Empowering K-12 Students With Disabilities to Learn Computational Thinking and Computer Programming

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TEACHING ExcEptional childrEn | SEptEmbEr/octobEr 2015 45
TEACHING Exceptional Children, Vol. 48, No. 1, pp. 45 –53. Copyright 2015 The Author(s). DOI: 10.1177/0040059915594790
Mr. Rose, a third grade general
education teacher, and Ms. Smith,
a special education teacher, co-teach
in an urban elementary school with
a high number of students receiving
free or reduced-price lunch. The school
integrates computer science and
computational thinking into curriculum
as part of their science, technology,
engineering, and mathematics (STEM)
initiative. Mr. Rose and Ms. Smith have
identified several challenges they will
need to address to meet the needs of
several of their students with learning
disabilities. These challenges include
difficulty with complex, multistep
problem solving, lack of access to
and experience with technology, and
difficulty with fine motor skills.
There is an increased focus on
including computing and
computational thinking in K-12
instruction within science, technology,
engineering, and mathematics (STEM)
education and to provide that
instruction in ways that promote access
for students traditionally
underrepresented in the STEM fields,
such as students with disabilities
(Israel, Pearson, Tapia, Wherfel, &
Reese, 2015). Several reasons drive this
focus on computing for a broad range
of learners. First, of all the STEM fields,
the greatest demand for workers exists
in computer science. In fact, the U.S.
Department of Labor has estimated
that there will be 1.4 million job
openings for computing-related jobs by
2020, but at the current rate of people
being prepared for those positions,
only approximately 30% of those
positions will be filled (Bureau of
Labor Statistics, U.S. Department of
Labor, 2014). The National Science
Foundation (2009) explained that
beyond traditional computer science
and programming positions, computing
is becoming necessary in other career
paths including journalism and the
creative arts. Second, Code.org (n.d.), a
nonprofit industry aimed at expanding
computing education opportunities in
K-12, has predicted that approximately
two thirds of all computing jobs will be
outside of the technology industry in
areas such as banking, retail,
government, entertainment,
manufacturing, and health care. Thus,
the demand for workers who are
skilled in computing will be across
industries. In addition to the pipeline
rationale, there are several instructional
benefits for students that result from
the inclusion of computing within K-12
programs. These include:
 Creating real-world applied contexts
for teaching mathematics,
algorithmic problem solving, and
collaborative inquiry (Fessakis,
Gouli, & Mavroudi, 2013; Jona et al.,
2014)
 Building higher-order thinking skills
(Kafai & Burke, 2014)
 Increasing collaborative problem
solving (Kafai & Burke, 2014)
 Increasing positive attitudes about
computer science and computer
science skills (Baytak & Land, 2011;
Lambert & Guiffre, 2009)
Thus, providing computing experiences
for K-12 students with and without
disabilities can open the doors to
multiple career paths and provide
broad educational benefits.
Despite national attention on
computer science, many teachers have
naïve conceptions about what
computational thinking and computing
entails because computing has not yet
been fully integrated into teacher
594790TCXXXX10.1177/0040059915594790Council for Exceptional ChildrenTeaching Exceptional Children
research-article2015
Empowering K–12
Students With Disabilities
to Learn Computational
Thinking and Computer
Programming
Maya Israel, Quentin M. Wherfel, Jamie Pearson, Saadeddine Shehab, and Tanya Tapia
Original Article
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46 council for ExcEptional childrEn
There is an increased focus
on including computing
and computational
thinking in K-12
instruction within science,
technology, engineering,
and mathematics (STEM)
education and to provide
that instruction in ways
that promote access for
students traditionally
underrepresented in the
STEM fields.
preparation. To address this confusion,
the Computer Science Teachers
Association and International Society
of Technology in Education (2011)
broadly defined computational thinking
as a “problem-solving process” that
includes
formulating problems in a way that
enables us to use a computer and
other tools to help solve them;
logically organizing and analyzing
data; representing data through
abstractions such as models and
simulations; automating solutions
through algorithmic thinking (a
series of ordered steps); identifying,
analyzing, and implementing
possible solutions with the goal of
achieving the most efficient and
effective combinations of steps and
resources; and generalizing and
transferring this problem solving
process to a wide variety of
problems. (p. 1)
It can be gathered from this definition
that students with disabilities who
struggle with complex problem
solving, mathematics, and abstract
reasoning may face numerous
challenges when presented with
instruction in computing. For
example, students with disabilities
may struggle with abstract computing
processes such as a multistep
procedure for using “if, then”
commands and with new vocabulary
such as algorithm (Israel et al., 2015).
Consequently, the national focus on
increasing computer science and
computing education directly
influences the work of special
educators. Teachers working with
students with disabilities must now
consider how to best support their
learners within these inclusive
educational environments so that
they can meaningfully engage in and
benefit from computing education.
How Is Computing Typically
Taught in K-12 settings?
There are many ways to integrate
computing education into K-12
instruction, and the resources to
support this instruction continue to
grow. Computing education may
involve either linear progression
through discrete computing skills with
tutorial software that teaches
computing (e.g., Code.org or the Khan
Academy) or open exploration/inquiry
where students and their teachers use
programming software for their
instructional purposes. Younger
students often begin learning
computing (i.e., how to use a
computer) and programming (i.e., how
to code) with graphically intuitive tile-
based software such as the open-source
software Scratch. Older students may
begin with these same programs or
learn how to program within
professional programming languages
such as Java or Python. Table 1
provides a list of popular computing
and programming curricula used in
K-12 settings, and Figure 1 provides an
example of an elementary student’s
project within Scratch. In addition to
teaching computing in isolation,
computer science instruction can also
be integrated into the content areas,
especially in math and science. For
example, when teaching geometry,
students can program animations for
different polygons. Israel and
colleagues (2015) found that
elementary school teachers often
integrated computing into content area
instruction due to a lack of dedicated
time for computing instruction.
Strategies That Increase
Access and Engagement in
Computing Education
Teaching Computing Through the
UDL Framework
Universal design for learning (UDL) is an
instructional planning framework for
meaningfully engaging a range of
learners, including students with
disabilities, by proactively addressing
barriers to learning (Center for Applied
Special Technology [CAST], 2011; Rose &
Meyer, 2002). There is a growing body of
research demonstrating the educational
efficacy of teaching through the UDL
framework (e.g., Marino et al., 2014;
Rappolt-Schlichtmann et al., 2013).
Within the context of computing
education, UDL can serve as the
instructional framework in which
teachers can embed the necessary
supports, technologies, and strategies
that lead to effective instruction for a
broad range of learners. Table 2
showcases how the UDL principles,
guidelines, and checkpoints can support
accessible computing instruction.
UDL encompasses three central
principles that can be applied to
computing education.
1. Multiple means of representation.
Principle 1 emphasizes that teachers
should present instruction in
multiple ways so that students have
different methods of accessing that
information. During computing
instruction, this principle is critical
as students often benefit from a
variety of different presentation
methods. Depending on their needs,
students can observe the teacher
model the use of computing software
(such as Scratch or Alice), see the
code that the teacher created
afterward, watch videos and demos
of that code used online, or break
apart existing code that their teacher
modeled. For some students a
combination of these engagement
options or variation in sequence of
presentation is required.
2. Multiple methods of action and
expression. Principle 2 emphasizes
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TEACHING ExcEptional childrEn | SEptEmbEr/octobEr 2015 47
the use of multiple methods for
allowing students to express their
understanding. In computing, this
principle can be achieved fairly
effortlessly because computing
activities inherently have flexibility
built into them. There is not
typically only one way of coding or
demonstrating understanding of that
code. Students can use
programming software in different
ways including creating their own
projects, replicating the teacher’s
program, expanding on the teacher’s
program, or using templates that the
teacher created with partially
created codes. They can also explain
how they designed their program
and provide directions to help peers
replicate their programs.
3. Multiple ways to engage students.
Principle 3 asserts that teachers
should include multiple options for
engaging students. Teachers can do
so by providing choices in computing
projects that involve the same skills
in different way and encouraging
collaboration. It is also important to
include culturally relevant computing
activities such as highlighting careers
of computer scientists with different
cultural backgrounds, genders, and
disabilities and helping students
Table 1. Computing Tools and Curricula
Resource Type and curricular aims
Scratch
http://scratch.mit.edu/
Software; tile-based, open-inquiry programming. Main topics cover both specific
operations and project-based instruction with a primary focus on how to use the
operations.
Alice
http://www.alice.org/index.php
Software; tile-based, open-inquiry programming. It is a 3D programming environment
focused on creating animations and story telling. It is focused on fundamental
programming concepts.
Code.org
http://code.org
Web site tutorials; basics of programming in a gamified linear “level up” process
of solving increasingly complex puzzles. Topics include fundamental concepts,
JavaScript, unplugged activities, and tutorial apps on a variety of platforms, app
development, and an “other” section.
Khan Academy
https://www.khanacademy.org
Web site tutorials; basics of programming through advanced Java Script, HTML, and
CSS including drawing, games, and simulations. There is also content about computer
science careers.
CS Unplugged
http://csunplugged.org
Web site unplugged computing lesson plans; activities that introduce students to
computer science concepts such as binary numbers, algorithms, and data compression
through play with cards, strings, cups, and other manipulatives.
Figure 1. Screenshot of an elementary student’s project in Scratch
Note. Scratch is developed by the Lifelong Kindergarten Group at the MIT Media Lab. See http://scratch.mit.edu.
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48 council for ExcEptional childrEn
make connections between
computing and their own lives.
Teachers can also provide different
learning options. Some students may
prefer “level up” practice within
gamified tutorial programs such as
Code.org and others may like to
practice those skills in a more open
exploration using software such as
Scratch.
Strategies to consider computing
through the UDL framework are
provided in Table 2.
Balancing Explicit Instruction With
Open-Inquiry Activities
Explicit instruction is a systematic and
direct approach to teaching. This type
of instruction has been demonstrated
as effective for students with learning
disabilities and others who struggle
with following multistep directions
within complex tasks inherent in
computing activities (Israel et al.,
2015). In their book, Archer and
Hughes (2011) researched 16 elements
of explicit instruction illustrating
roughly 30 years of evidence-based
instructional strategies. Table 3 offers
several of these strategies and how
they can be applied for computing
instruction.
Explicit instruction can reduce
students’ frustrations in computational
tasks because each step is explained
concisely and monitored until students
have mastered the step. Allowing
students ample opportunities to
develop and practice skills that have
been taught is an essential component
of delivering effective instruction. With
that said, it is important to balance
explicit instruction of discrete skills
with open-ended inquiry for students
to have the opportunity to use skills
learned through explicit instruction to
engage in open-ended, problem-solving
computing tasks (Israel et al., 2015;
Kafai & Burke, 2014).
The balance between explicit
instruction and more open computing
instruction can be a challenge for
teachers. Explicit instruction can be
either provided prior to open-inquiry
activities or embedded within those
activities. Israel and colleagues (2015)
described a model wherein teachers
cycled through computing mini-lessons
followed by short periods of open
exploration. Through this process,
teachers provided explicit instruction that
allowed for more successful open inquiry
for students who needed that level of
support. Israel and colleagues described
one teacher, for example, who modeled
how to animate an object in Scratch and
provided step-by-step directions on the
interactive white board. She then had
students use those skills within a
constrained inquiry activity wherein
Table 2. Teaching Computing Through the UDL Framework
Multiple means of representation
Multiple means of action and
expression Multiple means of engagement
Provide options for perception
 Model computing using an
interactive whiteboard, videos
 Give access to modeled code while
students work independently
 Provide access to video tutorials of
computing tasks
Provide options for physical action
 Provide teacher’s codes as templates
 Include CS Unplugged activities
that show physical relationship of
abstract computing concepts
 Use assistive technology including
larger/smaller mice, touch-screen
devices
Provide options for recruiting interest
 Give students choices (choose
project, software, topic)
 Allow students to make projects
relevant to culture and age
 Minimize possible common
“pitfalls” for both computing and
content
Provide options for language
mathematical expressions, and symbols
 Teach and review content specific
vocabulary
 Teach and review computing
vocabulary (e.g., code, animations,
computing, algorithm)
Provide options for expression and
communication
 Give options of computing software
and materials (e.g., Scratch,
Code.org, Alice)
 Give opportunities to practice
computing skills and content
through projects that build prior
lessons
Provide options for sustaining effort
and persistence
 Remind students of both computing
and content goals
 Provide support or extensions for
students to keep engaged
 Encourage peer collaboration by
sharing products
Provide options for comprehension
 Activate background knowledge by
making computing tasks interesting
and culturally relevant
 State lesson content/computing
goals
 Encourage students to ask questions
as comprehension checkpoints
Provide options for executive functions
 Guide students to set goals for long-
term projects
 Record students’ progress (have
planned checkpoints during lessons
for understanding and progress for
computing skills and content)
Provide options for self-regulation
 Communicate clear expectations for
computing tasks, collaboration, and
seeking help
 Develop ways for students to self-
assess and reflect on own projects
and those of others
Note. UDL = universal design for learning. For more information on UDL principles, see www.cast.org.
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students had choice in what they could
animate but had to use the discrete skills
she modeled. Finally, once the students
demonstrated proficiency in those skills,
they had options for independent
practice within more open computing
activities of their choice.
It should be noted, in computing,
students will know if they used code as
intended based on whether the
inputted code produces the expected
outcome. This is different from other
areas of instruction (such as writing a
grammatically correct paragraph)
because in traditional instruction, the
students may not always know if their
work is correct. Table 3 provides
strategies that account for this inherent
feedback within computing.
Mr. Rose and Ms. Smith are planning
to teach students about the process of
corn and soy production. To integrate
computational thinking with this content
goal, the teachers decide to engage
students in writing programs for a seed to
travel through a food production maze
using Scratch. Ms. Smith suspects that
students with learning disabilities will
struggle with “if, then” codes required to
complete this assignment. She, therefore,
models writing such a code explicitly, and
she leaves her example on the interactive
whiteboard for the students to view as
they create their mazes. Once students
finish writing their codes, they can either
continue to embellish their maze by
adding more features or discuss their
finished product with peers to gain new
perspectives and feedback.
Encouraging Student-to-
Student Collaboration
Computing is often highly collaborative
because of the focus on creativity and
finding solutions to ambiguous or ill-
defined problems. As in other areas of
student collaboration, students with
disabilities and their peers may need to
be taught the necessary skills to work
successfully in collaborative
environments. McMaster and Fuchs
(2002), in their review of collaborative
learning studies, described multiple
training procedures to prepare students
for collaborative learning activities. One
cannot assume that students will know
how to ask a peer for help when
problems occur or that they know how
to offer support to a peer who is
struggling in a manner that promotes
skill acquisition and independence.
Teachers can facilitate these interactions
through cooperative learning strategies.
Computing is often highly
collaborative because of
the focus on creativity and
finding solutions to
ambiguous or ill-defined
problems. Like in other
areas of student
collaboration, students
with disabilities and their
peers may need to be
taught the necessary skills
to work successfully in
collaborative
environments.
Table 3. Explicit Instruction in Computing Education
Select explicit instruction elements within computing
education Examples
Focus instruction on critical content by teaching skills
and concepts associated with the big ideas in computing
education.
Begin lesson with clear goals and expectations and review
prior learning. Provide content and computational goals.
Provide step-by-step demonstrations that break down
complex tasks. Model procedures the way you want the
student to perform the skills.
Use clear and easy-to-understand language that is consistent.
Avoid or clarify terminology that is ambiguous or confusing.
Provide numerous opportunities for guided practice. Provide
more scaffolds in the beginning and reduce scaffolds as
student is mastering the material.
Carefully monitor student performance and use data to
decide when to adjust and intervene to facilitate student
mastery.
Provide immediate and corrective feedback. Students
recognize errors if their code does not produce expected
results.
Decide which computational skills to teach such as
animating objects.
Give real-world example of the computing tasks in the
lesson to showcase its importance.
Model step by step the code that students will use and do
example on the interactive whiteboard.
When using language such as scripts and coding, provide
definitions and use these consistently in instruction.
Include support during computing time as students try new
scripts and skills. Encourage risk taking and independent
problem solving.
Based on student work and products, teachers note where
students had difficulties to address in the next class.
When students expect code to produce an animation and it
does not, the teacher can ask guiding questions that lead to
correction, provide a scaffold in finding solutions, or model
correct code for the student.
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50 council for ExcEptional childrEn
Cooperative Learning
Cooperative learning involves students
working together to help each other
learn content and discover new
information (Slavin, 1991). It requires
active student involvement and relies
on student interaction as a primary
means for promoting complex
reasoning, critical thought, and the
development of problem-solving skills
(Rose, 2004). It can span across all
grade levels from elementary through
high school and fits well within the
context of computing education. For
example, teachers can form groups and
assign roles for students to program.
Roles could include animation leader,
content leader, coding leader, and
sound effects leader.
Research indicates that successful
cooperative learning is dependent on
individual accountability and group
rewards (Slavin, 1991). Individual
accountability requires each member of
the group to perform an individual task
that contributes to the overall
completion of the assigned group goal
(Johnson & Johnson, 1999). Group
rewards is a form of recognition to all
team members upon the successful
completion of the task. Both factors can
be the incentives for students to actively
participate in cooperative learning.
McMaster and Fuchs (2002) found
that individual accountability and
group rewards have the potential to
increase achievement of students with
learning disabilities. The roles of
students with disabilities should
capitalize on their strengths and allow
for modified expectations if necessary.
For example, if they are collaboratively
creating a game in Scratch, students
who struggle with planning multistep
projects may require preplanning with
the teacher to determine individual
goals prior to the group collaboration.
Student-to-Student Help Seeking
When students cannot find a solution
to a computing problem, they often get
frustrated and want the teacher or
another student to help them find
solutions. To get students to articulate
those problems effectively, studies such
as Webb, Ing, Kersting, and Nemer
(2006) and Karabenick and Dembo
(2011) recommended providing
students with specific prompts to
encourage them “to give elaborated
explanations, to explain materials in
their own words, and to explain why
they believe their answers are correct
or incorrect” (Webb et al., 2006, p. 81).
Accordingly, teachers can encourage
collaborative discourse that provides
students with language to assist them in
seeking and giving help. For example,
Park and Lash’s (2014) collaborative
discussion framework (see Figure 2)
encourages students to collaborate
during computing activities. This
framework guides student conversations
through four questions when they are
stuck on difficult task: (a) What are you
trying to do? (b) What have you tried
already? (c) What else do you think you
can try? And (d) what would happen if
. . . ? (see Figure 2). This framework
should be explicitly taught to students as
a strategy to seek help from other
students before asking the teacher.
Mr. Rose and Ms. Smith encourage
collaborative problem solving in their
classroom. They introduced their
students to the Collaborative Discussion
Framework as a tool that promotes
collaborative problem solving and
reduces learned helplessness and
overreliance on teacher assistance.
Figure 2. Example of Collaborative Discussion Framework classroom
poster
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TEACHING ExcEptional childrEn | SEptEmbEr/octobEr 2015 51
Students with disabilities use this
framework as a prompt to seek help
from their peers without feeling
embarrassed for not knowing how to
solve the problem.
Experiment With Different
Software and Hardware to
Increase Accessibility
To include a broad range of learners in
computing, teachers should consider
whether the software and hardware that
the students access present barriers to
learning and participation. For example,
students with fine motor difficulties may
struggle with using a mouse. Because of
these barriers, teachers must examine the
accessibility of the hardware and
software their students use.
Assistive technologies (AT) and
instructional technologies (IT) go
hand in hand when considering
access issues during computational
thinking activities. Students with
disabilities who have access to AT
during traditional instruction that
includes technology (such as word
processing) will likely need access to
these technologies during
computational thinking instruction.
The same type of process for making
AT considerations in traditional
instructional areas should be afforded
to computational thinking instruction.
For example, teachers and
individualized education program
teams make AT determination
decisions based on students’ needs
and abilities, the required tasks, and
the learning environment. These same
areas should be considered during
computational thinking instruction.
Ms. Smith observed Thomas, a
student with fine motor difficulty. She
noticed that although he loves engaging
in computer-based learning, he is not
engaged in the planned computing
activity. Upon further observation, she
noticed that he had difficulty with
dragging the coding tiles and making
changes within those tiles. She first
gave him a different mouse to use, but
he still had a difficult time navigating
Scratch. She then allowed Thomas to
use the interactive whiteboard to do his
computing with his hands rather than a
mouse, which was much more effective
for Thomas.
Other tools that teachers can use to
respond to student challenges include
the following:
 Fine motor challenges: touch-screen
computers with either different
styluses or using finger gestures,
different size mouses, or the use of
interactive whiteboards
 Memory challenges: video tutorials
readily available or video models
created by the teacher, peers, or the
participating students
 Complex problem-solving challenges:
experiment with different software
that provide both linear and open
computing activities. For example,
Code.org and Khan Academy offer
linear lessons, whereas Scratch and
Alice offer a more open platform for
using those skills.
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52 council for ExcEptional childrEn
Final Thoughts
There are many strategies special
educators can employ to increase
opportunities for students with
learning disabilities to succeed in
computing education. Because
computing education is a new area of
instruction, many special educators
may not know how to provide support
to students as they learn computing. In
this article, several strategies and
resources were outlined that special
educators can implement to support
students who find computing
challenging. These instructional
practices should be considered
alongside the individual needs of each
student to develop meaningful,
engaging, and accessible computing
experiences for students with
disabilities.
References
Archer, A. L., & Hughes, C. A. (2011).
Explicit instruction: Effective and efficient
teaching (What works for special-needs
learners). New York, NY: Guilford.
Baytak, A., & Land, S. M. (2011). An
investigation of the artifacts and process
of constructing computers games
about environmental science in a fifth
grade classroom. Education Technology
Research Development, 59, 765-782.
doi:10.1007/s11423-010-9184-z
Bureau of Labor Statistics, U.S. Department
of Labor. (2014). Occupational
outlook handbook, 2014-15 edition:
Computer and information research
scientists. Retrieved from http://
www.bls.gov/ooh/computer-and-
informationtechnology/computer-
andinformationresearch-scientists.htm
Center for Applied Special Technology.
(2011). About UDL. Retrieved
from http://cast.org/publications/
UDLguidelines/version1.html
Code.org. (n.d.). Make computer science in
K-12 count! Retrieved from http://Code.
org/files/convince_your_school_or_
state.pdf
Computer Science Teachers Association and
International Society of Technology in
Education. (2011). Operational definition
of computational thinking for K-12
education. Retrieved from http://csta.
acm.org/Curriculum/sub/CurrFiles/
CompThinkingFlyer.pdf
Fessakis, G., Gouli, E., & Mavroudi, E.
(2013). Problem solving by 5-6 years
old kindergarten children in a computer
programming environment: A case
study. Computers & Education, 63, 87-
97. doi:10.1016/2012.11.016
Israel, M., Pearson, J., Tapia, T., Wherfel,
Q., & Reese, G. (2015). Supporting all
learners in school-wide computational
thinking: A cross case analysis.
Computers & Education, 82, 263-279.
doi:10.1016/j.compedu.2014.11.022
Johnson, D. W., & Johnson, R. T. (1999).
Making cooperative learning work.
Theory into Practice, 38, 67-73.
doi:10.1080/00405849909543834
Jona, K., Wilensky, U., Trouille, L.,
Horn, M., Orton, K., Weintrop, D., &
Beheshti, E. (2014, January). Embedding
computational thinking in science,
technology, engineering, and math
(CT-STEM). Paper presented at the
Future Directions in Computer
Science Education Summit Meeting,
Orlando, FL.
Kafai, Y. B., & Burke, Q. (2014). Connected
code: Why children need to learn
programming. Cambridge, MA: MIT Press.
Karabenick, S. A., & Dembo, M. H. (2011).
Understanding and facilitating
self-regulated help seeking. New
Directions for Teaching and Learning,
126, 33-43. doi:10.1002/tl.442
Lambert, L., & Guiffre, H. (2009). Computer
science outreach in an elementary
school. Journal of Computing Sciences in
colleges, 24(3), 118-124.
Marino, M. T., Gotch, C. M., Israel,
M., Vasquez, E., Basham, J. D., &
Becht, K. (2014). UDL in the middle
school science classroom: Can video
games and alternative text heighten
engagement and learning for students
with learning disabilities? Learning
Disability Quarterly, 37, 87-99.
doi:0731948713503963
McMaster, K. H., & Fuchs, D. (2002). Effects
of cooperative learning on the academic
achievement of students with learning
disabilities: An update of Tateyama-
Sniezek’s review. Learning Disabilities
Research & Practice, 17, 107-117.
doi:10.1111/1540-5826.00037
National Science Foundation. (2009).
A week to focus on computer science
education (Press Release 09-234).
Retrieved from http://www.nsf.gov/
news/news_summ.jsp?cntn_id=116059
Park, M., & Lash, T. (2014). The
collaborative discussion framework.
Champaign, IL: Unit 4 School District.
Retrieved from http://ctrlshift.mste.
illinois.edu/2015/04/03/collaborative-
discussion-framework/
Rappolt-Schlichtmann, G., Daley, S. G.,
Lim, S., Lapinski, S., Robinson, K.
H., & Johnson, M. (2013). Universal
Design for Learning and elementary
school science: Exploring the efficacy,
use, and perceptions of a web-based
science notebook. Journal of Educational
Psychology, 105, 1210-1225.
Rose, D. H., & Meyer, A. (2002). Teaching
every student in the digital age: Universal
Design for Learning. Alexandria, VA:
ASCD.
Rose, M. A. (2004). Comparing productive
online dialogue in two groups styles:
Cooperative and collaborative. American
Journal of Distance Education, 18(2),
73-88.
Slavin, R. E. (1991). Student team learning:
A practical guide to cooperative
learning. Washington, DC: National
Education Association. doi:10.1016/j.
ijer.2004.06.011
Webb, N. M., Ing, M., Kersting, N., &
Nemer, K. M. (2006). Help seeking
in cooperative learning groups. In S. A.
Karabenick & R. S. Newman
(Eds.), Help seeking in academic
settings: Goals, groups, and contexts
(pp. 45-88). Mahwah, NJ: Lawrence
Erlbaum.
Maya Israel, Assistant Professor, Quentin
M. Wherfel, doctoral student, Jamie
Pearson, doctoral student, Saadeddine
Shehab, doctoral student, and Tanya
Tapia, Master’s student, University of
Illinois at Urbana-Champaign.
Address correspondence concerning this
article to Maya Israel, University of Illinois
at Urbana-Champaign, 1310 S. 6th St., 276B
Education Building, Champaign, IL 61820
(e-mail: misrael@illinois.edu).
TEACHING Exceptional Children,
Vol. 48, No. 1, pp. 45–53.
Copyright 2015 The Author(s).
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