Relating curricular content coherence to learning: Examining high school students' emerging understanding of biology

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Running Head: COHERENCE AND BIOLOGICAL UNDERSTANDING
Relating Curricular Content Coherence to Learning: Examining High School Students’
Emerging Understanding of Biology
Candice Guy
University of California, Davis
crguy@ucdavis.edu
Julia Gouvea
Tufts University
julia.gouvea@tufts.edu
Cynthia Passmore
University of California, Davis
cpassmore@ucdavis.edu
Paper presented at National Association for Research in Science Teaching Conference,
Baltimore, MD. April 2016.

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Introduction
Current reforms in science education are calling for students to recognize and appreciate the
ways in which ideas are related within science (National Research Council, 2012; NGSS Lead
States, 2013). Connections such as these are often referred to as “meaningful connections”, such
as students should be able to form “meaningful connections” between biological ideas; however,
what types of connections students can make and what are the nature of these connections has
not been extensively explored (c.f., Ryoo & Linn, 2012; Ummels, Kamp, De Kroon, & Boersma,
2015). Oftentimes, the lack of connections is investigated rather than the types of connections
students can make (e.g., Bray Speth et al., 2014; Dauer, Momsen, Speth, Makohon-Moore, &
Long, 2013).
With new science reform, also comes calls for coherently designed curricula that promote
these types of connections (Carlson, Davis, & Buxton, 2014). Within the last several decades,
curriculum development research has explicitly addressed the logical, coherent sequence of
disciplinary content across multiple years and subjects (Neumann, 2013; Roseman, Linn, &
Koppal, 2008). However, it is also important to consider the iterative manner in which content is
developed over the course of a school year. Content coherence within a yearlong sequence has
the potential to catalyze the types of conceptual connections called for in NGSS and to allow
students to apply their synthesized knowledge to novel situations and phenomena (Roseman et
al., 2008).
In our work, we focus on the interplay between curricular coherence and student learning
through a National Science Foundation supported project focused on developing a yearlong high
school biology sequence designed to foster student engagement in the construction and
generation of models. The sequence integrates all four Life Science DCIs around a core family of

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models–evolution– (Passmore, Gouvea, Guy, Griesemer, in review). It is within this curriculum
design context that we conducted an exploratory study of the types of connections students can
make within and across biological ideas. Students who participated in this study were in the
classrooms piloting our model-based curricular materials and at the end of the school year
volunteered to take part in this separate investigation. In this paper we describe our method for
exploring the ways in which students perceive ideas to be related and how a study such as this
could be used to better understand curricular content coherence. Through analysis of student
discourse we were able to infer that students’ knowledge is organized in an emerging knowledge
system with varying ways for coordinating ideas. These pathways suggest that students are able
to make a diverse array of connections that resemble in some ways the overarching structure of
our curriculum; yet, still maintain idiosyncratic and dynamic organizational structures. In this
paper, we report on the results of this exploratory study, focusing in particular on the nature of
the connections that one student was able to make with regards to biological ideas. '
Investigations of Meaningful Connections in Biology
Most of the research focused on students’ abilities to make connections has been done through
explorations of student explanations of biological phenomena and student drawn concept maps.
Additionally, they tend to emphasize whether or not students can make connections between
genetics and evolution. In this section, we will highlight some of these studies, particularly those
focused on evolutionary understanding, as these types of investigations provide a foundation for
beginning to understand what students connections are possible between and within biological
ideas.

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Three recent studies have utilized concept mapping to explore the ways in which
undergraduate students connect biological ideas in science. One study utilized the maps as a
form of exploring conceptual retrieval (Dauer & Long, 2015), whereas others focused on concept
mapping as a way to infer what students know about biology (Bray Speth et al., 2014; Dauer et
al., 2013). Dauer et al. (2013) examined the changes in complexity over time of student drawn
concept maps representing relationships between genetics and evolution. Complexity was
defined by the number of connections students drew between particular ideas. Although students
were expected to describe the arrows drawn between ideas, they were assessed based on
correctness. Similarly, Bray Speth et al. (2014) utilized concept maps as a way to assess how
students were making sense of the connections between genes and evolution. In both studies,
students were given the same predetermined ideas to connect, and were told the types of
connections to include. For example, students were told to construct concept maps that explained
malformed vertebrae in a small pack of wolves; the connections should have included where
genetic variation originates, how genotype and phenotype are related, and how phenotype affects
fitness. Although Dauer et al. (2013) found that the complexity of students’ concept maps did
increase over time, Bray Speth et al. (2014) found that students failed to adequately represent
relationships between molecular genetics related ideas and natural selection. On the other hand,
looking to the examples provided, students were providing mechanistic accounts; there were just
no explicitly drawn lines from mutation to fitness. In other words, no direct causal relationships
were provided. This illustrates that these studies did not take into account, on a fundamental
level, the ways in which students were making connections. That is, they did not account for how
students perceived the ideas to be related, aside from viewing them through the lens of

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correctness or number of connections made. Even though a connection was there, it may have
been interpreted as incorrect because it was not explicitly drawn or verbally described.
Other studies focused on conceptual understanding look to students’ explanations of
biological phenomena. Many of these have focused on undergraduate students’ abilities to
explain evolutionary-based phenomena (Nehm & Ridgway, 2011; Nehm & Schonfeld, 2008;
Rector, Nehm, & Pearl, 2012), investigating how students explore and relate each of the “key
concepts” of natural selection (Mayr, 1982; Nehm & Reilly, 2007): causes of variation,
inheritance, reproductive fitness, competition; limited resources; and differential survival. In
each of these studies, students’ explanations were scored on whether or not the key concept was
included and whether or not students were appropriating naïve conceptions, misconceptions, or
expert-like conceptions. Nehm and colleagues illustrated that students could utilize the key
concepts for explaining evolutionary phenomena (Nehm & Schonfeld, 2008); however, the
concepts used varied across tasks. Kampourakis and Zogza (2009) also examined students’
explanations of evolution-based phenomena; however, they examined high school students’
work following an instructional sequence emphasizing the link between genetics and evolution.
They found that students could make those connections when given explicit instruction on how
to do so. The authors were also explicit in positing that the nature of the task matters, and that
different connections were drawn across tasks that drew on parallel ideas. Thus, the results of
these studies suggest thus is it possible for students to coordinate ideas in a variety of ways, and
that different tasks activate different connections. Moreover, the studies call attention to a need
to examine how students coordinate biological ideas.
Given the complexity of natural selection and its abstract nature, it is no wonder that
students struggle with developing the complex framework required to explicate the relationships

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between natural selection and its explanatory models. The college biology students participating
in the studies conducted by Bray Speth et al. (2014), Dauer et al. (2013), and Nehm and
Schonfeld (2008), had also participated in a model-based course further illustrating how a
modeling approach could provide opportunities for students to make links between and among
core ideas in biology; however, further investigation is needed to determine what these
connections look like and how students perceive these connections to be related to biological
phenomena. An understanding of these connections are important to consider given that
developing connections across biological ideas is a proposed student learning outcome for
NGSS.
Complex Knowledge Systems
To examine the types of connections students can make, we focused on the students’ abilities to
draw connections using the idea of complex knowledge systems (diSessa, 2002; diSessa &
Wagner, 2005). diSessa’s approach to understanding learning can be described as over time,
accumulated ideas become more interconnected until the pieces are dynamically organized in
relationship to broader concepts. The construction of a knowledge system requires the
coordination, or connection, of ideas into existing structures. Increased coordination specifies the
ability to draw on variety of resources in a given context, while fewer connections means fewer
accessible resources. In using this framework, we presuppose that as students reason through
ideas within the model-based curriculum they will build connections among components of their
knowledge systems. This means that students’ could develop coordinated knowledge around
evolution. This could include, for example, relationships between variation, fitness, and limited
resources. Moreover, these ideas could subsume other ideas, such as genotypic variation and
phenotypic variation and may themselves have smaller pieces that are interrelated.

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The ideas or resources that we draw on in given situations or when presented with
particular problems are inherently contextual (Hammer, Elby, Scherr, & Resdish, 2005), and
what is accessed in one context, may not be accessed in another. However, over time, as more
and more knowledge is coordinated, then reasoning can become more consistent as more
connections are made across resources (Jeppsson, Haglund, Amin, & Strömdahl, 2013). That is,
some resources may be easier to access than others, because it can be “activated” across different
contexts. For example, studies examining expert biologists’ knowledge structures have illustrated
that they tend to be organized around canonical models with multiple connections within and
between them (Ifenthaler, 2011). As we will illustrate in this paper, it is possible for students to
have the individual pieces needed to explain a phenomenon, and they may recognize that there
are some important connections between those pieces. In addition, when students are given
opportunities to discuss these connections, they can provide insight into the nature of the
connections made; that is how they are connecting all the pieces. However, after one year of high
school biology, we do not expect students to have an expert-like knowledge system containing
all connections between DCIs, phenomena, and the underlying models.
Modeling has the potential to allow students to synthesize information and to organize it
into a framework that helps them to explain a phenomenon. Thus, we refer to students’
connections as emerging knowledge systems. For example, a student may know what limited
resources are, understand that there is variation within a population, or that only some of the
organisms within a population live long enough to reproduce; however, until they are given
opportunities connect those ideas, they may not realize that as a whole, each idea helps to explain
a mechanism for evolution. On the other hand, if the curricular materials used in the classroom
make evident these connections, then there could be evidence that the materials have been

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coherently designed (Roseman et al., 2008). Therefore, it is possible for students participating in
this study to show evidence of an emerging knowledge system that could include the connections
between evolution and its supporting models.
We conjecture that when given an opportunity to describe connections in ways that are
meaningful to them, students can provide us insight into the different ways they have
coordinated their biological knowledge. Additionally, this investigation could give us insight into
whether or not this type of exploration can lead us to understand what it means for curriculum to
be coherent. Therefore, the research questions guiding the design and analysis of this study are:
1. What is the nature of the connections that students are able to make within and across
disciplinary core ideas within the context of our model-based curriculum?
2. How, if at all, do students’ emerging knowledge systems reflect the structure of our high
school biology sequence?
Study Design and Analysis
The students who volunteered for this study were participants in a larger project focused on the
development and implementation of a yearlong high school biology model-based sequence.
Students’ learning was mediated through a pilot of the curricular materials, of which their
teachers were co-designers along with the authors of this paper. The focus of the instructional
sequence was to organize biological ideas in a manner that emphasized biological models.
Therefore, for each instructional segment, or series of lessons, students were introduced to a
phenomenon that motivated a driving question. The driving questions for any given learning
segment served dual purposes: (1) to remind students that the goal was to make sense of the
underlying ideas that helped to explain the phenomenon; (2) to bound the lessons and activities

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in service of developing a model. The scientific model was then applied to other related
phenomena and in many cases led to other phenomena that could be explored either through the
same model or other related models. Within a single learning segment, therefore, three integral
components exist: a phenomenon, a question related to the phenomenon, and a model. The
learning segments were organized around the unifying theme of evolution, with students
revisiting the central model, extending or revising it as additional evidence was collected and
new models were explored. The lessons were designed with NGSS in mind and are therefore
aligned to the four life science disciplinary core ideas and scientific practices.
Participants
Towards the end of the school year, students were solicited by the author and a second
graduate student, asking them to participate in an interview. The open call was announced at the
beginning of each participating class, and it was explained that the interview would focus on
what they had learned throughout the school year. Eight students volunteered, and six were
ultimately interviewed. Because students volunteered to participate in this interview, the
interviewees cannot be considered representative of the student participation in the curriculum
pilot. However, because this study was exploratory in nature, we were mostly interested in what
this type of investigation could buy us in terms of understanding the types of connections
students can make within and across the life science DCIs. All of the students interviewed were
ninth-grade students. Table 1 includes the demographic information of the study participants.
Student demographic data for JPHS illustrates the diversity of the student population:
Hispanic/Latino (36%), white (24%) and Asian (22%) students. Over 50% of the students are
socioeconomically disadvantaged. Ms. Murphy, the biology teacher at JPHS considers her
biology class to be an advanced course for incoming freshman. In order to be placed in her class,

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students must be taking geometry as a freshman and have gotten B’s or better in both eighth
grade algebra and science. Sophomores taking the class, must have received C’s or better in their
freshman math and science courses. Ms. Murphy considers her students to be of higher academic
status than other students in the school. In contrast to JPHS, the AHS student population is
predominantly white (59%) and Hispanic/Latino (29%), with 36% considered socioeconomically
disadvantaged. For students to be placed in Mrs. Reynolds’ biology class, they must be pursuing
the A-G requirements for admission to the University of California higher education system.
Table 1. Demographic information of study participants, including the teachers’ descriptions of each
student’s academic performance. Interviewees were not representative of their school or of their individual
science classrooms.
Name
School
Grade
Teacher Description of Student
Shauna
JPHS
Freshman
Began year as one of the weaker students, but has
made progress. Has a solid ‘B’ in the class.
Linnea
JPHS
Freshman
Rarely turns in work, but it an active participant in
class discussions. Usually earns ‘B’s on exams.
Vanessa
JPHS
Freshman
One of the top students in class, and is a straight A
student.
Anna
AHS
Freshman
Speaks five languages fluently, but is generally a weak
student. Mrs. Reynolds believes there may be an
undiagnosed learning difference.
Aaron
AHS
Freshman
Rarely turns in homework, does poorly on tests, and
will most likely fail and retake the class next year.
Brian
AHS
Freshman
Good student, gets ‘A’s on homework and exams.
Participates often in class discussions.
The Interview Structure
Studies utilizing card-sorting tasks to examine knowledge structures have typically
compared experts and novices based on the categorical classification of problem sets. Most of
these studies are based on Chi, Feltovich, and Glaser (1981), wherein physics students and
physics experts were presented with problems that were sorted based on what was required to
answer the particular problem types. Nehm and Ridgway (2011) took a parallel approach to that
of Chi et al. (1981) providing experts and novices with premade evolutionary biology problem

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sets. Although Smith et al. (2013) examined the differences between experts and novices, they
did so utilizing pre-made cards containing biological concepts rather than problem sets as Nehm
and Ridgeway (2011) and Chi et al. (1981) have done. The results of each of these studies points
to the contextual specificity of the activated knowledge structures that individuals have, and that
novices may have fewer coordinated pieces to use in explanatory contexts. Experts are able to
“see” more in a problem because they have the potential to activate the relative resources
necessary to answer it. Novices have the potential as well, but may have fewer resources to do
so. Our focus here is not to emphasize the difference between expert and novice knowledge
structures; rather the goal is to provide some additional background into the purpose of using a
card-sorting task to elicit connections students make across biological ideas.
In our investigation, we were motivated by the results of these card-sorting studies and
the responses novices gave to describe why they categorize particular problems or concepts;
therefore, we designed a hybrid interview-card-sorting task that would work within the context
of our curricular pilot study. The aim of our task was two-fold: (1) to have students write
questions they were trying to answer during the academic year; and (2) to have students describe
relationships between the questions or ideas written on the cards. The task was essentially a
semi-structured interview (Merriam, 2009) that utilized a think-aloud process. The primary
author was the interviewer, and had to judge in the moment whether or not students had enough
ideas from stage one to describe connections between the cards they had written. It was also
necessary to determine when to probe or to ask clarifying questions. We wanted students to
generate the ideas on their own as much as possible; however, before conducting the interviews,
we created a protocol that contained a series of questions and phenomena that students grappled
with throughout the school year. If there were sticking points, the interviewer could refer to that

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protocol to assist the students in remembering questions or phenomena. Therefore, the interview
provided four opportunities for students to draw on ideas within their knowledge systems:
1. The initial prompt allowed students to recall from memory driving questions students
were trying to answer throughout the school year. [“What questions were you trying to
answer this year?”]
2. As the task unfolded, additional ideas may have been activated as students discussed
particular phenomena, questions, or models.
3. When clarifying questions or probing questions were asked, additional ideas may have
been activated by words used by the interviewer, or through the process of responding.
4. In stage two, additional ideas may have been activated when students were specifically
asked to make connections. As the second stage proceeded, more related ideas may have
been recalled that helps to explain a connection.
The card-sorting task took place in the students’ biology classroom, with one exception
(Shauna), where the interview took place on the front steps of her school. Before the task started,
each student was provided with a stack of 4-inch by 6-inch white lined index cards, and three
color markers. Even though students were primarily prompted to write down questions that came
to mind, some of the participants chose to write biological constructs. Some students even chose
to write multiple ideas or questions on a single index card. In stage two some students added to
existing cards, or added additional questions, constructs or models on separate cards. All
interviews varied in length due to the their semi-structured nature, and were video-recorded
using a hand-held camera. Because students were writing on the index cards as they were
talking, a tripod was not used so that the interviewer could easily zoom in and out on what the
students were writing. This also allowed the interviewer to focus on hand movements that were

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made in the process of illustrating or describing connections. All interviews were transcribed
using InqScribe software. Coding was done in both Microsoft Excel and Microsoft Word as
detailed below.
Data Analysis
To examine emerging complex knowledge systems, there were multiple phases of data
analysis that allowed for a thorough overview of the ways in which students were making
connections. First, the first author transcribed interviews, verbatim, using InqScribe software.
Next, the transcriptions were exported to Microsoft Word. Because we were interested in the
nature of the connections students were making within and across biological ideas, in the first
round of discourse processing, we utilized an approach similar to causational coding (Miles &
Huberman, 2013). We started with Vanessa’s interview, and for each utterance she made1, we
began to look at the context in which she was appropriating keywords she had been exposed to in
the curriculum and how she was linking them together. This allowed us to start to uncover
particular ways in which she would begin with an idea–the antecedent–and then how she
navigated the pathway from the antecedent to a particular end. It also allowed us to track the
context of how keywords were used throughout the entire interview. The excerpt below provides
an example from the beginning of her interview.
''''''''''''''''''''''''''''''''''''''''''''''''''''''''
An utterance is defined as a turn in speech not interrupted by the interviewer or not broken by a natural
pause in speech indicating a complete thought. An “Ok” or “Hm-mmm” does not count as an interruption
of an utterance unless it leads to a pause in speech.

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1 [00:01:04.00] Vanessa: So, why [writing
2 "why is there so much variation?" on a
3 card]. So, do I just answer this?
4 [00:01:19.02] Interviewer: Yeah, so if you
5 want to. So, why do you think there is so
6 much variation?
7 [00:01:23.00] Vanessa: Um. Well, I mean. I
8 think that would kind of be like what we're
9 talking about with Darwin's model. And,
10 um. So survival of the fittest, basically.
She begins with variation then mentions
Darwin’s model suggesting there could be a
relationship between the two ideas. She then
seems to link either Darwin’s model or variation
to relative fitness .
In the column on the right, we are beginning to see how Vanessa makes connections
between ideas and models. As we continued in this approach, we noticed that four themes were
emerging: (1) she was utilizing types of connecting talk (“would be kind of like” is an example
of an undefined connection; line 8); (2) she referred to phenomena “why is there so much
variation?”, lines 1-2); (3) she referred to models, although not always explicitly (e.g. “Darwin’s
model”, line 9) ; and (4) she used keywords to refer to components of a model. These emergent
themes were not surprising given that a lesson sequence within MBER Biology consists of three
main components: a phenomenon, a question students are trying to answer about the
phenomenon, and the model that provides a framework for answering the question. However,
this analytic pass did provide us with evidence that Vanessa was able to make connections in a
variety of ways, leading us to parse the types of connections in a separate analytic pass.
The second round of analysis was done in Excel. In this analytic pass, discourse excerpts
were tagged with the following codes based on the emergent themes found through causational
analysis: phenomena, connecting talk, referring to a model, and model constructs. Through this
pass, utterances may have been split depending on where codes applied to particular words used.
As coding proceeded, a description was provided so that the context of the statement was not

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lost. This was important for understanding how and why students were discussing the relevant
ideas and models. Table 2 illustrates a coded sample of Vanessa’s interview.
Table 2. An example of how the second pass codes was applied to understand how the participants were
making conceptual connections.
Line
Discourse
Keyword/Phrase
of Importance
Code
Context
26-27
how is there so much
variation
Variation
Phenomenon
question about a
phenomenon
35-36
I think that would kind of
be like what we’re talking
about with Darwin’s
model
be like
Connecting Talk
Possibly relating
variation to natural
selection
36
what we’re talking about
with Darwin’s model
Darwin’s model
Referring to a
model
model of natural
selection
36
And, um. so survival of
the fittest basically
Survival of the
fittest
Referring to a
model
Survival of the fittest is
another way to refer to
Darwin’s model of
natural selection
After looking for patterns in the second pass of Vanessa’s interview, we noticed that
connecting talk and references to model constructs appeared similar. When Vanessa would refer
to aspects of a model, she was actually describing how she connected those pieces; therefore, in
the third pass, model constructs were folded into the code of connecting talk. Additionally there
were cases when she was referring to a model while describing relationships that were recoded
as connecting talk. In the end, referring to a model was only used when she mentioned a model
without referencing relationships to other ideas. These final codes were then applied to a
segment of Linnea’s interview to ensure that the coding scheme was replicable.
The patterns resulting from the last coding pass began to look like a concept map where
the utterances would start with a phenomenon that would then be related through connecting talk
to models that help to explain the phenomenon. Because we were ultimately interested in the
nature of the connections, a final analytic pass was conducted that focused on the nature of the
connections, that is, how students were describing connections and at what level of specificity.
We noticed that students were primarily using three different types of “connecting talk”. They

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would provide us with “undefined” connections, such as when they would jump from one
concept to another. Other times, they would give us more context for how two ideas were
related, coded as “relational”. They could also provide more complex and explanatory details,
and those connections were coded as “explanatory. Table 3 provides examples of how
connecting talk was coded.
Table 3. Examples from Vanessa’s interview illustrating how “connecting talk” was coded as a means of
understanding the nature of the connections students can make. Connecting talk is in bold type font.
Lines
Connecting Talk
Types of Connecting
Talk
Context
35-36
I think that would kind
of be like what we’re
talking about with
Darwin’s model.
Undefined
Possibly relating
variation to natural
selection
62-63
I think crossing over
during meiosis adds a
lot to variation
Relational
Crossing over happens
during meiosis
62-63
I think crossing over
adds a lot to variation
Explanatory
Crossing over/meiosis
increases variation
11
Different traits is what
is an advantage and
what is not an
advantage
Explanatory
Explaining phenotype is
related to relative
fitness
In addition to delineating the types of connecting talk students used, detailed concept
maps were drawn in this pass and what emerged are similar to the types of maps diSessa used to
illustrate complex systems in individuals who are becoming conceptually competent (diSessa,
2002, p. 31). Concept maps have previously been shown to be useful in representing knowledge
structures (Dauer & Long, 2015; Nersessian, 1989), and students coordinated ideas (Bray Speth
et al., 2014; Dauer et al., 2013; Ummels et al., 2015). The resulting maps from this study are
based on our inferences of the students’ discourse and provide representations that assist in
illustrating students’ knowledge structures for connecting biological ideas. It is these analytic
maps that we will report on in the results section.

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Although six students were interviewed, we have chosen to solely report on the results of
Vanessa’s interview. We make no claims concerning the representativeness or correctness of her
interview. In fact, Vanessa was in Mrs. Murphy’s class, meaning that not only did she self-select
for a more rigorous biology course, but she was able to meet tougher entrance requirements than
that required of Mrs. Reynolds’s class. In addition, she was considered to be a successful student
by her teacher. Throughout the interview task, Vanessa referenced keywords nearly double that
of other students, with the exception of Linnea, but Vanessa’s connections were more explicit
and complex than what the other students provided. Moreover, fewer probing questions were
needed to facilitate her interview. We cannot make claims that Vanessa was better at describing
what was important or relevant to the task we provided, or if she was able to actually make more
connections than the other students. However, because of the exploratory nature of this study in
investigating the nature and types of connections students are able to make, we believe that
Vanessa’s interview highlights what we believe to be the types of connections that can be made
by a student when given the opportunity to do so.
Results
Vanessa’s interview lasted twenty-three minutes and five seconds, throughout which she used a
range of ideas to describe questions her class had tried to answer. She also used examples, when
prompted, which helped to provide additional context for the connections she was making. In the
first stage of the task Vanessa referenced 108 ideas, some more than once, with over half (n =
65) related to inheritance and variation. Throughout stage one, she used eight cards, writing
questions or ideas on that were relevant to what she was discussing. In some instances, those
ideas arose through discussion of another idea, or sometimes they came up due to prompting by

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the interviewer. Table 4 is a list of what was written or drawn on her cards during stage one, with
the context in which the words, phrases, or questions were written. Throughout the interview,
due to prompting by the interviewer, Vanessa would initiate discussion of a particular idea with
reference to a question about a phenomenon (“why is there so much variation”) or by providing a
specific example of a phenomenon (variation in peppered moths). The following turns in
discourse would then be types of connecting talk that identified how she perceived the ideas to
be connected to the phenomenon. The connections were sometimes implicit, meaning that
Vanessa would start with a phenomenon and verbally list ideas that could explain the
phenomenon, but did not reference mechanisms for how the things were related. Or, she did
reference explicit connections, describing in detail how the models or model constructs were
connected.
Table&4.&Throughout&stage&one&of&the&interview,&Vanessa&wrote&or&drew&biological&ideas&and&biological9related&
questions&on&cards.&These&cards&served&as&artifacts&she&or&the&interviewer&could&refer&to&throughout&the&task.&The&
content&is&written&here&in&the&same&order&that&Vanessa&used,&and&with&the&corresponding&statement&for&context.&
Written on Card
Statement Context
Why is there so much
variation?
[00:00:57.11] Vanessa: Um, I think a major thing we were talking about
was like, how there is so much variation.
[00:01:02.20] Interviewer: Ok.
[00:01:04.00] Vanessa: So, why [writing "why is there so much variation?"
on a card]. So, do I just answer this?
Reproductive Isolation
[00:02:15.22] Vanessa: Ok, let me think. Ok, so. I think reproductive
isolation that was a new one we talked about. I think that is really
important.
[00:02:23.10] Interviewer: Ok.
[00:02:24.28] Vanessa: [writing "reproductive isolation" on a card]. And
then, I think crossing over during meiosis adds a lot to variation. [writes
"crossing over"].
Crossing Over
Same as above.
Variation is within a
species. Biodiversity is
multiple species
[00:03:58.24] Vanessa: Ok. Um. [starts writing "variation is" then stops.]
I'm not sure how exactly to say this, I mean we spent all year trying like
bring this together so.
[00:04:19.23] Interviewer: No, that's fine.
[00:04:19.23] Vanessa: I mean there's a lot to it, but. Um. [continues
writing "within a species. biodiversity is multiple species.] Yeah, I mean
that was probably the major question we were talking about. Like, why is
there so much variation.
Why we’re so similar?
[00:07:27.26] Vanessa: Um. Well, we recently. Well we kind of have
talked about like. I think, not so directly, but how we're similar.

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19'
[00:07:39.13] Interviewer: Ok.
[00:07:41.02] Vanessa: So, like, why we're. And why without our species
we're so similar and why in separate species we're similar.
[00:07:51.24] Interviewer: Ok.
[00:07:51.24] Vanessa: [Writing "why we're so similar" on card].
Genetic code is the same
among all living things–
common ancestor
[00:08:31.02] Vanessa :Um, so, I think that was really interesting. So, um, I
think. I mean, that we all come from a common ancestor and like. Um, the
fact that like, my genetic code is the same as like, I don't know a mouse's is
kind of interesting. So I think that was one thing we talked about when we
were going through, like, nucleic acids and like that stuff so.
[00:08:55.12] Interviewer: Ok
[00:08:56.19] Vanessa: So, I guess that I should probably write that down.
Um. [writing " genetic code is the same among all living things-common
ancestor"].
Drawing of a cell with a
DNA strand.
[00:11:44.15] Vanessa: Um, well an allele would be like on our DNA
strand so like. We have like our DNA [draws a circle on an index card].
There's our nucleus, and there's our little DNA [draws a squiggly line in the
circle]. So then on our DNA you have different genes. So let's say this is
gene one and this is gene two [draws genes on a DNA strand next to the
nucleus]. So for gene one, where gene 1's at, there's going to be like 1, 2, 3,
and 4 [writes numbers on the card] so there's different types for gene one
and those different types would be alleles. I mean, that's probably a simple
way to explain it. So, for each allele you're going to have a different form of
that gene one. I think that like. Um, I don't know. 'Cause there's different
types of alleles. Like I don't know. So. Yeah.
How inheritance occurs?
[00:13:35.22] Interviewer: Ok, alright. Cool. Ok, and then I'm thinking.
Let's see. Can you think of any other examples? Um, not necessarily related
to alleles or genes right now, um, but any other examples of things you
were trying to figure out?
[00:13:54.21] Vanessa: Uh, during the year? Uhhh. Well, we did try to, I
mean these are more broad questions, but we did kind of talk about why or
how inheritance happens, so. [starts writing on card].
[00:14:10.01] Interviewer: Ok. that could be an important question.
[Vanessa is writing "how inheritance occurs" on a card].
Within the first minute of the interview, Vanessa states that her class was trying to
understand “how is there so much variation”, but she writes “why is there so much variation”,
which frames the next several turns in conversation. In this instance she is using “variation” in a
phenomenological sense, that is, variation is a class of something that can be explained. She then
begins to provide an explanation for both how and why variation can arise, as illustrated in the
following excerpt.

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
20'
1
2
3
4
5
6
7
8
9
10
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12
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18
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Vanessa: Like why [writing "why is there so much variation?" on a card]. So, do I just answer
this?
[00:01:19.02] Interviewer: Yeah, so if you want to. So, why do you think there is so much
variation?
[00:01:23.00] Vanessa: Um. Well, I mean. I think that would kind of be like what we're
talking about with Darwin's model. And, um. so survival of the fittest basically.
[00:01:33.02] Interviewer: Ok.
[00:01:33.02] Vanessa: And so, through evolution. Like, there's a lot of different components.
So we had meiosis and crossing over and those processes. And also, what the environment
had to do with it as far as them involving, like evolving to fit what they needed to.
[00:01:53.24] Interviewer: Ok.
[00:01:53.24] Vanessa: I think it, you have reproductive isolation with like. I don't know,
there's so much that we talked about.
…. [Interviewer prompts Vanessa to write down the ideas she’s mentioned]
[00:02:15.22] Vanessa: Ok, let me think. Ok, so. I think reproductive isolation that was a new
one we talked about. I think that is really important.
[00:02:23.10] Interviewer: Ok.
[00:02:24.28] Vanessa: [writing "reproductive isolation" on a card]. And then, I think
crossing over during meiosis adds a lot to variation. [writes "crossing over"].
[00:02:33.18] Interviewer: Ok, what do you mean by crossing over?
[00:02:35.13] Vanessa: Well, during the process of meiosis, they, the alleles they like, kind of
like, switch over [making a "moving back and forth" motion with her hands.] So, you are,
alone without crossing over there's already like, I forget the exact number. But, its like two to
the twenty-third power or something. And then when you add crossing over, the amount of
variation like, it increases to almost infinity so. I mean, it's actually surprising that we look
alike at all and the only reason I think we do is because like, we, like our siblings at least is
like, we have common parents and the gene pools and stuff like that. So, like, that is
important.
Vanessa is beginning to lay the groundwork for the connections she will continue to
explicate throughout the course of the interview; however, in this excerpt its clear that these
connections are at times undefined and at others more explanatory. For example, she is
beginning to answer her question when she states, “I think that would kind of be like what we’re
talking about with Darwin’s model (lines 5-6).” We can infer that she thinks variation is related
to natural selection; however, it is unclear how they are connected from this statement. Moving
forward, she lists what she says are components of evolution (lines 8-10), but again, is it not
clear how these things relate to one another. Finally, when pressed for what she means by
“crossing over”, Vanessa begins to provide more ideas that are possibly connected, ultimately

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
21'
leading to a second phenomenon: sibling resemblance (lines 21-28). Figure 2 illustrates the types
of connections Vanessa is beginning to describe in this excerpt.
'
Figure 1. The way in which Vanessa connects biological ideas is beginning to emerge. Here we are illustrating
how we perceive her initial connections between variation and natural selection.
In the next few turns in conversation, after being asked by the interviewer to further
explain what she means by variation, Vanessa exhibits the ability to describe variation as both a
phenomenon and a model. In doing so, she recapitulates the relationship between variation and
alleles (see Figure 2), but then references phenotype, demonstrating a potential connection to a
genotype-phenotype model that could help explain variation. Later, this connection becomes
explanatory when she explains, “alleles add to different traits”. In addition, as she continues to
reference variation, she delineates the difference between variation in a species and diversity
across species, stating, “When I think of variation now, I think of, like alleles and like
phenotype. So, I think that type of variation. And then, I don't know there's like variation within

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
22'
a population or like a species. And then like, diversity would be within multiple species.” She
has now used the term variation as both a phenomenon (“why is there so much variation”) and as
a model–“types of variation”. From there, she describes variation’s relationship to natural
selection, and how other models help to both explain variation and natural selection.
64
65
66
67
68
69
70
71
Vanessa: I mean there's a lot to it, but. Um. [writing “variation is within species and
biodiversity is multiple species”]. Yeah, I mean that was probably the major question we
were talking about. Like, why is there so much variation.
Interviewer: Ok.
Vanessa: and how exactly that comes about. So.
Interviewer: Ok.
Vanessa: Natural selection is important. [Writes “natural selection” on a card].
Interviewer: And, why do you think natural selection is important?
72
73
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75
76
77
78
79
Vanessa: Well, I mean, with natural selection, we kind of like, well what we were talking
about today in class is like. I think all the models kind of add onto natural selection but it's
like the basis for how exactly like, I don’t know, that we got it. It’s kind of like an outline
that we use and add onto. But, it’s like really important to kind of get an overall idea of how
like how, so for example, like mutation, that’s another, you can get into why exactly and how
that happen but mutation will relate back to natural selection in the way that mutation brings
more alleles and alleles and alleles adds to different traits and the different traits is what is an
advantage and what is not an advantage. So, like kind of like lead back up to that.
In this excerpt, Vanessa is relating variation to natural selection describing how mutation
leads to variation within species, and how variation is related to what traits are acted upon by
natural selection. We can infer that Vanessa understands variation to be a construct of the natural
selection model, and variation as a phenomenon that can be explained by both a mutation model
(“mutations brings more alleles,” line 78) and a genotype-phenotype model (“alleles add to
different traits,” line 78). Through these connections, Vanessa is exhibiting an understanding of
how biological models are related to one another and that more than one model can help to
explain a phenomenon or classes of phenomena in the case of variation. She is also
demonstrating an ability to connect variation to natural selection on two levels: at the model
construct level and at the phenomenon level. Furthermore, Vanessa displays an awareness that
natural selection acts only on phenotypic traits (“different traits is what is an advantage and what

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
23'
is not an advantage,” lines 78-79). Vanessa’s connections are now becoming more explicit and
explanatory in nature, providing us with further insight into how she organizes biological ideas.
Furthermore, she has now provided two instances of how she believes alleles are connected to
variation. We can now revise our initial representation of her thinking to include the more
explicit connections. Figure 3 illustrates these additional connections.
Figure 2. Our representation of Vanessa's knowledge system is becoming increasingly more complex as
additional constructs and models are added to explain both variation and natural selection.
'
When the interviewer asks her to provide an example related to the connections she was
making, Vanessa discusses the peppered moth phenomenon, moving from a general model of
natural selection to a more specific application. She also continues to outline her understanding
of the phenomenon, describing how environmental changes drive changes in allelic frequencies

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
24'
(“direct environment change.... relates to how the gene pool changes”). She also mentions
survival, which implies an understanding that it is a component related to the types and amount
of alleles remaining in the gene pool. Therefore, more connections exist: “alleles” make up a
“gene pool”; “environment change” causes changes in the frequency of “alleles” and thus
changes the make-up of the “gene pool”. In addition, survival is an additional construct that
helps to explain the general model of natural selection.
As stage one of the task continues Vanessa returns to the phenomenon of sibling
resemblance (see Figure 2), describing models of inheritance and their relationship to alleles and
gene pools. She also brings in a fourth phenomenon, common ancestry (“my genetic code is the
same as like, I don’t know a mouse’s is kind of interesting”). The types of connections she is
making, however, are still diffuse, as illustrated in the following passage:
I think we were going over like siblings and that type of thing we talked about, like. Uh,
like just the way inheritance works, too, so I think that could. I think that relates back to
why we're so similar. Um, because I think we all, like. And you take the idea of gene
pools, too, so, we kind of how the same alleles going on. So, some of the same. And
that relates to like why we are all kind of similar within our species but then we look
at other species and you have to look a little closer. Like it's not necessarily just like
phenotype. It's more like what's going on at the molecular level.
Although she is communicating an understanding that there are mechanisms for inheritance that
drive familial/sibling resemblance, she’s not explicitly describing how. On the other hand, she is
describing why when she states that “we kind of how [have] the same alleles going on”. She is
also illustrating undefined connections between alleles and gene pools and inheritance, while
also drawing another undefined connection to a genotype-phenotype model. The final minutes of
stage one of the interview focus on topics learned as Vanessa interacted with a bioenergetics
model that could explain matter and energy trade-offs within an individual’s lifetime. Her
discourse at this point reflects a limited ability to draw connections between matter and energy

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
25'
related concepts and to other biological ideas. Figure 4 illustrates a map based on her discourse
throughout stage one, excluding bioenergetics.
'
Figure&3.&Final&illustration&of&the&way&we&perceive&Vanessa's&cognitive&structure&following&stage&one&of&the&card9
sorting&task.
Discussion
Complex knowledge systems are constructed through coordination of various components, and
in biology, those components tend to be organized around core biological concepts (Ifenthaler,
2011). In this study, we analyzed high school biology students emerging knowledge systems by
constructing analytic maps based on discourse from a semi-structured interview that included a
card-sorting task. Overall, we were interested in the ways in which students who participated in a
model-based curriculum were able to articulate connections within and across disciplinary core

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
26'
ideas. We do not contend that the cases provided in this paper are representative of the students
who participated in the larger study; however, we do believe that Vanessa’s discourse illustrates
the types of connections that students can make.
Although this paper represents only a subset of the data collected, the connections
described from stage one provides us with evidence that when given opportunities to describe
ideas and related phenomenon, students can describe, in complex ways, how they have
coordinated biological ideas. Moreover, despite portraying a multitude of undefined or relational
connections, Vanessa’s interview gives us insight into how she has coordinated various
components into her knowledge system. Based on the finalized map of her discourse, it appears
as if Vanessa’s knowledge system may be organized around heredity and variation, especially
around the constructs of alleles and gene pools. The arrows displaying connections, however
nascent they may be, originate or end at these two ideas, more than any of the other ideas she
mentioned. However, these connections ultimately provide paths back to natural selection, and
she describes more than once how all of the ideas presented relate back to natural selection.
Studies that have been conducted at the undergraduate level have described students’ inabilities
to reason with natural selection (Ha & Nehm, 2013; Nehm & Schonfeld, 2008; Rector et al.,
2012). Other studies have indicated that undergraduate biology students (both majors and non-
majors) have difficulties in drawing connections between genetic variation and natural selection
(Bray Speth et al., 2014; Dauer et al., 2013); however, in this study, we illustrate how Vanessa
was very adept at making connections between a natural selection model and its supporting
models, such as variation and inheritance. The difficulties suggested by Bray Speth et al. (2014)
and Dauer et al. (2013) were described as students not recognizing the relationships between
mutations adding to variation and phenotypic variation being acted upon by natural selection;

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
27'
however, it could be interpreted that students did not find those ideas relevant in the moment for
the specific contexts they were provided. They may have coordinated ideas regarding those
things but it may not have been relevant to them for that particular context. Therefore, it should
not be construed as an indication that they are not able to make those connections. The same
could be true for Vanessa in that she may have been stating what was accessible to her in the
moment; however, she was able to draw connections between the constructs and their
explanatory models in ways that illustrated her understanding of what biological phenomena are.
When students are given opportunities to talk about ideas that are most salient to them, then it
may be possible to get a sense of the range of coordinated ideas they have. Furthermore, based
on our results, we posit that Vanessa was demonstrating an expert-like cognitive structure
because of her ability to describe biological ideas on two levels: at the phenomenon level, as
something puzzling to be explained by a model, and at the construct level where relationships
between models are important.
Taking a complex knowledge systems view of learning we have been able to illustrate the
nature of the connections students can make, while also showing that even less complex
connections, such as those that were undefined or relational can still be productive for making
sense of phenomena. We acknowledge that this study only accounts for the connections students
made by the end of the school year, something that diSessa (2002) cautions against; however, we
do believe we have presented work in the vein of complex knowledge systems by attending to
what the students’ words represent.

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Conclusion
Current reforms in science education are calling for student engagement in cognitive scientific
practices that result in an understanding of how scientific knowledge is constructed, and to
provide students opportunities to construct meaningful connections across the disciplinary core
ideas of science. As such, there is a push for curricular materials designed for NGSS that
transition secondary science instruction from a science-as-fact approach to one that provides
students opportunities to learn science as scientists do (Carlson et al., 2014). Curricular design
commitments focused on building understanding through scientific reasoning are essential to
meet this goal; and as teachers will soon begin implementing NGSS, it is important that we
consider what it means for students to construct “meaningful connections” within and across
disciplinary core ideas.
The purpose of this study was to examine how students organize and relate biological
ideas and phenomena. Based on our design conjecture we hypothesized that when given an
opportunity to describe connections in ways that are meaningful to them, students can provide us
insight into the different ways they link models to understand phenomena. In this paper, we
presented findings showing that Vanessa was able to make these connections, providing
explanations at varying levels of depth (general model of natural selection vs. a specific
application) and with increasingly more explanatory connections. Additionally, our overall
findings suggest Vanessa was able to draw on a number of ideas to describe a core idea such as
natural selection, where the components are phenomena and/or the supporting constructs and
models. Our inferred interpretations of student discourse are illustrated as concept maps, giving
us a tool for analyzing perceived student knowledge systems.

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
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Implications for Student Learning and Assessment
Following a need to develop curricular resources that foster connection building are ways
to assess the coherence of such curricula. Ummels et al. (2015) used a similar analytic strategy to
ours, finding that concepts maps used within their curriculum could be used to assess the
coherence of their developed curriculum and its relationship to student understanding. However,
in their study, the concept map used to analyze coherence was also a student handout where
teachers explicitly directed students towards conceptual connections. Despite the differences in
study design, their results suggest it is possible that the concept maps we derived from student
discourse could serve as tools for analyzing the content coherence of our curriculum. A second
iteration of the study reported in this paper could explore the usefulness of using concept maps to
assess content coherence. In other words, if our model-based curriculum provides opportunities
for connections, then we should see evidence of knowledge systems organized in similar ways.
Other studies within curriculum design have assessed content coherence through written
assessments, with the goal to determine if students are able to build complex understanding of a
particular idea, or set of ideas. For example, Fortus, Sutherland, Adams, Krajcik, & Reiser
(2015) posited that if they coherently designed the IQWST learning materials, then gains
between pre/post assessments should illustrate deepened understandings of energy. Student gains
could then be mapped to classes of phenomena and models explored. Overall, they found that
some units were better predictors for success than others, suggesting that only some of the
materials were effective in students building connections across energy-related phenomena and
the models that explain them.

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In an article forthcoming in Science Education, Hammer and Sikorski propose a new lens
for understanding student reasoning, that of complexity and the dynamic interactions that take
place within a learning system. The implication being a better understanding of the
idiosyncrasies in students’ learning progressions (LP). Our study, through its examination of
complex knowledge systems and the nature of connections students make between biological
ideas, could be a starting point for understanding where students are in a biological learning
progression at the high school level. Considering Vanessa was making connections that students
at the undergraduate level have demonstrated difficulty in making, particularly in connecting
alleles and variation to natural selection, our results could prove useful in developing the upper
end of these progressions assessments, with the goal to determine if students are able to build
complex understanding of a particular idea, or set of ideas. Future work with the dataset
presented in this paper will further explore the usefulness of using concept maps to assess
content coherence.

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References
Bray Speth, E., Shaw, N., Momsen, J., Reinagel, A., Le, P., Taqieddin, R., & Long, T. (2014).
Introductory Biology Students' Conceptual Models and Explanations of the Origin of
Variation. Cell Biology Education, 13(3), 529-539. doi:10.1187/cbe.14-02-0020
Carlson, J., Davis, E. A., & Buxton, C. A. (2014). Supporting the Implementation of NGSS
through Research : Curriculum Materials [Press release]. Retrieved from
https://narst.org/ngsspapers/curriculum.cfm
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and Representation of
Physics Problems by Experts and Novices. Cognitive Science, 5(2), 121-152.
doi:10.1207/s15516709cog0502_2
Dauer, J. T., & Long, T. M. (2015). Long-term conceptual retrieval by college biology majors
following model-based instruction. Journal of Research in Science Teaching, n/a-n/a.
doi:10.1002/tea.21258
Dauer, J. T., Momsen, J. L., Speth, E. B., Makohon-Moore, S. C., & Long, T. M. (2013).
Analyzing change in students' gene-to-evolution models in college-level introductory
biology. Journal of Research in Science Teaching, 50(6), 639-659. doi:10.1002/tea.21094
diSessa, A. (2002). Why "conceptual ecology" is a good idea. In M. Limón & L. Mason (Eds.),
Reconsidering conceptual change: Issues in theory and practice (pp. 29-60). The
Netherlands: Kluwer Academic Publishers.
diSessa, A., & Wagner, J. (2005). What coordination has to say about transfer. In J. P. Mestre
(Ed.), Transfer of learning: From a modern multidisciplinary perspective (pp. 121-154).
Greenwich: Information Age Publishing.

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
32'
Ha, M., & Nehm, R. H. (2013). Darwin’s Difficulties and Students’ Struggles with Trait Loss:
Cognitive-Historical Parallelisms in Evolutionary Explanation. Science & Education,
23(5), 1051-1074. doi:10.1007/s11191-013-9626-1
Ifenthaler, D. (2011). Identifying cross-domain distinguishing features of cognitive structure.
Educational Technology Research and Development, 59(6), 817-840.
doi:10.1007/s11423-011-9207-4
Jeppsson, F., Haglund, J., Amin, T. G., & Strömdahl, H. (2013). Exploring the Use of
Conceptual Metaphors in Solving Problems on Entropy. Journal of the Learning
Sciences, 22(1), 70-120. doi:10.1080/10508406.2012.691926
Kampourakis, K., & Zogza, V. (2009). Preliminary evolutionary explanations: A basic
framework for conceptual change and explanatory coherence in evolution. Science and
Education, 18(10), 1313-1340. doi:10.1007/s11191-008-9171-5
Mayr, E. (1982). The growth of biological thought: diversity, evolution, and inheritance:
Harvard University Press.
Merriam, S. B. (2009). Qualitative research: A guide to design and implementation: John Wiley
& Sons.
Miles, M. B., & Huberman, M. (2013). Qualitative Data Analysis: An Expanded Sourcebook
(Third ed.). Thousand Oaks, CA: Sage Publications.
National Research Council. (2012). A framework for K-12 science education: Practices,
crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.
Nehm, R. H., & Reilly, L. (2007). Biology Majors' Knowledge and Misconceptions of Natural
Selection. BioScience, 57(3), 263. doi:10.1641/b570311

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
33'
Nehm, R. H., & Ridgway, J. (2011). What Do Experts and Novices “See” in Evolutionary
Problems? Evolution: Education and Outreach, 4(4), 666-679. doi:10.1007/s12052-011-
0369-7
Nehm, R. H., & Schonfeld, I. S. (2008). Measuring knowledge of natural selection: A
comparison of the CINS, an open-response instrument, and an oral interview. Journal of
Research in Science Teaching, 45(10), 1131-1160. doi:10.1002/tea.20251
Nersessian, N. J. (1989). Conceptual Change in Science and in Science Education. Synthase,
80(1), 163-183. doi:10.1007/BF00869953
Neumann, A. (2013). Professing Passion : Emotion in the Scholarship of Professors at Research
Universities. 43(3), 381-424.
NGSS Lead States. (2013). Next Generation Science Standards: For States, By States.
Washington, DC: The National Academies Press.
Rector, M. a., Nehm, R. H., & Pearl, D. (2012). Learning the Language of Evolution: Lexical
Ambiguity and Word Meaning in Student Explanations. Research in Science Education,
43(3), 1107-1133. doi:10.1007/s11165-012-9296-z
Roseman, J. E., Linn, M. C., & Koppal, M. (2008). Characterizing curriculum coherence. In Y.
Kali, M. C. Linn, & J. E. Roseman (Eds.), Designing coherent science education:
Implications for Curriculum, Instruction, and Policy. New York, NY: Teachers College
Press.
Ryoo, K., & Linn, M. C. (2012). Can dynamic visualizations improve middle school students'
understanding of energy in photosynthesis? Journal of Research in Science Teaching,
49(2), 218-243. doi:10.1002/tea.21003

COHERENCE'AND'BIOLOGICAL'UNDERSTANDING'
34'
Smith, J. I., Combs, E. D., Nagami, P. H., Alto, V. M., Goh, H. G., Gourdet, M. A. A., . . .
Tanner, K. D. (2013). Development of the Biology Card Sorting Task to Measure
Conceptual Expertise in Biology. CBE-Life Sciences Education, 12, 628-644.
doi:10.1187/cbe.13-05-0096
Ummels, M. H. J., Kamp, M. J. a., De Kroon, H., & Boersma, K. T. (2015). Promoting
Conceptual Coherence Within Context-Based Biology Education. Science Education,
n/a-n/a. doi:10.1002/sce.21179