Betul C. Czerkawski, PhD.
The University of Arizona South bcozkan@email.arizona.edu
Abstract : Computational thinking (CT), while not a new concept, is becoming an increasingly important analytical skill that every child should master in order to be successful in the digital age. Computational thinking refers to a new set of problem solving strategies to tackle today’s complex issues. Because it is a thinking skill, CT has to be incorporated throughout the curriculum and span all academic areas rather than being isolated in the computer science curriculum. This paper presents a study where instructional designers are surveyed in instructional design strategies that could be used for CT integration in instruction.
“Computational thinking (or commonly referred as CT) involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science” (Wing, 2006, p. 33).
Originally from computer science, CT is increasingly becoming a multi-disciplinary skill where learners of the digital age use methods and thinking skills that are commonly used by computer scientists to solve complex with the use of computers. For instance, a person thinking computationally knows that complex and data-intensive information requires the use of algorithms to create automated solutions.
At the K-12 level, an operational definition of CT has been introduced by the International Society for Technology in
Education (ISTE) as “problem-solving process that includes (but is not limited to) the following characteristics:

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,

Identifying, analyzing, and implementing possible solutions with the goal of achieving the most efficient and effective combination of steps and resources,

Generalizing and transferring this problem-solving process to a wide variety of problems” (ISTE, 2011).
If CT is a cross-curricular subject that has to be infused throughout the curriculum, the issue becomes the way it’s conceptualized and implemented by the instructional designers. The purpose of this study is to answer this question by surveying instructional designers.
Instructional Design : Instructional design, as a field, deals with the learning and teaching processes and environments and ways to provide most efficient and effective instructional experiences to the learners. In order to achieve this goal, instructional designers start with the analysis of the learners, then determine learning goals, arrange learning activities and finally develop and implement assessment procedures. All these activities are driven by the learning theories and instructional methods and strategies.
Using instructional design (ID) theory as a guide to instructional development process is essential for a number of reasons. First, ID ensures quality for instruction. This means that content is delivered in the best possible manner, courses are consistent with each other, and learning outcomes are not coincidental but planned and expected. Second,
ID serves learners’ needs, expectations and desires so they can be successful in their pursuit of obtaining knowledge and developing skills.
There are many models that are used in instructional design but the most generic model is ADDIE (Analysis, Design,
Development, Implementation and Evaluation). (See Figure 1)

Figure 1: ADDIE Model
Instructional Design Process
Development Implementation Analysis
(Learners;
Learning
Outcomes; pedagogical considerations:
Adult Learning
Theory considerations;
Timeline)
Design
User interface; user experience; creating prototype; graphic design
Storyboarding; instructional materials; instructional technology
Instructor training; learner training; implementation of design
Evaluation
Formative evaluation; summative evaluation; feedback mechanisms
While all phases of instructional design are important, the first two phases, analysis and design, are where all the instructional planning occurs.
Computational Thinking : “Computational thinking provides intellectual tools to help manage information” (National
Research Council, 2010, p.41). These tools require using multiple step procedural thinking processes. A summary of these steps are provided in Figure 2 (ISTE, 2011).
Figure 2: Computational Thinking
Computational Thinking
Data Collection: The process of gathering appropriate information
Data Analysis: Making sense of data, finding patterns, and drawing conclusions
Data Representation: Depicting and organizing data in appropriate graphs, charts, words, or images
Problem Decomposition: Breaking down tasks into smaller, manageable parts (i.e. divide and conquer)
Abstraction: Reducing complexity and details to concepts
Algorithms & Procedures: Series of ordered steps taken to solve a problem or achieve some end
Automation: Having computers or machines do repetitive or tedious tasks
Simulation: Representation or model of a process.
Simulation also involves running experiments using models
Parallelization: Organize resources to simultaneously carry out tasks to reach a common goal
This study uses ADDIE Model as the theoretical framework of the study and survey instructional designers so best strategies to incorporate CT processes in instruction can be identified.
Higher order thinking skills including critical, creative thinking or problem solving have been studied widely by the instructional designers in the last two decades. However, there is a lack of research for computational thinking and the ways that it can be incorporated into the curriculum. This study attempts to generate ideas using the ADDIE instructional design model by surveying instructional designers. A qualitative questionnaire is employed in the data gathering. Twelve instructional designers were invited to participate in this study and 6 of them responded. These instructional designers were asked to mainly focus on analysis and design phases of instructional design process and provide suggestions and ideas as how to design instruction for promoting computational thinking.

The qualitative questionnaire that is used for data gathering includes 18 open-ended questions along with 2 demographic questions. The author validated the data gathering instrument with an expert faculty member who teaches computational thinking course in the computer science program. The results are presented below.
Demographics
Learners (Dispositions and
Characteristics)
Teaching Strategies
6 instructional Designers (2 of them are PhD students; 2 of them hold PhD and 2 hold Masters’ degrees)
Learner Characteristics

Students should have:
Not only Confidence, but also the Capacity to deal with complexity.
Not only Persistence, but also Patience in working with difficult problems
(and people)
Not only Tolerance, but also a Low Level of Frustration for ambiguity
Not only Open-Ended problems, but also dealing with Problems that have
Multiple Solutions
Not only a good Communicator, but also a Good Listener

Students should have:
Confidence in dealing with complexity—scaffolding/problem-based learning; using examples to model after
Persistence in working with difficult problems—practice and feedback.
Tolerance for ambiguity—Using more subjective assessments such as openended short answer questions and essays.
The ability to deal with open-ended problems—Using game-based learning to allow learner to implement a variety of strategies to solve the problem.
The ability to communicate and work with others to achieve a common goal or solution.—Incorporate collaborative learning strategies in all modalities
(face-to-face, online and hybrid).

Other student characteristics are:
Ability to think logically
Ability to think creatively
Ability to overcome problems and challenges
Ability to reflect on progress and learning

Thinking Skills Emphasized in CT are:
Critical thinking, implementation thinking, conceptual thinking, innovative thinking and intuitive thinking

Challenges : One participant mentioned following:
“A challenge during the learner analysis phase of instructional design is having sufficient time and resources to conduct a full analysis of the learners’ needs. In the real world, instructional designers have to go w/the best guess or anecdotal data to make their decision on the type of learning experience to develop for their target audience.
When analyzing learners to teach CT, the instructional designer should consider their proficiency in computers as well as their prior knowledge in applying critical thinking to computer skills.”
Strategies: One participants said:
“For teaching disposition, there are a few strategies that can help including: modeling the behavior, engaging and encouraging the student, seek opportunities to use CT in everyday life and lessons, and give insightful feedback as soon as possible to students.”

Learning Outcomes
Other strategies:
Encourage Mastery learning – set objectives, resources, guidance, reflection
Relevant, authentic, real life problems and/or case studies
Build analogies, cognitively visualize data
Build communities of learners
Challenges :
Keeping the student engaged and on track when this type of thinking may be difficult or near impossible.
Students not familiar with this method of teaching, learning and assessment
Expectations of progress and feedback
Problems with technology
Student time management - procrastination
Objectives: One participant stated following:
“In creating instructional objectives in general, and perhaps especially with CT, one needs to communicate in clear language targeting the group of learners.
What I mean to say is use terms that the audience will understand. If the audience is not familiar with computer terms, then use analogies to help them relate to the instructional objectives.”
Challenges :
 “The biggest limitation is the abstractions in defining CT and making sure everyone is on the same page when defining the term computation thinking.”

Instructional designers must recognize that computational thinking builds on the power and limits of computing processes, whether they are executed by a human or by a machine.

Having students reflect on how they breakdown problems and goals into small chunks and determining steps to solve or proceed.

Facilitating and assessing students communicating and working together requires access to technology and support.
 “How feasible is it to prepare students for global competitiveness and blend academics with real life? Our Educational System is still focused on remediating student with the basic skills they need to succeed (i.e., developmental education).”
Target Outcomes:
 “If I were teaching CT, my biggest learning outcome would be for students to demonstrate computational thinking by identifying the problems and solving the problems through a structured process. I would also want to give students the tools to think outside of the box to problem solve.”
 “My only outcome is to advance an instructor’s comfort level in utilizing technology”

Evaluate, Synthesize, Compare/contrast, design, create, critical thinking, social presence, persistence, grit
 “When looking at the operational definition for CT in K-12 education, the learning outcomes don’t seem to be easy at all. They seem like higher level concepts. But if I had to choose what seems to be the easiest, it would be logically organizing and analyzing data .”
Challenging Outcomes:
 “Abstract thinking is a hard learning outcome to pin down and teach.
Teaching a process is not difficult, but getting students to apply that process to various situations is challenging.”
 “Developing a methodological approach to developing instructional objectives is always a challenge; however, teaching computational techniques can be especially challenging because students are rarely

Pedagogical Considerations cognizant of their ability to develop an approach to problem solving using computational thinking.”

Dealing with ambiguity

Creativity

Formulating problems in a way that enables us to use a computer and other tools to help solve them; representing data through abstractions such as models and simulations; automating solutions through algorithmic thinking (a series of ordered steps)
Learning Theories:
Cognitive Load Theory.
Gagne’s Theory of Instruction.
Vygotsky’s Zone of Proximal Development (ZPD). neo-Vygotskian approach
Applied cognitive sciences – Expert/Novice
Cognition – information processing, analogies, chunking
Constructivism – experiential, simulations, relevance
Behavioral – reinforcement, repetition
Learner Centered – choice, personalization
Problem solving
Creative thinking
A combination of all four would be the best approach to use
Instructional Strategies:
Choose direction/application
Projects – research, evaluate, design, present, share, student feedback
Communities of practice – share research, ideas, problems, Questions/answers; feedback
Scaffolding
Modeling
Experiential learning
“I am not certain that a single instructional strategy will work best when teaching
CT. The pedagogical paradigm for the 21 st century is a distinct movement towards learner-centered instructing.”
Instructional Design Models:
ARCS Model of Motivation;
TCPAK
ADDIE because it’s the basic, fundamental model for all Instructional Design.
Adult Learning Considerations Learning Theories to Consider:
Malcolm Knowles Adult Learning Theory
Multiple Intelligences
Experiential Learning Theory
Elaboration Theory
Differences between Younger and Adult Students learning CT:

Adult – relate to experience and existing schemas of understanding, motivation and responsibility for learning,
Younger – need to make interesting, engaging, behavior design with positive/constructive feedback, gamification – (choice, rewards, experience points, level up), behavioral and learning discipline

Learning experience should encourage learner reflection and sharing of experiences and ideas
 “The most important distinctions would be to take into account adult learners’ prior knowledge, cultural background, skills and proficiency.”

User Experience
Instructional Prototype &
Curriculum Design
 “The major distinctions I find in working with the very young and adult learners is the “why am I learning this” question. Young children enjoy or are intrigued with learning in general. As we grow we want to know why I need to know this or how is this going to benefit me. So in teaching CT to adults we need to go a step further and show why CT thinking is important.”
Interface Considerations:

Visual Learning

Simulations

Importance of creating a safe environment where failing is OK
 “Learning environment for CT can foster a positive learning environment by developing basic objectives and assessments centered on expanding skills such as logic, creativity, algorithmic thinking. This category of skills provides an arena where the students have an opportunity to explore.”

Ease of use, familiar, apply visual cognition principles, fun, entertaining, rewarding, flexible simulations

To foster a positive learner experience, it depends on the design of the course (in any modality—F2F, hybrid and online), and how the instructor teaches it. Both work hand in hand to generate a positive environment.
Challenges:
 “The biggest hindrance to a positive learner experience is a complicated or confusing design. A positive beginning is a basic and easily explained beginning – complication should follow.”

Too many obstacles, negative or not enough feedback, misunderstanding of knowledge domain schemas, beliefs, previous experiences

Poor instructional design of a course, lack of learner-centered, active learning strategies being implemented, and lack of engagement from the instructor.
Prototyping
Having a detailed storyboard and audio script (if narration or audio files are used). The detailed storyboard should contain how each screen would function and engage the learner.
Instructional Design:

Using Gaming components to create user experiences

Better learning outcomes happen when there is a context, scaffolding and lots of supporting materials/strategies/resources to help the learner achieve the learning objective.
Assessment :

Presentations; Demonstrations; Explanations
 “Assessments are not “plug and play” but are designed with a focus on evidence.”
 “An effective instructional prototype in CT will involve an iterative process involving phases of development, pilot testing, and revision to produce instructional materials that will be useful as stand-alone curriculum modules or when collected into different packages to

support instruction.”

Tracking of data footprints, solicit and allow for readily providing feedback, reflections, formative/summative feedback, outcome/rubric evaluation, performance on standardized testing and professional certifications, performance and retention in field

Expert thinking, reflection, problem solving, critical thinking, communication/collaboration, social presence

A variety of assessment procedures should be considered, including formative and summative evaluation techniques or tools.
Visual and Multimedia Design Multimedia Considerations :

Visualization (e.g. concept maps)

Accessibility

Ease of Use

Intuitive/familiar interface
 “We still need to be specific about the definition of CT, especially in defining what problems can be solved using CT. It could vary, really, from simple (mathematical formula) to complex (programming algorithms).”
Technology & CT:

Design instruction

Facilitate access to instructional materials, resources, faculty/students

Track and assess learner behaviors, progress and outcomes

Mine, visualize, and predict models of learning
 “Technology always changes so it’s important to take that aspect into consideration when you think about computers supporting CT instruction. I don’t think it’s possible to teach CT without computers
(based on the operational definition of CT).”
Computational thinking is about approaching complex data and ideas using computers and offering solutions to the existing problems by using processes such as data gathering, manipulation, abstraction and automation. In today’s complex world, CT is a new way of solving problems and a required skill for students of all levels. According to
Phillips (2009) “learning activities that allow students to discover and explain scientific relationships, predict events, and learn procedural skills will enable them to better understand these subjects, to predict behavior, and to build computational thinking skills” (para. 4).
By identifying the most effective ways to integrate CT across the curriculum: 1. students can develop computational thinking skills, 2. instructors expand on the ways they teach thinking and problem solving skills including CT, 3. and instructional designers design learning environments most effective to teach CT. In addition, the most effective assessment procedures can be identified for CT. The results of this study could also be used to create illustrative case studies to improve the design of CT instruction.
ISTE (2011). Computational thinking teacher resources. Retrieved from http://www.iste.org/learn/computationalthinking/computational-thinking_toolkit.aspx.
National Research Council of the National Academies (2010). Report of workshop on the scope and nature of computational thinking . Washington, D.C.: National Academies Press.

National Research Council of the National Academies (2011). Report of workshop of pedagogical aspects of computational thinking. Washington, D.C.: National Academies Press.
Phillips, P. (2009). Computational thinking: A problem-solving tool for every classroom. Retrieved from http://education.sdsc.edu/resources/CompThinking.pdf.
Wing, J. M. (2006, March). Computational thinking. Communications of the ACM. 49 (3). pp. 33-35.