‘But, is it supposed to be a straight line?’ Scaffolding students’ experiences with pressure sensors and material resistance in a high school biology classroom

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‘But, is it supposed to be a straight line?’
Scaffolding students’ experiences with pressure
sensors and material resistance in a high school
biology classroom
Natalya St. Clair, A. Lynn Stephens & Hee-Sun Lee
To cite this article: Natalya St. Clair, A. Lynn Stephens & Hee-Sun Lee (29 Oct 2023): ‘But, is
it supposed to be a straight line?’ Scaffolding students’ experiences with pressure sensors and
material resistance in a high school biology classroom, International Journal of Science Education,
DOI: 10.1080/09500693.2023.2260064
To link to this article: https://doi.org/10.1080/09500693.2023.2260064
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UK Limited, trading as Taylor & Francis
Group
Published online: 29 Oct 2023.
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‘But, is it supposed to be a straight line?’Scaffolding students’
experiences with pressure sensors and material resistance in a
high school biology classroom
Natalya St. Clair , A. Lynn Stephens and Hee-Sun Lee
The Concord Consortium, Concord, MA, USA
ABSTRACT
This case study examines how material resistance (limitations posed
by the physical world) and graph interpretation intersected during a
high school biology investigation using digital sensors. We use an
extended episode from a small group to illustrate how, in an
inquiry-based unit, measuring near the resolution limit of a sensor
caused scaling issues in graphs. Qualitative videotape analysis
focuses on both the students’attempts to make sense of a
perceived lack of variation in the collected data and the teacher’s
and classroom researchers’misinterpretation of the students’
difficulties with graph interpretation. We suggest that these
educators, though experienced, could have benefited from
additional strategies to help them recognise and respond to graph
interpretation issues introduced by digital representations of real-
world data, and that the students could have benefited from
explicit prompts to discuss the limitations of their equipment. We
describe several implications for researchers, teachers, and
curriculum developers interested in implementing inquiry-based
biology investigations using sensor data. We argue that students
should be supported to recognise that encountering unexpected
results from their investigations and working to understand what
these results have to say about the real world is an important and
valid part of the practice of real science.
ARTICLE HISTORY
Received 20 September 2022
Accepted 13 September
2023
KEYWORDS
Biology education; sensor-
based science investigation;
scaffolding inquiry; sensor
data; material resistance
Introduction
The InquirySpace project aimed to facilitate open-ended inquiry for high school students
in biology, chemistry, and physics by providing a set of activities within a web-based
environment that integrated modern sensor equipment, data analysis tools, online simu-
lations, and provided teacher professional development. The teachers incorporated these
activities into their existing curricula. This study investigates the use of a pressure sensor
by ninth-grade biology students and the challenges they encountered when interpreting
real-time graphs of their sensor readings. One challenge stemmed from the small changes
being measured. The nature of this challenge was not recognised in the moment by the
teachers or researchers present.
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s)
or with their consent.
CONTACT Natalya St. Clair nstclair@concord.org The Concord Consortium, Concord, MA 01742, USA
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION
https://doi.org/10.1080/09500693.2023.2260064

Qualitative analysis is used to explore one student group’s engagement with the
‘Mangle of Practice,’a term referring to the challenges and complexities that arise
when trying to apply theoretical concepts and knowledge to real-world situations (Pick-
ering, 1995). In this particular case, the students attempted to reconcile their ideas about
what they would see with the actuality of what they did see in their osmosis data. While
grappling with the mangle of practice, the students encountered material resistance,
which describes the way the material world appears to resist human efforts to capture
data (Hardy et al., 2020; Manz, 2015; Pickering, 1995).
We review prior research on sensor use in three areas related to modern inquiry class-
rooms: sensor usage and inquiry, student interpretation of data generated by sensors, and
sensor-based teaching. Our theoretical perspective is that material resistance is inherent
in much inquiry-based learning and a necessary consideration for teacher noticing, which
refers to in-the-moment decisions teachers make in a classroom environment (Chan
et al., 2021; Jazby et al., 2023). To explore how encountering material resistance can
factor into the learning experience, we investigate how issues with scaling in data
graphs arose and persisted during a biology laboratory investigation. We conclude by
proposing several possible ways to notice and address the conceptual and perceptual
issues involved. We also discuss possible implications for future studies and curricular
design.
Review of literature
Using sensors in inquiry-based science classrooms
The goal of inquiry-based instruction is to facilitate learners’development of scientific
practices, including the process of developing an investigable question, designing an
experiment, collecting data, answering research questions through careful analysis, and
communicating results (National Research Council, 2000). Bell et al. write, ‘at its
heart, inquiry is an active learning process in which students answer research questions
through data analysis’(2005, p. 30). In the United States, modern inquiry instruction is
often viewed in connection with technology integration, including Internet research,
simulations, sensors, and wearables (Cooper et al., 2020; Lehtinen & Viiri, 2017;
Pedaste et al., 2015). The emergence of tools and technologies that enable students to
conduct activities such as investigation and data gathering more quickly (Pedaste
et al., 2015) has brought inquiry-based learning to the forefront of modern science edu-
cation research. The Next Generation Science Standards (NGSS) (NGSS Lead States,
2013), widely used in the United States, specifies that students are expected to use labora-
tory tools connected to computers for observing, measuring, recording, and processing
data (Appendix F). Sensors are among the technologies that teachers have used as they
seek to answer the NGSS call to engage their students in analysing and interpreting
data (NGSS Lead States, 2013, Appendix F). Sensor use in classrooms has been around
for decades (Mokros & Tinker, 1987). Recent research on sensor use reveals that 81%
of high school science classrooms in the United States have access to sensors used for
collecting data (Smith, 2019). They may serve as a valuable aid for teachers in overcoming
pedagogical challenges with collecting data during inquiry-based laboratory work
(Gericke et al., 2023, p. 32). As the use of various types of sensors in school laboratories
2N. ST. CLAIR ET AL.

increases, and teachers provide more inquiry-based learning opportunities, there is a
need for educators to address several new concerns related to data collected and
graphed through automated means (Lee & Wilkerson, 2018). This also has implications
for researchers, who need to keep up with emerging cognitive challenges.
Graphical representations of sensor data
Sensors are often paired with technologies that represent data in graphical form (Lee &
Wilkerson, 2018; Tinker, 2002). Although sensors have been shown to have educational
potential for science classrooms much in the way graphing calculators have influenced
mathematics education (Metcalf & Tinker, 2004), researchers have cautioned that stu-
dents who use sensors do not necessarily interpret graphs automatically. In a series of
studies conducted in the 1980s, researchers identified several difficulties students had
with interpreting graphs. For example, students seemed to have a weak interpretation
of slope, sometimes conflating the representation of the graph with a drawing or mista-
kenly viewing graphs as ‘pictures’of an event (Clement, 1989; McDermott et al.,
1987) and these issues have not disappeared with sensor use (Mokros & Tinker, 1987).
Metcalf and Tinker (2004) noted that while students seemed to identify broad sections
of a graph made with a temperature sensor, they faced challenges with reading points
or intervals on an axis. More recently, Lai et al. (2016) report that middle school students
struggle to interpret features of graphs produced digitally, including slope and axis labels.
This can be true, for instance, when sensor software autoscales (Hardy et al., 2020).
In spite of these challenges, researchers have reported multiple ways sensors can
enhance student learning. Many posit that when sensor data are recorded and displayed
in real time on a computer screen, students can collect data and immediately react to it,
opening possibilities for students to quickly generate more data, construct graphs, and
interpret and discuss results (Millar, 2005; Mokros & Tinker, 1987; Thornton &
Sokoloff,1990). Studies show that although use of real-time graphs seems to accelerate
student learning of graph interpretation, students do not begin with this knowledge
when they first use this technology (meta-review by Donnelly-Hermosillo et al., 2020).
Furthermore, while the topics of measurement and graphing tend to be covered in math-
ematics classes, they have not been well connected to explorations in science class (Lee &
Wilkerson, 2018; Leinhardt et al., 1990). Without adequate graph interpretation skills, it
would be difficult for students to extract information from their sensor readings or draw
formal mathematical conclusions to explain relationships in a scientific experiment.
Research on teaching with sensors
The cost of sensors has decreased substantially in the last 25 years, opening possibilities
for schools to adopt these technologies (Price, 2017) and for researchers to evaluate cur-
ricular approaches and assessment tools. A number of best practices have emerged,
including considering which experimental equipment is best to support student learning
and student agency in data production, as well as to develop supportive curriculum
materials and assessment tools available for teachers (Bernhard, 2018; Hardy et al.,
2020; Lee & Wilkerson, 2018; Price, 2017). Lee and Wilkerson (2018) caution that
while sensor technologies have been an effective tool for secondary classrooms, teachers
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION 3

and students must be mindful of their limitations and variability, especially when inter-
preting data. We will suggest that educators need more efficient ways for teachers (and
students) to assess graphing issues that arise in the context of classroom use of digital
measurement tools, particularly when scaling issues emerge as a result of sensor
resolution.
Overall, the literature on data interpretation and its importance is well established and
provides important insights into specific skills gained from using sensor technologies.
The automated generation of real-time graphs has been shown to give students a learning
advantage in their understanding of graphs in some situations (Price, 2017; Zucker et al.,
2008). However, it is still essential to understand how to identify and assist students
struggling with graphing issues when the primary focus of classroom activity is on pro-
ducing and comprehending data using sensors. In high school settings, students and tea-
chers encounter difficulties in managing such finer details spontaneously. Addressing this
issue can help educators identify strategies for enabling students to use inquiry-based
approaches to think and act more as scientists do.
Theoretical framework
A core tenet of the InquirySpace approach is the importance of exposing students to
authentic scientific experiences to support their ability to accurately interpret scientific
phenomena. Researchers have argued that part of an authentic science experience
involves students taking ownership of the data they collect and that using sensors to
collect data can provide valuable opportunities for students to work like scientists
(Hardy et al., 2020; Lee et al., 2021). Therefore, collecting data with sensors is a useful
way to offer students such opportunities.
Material resistance in school science laboratories
Education researchers have shown interest in Pickering’s(1995)‘Mangle of Practice’as
school science increasingly emphasises analysis of real-world data. The mangle of prac-
tice describes how scientific practices and ideas are developed through a dance between
human and material agency, with scientists developing hypotheses, procedures, and
measures that are applied to material phenomena that then respond with resistance.
Pickering discusses resistances that hinder a scientist’sefforts to capture a phenomenon.
When the results of an investigation confound theories, scientists engage in the mangle of
practice to realign theories with observations by adjusting theories, instruments, or both.
Manz (2015) suggests that Pickering’s ideas are congruent with current learning science
approaches and can aid in the design and analysis of disciplinary practices and under-
standing in the classroom.
Students who intend to gather data from sensor readings in science classroom labora-
tories may face obstacles due to material resistance. The concept of material resistance,
which refers to the physical world’s unyielding nature when attempts are made to
extract information using measuring tools, was first introduced by Pickering (1995).
Manz (2015) further defined material resistance as ‘push back from the world’(p. 90).
Despite the potential increase in data production when using digital measuring
devices, students must understand and respond to the information presented by these
4N. ST. CLAIR ET AL.

instruments. In the laboratory, students may need to engage in the mangle of practice to
reconcile their expectations with the data they acquire. To interpret the data successfully,
given the limitations of their measuring tools, students must address this issue.
Scaffolding student responses to material resistance in science instruction
Students require guidance and support from their teachers in order to handle their equip-
ment during inquiry investigations and interpet the results to gain an understanding of
natural phenomena (Bernhard, 2018; Wu & Krajcik, 2006). Our case study examines how
a group of students tackled the challenge of comprehending natural phenomena as they
encountered material resistance. We investigate the types of support that the students
received to help them understand, interpret, and improve their experimental methods,
as well as additional support that we believe would have been helpful. We also
examine how the students dealt with the challenges posed by the limitations of the avail-
able equipment.
Recent developments in research on teacher noticing and improvising in science class-
rooms have enriched our understanding of how teachers scaffold learning (Chan et al.,
2021; Jazby et al., 2023). The study of teacher noticing has its roots in mathematical edu-
cation but has recently gained more attention in science education (Chan et al., 2021).
Teacher noticing is defined broadly as the cognitive process involved during in-the-
moment teaching observations and pedagogical decisions in a loud and confusing class-
room environment (Jazby et al., 2023). In this study, the question of what the teacher and
researchers chose to scaffold in the moment in an inquiry-based environment is at the
heart of how to promote students’learning to link collected data to scientific phenomena.
Thus, we look not only at how students respond to a representation of their data, but how
they are supported by the teacher and researchers in the moment as they work to link that
representation back to the phenomenon.
In particular, we look at how the teacher and researchers noticed and supported stu-
dents as those students engaged in the mangle of practice in the course of data pro-
duction, struggling to align their conceptions with what their instruments were
recording. In this case, student encounters with material resistance involved scaling
issues when the collected data went near the resolution limit of the sensors. We focus
on the following questions:
.How did scaling issues manifest for the students when data collection approached the
sensor’s measurement capabilities?
.How did the teacher and classroom researchers interpret these manifestations and
attempt to provide in-the-moment scaffolding?
Methods
Case study
We found a dearth of literature on how to notice and address scale issues related to
sensor-based inquiry investigations at the high school level. We, therefore, use an
exploratory case study approach (Yin, 2018) to investigate how these processes developed
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION 5

in a classroom context with technologies, group work, and teacher scaffolding. We
selected one group of ninth-grade biology students for in-depth analysis according to cri-
teria described below. We examined their activity when they first encountered and
reacted to real-time data collected with a pressure sensor that approached the limits of
what the sensor could measure. A detailed account of classroom activity will illuminate
a particular instance of struggles we observed in this biology class and the effects of the
strategies that the teacher and researchers employed to attempt to support the students in
addressing those struggles.
The biology curriculum context
The InquirySpace project developed three inquiry-based curriculum units designed to
scaffold the development of students’independent experimental inquiry in high school
physics, chemistry, and biology. In our project’s curricular framework, students itera-
tively moved through cycles of design, collect, analyse, and explain when investigating
a phenomenon (St. Clair & Stephens, 2022). In each discipline, a unit of three investi-
gations, lasting two to three weeks, was designed so that explicit scaffolds fade over
the course of the investigations. The teacher introduced phenomena first to raise ques-
tions, then introduced the inquiry-based investigations. In the first two investigations
of the biology sequence, students started with a simulation and then, through trial and
error, learned how to design procedures with reproducible results. By the third investi-
gation, the curriculum was more open-ended, with the teacher selecting a phenomenon
and students taking charge of designing their experiments to investigate the phenom-
enon using sensors. In the experiments, the sensor readings were imported from our
real-time data collection tool, SensorConnector, into our open-source web-based data
analysis platform called the Common Online Data Analysis Platform (CODAP)
(Finzer, 2014), which is specifically designed to support data analysis and modelling
for students. CODAP is designed to import data from various sources such as sensors,
simulations, text files, data files, and manual data entry. The platform facilitates data
organisation and analysis through a drag-and-drop interface and linked representations
across graphs and tables, supporting student exploration of and reasoning with data.
The experimental setup. This study focuses on the second investigation in biology. The
objective of this investigation was to help students realise the importance of designing a
suitable experimental procedure and to emphasise the need for a class consensus on a
common procedure to facilitate the comparison of results. In this investigation, students
used a pressure sensor connected by an air-filled tube to a single-hole cork that was placed
inside a ‘well’bored into a potato (Figure 1). When water moved into the well from the
surrounding potato cells, the air column was compressed and its pressure increased.
When water moved out of the well and into the potato cells, the volume of the air
column expanded and its pressure decreased. The sensor detected these changes over
time and recorded them through the software interface running on the attached computer.
To conduct the trial runs, students filled the well in the potato with water containing
salt concentrations ranging from 0% to 20%. Each student group conducted multiple
trials for their assigned concentration value. With pure water in the well, water should
flow out of the well. In a period of 10 min, measurements should show a decrease in
pressure. Measurements of the change in pressure were very small with each timed
6N. ST. CLAIR ET AL.

observation, near the limit of the sensor to resolve that change. The overall change
observed during the 10-minute trial was typically only around 2 kilopascals (kPA).
The full range of the sensor is 0–400 kPA, with a 0.03 kPA resolution, meaning that
the 2 kPA change observed only accounted for 0.5% of the total range of the sensor.
These Vernier brand sensors are commonly used in high schools, and the teacher had
a set of them available in her classroom.
Case selection
The school. This study was carried out in a suburban high school located in California. At
the school, 43% of students were eligible for free/reduced lunch; 38% were proficient or
above average in Common Core Mathematics Standards. In 2019 when this study was
conducted, the school’s dropout rate was 6%, slightly above the state’s average. Students
came from diverse racial backgrounds: 25% of students were Black, 23% Filipino, 22%
Hispanic, 22% Asian, 4% White, and <5% other, according to school demographics.
The school schedule was organised around 58-minute periods, with a shortened
period on Wednesdays. The school provided a Chromebook tablet to each student,
which students carried around during the school day.
The teacher. This case study follows episodes in the classroom of Ms. T, a high school
biology teacher who taught a regular biology class that was part of the high school’s life
sciences graduation requirement. Ms. T taught 12 years in the Philippines and 17 years in
the United States. A fluent English speaker, Ms. T says that English is her second
language, and that she has that in common with more than half of her students. Ms. T
majored in chemistry and minored in biology in college, and her teaching credentials
were in high school chemistry and biology. Ms. T was also the biology chair at her
school, which meant it was her job to find curricula for her and her colleagues. One
major challenge she identified in her pre-implementation interview was that both she
and the school wanted their curriculum to be more NGSS-aligned and to include
more inquiry and classroom technology.
Figure 1. The experimental setup with the potato and pressure sensor [left] and an illustration of this
in the module [right].
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION 7

Before participating in the InquirySpace project, Ms. T had not used sensors in her
biology classes but had a set of Vernier sensors that she had used in her chemistry
classes previously. During her pre-implementation interview, she mentioned that she
wanted to do more with sensors in her biology curriculum. Over the summer, Ms. T par-
ticipated in a week-long summer workshop with 13 other teachers to learn more about
the InquirySpace curriculum and technology.
The class. Thirty-five of Ms. T’s students participated in the study (N = 35; 17 male, 18
female). Ms. T reported that the students had no prior experience using sensors or data
analysis platforms like CODAP or with inquiry-based science. The teacher reported that
a number of her students had parents who had been laid offfrom work and students were
staying in foreclosure homes or with foster parents, but many were still eager learners.
Students raised their hands during class discussion and responded to ‘cold calls’from
the teacher. They also seemed comfortable with group work; however, the teacher
reported that the English language learners were often shy to express themselves in
her class. The classroom researchers noted that one-tenth to one-third of the students
in the class engaged in behaviour such as side talk, outbursts, inappropriate cell phone
use/web browsing, or unexcused absences. Ms. T taught one biology class and five
regular chemistry/AP chemistry classes.
There were 16 desks in the centre of the room and 6 lab tables around the perimeter, all
of which were required to accommodate the 35 students. The classroom was organised for
lab and group work, but as the teacher commented, it wasn’t organised ‘for my big classes.’
With one whiteboard in the classroom, students crowded near the front of the room when
the teacher needed to present or write something on the board. There were four Dell desk-
tops in the corner of the classroom, which students sometimes used if they forgot their
Chromebooks or had technology issues. Classroom observers and researchers often
bumped into objects or people while attempting to move around the room.
The student group. The focus group consisted of three boys and one girl. Two identified
as Asian and two as Hispanic or Latino. One was new to the school this semester. Two of
the students were in ninth grade; one was in eleventh grade; and one was in twelfth grade.
Three scored in the median range for the pre-test and one student scored in the upper
quartile; the student who scored highest appeared to be a leader in the group.
Project staff.The first author of this study (Ms. S) and another researcher (Ms. K) were
present for the first and second investigations of this study (potato osmosis investi-
gations). In addition, the project curriculum developer (Mr. R) observed during the
week of the second investigation that is the focus of this study. Students were familiar
with the observers and greeted them as they came into the classroom. The observers pri-
marily focused on collecting field notes and helping with technology issues. At times the
observers asked students questions about what they were working on or responded to
student questions. During the week when Mr. R was present, he collected field notes
and sometimes assisted with full-group discussions.
Data collection, reduction, and analysis
Every class period was videotaped and we gathered field notes to capture class activities.
In addition, we collected the focus group’s screencasts every day, which captured the
graphs generated by the students in real time as their sensors recorded data. We had
8N. ST. CLAIR ET AL.

access to all the data that the students submitted through our online student portal,
including student answers to questions and CODAP documents that showed data collec-
tion and analysis. We debriefed with the teacher every day after an InquirySpace lesson
was taught and took notes of those meetings. In addition, we interviewed the teacher after
the implementation to learn her perceptions on inquiry, experimentation, and data col-
lection; and what she noticed and wanted to work on. Table 1 outlines the timeline of the
classroom implementation.
Our case selection was opportunistic. One group in the class fully consented to the
video study. In total, 65 screencast videos capturing the computer screen of the focus
group, the portion of whole class videos that contextualised the videos of the focus
group, and the associated field notes were imported into Transana qualitative analysis
software and reviewed. We then reviewed student artefacts and 40 additional field
note documents. The student artefacts collected included lab notebooks and CODAP
documents, which included tables and graphs.
During review, we identified five whole class videos that showed the teacher scaffold-
ing the whole class in working with data and five screencast videos that involved the focus
group working with unexpected data collected from a pressure sensor. For further in-
depth analysis, we selected the first two screencast videos in which the students were col-
lecting data from the pressure sensor so that we could better understand how they asked
questions about their graphs and how Ms. T and the observers responded in the moment.
A researcher on the team transcribed the screencast videos verbatim and the first author
reviewed the videos and transcription to improve accuracy. The first author and the
researcher annotated the transcripts from the field notes. The goal at this stage was to
identify (1) moments that involved a high degree of experimentation and engagement
with the sensors and students’interpretation of the graph inscriptions they saw on the
computer screen and (2) what Ms. T and observers noticed and the corresponding
actions taken.
We looked in the screencast videos for graphical representations of student data to
help us understand how students interpreted them. This included real-time data collec-
tion in SensorConnector, as well as CODAP graphs that the students created later.
During this stage, scaling issues were identified as an important theme. In some screen-
casts, the real-time data appeared as straight lines in SensorConnector because the
change being recorded appeared too small in the default graph display. The second
round of qualitative analysis also incorporated field notes as the author sought to
Table 1. Timeline of classroom implementation. The * represents the analysed portion of the case
study. Students also took pre/post assessments (1 class day for each).
Date Investigations Collected data
Unit 1 –October 2019, 5
class days
Enzyme function lab with glucose
metres, introductory unit
Pretest, video recordings of whole class,
screencasts, fieldnotes based on in-person
observations, digital artefacts of student work
Unit 2 –November-
December 2019, 11
class days
Potato osmosis with pressure sensors,
5 class days* Diffusion simulations, 6
class days
Video recordings of whole class, screencasts, in
person observations, digital artefacts of student
work, photographs of students’‘Do Now’
activity
Unit 3 –January 2020, 6
class days
Independent lab/photosynthesis Video recordings of whole class, screencasts, in
person observations, digital artefacts of student
work
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION 9

understand how this issue manifested itself in this setting and what the teacher and class-
room researchers noticed in these interactions.
The episodes of interest occurred when students were constructing the experiment,
debating next steps, seeking help, engaging with the data, and engaging with classmates
or their teachers. These segments generally took place during small group sessions, but
there were moments where this happened in whole class discussions. During this stage of
analysis, the research team met often and shared representative episodes as a check on the
validity of our interpretations. We sought to understand students’purposes within epi-
sodes to identify the challenges that appeared to be affecting them. We examined patterns
across episodes, used analytical memos to describe each of these moments, and fre-
quently consulted with other project researchers as a check on the validity of our
interpretations.
Results
Day 1: orientation of phenomenon through class discussion and teacher
demonstration
On Day 1, the teacher introduced the concept of osmotic pressure, but the question of
how to measure the phenomenon in the upcoming sensor-based investigations was
not discussed. Rather, the focus was on reviewing the vocabulary of osmosis and
diffusion and making sure the students understood it.
Day 2: the appearance of the scaling issue
On Day 2, Mr. R, the curriculum developer, briefly introduced how to create a setup
using the materials for experimentation: ‘You are going to test to see how water will
move into cells or out of cells in a potato. So, here’s a potato, right? This potato is
made of cells.’He then said that students would need to bore a hole into the potato
‘so that the hole has cells all around it’and fill the hole with deionized water. Mr. R
then demonstrated how to put a seal on the pressure sensor, connect it to the computer,
set the duration of the trial to 10 min, then press ‘Run.’The directions were intentionally
under-specified in order to elicit student questions and ideas about the experimental
design. One student asked how much water to add to the potato. Ms. T responded
‘more or less; you figure it out’in line with the unit goal of getting students to agree
on an experimental procedure after a trial run. After that exchange, the students came
to the front of the classroom to collect their materials and then began setting up the
experiment. The teacher and observers walked around the classroom to ensure that stu-
dents’equipment and sensors were working properly and help with student questions.
The students in the focus group worked with a member of the research team, Ms. K, to
make sure that the pressure seal was on the potato correctly. At this point, the students
were concerned about how full the hole in the potato should be with deionized water.
After Ms. K assured the group that their setup was fine, the students collected data.
The teacher, Ms. T, reminded the class to set the sensors to run for 10 min. Mr. R
walked by the group and the students asked Mr. R and Ms. K whether the sensor was
collecting data or not. Mr. R confirmed that the sensor was collecting data and walked
10 N. ST. CLAIR ET AL.

away. The students asked Ms. K whether they needed to enter data manually or if the
sensor worked on its own. Ms. K noted that the sensor was collecting data automatically.
Four minutes into the experiment, students indicated that they perceived the data as a
‘straight line,’but they had questions:
S1: Why is ours just flat? [See Figure 2.]
Ms. K: Well, it’s a 10-minute experiment. Why do you think it’sflat?
Ms. K: Do you guys know what it’s measuring?
S1: Pressure inside.
Ms. K: What? Pressure?
S1: Well, it’s measuring the pressure inside.
Ms. K continued to probe, asking the students, ‘Pressure from what?’After this exchange,
Ms. K wrote, ‘We had a little discussion on if it’s supposed to be a straight line or not.
And what it’s really measuring.’The students checked their data with other groups to
see if what they were seeing was expected, but they seemed unsatisfied with what they
saw. While this was happening, Ms. S, the first author, walked by and looked at the
screen as Ms. K made a suggestion about how to answer a question in the module.
Ms. K: Oh, it gives you –go back and change it.
(Ms. S and S1 work on S1’s computer to troubleshoot.)
S1: Was this supposed to be a straight line?
Figure 2. A representative graph showing what students saw when they were collecting data on the
first day about 45 s after data collection started.
Note: This is a recreation.
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION 11

Before the observers had a chance to respond to the student’s question, Ms. T instructed
the class to write down the steps to their procedures, including the unsuccessful ones. She
also informed the class that the group with the most favourable trial run results would be
selected to develop a standard procedure the following day.
The data display in Figure 2 shows what students and observers saw 45 s into the first
data collection run of this investigation. These students reacted to their graphical display
with some concern –they seemed to think that they had made a mistake but did not
know what to do about it. In the end, they saved their data in CODAP in a tabular
form. They wrote in their lab notebooks that they ‘waited too long to collect the data’
as a possible problem in the trial run.
For each experimental condition, with varying salt concentrations used in the well of
the potato, the pressure change was very small when compared to pressures typically
measured by this type of sensor. Typical pressure changes over the period of a trial
run ranged between 0–2 kPA, while the default scaling on the graph for the typical
range of this sensor was 0–400 kPA. From a student’s perspective, the 5-to-10-minute
wait during data collection and the nearly imperceptible change in pressure (at the
default scaling) made it appear as though nothing was happening. Students viewed the
sensor readings in a tabular format and noticed minor variations of one-tenth of a
kPA. However, they were unaware that the fluctuations sometimes fell below the display’s
resolution. They were not aware that changing the y-axis scale (i.e. minimum and
maximum values) would have given them a different perspective on their data.
Day 3: class discussion on refining the experimental setup
The next day, Ms. T rescaled the focus group’s graph prior to the class period and pro-
jected it to the class for sharing:
Figure 3. The teacher rescaled the data to show the correct scale, but the students did not seem to
recognise their data when it was projected on the board.
Note: The graph was recreated by the authors from the same student data table and scaled to match the image from
classroom video.
12 N. ST. CLAIR ET AL.

Ms. T: Can we share our –what procedures did we use so that we have the data? Can you
share the procedure that you think actually works?
S1: Umm, I don’t think it was our group that shared data. None of these look like our
–
Ms. T: In all of these, I have made it bigger.
S1: Oh.
Interestingly, the student did not recognise their own graph when rescaled in this way.
The scaling (and hence the shape) was noticeably different from what the students had
seen the previous day (see Figure 3). When the teacher explained, the student responded
‘Oh,’indicating acceptance –but not necessarily understanding –of what the teacher
said. If the student understood, the episodes below cast doubt on the robustness of
this understanding. From the teacher’s point of view, she had addressed the student con-
fusion –‘In all of these, I have made it bigger’–but S1’s question about ‘the straight line’
from the previous day was never directly addressed. Instead, the teacher now focused the
class on thinking about the group’s experimental procedure.
The teacher encouraged the class to carry out a revised experimental procedure, with
each run lasting five minutes. The students then returned to their respective groups to
collect another trial run, following the agreed-upon procedure. About 10 s into their
new trial run, the focus group tried again to determine if their seal was good and if
they were collecting data. The following exchange occurred:
Figure 4. The graph that the students produced when they seem to have accidentally rescaled the
data before the first author approached the group. The graph oscillates, jumping between two
values that represent miniscule changes in pressure, because the sensor is approaching its resolution
limit.
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION 13

S1: Are we supposed to make a line or something?
S3: Restart?
S1: It’s going.
S2: It’s just straight. Is this (inaudible)?
This suggests that the issue of ‘the straight line’had not been fully resolved. During a
post-implementation interview, the student who had been using the mouse cursor
explained that at this point they ‘started clicking around to see what happens.’In the
screencast, the mouse can be seen clicking an icon in the onscreen data representation,
and this caused the data to rescale (see Figure 4):
S3: Whoa, what I am doing, what (inaudible)?
(Rather than clicking ‘Stop,’the student, apparently flustered, clicked the ‘discon-
nect sensor’button.)
S3: Oh, sh –.
(sound of laughing)
S1: I’ll have to restart it.
After attempting to refresh the page, the data were still there but not rescaled. Once again,
the graph appeared as a straight line. Students saw what looked like a flat line, so they
called the first author over for help. The author helped them to begin a new data collec-
tion for a different time duration, but did not address the issue of the flat line.
The author then proceeded to change the duration of the run from 10 min to 5 min,
and then started a new run for the students. After this exchange, students waited for the
Figure 5. What was on screen on Day 2 when the researcher/first author approached the group. Note
the small ‘bump’at approximately 160 s.
14 N. ST. CLAIR ET AL.

run to complete. They made off-topic comments about grades and other schools they
attended. About halfway into the trial run, the author asked about their graph (Figure 5):
Ms. S: Can I see your data?
S1: Yeah.
S2: Is it supposed to look like that on the screen?
S3: Oh, yeah, that’s because –that’s where I touched it.
Ms. S: Oh, interesting! OK.
The author never saw the graph as it appeared in Figure 4 and did not realise the issue
had again been a scale issue. The readings were fluctuating several hundredths of a
kPA, at the limit of the sensor’s resolution. When the student rescaled, the graph was
too zoomed in and the tiny changes in pressure were amplified by having such a small
range on the y-axis after clicking the ‘rescale’button. The students did not know what
to make of the graph’s appearance. For the next run, it is not clear whether the issue
was one of scale or whether the problem for S2 was the ‘bump’in Figure 5. In the
moment, the author, who was tasked with supporting students in technology glitches,
interpreted the first interaction as a problem with the length of the run. She validated
the students’reasoning and decided to allow them agency in continuing their data run.
The question of the straight line continued to come up for this group of students even
after a whole class discussion and a second chance to work on the data collection run.
They collected another set of data before the class period ended, but did not voice ques-
tions about it. At the end of class, they returned their pressure sensors with minimal dis-
cussion about the investigation. The graph continued to appear as a straight line in the
onscreen data representation and the students continued to express confusion about this
by the end of Day 3.
Day 4 and on: working beyond the data collection runs
Ms. T asked students to draw a picture of what they thought was going on with the water
from the salt solution as it moves via osmosis between the well and the cells of the potato.
In the caption, one student wrote: ‘The water molecules move from a higher concentration
to a lower concentration, meaning the concentration inside the cell is higher than the con-
centration inside [the well] (20% or 5% NaCl), so the water moves out of the cell.’This indi-
cates some level of understanding of the phenomenon that there is a higher ratio of water to
salt inside the cell (i.e., higher concentration of water) compared to the solution in the well
bored into the potato. The small groups spent one more day collecting data, this time with
each group trying different initial concentrations of saltwater solutions.
After completion of the data collection runs with three different concentrations, stu-
dents continued to work with the teacher to better understand osmosis and diffusion for
two weeks after this investigation. One week included InquirySpace curriculum with two
simulations, including a pressure change simulation with graphs. They discussed what
causes pressure changes and why NaCl will not cross a cell membrane. A second week
was devoted to osmosis vocabulary, which was not part of the InquirySpace curriculum
and was not videorecorded or observed.
Twelve days after the start of the InquirySpace investigations under study, the groups
of students produced video lab reports to explain their findings from the pressure sensor
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION 15

and other investigations. By this time, the scaling issue appears to have been resolved for
the focus group. In their report, they discussed their earlier misconceptions:
S1: Our first attempt was challenging. We thought we had messed up because on our
graph the line was just straight, but later on when we zoomed in, it didn’t look straight.
Also, we waited too long to connect the sensor to the potato after putting the water in.
It appears that they had resolved their graphing issues, although the simulation they had
worked with yielded the properly scaled graphs in Figure 6.
Because this issue was missed by four experienced educators in the room and only
recognised in retrospect, it is worth reflecting on how the educators interpreted and
responded to the issue at the time, its impact on the small group exploration, and the
potential insights that could be gained from re-examining it through the lens of the
mangle of practice.
Findings and discussion
Finding 1: scaling issues manifested in the form of student dissatisfaction with
their graphed data when the collected data approached the sensor’s resolution
Our case study reveals that students encountered two types of scaling issues when
working with graphs. The first type occurred when the graphs were zoomed out too
far (Figure 5), causing the curves to compress and obscure trends in the data. This
made it difficult to discern the subtle pressure changes, as the graph appeared to be a
straight line. The second type of scaling issue occurred when the graph was zoomed in
too close (Figure 4), resulting in chaotic data that was difficult to interpret. We
provide a detailed description of each scaling issue and how students expressed their dis-
satisfaction with them.
When students encountered graphical representations that were zoomed out too far,
the students did not ask about scale, per se. This is not surprising since they did not know
what they did not know. Instead, they asked about the graphical patterns they saw, with
questions such as ‘Is this supposed to be a straight line?’and ‘Are we supposed to make a
line or something?’These questions were about the graphs and stemmed from the default
scale of the graph and the overall small changes in pressure observed in the experiment.
However, we can look beyond this to see that students’conceptual difficulties were ulti-
mately produced by the fact that the sensors did not appear to them to yield information
about a change they felt should be occurring. They could not see any change in pressure
and were unsure why. Similar to the experience of experimental scientists, these students
questioned whether something was wrong with their instruments, their experimental
design, or their interpretation of the results.
The second type of scaling issue involved zooming in too close on the data, which
made it difficult for students to interpret the graph. They observed spikes in the
graph, which result from the sensor readings oscillating between two values near the
sensor’s resolution limit. This happened when the pressure change was very small.
Over the course of several days, they witnessed the numerical values for the pressure
(at the top of the SensorConnector window) fluctuate rapidly in real time, further com-
plicating their understanding of the collected data. As a consequence, the students
16 N. ST. CLAIR ET AL.

struggled to make sense of their own sensor data on pressure change caused by osmosis at
the cellular level. The functioning of their measuring tools impacted how the students
perceived their data. Even though they were not successful at either point in creating
alignments between their observations and their conceptions about what they should
be seeing, it seems clear they were attempting this, and, unbeknownst to them, engaging
in the mangle of practice in a sustained fashion.
The students were trying to comprehend the graphs with misleading scale by connect-
ing the phenomenon back to the graph or the graph back to the phenomenon. Their
question, ‘Is this supposed to be a straight line?,’is consistent with Leinhardt et al’s
(1990) observation that after rescaling, the ‘picture’of a graph can look quite different,
and with the finding of Ben-Zvi’s case study (2004) that questions students have about
the overall shape of the graph could be an indication that students are trying to make
sense of larger trends in the graph for summarising the data and connecting it with
the phenomenon. When students saw their rescaled graph in CODAP projected on
the whiteboard, they did not recognise the data as their own. Simply having someone
Figure 6. The graphs that the students produced with a simulation that explores diffusion across a
cellular membrane.
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION 17

else rescale their data, and tell them they had done so, was not helpful enough. The
problem was not with the act of rescaling itself, but rather with the understanding that
real-world data often requires rescaling and the ability to do so effectively. In other
words, the students lacked the knowledge and awareness necessary to understand that
they needed to rescale and to figure out how to do so.
Finding 2: the teacher and observers interpreted these manifestations in the
moment as procedural issues, and their attempts at in-the-moment scaffolding
focused primarily on experimental procedure
The depth of the perceptual and interpretation issues that arose for the students related to
scaling were not initially recognised as such. At the point when the graphs were zoomed
out too far, the teacher and observers had several objectives in mind. They were focused
on ensuring the equipment was functioning, encouraging reflective thinking, directing
attention to the experimental procedure, and troubleshooting technical issues (Table
2). One potentially powerful teaching move they used was the reflective toss (Van Zee
& Minstrell, 1997) where they asked questions such as, ‘Do you guys know what it’s
measuring?’in order to foster reflective discourse within the group. Despite these
efforts to encourage reflective thinking, and the valuable insights generated from these
questions, the depth of perceptual and interpretation issues related to scaling went unrec-
ognised at that time, leaving these students without the necessary guidance to manipulate
the data representations and develop a more accurate understanding. Consequently, their
primary concern remained whether they had set up the experiment correctly, rather than
exploring their understanding of the data representation.
When the graphs were zoomed in too closely, the educators’primary focus was on
addressing technological issues, inadvertently leaving the students with a lack of compre-
hension regarding the spikes shown in Figure 4. As a result, the students remained con-
fused despite the educators’reassurances during class discussions that their data looked
good. Unfortunately, this reassurance failed to bridge the gap between the students’
understanding of the graph, seen at a misleading scale, and their perception of what
was happening in their experiment. The scaling and resolution problems were never
explicitly addressed, and the students continued to have uncertainty and doubts about
the accuracy of their experimental setup.
In line with Jazby et al.’s(2023) observations regarding the time pressures faced by tea-
chers when attempting to notice students’thought processes during lessons, this study’s
Table 2. Instances of students asking questions about the data and how adults scaffolded student
questions.
Instance The support that adults opted to offer
Students asked if the data ‘is going’(can’t see the
line at current scale)
Curriculum developer verified that the data ‘is going’
Students asked ‘is this supposed to be a straight
line’
Observer provided a reflective toss
Students did not recognise their own data,
rescaled and in different representation
Teacher responded ‘in all of these I have made it bigger’and then
redirected attention to experimental procedure
Students rescaled graph by mistake and asked for
help re-configuring graph
Observer proceeded to fix technical issue
18 N. ST. CLAIR ET AL.

findings further support the notion that enhancing teachers’capacity to effectively attend
to and make sense of significant classroom occurrences is crucial for gaining insight into
student difficulties. In turn, this reinforces our awareness of the pivotal role teachers play in
recognising and addressing student challenges, while also shedding light on the challenges
imposed by time constraints in the classroom. In the specific context of this study, it is
evident that creating opportunities for teachers to notice and address these scaling
issues and other challenges is vital for optimising student learning experiences. When
experiments are opened up for students to exert more of the design decisions, it may
not be possible to anticipate the results. This can lead to a greater diversity of results,
and issues of scale and limitations of the sensor resolution should be something that tea-
chers are prepared to support when students have questions about their results.
In summary, despite the educators’reassurances regarding the quality of the students’
data during class discussions, their questions about the straight line in the graphed data
were not adequately addressed at the time. This lack of alignment between their under-
standing and the actual events in the experiment persisted, leaving the students with
uncertainty regarding their findings. We suggest the students could have benefited
from more explicit support to understand the measurement process, to make connec-
tions between the range of measurements the sensors were designed for (0–400 kPA),
and the range of measurements being made (0–2 kPA). We hypothesise that their later
work with a simulation of pressure change (which included a properly scaled graph of
data and was conducted after their hands-on work with sensors but before their screen-
cast assignment) helped them understand the relationship and may have helped resolve
their graphing questions.
The teacher and observers underestimated the difficulty of linking the data back to the
phenomenon in this situation. The students struggled to understand what was happening
in the experiment at the cellular level, and the ‘straight line’that students saw confused
them because a finding of ‘no change’was not in alignment with their conceptions.
Rather than recognising and framing this lack of alignment and the struggle to resolve it
as an important part of the scientific process, it functioned to reduce chances for these stu-
dents to act independently and autonomously in their data production and analysis.
In addition to issues related to graph scaling, material resistance from sensors contin-
ued to pose a challenge in subsequent implementations in the biology classes of this
teacher and others. To address this issue, a new InquirySpace curricular unit was
created with activities to help students better understand the available sensors and
their limitations. These activities include teacher-led discussions on the equipment’s
limitations, an optional exercise focusing on exploring sensors and their measurement
techniques, and guidance on rescaling graphs for obtaining more precise real-time
data representation. These were designed to provide students with opportunities to
think about and rescale their graphs while collecting data and to have productive conver-
sations about the osmosis phenomenon and its relationship to the changes in pressure
data. The revised curriculum is available and being used in classrooms.
Implications for teaching and future research
Overall, this case study highlights the importance of supporting students to understand
material resistances they are likely to face when using modern sensor technologies to
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION 19

carry out preliminary investigations. It emphasises that teachers must be mindful of these
challenges and provide support to their students as they engage in data-rich investigations,
particularly in light of the NGSS and recent advancements in affordable sensor technol-
ogies. The use of inquiry-based teaching methods in classrooms can make data collection
and graphing more challenging. This is due to the fact that students often design their own
experiments, which can lead to significant variations in how they use sensors. To address
this issue, it is important to reframe sensor and data graphing difficulties as opportunities
for students to align their conceptual understanding with their experimental results and to
empower students to realise they are engaging in valid scientific practices. This can help
position them as data producers (Hardy et al., 2020).
In light of the increasing availability and usefulness of sensors for NGSS, we see the
following implications arising from this study.
Engaging in more activities exploring sensor capabilities could help students understand
the mangle of practice. Teachers could incorporate classroom activities where the primary
goal is to discuss the measurement range of the sensor, what it can measure, and how to
handle unexpected behaviour. By actively exploring the sensor’s capabilities and limit-
ations, students may gain a better understanding of how to effectively use the technology
in their experiments. Manz et al. (2020) argue that if teachers do not support students in
making sense of uncertainties inherent in measurement, then teachers will be left with
the task of ensuring that students’explanations fit the model that the teacher or curricu-
lum developers were trying to create. Incorporating activities that encourage students to
grapple with these uncertainties can empower them to develop a more robust under-
standing of the scientific concepts and the role of measurements in supporting their
claims (Manz et al., 2020).
These kinds of activities often require the teacher to embrace the unexpected more than
usual and to be prepared to answer student questions in depth. These episodes highlight
the importance of empowering teachers to establish supportive classroom structures that
facilitate addressing student questions within an inquiry-based learning environment
(Jazby et al., 2023). This shift in approach means helping students anticipate potential
sources of issues in their data, and support them in articulating how they think their
data should look.
We suggest an increased focus on analysing computer-generated graphs and other rep-
resentations of student-produced data. Pols et al. (2021) suggest that high school students
face difficulties in understanding various graph features, such as labelling axes or identi-
fying trend lines, when working with computer-generated graphs. Teachers can enhance
students’understanding of data representation by questioning them about the graphs
and other data visualisations they create. When a student asks, ‘Should this be a straight
line?,’a teacher could respond by prompting students to consider their expectations,
potential sources of noise, and the appearance of small changes in their data. The objec-
tive is to encourage students to think critically about their experimental results and to
foster deeper engagement with the scientific process. This approach aligns with the per-
spective of Ben-Zvi (2004), who emphasises that teachers can support student under-
standing by fostering an environment that encourages students to interpret and derive
meaning from the graphs they generate, rather than simply providing directives.
In the context of high school biology, educators may face distinct challenges using
sensor technologies compared to physics, for instance, because biology labs often deal
20 N. ST. CLAIR ET AL.

with data that approach the limits of the sensors available in schools. When sensor use
spreads to more realms of science instruction such as biology, there are new challenges
for teachers in noticing the discrepancies between students’expectations and actual
outcomes.
Limitations and recommendations for future research. This case study is of a single
group of high school biology students working to make sense of the data they collected
with one sensor in a particular investigation. Other students may interact with sensors
and software for collecting and analysing data differently. Similarly, we recognise that
the specifics of the teacher and classroom setting play a role in students’experiences
working with the data they collect.
Future research could further investigate the difficulties that teachers face while
guiding students in data-oriented inquiry as sensor use moves increasingly into other
areas of high school biology. The objective would be to identify pedagogical scaffolds
appropriate to the new kinds of investigations suitable for those areas, as well as identify-
ing the kinds of material resistance students will likely encounter so that those encounters
can be reframed as valid science practice.
Conclusion
A major premise we are working from is that data collection with sensors has the
potential to enhance inquiry-based learning activities in the classroom, provided appro-
priate scaffolding is in place. An unanticipated finding in our study was the crucial role
of material resistance when working with sensors in investigations that challenge the
sensor’s measurement capabilities. Students struggled with the graphs they produced
from sensor readings and they grappled with understanding whether they were produ-
cing data at all. Furthermore, scaling was a significant challenge in this classroom, as
evidenced by the example in this case study. It was surprising to realise that even the
adults in the room, including the first author, were unable to recognise the scaling
issue in the midst of all the classroom activity. We suggest that although grappling
with material resistance can be a source of frustration for students, it can also
present opportunities to discuss aspects of measurement in general. We see value in
placing material resistance within the context of valid scientific inquiry, as rec-
ommended by Manz (2015).
As the popularity of inquiry-based teaching and learning and the adoption of NGSS
continues to grow, it is imperative for educators to leverage both new points about
NGSS and advancements in technology to enhance teaching practices. In this
context, it is crucial for teachers and students alike to acknowledge the limitations of
sensors, particularly when it comes to interpreting the graphed data they generate.
By developing a deeper understanding of material resistance and its implications for
inquiry-based learning, teachers and students can harness the full potential of
sensors in the classroom.
Acknowledgements
Any opinions, findings, and conclusions or recommendations expressed in this material are those
of the author(s) and do not necessarily reflect the views of the National Science Foundation. The
INTERNATIONAL JOURNAL OF SCIENCE EDUCATION 21

authors would like to acknowledge Dan Damelin, Sarah Haavind, Steve Roderick, Linda Cohen,
George Jennings, and Cynthia McIntyre for their input.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
This work was supported by the National Science Foundation: [Grant Number DRL-1621301,IIS-
1147621].
Ethics statement
This study was approved by the Ethical & Independent Review Services (E&I Assigned
Study ID: 19106-01) on July 19, 2019, date of check-in July 18, 2021. All participants pro-
vided written consent prior to participating. Written informed consent to participate in
this study was provided by minor participants’legal guardian/next of kin. Identifying
information, such as names and places, have been anonymised to ensure participant
safety and privacy.
ORCID
Natalya St. Clair http://orcid.org/0000-0003-0654-1652
A. Lynn Stephens http://orcid.org/0000-0002-4343-340X
Hee-Sun Lee http://orcid.org/0000-0002-4673-5008
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