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Citation: Starks, F.D.; Reeves, S.M.;
Rickert, J.; Li, K.; Couch, B.;
Millunchick, J. Rethinking
Undergraduate Computer Science
Education: Using the 4Es Heuristic to
Center Students in an Introductory
Computer Science Course. Educ. Sci.
2024,14, 514. https://doi.org/
10.3390/educsci14050514
Academic Editor: João Piedade
Received: 30 January 2024
Revised: 8 March 2024
Accepted: 17 April 2024
Published: 10 May 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
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4.0/).
education
sciences
Article
Rethinking Undergraduate Computer Science Education: Using
the 4Es Heuristic to Center Students in an Introductory
Computer Science Course
Francheska D. Starks *, Shalaunda M. Reeves *, Jonathan Rickert, Kyle Li, Brock Couch and Joanna Millunchick
College of Education, Health, and Human Sciences, University of Tennessee, 1122 Volunteer Boulevard,
Knoxville, TN 37996, USA
*Correspondence: [email protected] (F.D.S.); [email protected] (S.M.R.)
Abstract: There is a nationwide effort to increase the representation and engagement of minoritized
students in computer science education. Discourse about the barriers to diversity among computer
science majors is often characterized by student pathologies and does not consider the impacts of
classroom culture and instructor pedagogies. Amid the push for strategies to recruit and retain
minoritized students in computer science, little has been done to transform curriculum and analyze
faculty perspectives on curriculum and pedagogy as methods to increase students’ access to the
computer science major. This paper presents an example of curriculum redesign for an undergraduate
introductory computer science course (ICS) that sought to address issues of inequitable representation
by centering student identities and redistributing power in favor of students. The authors draw upon
critical sociocultural and the 4Es heuristic for disciplinary literacy to reimagine the ICS course as a
space that centers on the important roles of identity and power in solving for diversity in computer
science education. We highlight for researchers and practitioners how our work may be used to
disrupt meritocratic practices that alienate minoritized and economically disadvantaged students
and to expand definitions of mastery and expertise in computer science education.
Keywords: computer science; STEM education; disciplinary literacy; equity; undergraduate education
1. Introduction
There is a well-recognized lack of representation of students and professionals with
various social locations within science, technology, engineering, and mathematics (STEM)
broadly and more specifically in the computer science discipline. The lack of representation
has resulted in efforts to increase the participation and engagement of marginalized and
economically disadvantaged students in STEM education [
1
]. Some of the reasons attributed
to the homogenous makeup of computer science classes and in the profession are the lack
of early exposure to related concepts, challenges with developing positive science identities,
a sense of belonging, and narrow approaches to pedagogy and instruction [
2
,
3
]. Much of
the efforts to diversify computer science classrooms and the computer science industry
have been aligned with relieving the impact of the aforementioned barriers.
Alongside efforts to create heterogeneous educational and workforce environments,
there is a risk of characterizing systemic barriers as student pathologies. Curiosities about
the attribution of inequities in computer science education (CSE) and the waning participa-
tion of students and professionals from marginalized groups have spurred researchers to
investigate the issue from sociological perspectives [
1
,
4
]. Sociological theories illuminate
the importance of considering students’ and instructors’ identities, agency, and the role
of power in society as important elements for informing our understanding of students’
experiences and instructor practices in undergraduate computer science education [
5
,
6
].
Acknowledging the significance of social locations for creating and maintaining inequities
Educ. Sci. 2024,14, 514. https://doi.org/10.3390/educsci14050514 https://www.mdpi.com/journal/education
Educ. Sci. 2024,14, 514 2 of 22
in CSE guided us to consider ways to “restructure the rules of participation” by provid-
ing student-centered curriculum and instruction [
1
]. Increased participation in diverse
gendered enrollment in undergraduate CSE courses has not eradicated racialized and gen-
dered disparities. Despite increases in enrollment due to strategic efforts to diversify CSE
classrooms, long-term changes such as those that translate into careers require policy-based,
structural changes. Intentional shifts toward equity-focused curriculums and pedagogies
can serve as structural changes that support long-term diversity in the computer science
field [7].
The authors of this paper are a team of researchers, former ICS students, and post-
secondary faculty with expertise in the disciplines of computer science, engineering, cul-
tural studies and learning experience design. For this paper, we designate “Faculty” as the
2 instructors of the current ICS course, and “Researchers” as other post-secondary faculty
and the former ICS students that supported the course redesign. Our collaboration was
born from a need to address the retention and enrollment of students in an ICS course at a
public university located in the midwestern United States. Concerns about enrollment rates
(drops, withdrawals, and failures) that mirrored national statistics prompted an examina-
tion of student feedback on why they dropped the course [
8
] and an analysis of the course
content and facilitators’ instructional methods. Our research team subsequently began the
course redesign process and used this paper to reveal findings about faculty responses to
the redesign alongside artifacts from the revised curriculum.
Our research includes a theoretical analysis of an existing Introductory Computer Science
(ICS) course, a demonstration of how we used the course analysis to inform our ICS course
curriculum and instructional redesign, an analysis of faculty responses to the curricular
and instructional changes, and examples of the redesigned curriculum. We used the four
resource model [
9
] and critical sensemaking [
10
] to analyze our collected data (current online
curriculum, observational notes from class meetings, transcriptions from faculty meetings,
and the redesigned curriculum) and respond to the following research questions:
1.
How does the ICS course curriculum (including teaching lectures) align with the four
resource model practices of code breaking, text participant, text user, and text analyzer?
2.
How can the 4Es heuristic be used to redesign the initial ICS course curriculum and
teaching methods to better align with the broad use of the FRM?
3. What are ICS faculty perceptions of the ICS course curriculum redesign process?
In the remainder of this paper, we present the issue of inequitable representation in
CSE in extant research literature, describe the theoretical underpinnings that informed our
study, and share how we used the 4Es heuristic to redesign an ICS course to center students
in their learning along with faculty responses to the curriculum changes.
1.1. Representation and Engagement in Computer Science Education
There is a large body of computer science education research that examines underrep-
resentation for gendered, economically, and racially diverse students in computer science
education [
1
]. Socially constructed identity markers such as race and gender are connected
to students’ domain-specific self-efficacy, their experiences of academic exclusion, and their
perceptions of individual academic performance and sense of belonging in STEM fields [
11
].
We find computer science particularly interesting, as women account for more than half
of graduates with majors such as chemistry and biology; yet less than 20% for computer
science. Cheryan and colleagues [
11
] identify exclusionary environmental culture, low self-
efficacy, and disproportionate early learning of computer science concepts as contributors
to the differences in women’s engagement with computer science as a major.
Within computer science classrooms, researchers found that female-identifying stu-
dents’ sense of belonging as members of their classroom community was correlated with
their perceptions of personal academic and social exclusion [
12
]. Belonging uncertainty, or
concern about the quality of one’s social relationships, is of specific concern for students
who have intersectional social locations that may include one or more minoritized socially
constructed identities such as race and gender [
12
]. As an example, Black and Latina
Educ. Sci. 2024,14, 514 3 of 22
women have reported experiences of racism and sexism that led to their feelings of isolation
and decreased their sense of belonging within CSE classrooms, resulting in unique and
compounded challenges in computer science professions and education [
13
]. Low sense
of belonging and belonging uncertainty are also associated with low program completion
rates, which may contribute to the racial and gender gap of women of color in STEM
professions [14–16].
A sense of belonging is critically important for the pursuit of computer science ed-
ucation, and students need a developed sense of belonging to pursue careers in STEM
education [
17
]. Thus, efforts to improve diversity within STEM fields have focused on fos-
tering computer science identity among underrepresented student groups. Students agree
that engaging in computer science programs designed with intentionality and working
with teachers and faculty who are perceived as caring were factors before and during col-
lege that contributed to their development of positive science identities [
18
]. Consequently,
there has been a call for educational institutions to systematically improve inclusivity
in CSE [18].
Shared language and participation within a discourse community, a knowledge-
producing community with shared assumptions, values, and characteristics, can help foster
a sense of belonging for computer science students. Thus, instructors must explicitly
support students’ participation within the discourse community by teaching them the
associated literacies and ways of knowing and doing within the discipline while also
incorporating students’ individual literacies within the discourse community. Disciplinary
literacy “emphasizes the specialized knowledge and abilities possessed by those who create,
communicate, and use knowledge within each of the disciplines [
19
]. Explicit teaching
of disciplinary literacy, which differs from content area literacy, is promoted in middle
and high school science and mathematics education [
19
]. However, we also recognize the
need to attend to disciplinary literacy in post-secondary CSE in ways that affirm and center
student identities and experiences.
Disciplinary literacy-focused instruction has been used to change curriculum by offering
first-hand/hands-on experiences related to scientific content, eliciting personal experiences
that support students’ learning of disciplinary-specific content, designing activities for stu-
dents to practice disciplinary literacies such as debate, and examining how arguments are
developed and executed during debates [
20
]. The 4Es heuristic has been examined as a
method to support the teaching of disciplinary literacy in mathematics [
21
–
23
], science [
24
],
and other disciplines. Use of the 4Es heuristic in secondary education has also resulted in cur-
ricular changes that prompted instructors to merge science and English language arts learning
standards, prioritize inquiry and exploration, recognize connections across disciplines, and
use student-guided inquiries [
19
]. Hayden and colleagues [
20
] found the effects of teaching
disciplinary literacy using the 4Es resulted in students’ increased abilities to read and analyze
science-related literature and to understand main arguments and evidence-based conclusions.
Students also improved their abilities to read figures and tables. Instruction grounded in the
4Es provides students with access to the science discourse community, and participation in the
discourse community is connected to students’ academic engagement and performance [25].
In the following section we discuss the theoretical foundation of our research and provide a
more detailed explanation of the 4Es heuristic.
Self-perception and identity are important parts of women’s and girls’ perceptions
of careers in computer science. Akkus and colleagues [
26
] found that working with mid-
dle school girls using a curriculum that combined software programming and identity
exploration resulted in improved attitudes toward computer science and increased compe-
tence and confidence in their work with computers. Main and colleagues [
27
] examined
the underrepresentation of women in STEM at several different levels (pre-college, col-
lege, industry, etc.) and found that the classroom environment, including the design and
team/peer interactions, influenced students’ decisions to major in computing, and their
sense of belonging and self-efficacy were connected to participants’ willingness to persist in
the computer science majors. High school student dropout rates, coupled with high work-
Educ. Sci. 2024,14, 514 4 of 22
force needs in STEM and computer science, have spurred interest in student perspectives
in higher education. Examining students’ expectations about computer science courses
and careers is one way that educators can effectively prepare students to complete their
coursework and enter the computer science industry.
1.2. Theoretical Perspective
Critical theories are well-suited to address underrepresentation in CSE because of their
ability to illuminate power distributions that cause inequitable societal conditions [
28
,
29
].
Our research responds to Hubbard Cheuoua’s [
29
] call to action to view issues of participa-
tion in computer science courses through a critical lens by using critical sociocultural theory
to consider issues of underrepresentation. We view the issue of inequity and participation
in CSE from a sociological perspective, which draws our attention to the role of power
within systems and structures such as schools and classrooms, and how power distributions
may support or constrain curricular and pedagogical revisions. Critical sociocultural theory
(CST) holds learning, identity, agency, and power as central and related constructs [
30
].
According to CST, learning is an act of participation that creates a history for the learner or
participant; however, learning is not reduced solely to participation. Learning occurs within
the process of participation if students’ engagement impresses upon them in a way that
creates a history of participation [
30
]. Learning takes place within a discourse community,
a group of people with shared views, practices, and vocabularies/languages [
31
]. Learning
requires gaining access to the discourse community, where students may start as beginners
on the periphery and then move closer to being central participants as they hone their
expertise. Thus, conditions of learning for students enrolled in computer science courses
require their participation and retention of the ways of thinking, knowing, acting, and
communicating like computer scientists.
Further, a sociological perspective suggests that full engagement and participation in
the CSE discourse community requires students to possess the cultural capital to fully en-
gage in that community and that students’ views and dispositions (habitus) may influence
successful assimilation into the discourse community [
32
]. Cultural capital includes the
knowledge and skills relevant in CSE, and undergraduate students access cultural capital
from activities such as early exposure to computer science concepts and programming
exposure in high school. Habitus or students’ dispositions toward computer science include
stereotypes that students may hold, such as who is/can be a computer scientist. While
students’ success may be indicative of their possession of and orientations toward capital
and habitus, it is the experts, in this case, ICS faculty, who control access to the cultural
tools and resources within the discourse community. Access to and control of cultural tools
and resources can also be influenced by what Moje and Lewis [
30
] call “qualities of differ-
ence”, which are marginalized social identities that result in students of non-dominant race,
gender, ability, and social class having less accessibility to the cultural capital required for
participation in discourse communities. CST allows us to acknowledge the role of power in
creating and maintaining inequities in CSE and guides us to examine how faculty teaching
the ICS course supported students’ developing disciplinary literacies to participate (and
learn) as part of the computer science discourse community. Because disciplinary literacies
provide access to the discourse community, we used the four resource model to examine
the range of literacy practices that the ICS curriculum supported.
1.3. Four Resource Model
We sought to analyze the existing ICS course prior to making changes to its curriculum.
Thus, we needed a framework to help us understand the breadth of literacy practices that
faculty used to support students’ disciplinary literacy development. We referenced Luke
and Freebody’s [
9
] four resource model (FRM) for the task of analyzing the pre-existing ICS
course curriculum. The FRM is a theoretical perspective that describes how readers engage
with texts, where reading is broadly defined as meaning-making and texts are not limited to
print but may also include visual images and the world around us [
33
]. The FRM is a useful
Educ. Sci. 2024,14, 514 5 of 22
approach for understanding how students in the current ICS course access disciplinary
literacies through a set of resources that represent a range of reading practices that students
draw upon when developing disciplinary literacies [
34
]. The FRM perspective highlights
four key activities produced by proficient readers (called resources); they are codebreaker,
text user, text participant, and text analyst. Codebreaking refers to the understanding of the
conventions, signs, and symbols of how the text is constructed; it is also called decoding
the language. Text participants can make meaning (literal and inferential) from a text by
differentiating it from others, and describing its salient characteristics as they relate to
computer science. Text users can harness their knowledge of a text to use it appropriately;
communicating within the context of the discipline. Text analysts recognize the text as
being associated with power and question how the text may act to position them; this is a
critical form of engagement that empowers students to move beyond blind consumption of
disciplinary knowledge [35].
Codebreaking, text user, text participant, and text analyst are interdependent resources
that readers do not follow in a specific sequence during the reading process or during the
developmental stages of learning to read texts [
36
]. However, the four resources vary in how
deeply readers understand, access, and engage with texts. The FRM helped us understand
if and how the ICS course curriculum and teaching methods positioned students to engage
with texts in the ways necessary to support the breadth of practice required for disciplinary
literacy. After our analysis of the ICS course curriculum, we sought to expand students’
opportunities to develop disciplinary literacies and used the 4Es heuristic to support our
course redesign.
1.4. 4Es Heuristic
The 4Es heuristic (engage, elicit/engineer, examine, and evaluate) is grounded in criti-
cal sociocultural theory and related to disciplinary literacy. It is a method for (re)designing
curriculum and instructional content that contextualizes the required cultural capital for
success in CSE within the scope of students’ lived experiences. The 4Es heuristic is designed
for teaching disciplinary literacy, where ‘literacy’ refers to the mastery of not only the con-
tent of a discipline but also the breadth of cultural capital required to fully participate in the
discourse community of a CSE course. The 4Es also guide instructors to support students
as they form their habitus and expand their views of computer science overall (including
who can be a computer scientist). The 4Es represent teaching practices that instructors
may use to support students drawing upon their existing cultural capital to engage in the
computer science discourse community. The heuristic is organized by four directives that
guide instructors to support students’ full engagement with both disciplinary content and
disciplinary literacy, which serve as capital within the discourse community and include
the ways of being, doing, and knowing computer science. The 4Es guide instructors to do
the following:
1.
Engage with the practices of the discipline as part of students’ daily routine, including
the vocabulary, habits of practice, etc.
2.
Elicit/engineer opportunities for students to engage in disciplinary practices and the
discourse community. Instructors should first elicit and consider students’ current
skills as they engineer experiences for students to gain new knowledge.
3.
Examine reminds instructors to guide students to closely examine words, such as
disciplinary-specific vocabulary.
4.
Evaluate has instructors guide students through a meta-discursive process by asking
themselves when, where, and why disciplinary language is useful.
Working with students through the 4Es heuristic positions instructors as responsible
parties for uncovering a hidden curriculum within discourse communities; thus provid-
ing access to a wider range of perspectives and practices from those who are typically
marginalized from the discourse community due to qualities of difference, such as gender,
or less accessibility to the cultural capital required for success in existing CSE courses.
Educ. Sci. 2024,14, 514 6 of 22
Faculty instructor feedback was also a critical element in our research. We used critical
sensemaking to understand how faculty responded to curriculum shifts.
1.5. Critical Sensemaking
The critical sensemaking framework (CSM) was designed to understand how minori-
tized undergraduate students made meaning of their campus climate [
10
]. Within this
study, we utilized CSM to explore how faculty make sense of implementing a curriculum
that centers students. CSM is grounded in the work of Weick [
37
], which determined seven
components that are engaged as one constructs meaning or understanding. According to
Weick [
37
], the sensemaking process occurs when an individual is faced with an experience
that creates uncertainty (new curriculum). The framework outlines that meaning-making
is a social construction that includes one’s own experience and the requirements of their
environment [
38
]. Sensemaking is also cultivated through how an individual perceives
oneself (identity) and their ability to both experience and influence their environments
(enactment) [
39
]. As individuals engage in new experiences, previous events in their lives
also inform how they interpret the novel encounter (retrospection). The process of sense-
making is continuous (ongoing) [
39
]; as new situations arise, one is constantly identifying
information to inform their perception of it being applicable (extraction of cues) [
40
] and
reasonable given their previous experiences and beliefs (plausibility) [9].
Despite Weick’s attempt to identify and define the tenants of sensemaking, one major
critique is the lack of consideration for power within the process [
38
]. Given the role
of instructors in deciding curriculum materials and learning experiences, while situated
within the requirements of the institution as faculty members, there are systems of power
at play within this dynamic that shape their perspective and subsequent sensemaking [
39
].
“CSM builds on the foundational notions [of sensemaking] by considering the role of
power—especially power as exerted by context, structure, and discourse” [
10
], p.87. Schildt
and colleagues [
38
] discuss that power is not solely an act being imposed on another; it
may derive from historical discourse and can exist both through deliberate action and
historical structures that have upheld perceived power, though not explicitly stated. Thus,
CSM allows us to explore the discursive elements involved in faculty sensemaking while
acknowledging the role of power in their meaning-making process.
2. Materials and Methods
We conducted a theoretical analysis of an existing ICS undergraduate course to exam-
ine the literacy practices and understand how students are positioned in relation to the
discourse community, then we used the 4Es heuristic to redesign a portion of the course,
and finally we examined faculty feedback on the redesigned course. The 4Es heuristic
provided a framework for us to restructure the course in a way that foregrounds the sig-
nificance of disciplinary literacy and access to the discourse community. In this section,
we describe the research participants, the ICS course that was our subject, our strategy for
redesigning the curriculum, and the perspectives of faculty instructors on the process.
2.1. Context
2.1.1. Research Participants
The initial ICS course analysis and redesign were completed by the co-authors of
this paper, which included faculty instructors of the ICS course and other members of the
research team. The two faculty instructors of the ICS course and the other members of the
research team also contributed to the conversations that were recorded, transcribed, and
analyzed to better understand faculty perspectives on the curriculum changes. Both faculty
instructors have taught the ICS course for more than two years at their current university.
2.1.2. Course Description
We conducted the analysis and curriculum redesign for an ICS course at a large
midwestern Research 1 institution. The course was designed for any student seeking
Educ. Sci. 2024,14, 514 7 of 22
to learn beginning computer science concepts and possibly persist into the major; high
school math was the only course prerequisite. Students learn a functional programming
language that covers arithmetic, definitions, design recipes, enumerations, and other
common introductory topics. The course has been offered for almost two decades, with
some variations made by faculty to support student success. Prior to the redesign, the ICS
course followed a flipped classroom format. Students were required to view video lectures
and complete short exercises before attending class. During the 75 min in-person class
sessions, the instructor provided additional details related to the video lecture content, and
students completed additional in-class exercises. Graduate or upper-level undergraduate
students facilitated weekly, mandatory laboratory sessions where students were required to
complete assignments that supported their continued engagement with the content covered
during weekly instruction. To support engagement outside of the scheduled instructional
time and offer just-in-time assistance, the instructors incorporated an instant message
platform for students to ask questions and engage with their peers.
In a previous study data regarding drop-out rates for the ICS course was examined for
three consecutive semesters (fall 2021, winter 2022, and fall 2022) [
8
]. The results revealed
that undeclared students dropped the course at a significantly higher rate compared to those
who were declared CS majors. Further, women drop out of the course at a significant rate
compared to their male counterparts. There was no significant difference in the drop-out rate
based on race. However, minoritized students only made up approximately twenty percent
of those enrolled in the ICS course. As well, the institution being examined requires students
to provide an open-ended reason for dropping a course. An analysis of the drop-out reasons
over the three semesters revealed the most common reasons for dropping a course could be
categorized as “academic struggles”, “dislike/not required”, “not prepared”. We drew on the
results of this previous study to inform the curriculum redesign.
2.2. Analysis
2.2.1. ICS Course
We conducted a content analysis of the curriculum materials within the original ICS
course. The original course materials were housed within an online learning management
system and organized by the corresponding lecture (e.g., Lecture 1). There were a total of
18 lectures in the course. Each lecture folder consisted of recorded videos of the faculty
explaining the topic for the week and demonstrating how to complete the subsequent
activities. There were multiple videos within each lecture folder, and a number of exercises
were to be completed after watching the video(s). We followed the method of analysis
outlined by Caro and colleagues to code each video and the accompanying exercises for
how they supported and/or activated certain learner roles identified in the FRM [
41
]. Two
researchers from the design team reviewed all the assignments and lectures that were
assigned for the first two weeks of the course. Similar to Caro and colleagues, each item
was categorized according to the resource/role it supported learners in performing while
completing the learning experience, and more than one category could be identified within
a single activity [
41
]. For example, when students were asked to write down data definitions
for structures to identify their functions, they were coded as codebreakers. Also, when
learners were instructed to create two new data definitions, they were labeled as content
users. When learners were asked to look for non-essential differences in data definitions,
they were labeled as content participants. When learners were asked to share three things
they would advise future students, they were coded as content analyzers.
2.2.2. Curriculum Redesign Strategy
Big Ideas.
Our goal for the curriculum redesign was to create a more student-centered approach
to teaching in the ICS course that would engage students’ cultural capital, and provide
them with explicit opportunities to learn the disciplinary content and literacy required for
engagement in the discourse community. Stevens and colleagues [
42
] define “Big Ideas”
Educ. Sci. 2024,14, 514 8 of 22
as the central and fundamental aspects of a discipline that lead to students’ sustained
understanding and progress. We began the curriculum redesign by working collaboratively
to identify the five big ideas that would support students’ mastery of computer science’s
disciplinary content.
Next, we organized all of the course content around the five big ideas, ensuring that
each topic was directly connected to one of these core concepts. We then developed learning
goals (specific to this course) to reflect the desired conceptual understanding that students
should have as a result of engaging in related learning experiences and specified the
prerequisite knowledge for learners to successfully master the learning goal. The following
is an example of one big idea, followed by the learning goal and prerequisite knowledge:
Big Idea
Computer programs that work with similar types of data follow similar patterns in
how they are written. As learners practice more and more computer science they learn to
recognize these patterns and then apply them.
Learning Goal
By the end of the course, students should be comfortable working with a variety of
built-in and custom data types/structures. Often, the types of input and output required
for a function suggest an appropriate template to use, only with a couple of small details
left to fill in. By learning to recognize similarities and common patterns between functions,
students will be able to quickly orient themselves when writing new functions. This will
also speed up debugging, as students can note whether they made an error with the overall
template/flow of their code or with a specific detail.
Prerequisite Knowledge
(a) Pattern recognition—being able to notice commonalities and make generalizations.
(b) In contrast, being able to identify essential differences between similar things.
Finally, we listed potential difficulties and misconceptions related to each big idea. As
an example, students commonly attempt to combine data types, which often leads to error
as data types follow specific and uncompromising rules. Breaking the rules of a data type
may result in breaking a function.
After outlining the big ideas of computer science and using them to align the learning
objectives, we identified the prerequisite skills, not to qualify or disqualify students but
to assist instructors with locating the source of misconceptions and identifying potential
difficulties. After outlining the full picture of the course, we turned our attention to the
4E’s heuristic as a method to address our concerns about centering student identities
and broadening literacy participation to develop disciplinary literacy and increase their
opportunities to participate in the discourse community.
4E’s Heuristic.
We utilized Moje’s [
25
] 4Es heuristic to guide our development of weekly lesson
plans that would support students’ identity, agency, and power within the community of
practice while sufficiently supporting their content knowledge of the domain. The 4Es
heuristic appealed to us as a design strategy because it is descriptive, but not prescriptive.
Using it as a framework provoked faculty/instructors to consider ways to prioritize student
experiences and identities while offering flexibility for individual instructor preferences and
expertise. The following represents how we engaged the elements of the 4Es to accomplish
our redesign goals:
1.
Engage helped us support students in exploring the “everyday practices” of the disci-
pline of computer science. We utilized the problem-framing component to support
learners’ motivation.
2.
Elicit/engineer helped us support beginning computer science students, especially
those in their first year of college and those with varied previous experiences, by
emphasizing the meta-strategies that will elicit the thinking necessary for success in
the course, and computer science work in the future.
3.
Examine guided us to ensure that all students can access the course content regardless
of their level of prior knowledge by questioning all terms, including how and why
Educ. Sci. 2024,14, 514 9 of 22
they are used. This also allows instructors to assess the schema and preconceptions
students are bringing into the course.
4.
Evaluate guided us to provide students with the opportunity to challenge con-
cepts and explanations that are bogged down in jargon and unnecessary complex-
ities. If a term or concept is difficult to refine without referencing a prior assump-
tion/understanding, then students are encouraged to interrogate why that might be
the case.
Pacing Guide.
The pacing guide was an important artifact from the redesign that incorporated what
we learned from organizing the course content around the five big ideas and using the 4Es
heuristic to support students in developing their disciplinary literacy. The pacing guide
outlined the semester by week, where each week instructors taught based upon one of the
five big ideas. We described the weekly concepts to be covered, delineating the disciplinary
concepts from the disciplinary literacy practices. We did this to ensure that instructors
can explicitly identify how they are responding to the call from the 4Es (centering student
experiences and identities) as they teach the content. The pacing guide also includes
a weekly note to students that explains why that week’s content is important for their
domain knowledge.
2.2.3. Faculty Responses
We applied ordered network analysis to our faculty response data using the ONA
Web Tool (version 1.7.0) [
43
,
44
]. Our units were divided into two groups: the faculty and
the research team, and we analyzed the conversation using a moving stanza window of
four lines. The resulting directed networks are aggregated for all lines for each unit of
analysis in the model. We created a model that represents aggregated networks using a
binary summation in which the networks for a given line reflect the presence or absence
of the co-occurrence of each pair of codes. Our ONA model included the following codes
outlined in the CSM framework: identity, social, plausibility and retrospection. Some codes
accepting, student learning, curriculum, pedagogy, curriculum, struggling, and theoretical
were not outlined in the CSM framework but added to the code book to account for their
occurrence in the discourse. We defined conversations as all lines of data associated with
group discussion on curriculum design. The ONA model normalized the networks for all
units of analysis before they were subjected to a dimensional reduction, which accounts for
the fact that different units of analysis may have different numbers of coded lines in the data.
For the dimensional reduction, we used a singular value decomposition, which produces
orthogonal dimensions that maximize the variance explained by each dimension [
45
,
46
]. We
visualized networks using directed network graphs where nodes correspond to the codes
and edges reflect the relative frequency of co-occurrence, or connection, and connection
direction between two codes. The result is two coordinated representations for each unit
of analysis: (1) a plotted point, which represents the location of that unit’s network in
the low-dimensional projected space, and (2) a directed weighted network graph. The
positions of the network graph nodes are fixed, and those positions are determined by
an optimization routine that minimizes the difference between the plotted points and
their corresponding network centroids. Because of this co-registration of network graphs
and projected space, the positions of the network graph nodes—and the connections they
define—can be used to interpret the dimensions of the projected space and explain the
positions of plotted points in the space. Our model had co-registration correlations of
1 (Pearson) and 1 (Spearman) for the first dimension and co-registration correlations of
1 (Pearson) and 1 (Spearman) for the second. These measures indicate that there is a strong
goodness of fit between the visualization and the original model.
ONA can be used to compare units of analysis in terms of their plotted point positions,
individual networks, mean plotted point positions, and mean networks, which average
the connection weights and directions across individual networks. Networks may also be
compared using network difference graphs. These graphs are calculated by subtracting the
Educ. Sci. 2024,14, 514 10 of 22
weight and direction of each connection in one network from the corresponding connections
in another. To test for differences between the networks, we applied a Mann–Whitney
test to the location of points in the projected ENA space for units in the faculty and
research team.
3. Results
Our first research question examined how the initial ICS curriculum and teaching
lectures aligned with the FRM practices of codebreaking, text participant, text user, and
text analyzer. The initial ICS course curriculum dedicated less than 40% over the course of
the 18 weeks to breaking down the basic components of syntax and grammar in computer
science-related language and practices. Instructors spent more time/more of the course
content with foundational elements of codebreaking later in the course than at the begin-
ning. The learning experiences provided more frequent opportunities for students to be
guided/supported in making meaning from course content, but the majority positioned
them as text users and they very rarely were guided to engage with course content as text
analysts. Figure 1represents the distribution of resources that instructors addressed to
students in the ICS curriculum.
Educ. Sci. 2024, 14, x FOR PEER REVIEW 10 of 22
of that unit’s network in the low-dimensional projected space, and (2) a directed weighted
network graph. The positions of the network graph nodes are fixed, and those positions
are determined by an optimization routine that minimizes the difference between the plot-
ted points and their corresponding network centroids. Because of this co-registration of
network graphs and projected space, the positions of the network graph nodes—and the
connections they define—can be used to interpret the dimensions of the projected space
and explain the positions of ploed points in the space. Our model had co-registration
correlations of 1 (Pearson) and 1 (Spearman) for the first dimension and co-registration
correlations of 1 (Pearson) and 1 (Spearman) for the second. These measures indicate that
there is a strong goodness of fit between the visualization and the original model.
ONA can be used to compare units of analysis in terms of their ploed point posi-
tions, individual networks, mean ploed point positions, and mean networks, which av-
erage the connection weights and directions across individual networks. Networks may
also be compared using network difference graphs. These graphs are calculated by sub-
tracting the weight and direction of each connection in one network from the correspond-
ing connections in another. To test for differences between the networks, we applied a
Mann–Whitney test to the location of points in the projected ENA space for units in the
faculty and research team.
3. Results
Our first research question examined how the initial ICS curriculum and teaching
lectures aligned with the FRM practices of codebreaking, text participant, text user, and
text analyzer. The initial ICS course curriculum dedicated less than 40% over the course
of the 18 weeks to breaking down the basic components of syntax and grammar in com-
puter science-related language and practices. Instructors spent more time/more of the
course content with foundational elements of codebreaking later in the course than at the
beginning. The learning experiences provided more frequent opportunities for students
to be guided/supported in making meaning from course content, but the majority posi-
tioned them as text users and they very rarely were guided to engage with course content
as text analysts. Figure 1 represents the distribution of resources that instructors ad-
dressed to students in the ICS curriculum.
Figure 1. The content analysis results from the original ICS course. The semester was divided into
three sets of 6 weeks in chronological order, for a total of 18 weeks. Each set illustrates the frequency
with which one of the four resources/learner roles (codebreaker, text/content participant, text/con-
tent user, and text/content analyzer) was supported within a learning activity.
For question two, we wanted to explore how the 4Es heuristic could be used to rede-
sign the ICS curriculum and teaching methods to beer align with the more
Figure 1. The content analysis results from the original ICS course. The semester was divided
into three sets of 6 weeks in chronological order, for a total of 18 weeks. Each set illustrates the
frequency with which one of the four resources/learner roles (codebreaker, text/content participant,
text/content user, and text/content analyzer) was supported within a learning activity.
For question two, we wanted to explore how the 4Es heuristic could be used to
redesign the ICS curriculum and teaching methods to better align with the more compre-
hensive use of the resources named in the FRM. We used the 4Es heuristic to support the
reimagining of the ICS course in the following manner: During the first week of the course
learners, are engaged in the “everyday practices” of the discipline (Figure 2). This begins by
contextualizing the work for the week within a problem or overarching question. For week
one, the topic of rocket science is used to contextualize the material for students. They are
asked to think of the many pieces that are required to successfully launch a rocket. Items
such as the design would be discussed since the rocket has to be able to propel itself with
incredible force and accuracy, survive exiting and re-entering Earth’s atmosphere, and be
livable for the astronauts. Students would recognize that the construction (e.g., using proper
materials, being precise in measuring and welding) of the rocket would be important in its
launch. Training could be recognized as a vital component of a rocket launch, as astronauts
and ground controllers have to be versed in a variety of procedures and prepared for
all scenarios (Apollo 11 vs. Apollo 13). Additionally, calculations would be necessary to
determine how much fuel is required, what path the rocket should take to reach its destina-
Educ. Sci. 2024,14, 514 11 of 22
tion, and how gravitational forces or weather will impact its trajectory. Students would
discuss the importance of communication, as constant interaction and engagement have to
be maintained between ground control and the astronauts, even as the latter are in space.
Contextualized within the overarching question, students are provided the opportunity
to engage with data that will encourage them to identify patterns that appear within the
data. As well as salient features of the data that will be most relevant to their analysis.
The goal would be for students to identify patterns and ways that data can be grouped
and interpreted.
Educ. Sci. 2024, 14, x FOR PEER REVIEW 12 of 22
Figure 2. This is a revised figure of Moje’s (2015) 4E’s heuristic figure that explores how “engage” is
achieved through the disciplinary cycle. This figure includes examples of how each aspect of the
disciplinary cycle can be used to support ICS students’ disciplinary literacy.
There were two key changes that allowed for shifts in the curriculum toward identity
and power. The first key change was the development of big ideas for computer science.
In addition, we identified why each big idea was important and what students should
learn in the specific course related to the big idea. With each big idea, we list the types of
previous knowledge we deem necessary to support successfully mastering the discussed
concept, along with potential student difficulties and misconceptions.
BIG IDEA 1: Computer programs are modular, combining simple pieces into a com-
plex whole. Similarly, difficult problems in computer science can be solved by breaking
them down into easier subproblems.
Significance: Some problems can look intimidating, but breaking them down into
linear steps helps to make sure the problem is solved correctly. Throughout their careers,
the big, complicated things that people create could be made by less experienced individ-
uals, but they do not know that the complex codes or problems are really a combination
of smaller, more aainable problems. If you are working with a larger team, they are
working on separate tasks, which are then all put together. So, working on smaller parts
of a whole is common in CS.
What students should learn: By the end of C211, students should be able to work
through multi-step coding problems by decomposing them, building smaller programs to
perform subtasks, and then combining these programs logically. The course will start with
teaching how to write these simple programs, often consisting of functions that take a
small number of inputs and produce a single output, and variables that are defined as the
application of a single function. Once students are comfortable working with these basic
building blocks, the course will teach them how to combine them in increasingly sophis-
ticated ways, such as function nesting, conditionals, helper functions, recursion, and ab-
straction. Emphasizing this approach will prepare students for the modularity inherent in
computer science.
Specific prerequisite knowledge:
• Algebra (especially working with functions);
• Vague familiarity with modular design.
Potential student difficulties and misconceptions:
• A common difficulty is trying to tackle the entire problem at once, instead of taking
it one piece at a time;
• Not checking that every part works before combining them, then being unable to
identify the problem when the whole fails;
Figure 2. This is a revised figure of Moje’s (2015) 4E’s heuristic figure that explores how “engage”
is achieved through the disciplinary cycle. This figure includes examples of how each aspect of the
disciplinary cycle can be used to support ICS students’ disciplinary literacy.
Continuing to build students’ identities and beliefs, we expand their sense of what
it means to do computer science beyond coding. This includes showing them what an
algorithm looks like in words versus a coding language or a physical simulation utilizing
a Turing machine. To build students’ agency and redistribute power within the learning
experience, we provided learners the opportunity to reflect on their understanding of what
the field of computer science entailed and to examine various perspectives and evaluate
them for both their merit and potential limitations. Week one’s learning experiences would
culminate in a discussion of their position in both small and large groups. This formative
assessment technique positions both the instructor and the student as learning resources.
Elicit/engineering within the course redesign is nurtured as students are allowed to
experience computer science beyond coding. The design makes no assumptions about
the level of prior experience students bring to the course. The focus in week one and
throughout the course is on developing the meta-strategies that will elicit the thinking
necessary for success in this course, and computer science work in the future. Within
the course redesign, we acknowledge that students are typically in their first or second
semester of college and require more support and scaffolding as they engage with the
disciplinary practices (Figure 2). The practice of examining is nurtured within the learning
experience as terms such as computers and science are called into question. The discourse
within the course is focused on centering students’ voices in lieu of lecturing. For example,
in a large group setting, students completed a mind map for a concept in computer science.
The map consisted of purely students’ suggestions, with the instructor providing feedback
only at students’ request. This type of formative assessment can identify what schema
preconceptions are informing students’ conceptions. The course redesign assists students
in examining the ways of thinking and knowing in computer science. Throughout the
learning experience, students (and teachers) are encouraged to redefine definitions and
explanations to their essence, so that they are free of jargon and overcomplication. If a
concept or term is deemed too complex and cannot be simplified or explained without
Educ. Sci. 2024,14, 514 12 of 22
reference to a prior assumption/understanding, students are to interrogate why that might
be the case.
There were two key changes that allowed for shifts in the curriculum toward identity
and power. The first key change was the development of big ideas for computer science. In
addition, we identified why each big idea was important and what students should learn in
the specific course related to the big idea. With each big idea, we list the types of previous
knowledge we deem necessary to support successfully mastering the discussed concept,
along with potential student difficulties and misconceptions.
BIG IDEA 1: Computer programs are modular, combining simple pieces into a
complex whole. Similarly, difficult problems in computer science can be solved by breaking
them down into easier subproblems.
Significance: Some problems can look intimidating, but breaking them down into
linear steps helps to make sure the problem is solved correctly. Throughout their careers, the
big, complicated things that people create could be made by less experienced individuals,
but they do not know that the complex codes or problems are really a combination of
smaller, more attainable problems. If you are working with a larger team, they are working
on separate tasks, which are then all put together. So, working on smaller parts of a whole
is common in CS.
What students should learn: By the end of C211, students should be able to work
through multi-step coding problems by decomposing them, building smaller programs
to perform subtasks, and then combining these programs logically. The course will start
with teaching how to write these simple programs, often consisting of functions that take
a small number of inputs and produce a single output, and variables that are defined as
the application of a single function. Once students are comfortable working with these
basic building blocks, the course will teach them how to combine them in increasingly
sophisticated ways, such as function nesting, conditionals, helper functions, recursion, and
abstraction. Emphasizing this approach will prepare students for the modularity inherent
in computer science.
Specific prerequisite knowledge:
•Algebra (especially working with functions);
•Vague familiarity with modular design.
Potential student difficulties and misconceptions:
•
A common difficulty is trying to tackle the entire problem at once, instead of taking it
one piece at a time;
•
Not checking that every part works before combining them, then being unable to
identify the problem when the whole fails;
•
The misconception that all coding questions are solved in one function; this often leads
to struggle as there are many times where a solution involves a helper function, which
makes the question much easier;
•
Combining functions arbitrarily rather than reasoning about how data should flow
through them (often helps to use step-by-step breakdowns).
BIG IDEA 2: Code should be as efficient as possible, optimized for time, memory, and
storage space.
Significance: Human usability is important. Time matters in how long it takes to
execute a task. If a program takes up too much hard drive space, then computers will not
be able to store it. This topic is less talked about, but optimization is necessary later on.
Doing something by hand vs. on a computer to emphasize optimization.
What students should learn: While optimization is explored more thoroughly in later
computer science courses, ICS students should be able to explain why time and memory
efficiency are important programming tenets. Fast and slow algorithms (e.g., for sorting
and exponentiation) can be demonstrated and compared, illustrating how increasing the
magnitude of the input can dramatically increase the runtime of a program, especially
if optimization strategies are not taken. Real-world scenarios (e.g., a search engine in a
Educ. Sci. 2024,14, 514 13 of 22
hospital database, real-time GPS navigation) should also be explored to underline the
importance of this concept.
Specific prerequisite knowledge:
•Having been on the user end of computer programs;
•Experiencing fast and slow runtimes (motivates the big idea).
Potential student difficulties and misconceptions:
•
The misconception that a single problem only has a single solution—a coding question
has countless different solutions, each one varying in its speed and memory usage;
•
Not taking these principles to heart when they start learning to code, then eventually
reaching a point where they struggle with code efficiency.
BIG IDEA 3: Computer programs that work with similar types of data follow similar
patterns in how they are written. As you practice more and more computer science, you
learn to recognize these patterns and then apply them.
Significance: Help students identify commonalities, reduce coding time, and
support debugging.
What students should learn: By the end of ICS, students should be comfortable
working with a variety of built-in and custom data types/structures. Often, the types of
input and output required for a function suggest an appropriate template to use, only with
a couple of small details left to fill in. By learning to recognize similarities and common
patterns between functions, students will be able to quickly orient themselves when writing
new functions. This will also speed up debugging, as students can note whether they made
an error with the overall template/flow of their code or with a specific detail.
Specific prerequisite knowledge:
•Pattern recognition—being able to notice commonalities and make generalizations;
•On the flip side, being able to identify essential differences between similar things.
Potential student difficulties and misconceptions:
•
A very common difficulty students experience is trying to mix data types together, which
often leads to an error, as data types follow very strict rules that break an entire function
when not followed (using a function that takes in a number but giving it a string);
•
Not knowing/following the usual template for a function—making one up on the
spot is usually going to be incorrect;
•
The misconception that a computer knows exactly what you want it to do—just
because you understand the purpose of your code does not mean the computer does;
•
Trying to solve the problem before analyzing it and figuring out what pattern the code
should follow.
BIG IDEA 4: Writing computer programs requires lots of testing to figure out what
does and does not work and to account for all use cases.
Significance: One of the core things in computer science is not how you solve it but
how you break it (you are not designing your problem for yourself but for other people— a
lot of people do not know how to use computers perfectly, so write code as if the person is
not going to use it perfectly) and then how you will patch up those possible breaks. If you
have a hypothesis and run experiments to test the hypothesis, your example should cover
all inputs. Being willing to write and rewrite your code until it works. Supports failing
and retesting.
What students should learn: After ICS, students should be able to identify both
the regular uses and edge cases within a standard programming question. There are
several common edge cases that can be identified, such as using negative numbers or
using incompatible data types, and these should be preemptively dealt with by students
using tests/examples. After covering the common edge cases, students should also be
able to identify more subtle errors that their code may not account for and effectively
patch them while maintaining a solid code structure (ex: not just adding a conditional that
covers a single error, but rather dealing with the root of the error itself). In debugging,
Educ. Sci. 2024,14, 514 14 of 22
students should be willing to try a variety of solutions to get their code working properly,
experimenting with what does and does not work—this process is essential in all kinds of
computer science.
Specific prerequisite knowledge:
•Familiarity with the scientific/empirical method (hypothesis -> experiment);
•Previous experience with detail-oriented work.
Potential student difficulties and misconceptions:
•
Writing tests that fit the result of your code, rather than writing code that fits the result
of your tests;
•Not writing enough tests/not writing any tests;
•
Immediately giving up when the code does not work instead of first trying some
plausible fixes;
•
When a test does not pass, tweak the code so it now passes that test instead of actually
analyzing why the test did not pass;
•
Not doing step-by-step breakdowns for one or more example inputs, especially for
more sophisticated problems;
•
Believing that if all the tests they’ve written pass, their function must be working
properly (there could be edge cases and other types of input that they have not
considered and tested).
BIG IDEA 5: Code should be precise and detailed to be understood by the computer
and well-annotated to be understood by the student and other programmers.
Why this is important: Knowing what your functions do is important, especially when
you have more than one. Focus on clarity. Part of annotation is choosing good variable and
function names. It is important for not only how everyone else understands your code but
also for how you understand your code. Computers do not automatically understand the
purpose of code—they need everything spelled out in detail in order to work properly.
What students should learn: ICS students should learn the proper foundations to
write well-formatted and legible code that can be understood by themselves, the computer,
and other programmers. This includes choosing good variable and function names, as
those can help the student figure out how elements should be used or combined as they
write their code, as well as proper indentation to illustrate the flow of the code. Someone
else should be able to view their code, and although they may not be able to know its exact
functionality, they should be able to recognize the inputs that it deals with and have a
vague idea of its purpose. It is also much easier to spot errors and bugs when the program
is written cleanly; otherwise, simple mistakes in punctuation or structure can be masked
by the “noise” of poor formatting.
Specific prerequisite knowledge:
•Background in English writing (e.g., proper grammar, punctuation, and formatting);
•
Experience with miscommunication—how has imprecision/ambiguity led to misun-
derstandings in social interactions?
Potential student difficulties and misconceptions:
•
The misconception that code should be written in a way that only they can understand.
Code clarity means that anyone should be able to read and roughly understand
its purpose;
•
The misconception that having the code work properly is good enough, even if their
solution is indecipherable;
•Random/undescriptive variable and function names (x, var1, blah, etc.);
•Poor indentation that leads to confusion about the structure of the code;
•Not keeping track of what kinds of inputs and outputs functions take;
•
Not keeping track of the purposes of individual functions or what certain variables mean;
•
Getting into bad habits early—as the course progresses and the problems become
more challenging, the sloppiness of the code will catch up to them.
Educ. Sci. 2024,14, 514 15 of 22
The second key change was the development of a weekly pacing guide for the entire
semester-long course (Figure 3). Each week, identify which big idea will be covered to
assure alignment with the major ways of thinking and knowing within the discipline.
The facts and concepts that will be covered that week are outlined in the pacing guide.
Instructors are provided with examples of problems they can provide students with to
apply their knowledge. The pacing guide is designed for both students and instructors to
view as a way to communicate the expectations of the instructors to the students. As such,
it includes skills that students should be able to demonstrate at the end of each week, as
well as types of projects/assignments that will support students in demonstrating mastery
of the concepts. Each week contains a section that identifies what knowledge from previous
weeks students are expected to engage in order to master the concept or support their
disciplinary literacy. The pacing guide also includes a weekly note explaining why the
concepts for that week are important both within the course and the discipline.
Educ. Sci. 2024, 14, x FOR PEER REVIEW 16 of 22
plausibility codes). Interestingly, despite its central role in the faculty network, there was
lile connection to identity for the research team.
Figure 3. Illustration of the pacing guide for the first 3 weeks of the redesigned course.
Figure 4. The model represents the discussion between the research team (blue lines) and the faculty
team (orange lines). The model indicates the co-occurrence of codes derived from the CMS frame-
work. The size of the nodes (circles) indicates the number of occurrences of the code during the
discussion. The thickness of the lines indicates more co-occurrences of the two codes. The color of
the lines and nodes represents the group (research team or faculty team) it is most represented by.
Figure 3. Illustration of the pacing guide for the first 3 weeks of the redesigned course.
Our third research question required us to examine the ICS course instructional faculty’s
perceptions of the redesign process. Figure 4illustrates that along the X axis (SVD1), a Mann–
Whitney test showed that the faculty (Mdn =
−
0.47, N = 2) was not statistically significantly
different at the alpha = 0.05 level from the research team (Mdn = 0.24, N = 3, U = 0, p= 0.20,
r = 1.00). Additionally, we found that the faculty (Mdn = 0.23, N = 2) was not statistically
significantly different on the Y axis (SVD2) at the alpha = 0.05 level from the research team
(Mdn =
−
0.39, N = 3, U = 4.00, p= 0.80, r =
−
0.33). Although the networks were not statistically
significant, there are differences highlighted by the connections within the networks. For the
faculty network, identity played the largest role in their sensemaking during the conversation.
When looking at the connections to identity, student learning, curriculum, and struggling had
the largest ties to it. This highlights that the faculty were struggling to find the connection
between their own identity and practices that help students learn in the classroom.
Educ. Sci. 2024,14, 514 16 of 22
Educ. Sci. 2024, 14, x FOR PEER REVIEW 16 of 22
plausibility codes). Interestingly, despite its central role in the faculty network, there was
lile connection to identity for the research team.
Figure 3. Illustration of the pacing guide for the first 3 weeks of the redesigned course.
Figure 4. The model represents the discussion between the research team (blue lines) and the faculty
team (orange lines). The model indicates the co-occurrence of codes derived from the CMS frame-
work. The size of the nodes (circles) indicates the number of occurrences of the code during the
discussion. The thickness of the lines indicates more co-occurrences of the two codes. The color of
the lines and nodes represents the group (research team or faculty team) it is most represented by.
Figure 4. The model represents the discussion between the research team (blue lines) and the faculty
team (orange lines). The model indicates the co-occurrence of codes derived from the CMS framework.
The size of the nodes (circles) indicates the number of occurrences of the code during the discussion.
The thickness of the lines indicates more co-occurrences of the two codes. The color of the lines and
nodes represents the group (research team or faculty team) it is most represented by.
For the research team, the social aspect of sensemaking played a large role in their net-
work, with the largest connections to curriculum and student learning. These connections
show the conversation required the research team to provide broad group conversations
on practices to help students learn in the classroom. When looking across the networks, we
can see that the research team was continually helping the faculty through their struggle
with student learning and curriculum, but the discussion did not shift towards the faculty’s
acceptance of the proposed curriculum (low instances of accepting and plausibility codes).
Interestingly, despite its central role in the faculty network, there was little connection to
identity for the research team.
4. Discussion
There is a need to address the lack of gender, ethnic, and experiential diversity among
students in computer science education courses. Efforts to solve this issue have previously
focused on increasing students’ content knowledge by providing them with early exposure
to computer science concepts and increasing experiential learning opportunities. While
these methods have been marginally effective, they do not address the issue of inequity
from critical perspectives that can illuminate structural barriers that limit access to power,
such as students’ limited access to computer science discourse communities. There is also
the danger of pathologizing student performance in ways that label students as under-
performing and underachieving without examining the course curriculum and instructor
pedagogy. One way to broaden students’ accessibility and to diversify perspectives within
the computer science discourse community is to support students’ disciplinary literacy
development. Learning the ways of knowing and the common language and practices
used in computer science education and industry affords students from varying back-
grounds greater opportunities to understand, engage with, and critique the discipline, thus
diversifying perspectives and growing the field.
Educ. Sci. 2024,14, 514 17 of 22
Disciplinary literacy development is a process where students practice making mean-
ing of the words, habits, and practices of computer science as a discipline. Students may
investigate what terms and vocabulary are used and why, learn how to appropriately use
related equipment and tools, observe and practice interpersonal communications within
the computer science industry, use their knowledge to execute tasks, and critique existing
processes and procedures to push the current boundaries and bring new ideas into the
field. The four resource model represents a range of literacy practices that occur when
students engage in meaning-making. We used the FRM as a framework for analyzing
the initial ICS course to understand how the curriculum aligned with the framework and
if students were provided with opportunities to practice the range of meaning-making
processes (from codebreaking to critiquing) that are required for developing disciplinary
literacy and engaging in the computer science discourse community.
Students were positioned in the curriculum most often as text or content users because
they were provided with a list of instructions for executing functions with very little
background on the origin of the concepts (codebreaking), how to make meaning of the
language/vocabulary in multiple ways related to computer science (text participant), and
encouragement to challenge the status quo of how and why processes are executed in the
discipline. Limiting students’ opportunities to deconstruct discipline-specific language
through codebreaking, their literal and figurative meaning-making through text and content
participation, and their critical thinking through text and content analysis is problematic,
in part because it limits their full participation in the CSE discourse community. Although
interventions have focused on approaches that mostly address gendered inequities in
CSE, research has demonstrated the need to broaden, more generally, perspectives on
computer science and computer scientists [
47
]. Curriculum that foregrounds disciplinary
literacy, specifically through the 4Es heuristic, provides opportunities for diverse students
and their perspectives to participate in widening CSE perspectives. This may include
students revising and challenging longstanding ideas about the field of computer science
and participating in the full range of literacy practices identified through the FRM.
We found the 4Es heuristic to be most useful in aligning with the FRM framework
when we focused on engagement, eliciting/engineering, and evaluation. We designed
opportunities for instructors to elicit and engineer opportunities for students to participate in
computer science education in ways that draw upon their prior knowledge and understand-
ings. This aligned well with codebreaking because it does not assume all students have
prerequisite knowledge and builds capacity for broadening their schema from their current
understandings. When using engagement as a design principle from the 4Es heuristic, we
were guided to include hands-on opportunities for students to explore and experience
concepts reflected in the ICS course content, which we believe will support their practices
as text or content users. Students are guided in the curriculum redesign to take a group
of derived patient data and identify patterns and ways that the data can be grouped and
interpreted. We used examination from the 4Es heuristic to guide instructors to support stu-
dents’ in paying close attention to domain-specific language and practices, something that
is required across the breadth of meaning-making processes in the FRM. Finally, evaluation
aligns with positioning CSE students as critical thinkers and encourages them to complete
steps related to tasks as text or content users, but also to evaluate and question things about
the process. For example, does it accomplish the intended goals? Is it efficient? Equitable?
Viable in multiple environments, etc.?
Identity was a major factor in faculty critical sensemaking of centering student needs
in their courses. This aligns with the concern-based adoption model (CBAM), which
supports that attending to one’s self-concerns is a vital part of the adoption process. Wick-
erman and Wang [
39
] explain that faculty take on many identities while sensemaking
curriculum changes, establishing identity as the most important element of the process.
During the discussion, faculty were struggling to find the connection between their own
identity and practices that help students learn in the classroom. According to Wickerman
and colleagues [
39
], during professional development opportunities, faculty take on an
Educ. Sci. 2024,14, 514 18 of 22
adult learner identity, and if they are unable to make meaningful connections between
the proposed change and their previous experience and assumptions, they are resistant
to change. Centering the student needs redistributes power within the course and im-
proves opportunities for all people to learn. However, as existing members (faculty) of
the discourse community, there may be hesitation about relinquishing control to whoever
has access [
30
]. Though Schildt and colleagues [
38
] suggest that power is not necessarily
restricting access in explicit terms, it may be simply upholding a long-held understanding.
As institutions continue to provide professional development focused on increasing
participation, it is vital to not solely focus on how to redesign curriculum and implement
pedagogical strategies. Yet, there must be space and opportunity for faculty members to
remake their identities regarding their role as instructors and support capacity building
for tailoring their course to meet the needs of their students [
39
]. Power can be both
negative/constraining and positive/enabling [
38
]. Further research is needed to explore
how professional developments can be designed and implemented to support faculty
identity reconstruction when adopting equitable curriculum redesign and the role of power
in that process.
The 4Es heuristic guided faculty to center student identities, experiences, and interests,
thus using students’ cultural capital as a tool to provide them with access to the discourse
community of computer science education. By drawing upon students’ existing knowledge
and not assuming early or prior exposure to computer science-related concepts, instructors
widen the possibilities for participation in the discourse community. Valuing students’
opinions and providing opportunities for them to challenge and agree with often taken-
for-granted concepts in computer science may also shift their habitus by expanding their
thoughts about who gets to be a computer scientist.
4.1. Centering Student Identities
Faculty instructors center student identities most frequently by considering students’
interests and experiences as the starting point for engaging in disciplinary practices. Of
the seven disciplinary practices connected to engagement, we found that faculty actively
centered student identities in several important ways. For example, a discussion about
framing the question/problem prompted an instructor to say during an instructional team
meeting, “We can’t jump right off the bat into how to code—first we need motivation!”
The faculty subsequently designed an introductory activity called Rocket Science that
they thought would engage their current students based on their interests and agreed
on the importance of getting to know the students in each class to tailor future problems
and inquiries around their curiosities. The redesign team meeting minutes also provide
evidence that faculty were spurred to think about students’ prior experiences with coding,
opportunities to demonstrate their learning in various modalities, and providing students
with space to challenge claims in existing CSE literature and contrast them with their
own experiences.
Instructor statements gathered from collaborative planning sessions demonstrated
how focusing on eliciting and engineering in the ICS course resulted in conversations
among the instructional team of faculty that centered students’ previous experiences (“We
should also not assume that students are already computer-savvy or have coded before”)
and guided them to design instructional experiences that scaffold from their existing skills
and consider their overall experiences as undergraduate students (“It is important to
note that most students in this class are in their first or second semester of college, and
could use a little encouragement to build positive study habits in general!”). Instructors
also commented on the computer science curriculum: “How would this course change
if we truly assumed no prior knowledge? No term is too simple to define those may be
the best terms to try to define! The simpler the better!!!” Finally, we found that faculty
encouraged student voice in ways not previously performed in the existing curriculum.
Faculty designed multiple opportunities for students to comment on their perspectives
regarding literature about computer science (various multimodal formats, including the
Educ. Sci. 2024,14, 514 19 of 22
textbook), to work in small groups to orally present their ideas and questions, and to learn
from others.
The intentional practice of beginning from the beginning and not assuming prior
knowledge about computer science and computer functioning actively addresses the
inequitable representation of students who have early exposure to STEM education or
previous experience with computer science. The curriculum redesign represents the ICS
faculty’s willingness to suspend assumptions about students’ knowledge and to explicitly
identify, define, and explain both disciplinary content and habits of practice. Faculty also
address inequitable representation due to “qualities of difference” by centering students’
experiences and feedback. Faculty-centered student experiences by using their knowledge
about students’ backgrounds to inform the design of their learning activities. Students’
interests were used to create contexts for inquiries designed to engage students in computer
science practices. The faculty also designed opportunities for students to share their
feedback in the form of agreement and challenge as it relates to the multimodal forms of
computer science-related content.
4.2. Shifting Power Relations
We sought a redistribution in power relations because students, as novices in the field
of computer science, are typically at a disadvantage within CSE. While the faculty are the
experts because of their knowledge and experience within the discourse community, it
is imperative to acknowledge the value of opening the discourse community to various
perspectives that may support and challenge the existing cultural capital and habits of
practice. Moje [
25
] identifies the potential of the 4Es heuristic as a framework in support of
achieving social justice in education.
As the faculty considered how to elicit student perspectives and engineered opportuni-
ties for them to share their perspectives and feedback, they were simultaneously widening
opportunities for participation within the discourse community for students who were
previously excluded because of their previous experiences or qualities of difference. Ul-
timately, drawing upon student experiences and taking nothing for granted as it relates
to the content and disciplinary practices of CSE, power was redistributed more in favor
of the students than it had been in the original course design. Key changes allowing for
shifts in the curriculum toward centering student identity and a redistribution of power in
favor of students began with the five big ideas of computer science course content, which
illuminated our need for a supplemental framework (4Es heuristic) to attend to supporting
student engagement in the discourse community. We identified the 4Es heuristic as a
feasible method to support students as they engage with the five big ideas of computer
science within their discourse community.
4.3. Limitations and Challenges
Our study reflects data collection and analysis of one ICS course curriculum and two
class lectures. We had, as part of our team, two faculty instructors for the ICS course. Also,
due to time constraints, we were only able to redesign the curriculum for one course. Thus,
we are limited in generalizing the findings of our research to other CSE contexts. In the
future, we would recommend recruiting a larger number of faculty to elicit additional
responses and feedback on the course redesign. We would also recommend exploring
curriculum redesign in multiple contexts before making broad recommendations for CSE.
5. Conclusions
We found the 4Es heuristic to be a useful tool to support the reimagining of an
undergraduate ICS course to center student identity and redistribute power in favor of
students in CSE. Key changes to the curriculum included identifying the five big ideas
of computer science education and designing the pacing guide to encompass instructor
guidance for teaching both the disciplinary content and literacy practices required for
participation in the discourse community. Using the 4Es framework guided ICS faculty
Educ. Sci. 2024,14, 514 20 of 22
to consider changes in the curriculum that made the content and disciplinary practices of
CSE accessible to all students, regardless of their prior experiences, level of exposure to
computer science education, or decision to pursue computer science as a major. The faculty
made strides to design activities and opportunities centered on students’ experiences,
both to help students connect to the content and to honor the value of their individual
experiences in support of their learning.
Although the 4Es work in tandem to fully open access to discourse communities, we
recognize the significance of the incremental progress that was made when we focused
on engagement and elicitation/engineering. Thus, we would recommend beginning with
these two areas if one desires to use the 4Es framework to make small changes in their
curriculum and instruction. Specifically, the seven disciplinary practices of engagement
are: (1) framing questions/problems, (2) working with data, (3) using varied media to
consult and produce multiple texts, (4) analyzing, summarizing, and synthesizing data
into findings related to the questions posed, (5) examining and evaluating one’s claims and
the claims of others, (6) communicating claims orally or in writing, and (7) using the cycle
of disciplinary practices to provide robust opportunities for educators and CSE faculty to
consider centering students and shifting the power dynamics in favor of students in their
ICS courses.
Future research could extend this work by using the 4Es heuristic to redesign ICS courses
or other courses in CSE. It would also be beneficial to continue working with faculty that are
instructors of CSE courses to investigate how to support them through the process of shifting
their curricula and pedagogies. Researchers may also consider eliciting pre- and post-student
evaluations and feedback on their experiences as students in the CSE courses that are designed
using the 4Es heuristic. It may be particularly useful to consider the perspectives of students
who are uniquely impacted and marginalized by inequities in CSE.
Author Contributions: Conceptualization, F.D.S., S.M.R. and J.M.; methodology, F.D.S., S.M.R. and
B.C.; formal analysis, F.D.S., S.M.R., B.C.; investigation, K.L., J.R. and J.M.; writing—original draft
preparation, F.D.S. and S.M.R.; writing—review and editing, F.D.S. and S.M.R.; visualization, F.D.S.
and S.M.R. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The raw data supporting the conclusions of this article will be made
available by the authors on request.
Conflicts of Interest: The authors declare no conflicts of interest.
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