the lab impact
these learning environments? Specifically, what impact
does pedagogical structure have on the equitable division
of roles within groups?
II. MATERIALS AND METHODS
All participants in this study were undergraduate stu-
dents at a major research university enrolled in the honors-
level mechanics course of a calculus-based physics
sequence. The course was designed for physics majors
and open to students across the sciences and engineering.
The sample of prospective physics majors is an important
population for this study, given the potential link between
students’experiences, roles, identity, and persistence in
physics [26]. We explored students’behaviors in two
different types of lab instruction.
The highly structured traditional labs were designed to
reinforce physics content knowledge presented in lecture.
Students were provided with detailed paper worksheets to
follow during lab, guiding them through experiments that
provided them with hands-on experience. The lab guides
provided explicit details about what and how much data to
collect and posed targeted conceptual physics questions to
support making predictions and interpreting results.
Students worked in groups to collect data for the experi-
ments and submitted individual paper worksheets.
In contrast, the less structured inquiry labs were
designed to emphasize the process of experimentation in
physics (see, for example, Refs. [39–42]). Students were
provided with a specific goal, but were expected to design
their own experiment to achieve that goal. Lab guides
prompted students to design data collection methods to
reflect on results, and to design follow-up investigations to
improve or extend their investigations. Students worked
collaboratively to design and implement their experiments
KATHERINE N. QUINN et al. PHYS. REV. PHYS. EDUC. RES. 16, 010129 (2020)
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and submitted only one electronic notebook as a group.
Reference [40] includes additional detail about the
differences between the conditions, including differences
between students’learning engagement with experimenta-
tion, and attitudes towards experimental physics.
The same mechanics course was taught twice during the
academic year, once in the fall semester and then again in
the spring semester. Students from both semesters were
included in this study. During the first semester, all students
attended the same lecture, mixed together in discussion
sections, but were separated into two pedagogically differ-
ent lab types discussed below (three traditional lab sections
and two inquiry lab sections). During the second semester,
the two lab sections under study were both inquiry labs.
Note that we observed students across multiple lab periods
throughout the course of the semester (and each student
appeared in only one semester), and so while each student
is in one lab section they appear in multiple lab periods. All
participants were unaware of the differences between lab
types: students in the first semester self-selected into their
lab sections prior to the start of the course by registering for
the course, and only the inquiry lab sections were available
to students in the second semester. Student groups varied
every period, and were randomly assigned.
The role a student takes on in their group is a highly
complex reflection of the function they serve in the group,
and depends on numerous factors from the individual to the
cultural level. Because this study explores student roles in
physics labs, we assume that these roles are in some way
correlated with their behavior in these labs, such as
handling of equipment or of computer usage. To probe
the roles that students assumed in physics labs, we analyzed
the quantified behavior profiles of 143 students across
multiple lab periods. We collected data for this study at two
levels of granularity.
First, coarse behaviors were captured at five minute
intervals for all students in multiple lab periods. The codes
were determined by what the students were handling:
(1) lab desktop computer, (2) personal laptop or other
device, (3) writing on paper, (4) handling equipment, or
(5) engaging in some other activity. We used the “other”
code to capture all other behaviors, such as discussing
within their group, with another group, or with the
instructor; engaging in whole-class discussions; writing
on whiteboards; or engaging in off-task behaviors. Note
that the other code was constructed to ensure all time was
coded for every student, and therefore captures many
different behaviors. The choice of codes were designed
to capture enough detailed information as possible about
every student, coded in real time, while reflecting the lack
of a priori knowledge of what the exact group roles were.
The behaviors of each student in each lab period were
amassed to create a profile of their behaviors during that lab
period. Unfortunately, given the observation protocol (dis-
cussed in detail in Sec. II B) where each student was
observed over the course of an entire lab period, subdivid-
ing the other code could not be done quickly enough and
with enough accuracy by the researchers. Instead, a second
analysis of such detailed behavior was performed using
video from single groups, and discussed in greater detail in
Sec. II D.
A. Collecting demographic information
We used in-class surveys to obtain student demographic
information. In all, 143 students across multiple lab
sections were used in this study. While they had the option
to disclose a gender other than woman or man, no student
chose to do so, and only two students did not disclose their
gender identity. As a result, all students were included in
the initial cluster analysis, however the gender analysis
follows the traditional gender binary of woman or man
(with the two undisclosed students omitted from the graphs
in Figs. 4and 6due to insufficient statistics). Table Ishows
the demographic breakdown of student participants in this
study. To obtain the standard error on the fraction of a
population (such as in Table Ior Fig. 6), we used the
following:
Errðp; NÞ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
pð1−pÞ
N
r;ð1Þ
where pis the fraction of the population, and Nis the size
of the total population.
B. Quantifying coarse student behaviors
In all lab sections, observers documented student behav-
iors following the observation protocol used in Ref. [34].
Every 5 min, an observer noted each student’s actions in the
lab using one of five codes: desktop, equipment, laptop,
paper, and other. One code was applied to each student in
the class at each 5 min interval, except in cases where
students could not be observed (e.g. because they were late
or left early). The codes are described in Table II, and were
based on what a student could be handling in the lab. The
other code captured all other behaviors such as engaging in
whole-class discussions, writing on whiteboards, discus-
sing with the teaching assistant (TA) or undergraduate
teaching assistant (UTA), and off-task behaviors, ensuring
that all in-lab time was coded. The desktop code was
TABLE I. Student demographics of this study. Errors were
computed using standard error for population fractions, shown in
Eq. (1). In all, 143 students were considered in this study.
Traditional labs Inquiry labs
N%N%
Women 11 19 521 25 5
Men 46 79 563 74 5
Undisclosed 1 22111
GROUP ROLES IN UNSTRUCTURED LABS …PHYS. REV. PHYS. EDUC. RES. 16, 010129 (2020)
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separated from the laptop code because the desktop was
often required for data collection (e.g., because it was
directly connected to a detector or piece of equipment).
Furthermore, desktops were shared within groups whereas
the laptop code was ascribed to students handling personal
devices. While desktops were present in both lab types,
only students in the inquiry labs actively used laptops to
analyze data, document their lab procedures, and submit
their electronic notebooks.
The codes used in this study, in particular the other code,
are very coarse and so multiple behaviors can fall under the
same code (e.g., the laptop code includes using a laptop for
data analysis as well as for note taking, the other code
captures activities such as discussing the lab with group
members or engaging in off-task talking with group
members). Given the observation protocol, it was not
possible for an observer to differentiate between these
different and more nuanced behaviors in real time for every
student, and so a second analysis was performed, and the
details of which are outlined in Sec. II D.
To validate our observation procedure, two observers
coded student actions in the same lab period using the
described protocol but at different 5 min intervals. If we had
each observer code the same student at the same time, we
would have only evaluated the reliability of the codes.
Instead, observers were specifically not coding the same
student at the same time. Thus, comparing the overall code
count for each student provides a measure of reliability of
the codes recorded at 5 min intervals. By comparing the
overall code count for each student, we provide a measure
of reliability about the overall method. This method limits
us, however, from comparing individual student behavior
over time in the lab period. Thus, all analysis is performed
on the student profiles, which aggregate their behaviors
throughout the lab period. Note that because observers
were explicitly not observing the same student at the same
time, percent agreement or calculating Cohen’s kappa
would not provide the necessary information to validate
the method. Instead, a standard chi-squared analysis was
performed on the contingency table constructed from the
accumulated codes (the frequency each observer noted each
code, summed over all students). We used the criteria that if
two sets of observations are statistically indistinguishable
from each other, then the observers captured the same
overall profiles for the students in the lab session. Note that,
if either (i) there was not agreement between the codes, or
(ii) the 5 min interval did not accurately capture student
behavior when averaged over a lab period, then there would
be disagreement in these overall distributions.
In all cases observers’distributions were statistically
indistinguishable, and so single observers coded subsequent
lab periods. When attempts were made at subdividing the
codes, for instance, to capture students performing data
analysis vs note taking or identifying if group discussions
were off task, we were not able to obtain agreement between
observers. As such, we used the protocol detailed in this
section. We provide an example of observer comparisons for
illustrative purposes. A sample graph of the accumulated
codes for two observers in a traditional lab section is
presented in Fig. 1. The contingency table constructed from
these observations is given by Table III. Because the two
distributions are statistically indistinguishable, the observers
captured the same distribution of student actions.
TABLE II. Action codes used in observations. The laptop code
is used for both handling a laptop or personal device (students
used laptops, phones, and tablets for the purpose of notetaking,
write up, data analysis, and reading instructions in the inquiry
labs).
Code Description
Desktop Using the desktop computer at the lab bench.
Equipment Handling equipment.
Laptop Using a laptop or personal device.
Paper Writing on paper or in a notebook.
Other Other action or behavior.
FIG. 1. Bar plot of code counts from two observers used to form
the basis of a chi-squared test to validate the observation protocol
used in this study. Two observers documented the same lab
period, and the resulting contingency table (given by the raw
counts displayed on the graph and shown in Table III) was used to
determine statistical validity of the method. Here, the two
distributions are statistically indistinguishable indicating that
the observers captured the same distribution of student actions.
TABLE III. Sample contingency table used to determine if two
distributions are statistically different. Two observers docu-
mented the same lab period, and a chi-squared test was performed
to determine if the resulting distributions are statistically similar
or dissimilar. Here, we obtain p>0.1, indicating that the
observers captured the same distribution of student actions.
Observer Desktop Equipment Laptop Paper Other
1 41 14 9 182 161
2 32 22 20 174 154
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Because students were observed during multiple lab
periods over a full semester, we were able to document
individual students more than once. As a result, we obtained
522 unique student profiles, each quantifying the actions of
one student in one lab period through the frequency of
associated codes. Table IV shows a demographic breakdown
of the student profiles used in this study.
C. Cluster analysis
The distribution of coarse behavior code frequencies are
highly skewed, with most students engaging in a particular
activity infrequently or not at all and some students
engaging in an activity a lot. Figure 2shows box plots
of the raw data, illustrating the non-Gaussian features of the
data. For this reason, we performed a cluster analysis
instead of methods that rely on the assumption of Gaussian
distributions. Clustering can account for nonlinearities
missed in common regression analyses, capturing dominant
behavior as opposed to average behavior, and has been
used in similar studies of this type to provide fruitful results
[43]. By performing a demographic analysis on the student
groupings (i.e., clusters) we can quantitatively characterize
coarse gendered behavior.
To perform a cluster analysis on multidimensional data,
the scales for each measure must be the same. In this study,
there were two major effects present that caused differences
in scales that we accounted for. First, the amount of coded
time for each student was highly variable, ranging from less
than 45 min to over 175 min. To account for this effect, we
normalized each student profile. In this way, each measure
represents the fraction of time spent on a particular task.
Second, there is the inherent differences in the five
measures. For instance, from Fig. 2, we can see that the
distributions for other is more spread out than for equip-
ment. To account for this, each measure was grand mean
scaled so that, averaged over all students, each measure had
mean 0 and standard deviation of 1. In doing so, each
measure becomes a Zscore [43,44]. Thus, each student’sZ
score tells us whether the time they spent on a particular
activity was above or below average as compared to other
students. Moreover, the Euclidean distance between two
profiles has a statistical interpretation in this Z-score
format: it measures the dissimilarity of two student profiles
in units of standard deviations [43].
We performed a standard k-means clustering on the
rescaled student profiles. k-means clustering is an iterative
algorithm, where the researcher specifies the number of
clusters. The algorithm clusters and then reclusters the data
in an iterative manner until the sum of the square of the
distances from all points to their respective cluster’s center
is minimized and no point changes cluster between
iterations [45].
Note that not all data can be meaningfully clustered.
For example, even if all data form a structureless blob, a
researcher can still input two or more clusters and the
algorithm will converge to a solution. Therefore, in order to
determine (i) if the data are clusterable, and (ii) if so, what
the optimal number of clusters is, we used the elbow
method [46]. We plotted the average squared distance from
each point to the center of its assigned cluster, as a function
of the number of clusters, and compared the results to
10 000 randomly generated studentprofiles. We used enough
random data to numerically generate a smooth function and
ensure that the comparison is not hindered by statistical
fluctuations. The results of the elbow plot are shown in Fig. 3.
The plot for our collected data was substantially below
random, indicating that the data is clusterable. There is a
distinct kink in the plot for five clusters, indicating that the
optimal number of clusters is five.
From the elbow plot in Fig. 3, specifically from looking
at the drop in average squared distance from each point to
the center of its cluster for five clusters compared to one, we
TABLE IV. Demographic breakdown of student profiles mea-
sured in this study. Errors were computed using standard error for
population fractions, shown in Eq. (1). In all, 143 students were
observed across multiple lab periods, resulting in 522 unique
student profiles.
Traditional labs Inquiry labs
N%N%
Women 34 18 387 26 2
Men 152 81 3246 74 2
Undisclosed 2 1110.30.3
FIG. 2. Box plots of raw data revealing the highly non-
Gaussian nature of the code distributions. Each faded point is
the accumulated codes for a student in a lab period for a particular
category (the horizontal spread of the points is just to visualize all
the points), and so darker regions represent more total codes of
that value (with the darkest regions near zero). Note that the
median for all codes except other is less than or equal to one,
reflecting the fact that over half of students were observed
engaging in that behavior once or less than once. This, combined
with the fact that there are a large number of outliers, is an
indication that students either engage in a particular activity a lot
or not at all.
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can see that the five optimal clusters account for 70% of the
variance in the data. By looking at the distances confined to
each of the five measures (i.e., generating similar figures as
that of Fig. 3for each measure, where the max value would
be 1 instead of 5), we found that the five optimal clusters
account for 73% of desktop use, 60% of equipment use,
78% of laptop use, and 59% of other activities). This is
well above the 50% threshold used for a study of this
type [43,44].
We provide a 2D visualization of the clusters using
t-SNE [47], with each dot representing a profile colored by
its assigned cluster (Fig. 4). Figure 4is a two-dimensional
representation of a five-dimensional space, and so is used
primarily for qualitative illustration.
Clusters from kmeans are characterized by their centers.
Here, the centers of the five clusters matched the five codes
used in this study and so we labeled the clusters accord-
ingly. Therefore, the clusters characterize “high users”of a
particular measure. Note that this description fits with the
raw data, shown in Fig. 2, which illustrates that the majority
of students engage in a particular task either frequently or
very rarely. For example, students in the yellow cluster of
Fig. 4spent a larger fraction of their time on the equipment
than the average student, so this cluster is referred to as the
equipment cluster. This is a feature of student behaviors,
FIG. 3. Elbow plot used to determine the optimal number of
clusters for the data. The average squared distance from each
point to the center of its assigned cluster is plotted as a function of
the number of clusters. There is a kink at five, indicating that the
optimal number of clusters for the data is five. Our results were
compared against 10 000 randomly generated student profiles.
Note that the elbow is well below random, a sign that the data can
be clustered. Superimposed on the graph is a two-dimensional
visualization of the data and random points for qualitative
comparison. The data show structure (brown points in lower
left), whereas the random points form a blob (gray points in
center right).
FIG. 4. Two-dimensional visualization of behavior clusters and their centers. Each point represents a unique student profile, with
profiles from the same group connected by a gray line (solid for less-structured inquiry labs, and dashed for highly structured traditional
labs). Circles represent students in the traditional labs and stars in the inquiry labs, and black edges indicate women’s profiles. All points
in the laptop cluster are stars, whereas all points in the paper cluster are circles, a reflection of the pedagogical differences in the labs
(students in the traditional labs were filling out paper worksheets, whereas in the inquiry labs were filling out electronic notebooks).
Clusters are characterized by their centers, and here the centers of the five clusters are given by large Zscores for each of our codes.
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and not due to the number of codes used in this study. For
instance, one could imagine a scenario in which all students
behave nearly identically, with minor differences described
by fluctuations: in that case, the data would form a five-
dimensional Gaussian cloud centered at zero, and an elbow
plot that matches random noise. Or, one could imagine a
scenario in which only students handling equipment handle
the lab desktop, in which case a cluster would emerge that
couples the two respective codes.
We used the clusters that emerged from the data to
coarsely characterize the roles students take on in labs.
Generally, roles within groups are complex and multidi-
mensional and could be further explored in greater
detail through more detailed video analysis (discussed in
Sec. II D), student interviews, or anthropological inves-
tigations. The analysis performed here provides a coarse-
grained perspective on the division of roles within groups,
and will ultimately reveal the unexpected inequities in role
divisions (discussed next).
Because each student had multiple profiles, arising from
several lab periods over the course of a semester, we
investigated whether or not it is possible to further collapse
the profiles to determine “semester-long”behaviors. We
did this by analyzing whether or not individual students’
profiles appear in multiple clusters over the course of a
semester. In the traditional labs, 87% 4% of students
have profiles appearing in more than one cluster. Similarly,
86% 4% of students in the inquiry lab appear in more
than one cluster. Because so many students have profiles
appearing in multiple clusters, the weekly variation in an
individual’s profile is too great to further collapse (for
numerous reasons, such as variability in lab content and
students changing lab partners).
D. Describing detailed student behavior
We used video recording of single groups during full lab
periods to better describe student behavior in more detail
than captured in the previous section. In all, ten videos were
coded, decomposing 23 profiles from 17 students (five
students appeared in more than one video). BORIS soft-
ware was used to code videos [48], specifically the fraction
of time students engaged in different behaviors.
The five codes in Table II were further broken down by
what a student was doing (e.g., analyzing data) while
engaged in that coarse behavior (e.g., using the desktop) as
shown in Fig. 5. The paper code was used to predominantly
describe students filling out paper worksheets in the
traditional labs, and so it was not further decomposed.
Students in the inquiry labs predominantly used white-
boards for calculations, and very rarely used paper. Both
the desktop and laptop codes were used to describe students
analyzing data, collecting data, or writing lab notes, and
so both of these codes were broken down in this way.
FIG. 5. Breakdown of codes by decomposing coarse behavior (e.g., “handling laptop”) into more fine-grained behavior (e.g.,
“analyzing data”). Ten videos were coded, resulting in 23 decomposed profiles from 17 different students (five students appeared in
more than one video). (a) A breakdown of each code, showing the fraction of time students engaged in a particular task while coded as a
particular behavior. Three of the five codes (desktop, equipment, and laptop) were directly decomposed into subcodes while analyzing
videos, as shown in (b) illustrating a sample coded time series. Four additional “group states”were coded in the videos, representing
large group behavior (discussing with a TA or UTA, conversing with other groups, whole class discussions and announcements, and
using a whiteboard). We decomposed the other code by overlapping it with these larger group states. The paper code was purely
represented by students filling out paper worksheets in the traditional labs.
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However, when collecting data, the desktop was often
connected directly to equipment whereas gathering data on
a laptop was purely represented by students manually
entering data into their electronic notebook or analysis
software. Students handling equipment were primarily
doing so to either collect data or manipulate the setup in
some way (setup, cleanup, calibration, playing) and so the
equipment code can be further decomposed into these two
tasks. In this way, the desktop, equipment, laptop, and
paper codes were explicitly decomposed.
To better describe student behavior while coded as other,
we introduced four new state codes. These were used to
describe significant events in lab, and are elaborated in
Table V. By overlapping the event codes with other, we
broke down the other code and provide a more qualitative
picture of classroom activities, such as engaging in whole-
class discussions, using whiteboards to sketch out ideas
and concepts, single group discussions with the TA or
UTA, or engaging in intergroup discussions with neighbor-
ing groups.
To validate this method, two observers coded the same
video as a means of testing the interrater reliability. The
level of agreement was assessed with Cohen’s kappa where
a value of 0.61–0.80 represents substantial agreement. Two
observers coded the same video, and obtained a Cohen’s
kappa value of 0.79, indicating substantial agreement
between the two. As a result, only one researcher coded
the subsequent videos.
Video analysis was also used to better understand task
allocation. Point events were identified when one student
explicitly instructed another to perform a task. We broke
down the criteria for inclusion as a point event and
exclusion as a point event in the following way:
•Criteria for inclusion: A student needs to be address-
ing another, and explicitly direct them in some way,
such as by saying “You should do X.”
•Criteria for exclusion: Suggesting a task should be
done that a student assumes without being asked is not
included. Examples of such events are characterized
by statements such as “We should do X,”“I think we
should focus on X,”“Does someone want to work on
X?”Additionally, a student asking another for help
performing a task is excluded (such as asking another
student how to sum a row in a spreadsheet, and the
student telling them how).
In total, we found eight point events for inclusion from all
ten videos. All such events were quick, directed comments
related to a task the student was already engaging in.
Therefore, as described in the main text, we conclude that
no tasks were explicitly assigned by another student.
III. RESULTS
A. Identifying course-wide behavior patterns
through cluster analysis
We analyzed the demographic composition of each
behavior cluster by lab type (highly structured traditional
or less-structured inquiry based), gender (students’self-
reported gender identity of man or woman), and group
composition (mixed-gender or single-gender groups). In all
cases, when comparing the composition of behavior clus-
ters, we used a chi-squared test of frequencies on the
contingency tables of the raw counts.
When broken down by lab type [shown in Fig. 6(a)],
60% of the student profiles in the traditional labs were in
the paper cluster, indicating that the majority of students in
the traditional labs were high paper users. Students in the
inquiry labs engaged in a more varied set of activities,
demonstrated by the uniform distribution of student profiles
across clusters. In the traditional labs, however, student
profiles were predominantly found in the paper cluster,
with few profiles in the remaining clusters.
Our data support the notion that labs with reduced
structure provide a wider range of available roles. We
tested this explanation by examining the range of roles
within individual groups in each class type: Do members
within a group predominantly fall into the same or different
clusters? In the traditional labs, 43% of groups had all
members in the same cluster (predominantly the paper
cluster), whereas only 14% of groups in the inquiry labs
had all members in the same cluster (Fig. 7).
We note that groups in the traditional and inquiry labs
were of varying sizes. Groups in the traditional labs
typically had three or four students, whereas groups in
the inquiry labs typically had two or three members, with
group sizes determined by logistical constraints of the lab
TABLE V. Event codes used in video observations. These codes described significant events in the lab, and were used to decompose
the more coarse-grained other code. A sample time series illustrating a coded video is shown in Fig. 5(b).
Code Description
Whole class discussion The TA or UTA makes an announcement to the class, or holds a whole class discussion.
Whiteboarding Students perform invention activities in the lab, and use a white board to
sketch out ideas and concepts.
Single group discussion with the TA TA or UTA engages in a discussion with the group (but not as part of a whole class
discussion).
Intergroup discussion Groups compare results or discuss among each other (not as part of a whole class
discussion).
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spaces (such as the number of available lab benches given
the size of each class) and mainly assigned randomly by
the instructor. Moreover, mixed-gender groups also had
between 1 to 3 women and 1 to 3 men. Observers
documented the behavior of all students in every group,
and kept track of which student was in which group. One
could expect that, in groups with more members, there is an
increased chance of task division occurring. While groups
in the traditional labs typically had more members than
those in the inquiry labs, Fig. 7in fact shows proportionally
fewer groups in the inquiry labs with members in identical
clusters, supporting the conclusion that groups in the
inquiry labs were more likely to divide tasks.
We infer that the set of available roles is much greater in
the inquiry labs and that students assumed distinct roles
from one another. The traditional labs were highly guided,
leaving students little room for active decision-making
about the experiment. While they worked in groups, each
student was responsible for completing their own
individual worksheet. As a result, the set of available roles
was both confined and manifestly similar for all students. In
contrast, the inquiry labs were designed to emphasize the
process of experimentation and thus students supported in
exercising agency for active decision making about the
experiment. As a result, the set of available roles was larger
and students could divide tasks in a variety of ways.
We next sought to evaluate whether men and women
assume different roles. We decomposed the behavior clusters
by gender and lab type, as shown in Fig.6(b).Throughachi-
squared test of frequencies, we found a statistically signifi-
cant difference between men and women in the inquiry labs
[χ2ð3Þ¼10.77,p¼0.01,VCramer ¼0.15], but none in the
traditional labs [χ2ð3Þ¼3.27,p¼0.65,VCramer ¼0.08].
There were disproportionately more women in the laptop
cluster than men and disproportionately more men in the
equipment cluster than women.
Statistically significant differences also existed between
men in mixed-gender versus single-gender groups, shown
FIG. 6. Cluster compositions for each of the five clusters, broken down both by lab type, gender, and group composition. In all plots,
the yaxis represents a fraction of student profiles and errors are calculated using the standard error on the fraction of a population shown
[see Eq. (1) for additional details]. (a) Cluster distributions broken down by lab type. (b) Clusters further broken down by gender. We see
that there are disproportionately more women in the laptop cluster than men, and disproportionately more men than women in the
equipment cluster. (c) Cluster distributions were further broken down in the inquiry lab by group type (men and women in mixed-gender
groups and single-gender groups). Upon inspection, we see that the laptop difference remained, while a difference emerged in other.
Furthermore, far more men are high-equipment users when in single-gender groups. Because of insufficient statistics, no comparison can
be made with women in single-gender groups, and the data are presented for completeness.
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in Fig. 6(c) [χ2ð3Þ¼12.10,p¼0.007,VCramer ¼0.15].
When men were in single-gender groups, they were more
likely to be in the equipment cluster and less likely to be in
the other cluster than men in mixed-gender groups. Men in
mixed-gender groups were more likely than their female
group members to be in the other cluster, and women in
mixed-gender groups were more likely than their male
group members to be in the laptop cluster [χ2ð3Þ¼10.34,
p¼0.02,VCramer ¼0.15]. Because of the small number of
women in single-gender groups, we did not have statistical
power to detect whether there are differences for women
who were in mixed versus single-gender groups (p>0.17
in all cases). Furthermore, due to insufficient statistics, we
were unable to perform a similar analysis for groups of
varying sizes.
The difference in men’s behavior when in mixed-gender
and single-gender groups may be indicative of the impact of
social context on the roles students assume. In groups with
only men, there may be different social dynamics compared
to groups that include women, changing the set of available
roles (and thus observed behaviors). For instance, the
increased number of high-equipment users in men-only
groups may be the result of “playfulness”[49] when
women are not in the group, or that in mixed-gender
groups members were more efficient with equipment use.
B. Quantifying the relative behaviors
of students within groups
The cluster analysis in the previous section indicates that
individual students took on different roles on a course-wide
level but suggests that group composition may impact the
group dynamics in a nontrivial way. To investigate roles
within individual groups, and to ensure that different
analysis methods obtain nonconflicting results, we com-
pared each student’s profile to those of their group
members. We quantified the relative behaviors by con-
structing a deviating profile for each student to describe
how they differed from their group’s average profile
(quantified as the numerical difference of the student
profile from the group average, see Appendix for additional
details). For example, if all students in a group behaved the
same, the profiles of every student would match their
group’s average, and thus they would each have a deviation
of zero for each code. The distribution of all students’
deviations for each code has a mean of zero, as the
deviations in every group must cancel each other out.
FIG. 7. Fraction of groups with members in identical clusters
(light ring) and different clusters (dark ring) illustrating role
division in the different labs. Almost half of groups in the
traditional labs had all members in the same cluster (primarily
paper cluster), whereas the majority of groups in the inquiry labs
had members in multiple clusters indicating an increase in task
division.
FIG. 8. Intragroup variances of the relative behaviors among
students, signifying the amount of task division within groups.
Each plot shows VAR(ΔN) for all student profiles contained
within the labeled lab and group types along with their Bayesian
confidence intervals. (a) Comparing across lab types, the intra-
group variances are remarkably larger in the inquiry lab groups
than in the traditional lab groups for all codes besides paper,
indicating a greater range of behaviors and an increase in task
division. (b) Within the inquiry labs, the intragroup variances are
comparable among groups of differing composition suggesting
that similar degrees of task division were taking place. (Female
single-gender groups not included due to insufficient statistics).
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However, the variances of these distributions (defined here
as the intragroup variance) are not constrained and indicate
the degree of task division. An intragroup variance of zero
implies that any student’s behaviors are completely indis-
tinct from their group, while a large intragroup variance
reveals a greater degree of divide and conquer.
In the traditional labs, the intragroup variance was very
small for all coded behaviors other than paper [Fig. 8(a)].
This result supports the analysis and interpretation from the
cluster analysis: groups in the traditional labs did not divide
roles and each student behaved similarly to their group
members. In the inquiry labs, intragroup variances were
much larger for all codes apart from paper, which indicate a
high degree of task division took place.
Within the inquiry labs, we found comparable intragroup
variances among all coded behaviors regardless of the
group’s composition [that is, single-gender versus mixed-
gender groups; Fig. 8(b)]. This result suggests that the
group composition does not impact the group dynamics
with respect to the amount of task division; that is, single-
and mixed-gender groups divide roles to similar degrees.
However, within mixed-gender groups, these roles
are divided along gender lines. The distributions of devia-
tions for men and women in mixed-gender groups dif-
fered significantly for the laptop (p¼0.001) and other
(p¼0.009) codes. Women handled a laptop or personal
device more than their group members, and men partici-
pated in other activities more than their group members.
Furthermore, men in mixed-gender groups appeared to
handle equipment more often than the group average,
however, this result was only marginally significant with
the Bonferroni correction (p¼0.012). See Appendix and
Fig. 9for supporting data. To better understand these
dynamics within mixed-gender groups, we sought a more
fine-grained description of the roles students take on and how
they are assigned.
C. Understanding specific student tasks
and identifying role assignments
We captured video of a subset of individual groups for
entire lab periods and identified the specific tasks asso-
ciated with the coarse behaviors discussed in the previous
sections. For example, when a student was handling the
equipment, were they collecting data or setting up the
apparatus? We identified the specific tasks through visual
cues and students’speech. We then measured the total
amount of time each student spent on each specific task.
The cluster and intragroup analyses found significant
differences between men and women in mixed-gender
groups with regards to laptop usage and other activities.
The individual group video analysis found that women
spent about twice as much time as men analyzing data on
laptops (14% 7% of the lab period for women and 6% 
3% for men). However, we did not find a clear difference in
the specific tasks associated with the other behavior
between men and women in this subset of groups. The
biggest difference among other tasks came from within-
group behaviors such as talking, observing, or interacting
with group members (30% 4% of the lab period for men
and 26% 5% for women).
We also used the single-group video analysis to identify
that in almost all cases, students did not discuss the roles
they would assume. Notably, there were no instances of
explicit role allocation from peers in the group or from lab
instructors. We conjecture students either self-assigned
roles within groups, “fell into”roles, or directed each other
through positioning (subtle verbal and nonverbal social
cues [50,51]). Exploring mechanisms for role allocations is
the focus of future study to better understand how roles
become gendered. Tentatively, we conclude that the sig-
nificant difference in roles is not the result of overt, explicit
allocation. Rather, we infer that subtle interactions at the
individual level accumulate to create class-level patterns.
IV. DISCUSSION AND CONCLUSIONS
In this study, we identified how student behaviors in a lab
vary by lab type, gender, and group composition. From
coarse-grained observations of what students were han-
dling in the lab, we found that students in traditional labs
generally behave similarly, spending most time writing on
the lab worksheets. Behaviors in the inquiry labs were
much more varied, with behaviors focused on using
equipment and computers. Furthermore, women in the
inquiry labs tended to be high laptop users (primarily
analyzing data), while men were high equipment users
(collecting data or manipulating the equipment). This
pattern varied by group composition, however, where
men in mixed-gender groups were much more often
engaged in other behaviors (primarily talking to their
peers), while men in single-gender groups were the high
equipment users. Within-group analyses indicated that
these differences were a result of group members taking
on distinct roles, rather than whole groups tending towards
similar behaviors. The role division was not a result of
explicit allocation between group members.
Research indicates that providing students with more
authentic lab experiences, often by removing structure to
grant students more agency, improves student attitudes
towards science and engagement in high-level scientific
practices [39,52–55]. The results here suggest that by re-
moving structure in labs, these curricula facilitate student-
driven group work and open up a new set of group roles, but
may unintentionally create inequitable learning environ-
ments or provide the opportunity for underlying inequities
to manifest. Increased student agency, on its own, is
insufficient for the creation of a supportive and equitable
learning environment, where each student has the oppor-
tunity to freely pursue their own path in physics. Equitable
participation must be actively built into curricula, to elimi-
nate implicit inequities that can go on behind the scenes.
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We have found that inquiry-based labs, designed to
support student decision making, increased the variation in
student behaviors when compared to the more traditional
lab structure. Working collectively in groups, with a
pedagogical structure that facilitated group work (such
as having one electronic notebook per group as opposed to
identical, individual worksheets) opened up new group
roles and increased the range of behaviors students took on.
Removing structure in lab activities so that students may
take on a variety of roles supports a variety of students
experiences during an activity. Through these experiences,
we may communicate to students that there are multiple
ways to contribute to science and to be a physicist.
However, the freedom for students to fall into roles without
any guidance or pedagogical structure has the potential to
introduce problematic inequities. While one could argue that
allowing students to assume the roles they are more com-
fortable with may increase persistence in the course (regard-
less of whether or not they are gendered), we note that in the
absence of structuring equitable participation and group
work students may inadvertently fall back on cultural norms
and expectations when taking on roles within their group and
may rely on implicit biases when making these decisions.
Each student’s experience is unique in a classroom, but
systematic differences in these experiences may have unin-
tended, detrimental consequences. In this study, systematic
gendered inequities (with men and women systematically
taking on different group roles) and group behavior that
depends on group composition (men behaving differently
when in groups with other men versus when there is at least
one woman) were statistically apparent only in a curriculum
that provided ample agency. If such differences are supported
in institutional settings, they can contribute to increased
gender segregation through students’educational experi-
ence, and ultimately contribute to the large gender imbalance
seen in the field as a whole.
The focus of this study was intentionally directed at
students primarily intending to major in physics. While this
narrow population limits generalization to nonphysics
majors, it provides vital information with regards to group
work, which has potential implications on students’iden-
tities as physicists and decisions to persist in physics [26].
This work also has implications for instruction. Our
data, however, do not speak to the efficacy of different
approaches at mitigating the issues observed. We can draw
from previous literature to propose strategies that should be
studied. For example, it has been shown that increased
pedagogical structure combined with active learning can
reduce the achievement gap in class work [56]. Therefore,
actively building into lab curricula group roles (similar to
those of cooperative grouping [57]) such as “group PI,”
“reviewer,”or “science communicator”that have students
actively think about how roles are assigned and make
deliberate choices regarding role division could alleviate
the unintended consequences of subconsciously acting on
implicit biases, and is the focus of further research.
Previous work has identified many structural manipu-
lations that support equitable participation in other learning
environments [57,58]. Our results highlight that there
may be unique challenges to equity in inquiry lab envi-
ronments, where students divide roles associated with
distinct experimentation tasks (such as analyzing data or
handling equipment). The existence of role division is not
inherently problematic. However, the different roles phys-
ics students take on can greatly influence their unique
experience, identity formation, and sense of belonging,
which, in turn, ultimately impact persistence and repre-
sentation in the field [26,59,60]. With many calls to reform
lab instruction to provide students with more authentic
experiences and less structure, researchers have a respon-
sibility to evaluate the potential side effects of such
interventions. Given the many issues in representation
and persistence in STEM, students’experiences should
not be sacrificed for the increased learning benefits of these
kinds of labs. Instructors have the responsibility of ensuring
that the desired aspects of research and academia are being
reinforced in these learning environments, and that we are
not inadvertently reinforcing gendered roles by failing to
actively intervene.
ACKNOWLEDGMENTS
We thank the teaching assistants and lab instructors for
the course used in this study for their invaluable support
and cooperation. We also thank Chris Gosling for valuable
conversations and insight, and James Sethna for helpful
feedback. This material is based upon work supported by
the National Science Foundation under Grant No. 1836617,
the President’s Council for Cornell Women’s Affinito-
Stewart Grant, and the Cornell University College of
Arts and Sciences Active Learning Initiative.
APPENDIX: STATISTICAL ANALYSIS OF
INTRAGROUP VARIANCES
Here we present our intragroup analysis procedure to
investigate whether roles emerged within individual groups.
Each lab period involved groups of students working
together as a team to progress through an experiment. We
compared each student profile in a group to their group’s
average profile and quantified how a student deviated from
their group’s average for each code. Rescaling each profile in
a group with respect to that group’s average reveals the
variations between the group-members’behaviors. We then
compared whether there were any significant differences
between the relative behaviors of men and women.
We quantified the relative behaviors of students by
constructing each student’s deviating profile. If the coded
behaviors were distributed equally within a group, then the
observations of each student would match the group’s
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average for each code. Denoting the observed count of a
coarse behavior code for student Sin group Gwith NS
code,
the expected count of that code for a student in that group is
hNcodeiG¼1
MGX
MG
S∈G
NS
code;ðA1Þ
where the sum runs over each student in one group with MG
total group members. From this expectation value, we
calculate how student Sdeviates from their group’s average
ΔNS
code ¼NS
code −hNcodeiG:ðA2Þ
These deviations reveal interesting behavior trends within
groups. For instance, a student engaging in a particular task
more than their group members would be revealed with a
large and positive ΔN.
The distribution of deviations ΔNfor each code provides
information about task division within groups. When the
distribution of deviations contains all group members, the
mean is constrained to zero since each student’s deviation
cancels each other out by definition. However, the variance
of these distributions for each code [the intragroup variance
defined as VARðΔNÞ] is not constrained and provides a
measure of the amount of task division within a group. Zero
variance among deviations would imply the students’
behaviors are completely indistinct from another while a
large variance would reveal a greater degree of divide and
conquer.
In Fig. 8(a), we plot the intragroup variances for the two
lab types. The relative behaviors within groups from the
inquiry labs were highly varied when compared to the
traditional labs, which exhibited remarkably less variance
for all codes except paper. This result was confirmed with
Levene’s test to assess the equality of variances, where
none of the pvalues from the test statistic for each code
exceeded 10−5. This disparity among intragroup variances
was expected as the traditional labs were highly guided and
students were required to fill out their own worksheet, while
the inquiry labs were less guided and students were given
more agency for active decision making about the experi-
ment. The large intragroup variances in the inquiry lab
groups signify a higher degree of task division taking place.
To investigate task division in the inquiry lab groups, we
compared the intragroup variances for different group
compositions [Fig. 8(b)]. We found comparable intragroup
variances regardless of the group’s composition for all
behavior codes (every code’spvalue from Levene’s test
exceeding the p¼0.01 cutoff, with pvalues ranging from
p¼0.03–0.9). The comparable intragroup variances sig-
nify that there was no significant difference in the degree of
task division in the inquiry lab groups with respect to group
composition.
To examine the relative behaviors of men and women,
we shifted our focus to within mixed-gender groups. In
Fig. 9, we plot the histograms of deviations for men and
women in mixed-gender groups for the desktop, equip-
ment, laptop, and other codes. We performed a Mann-
Whitney U test as a nonparametric test to determine
whether there were any significant differences among the
distributions from men and women. We find significant
differences in the deviations from men and women for the
laptop (p¼0.001) and other (p¼0.009) codes. Women
handled a laptop or personal device more than their group
members, and men participated in other activities more than
their group members. We also find that men in mixed-
gender groups appear to handle equipment more often than
the group average (p¼0.012), however, this result was
only marginally significant with the Bonferroni correction.
FIG. 9. Histograms of intragroup deviations for men and
women within the inquiry lab’s mixed-gender groups for the
desktop, equipment, laptop, and other behavior codes, with the y
axis representing the number of student profiles. Each student
deviates from their group’s average by ΔN[defined in Eq. (A2) in
Materials and Methods A]. A positive ΔNdenotes a student
engaging in a behavior more often relative to their group
members. We quote pvalues calculated from the Mann-Whitney
U test statistic on all plots and find significant differences
between men and women for the laptop and other codes. We
also find a borderline result of men handling the equipment more
than their group members.
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