Research Study Summary: Computational Thinking

Introduction   

      Computational Thinking (CT) is a problem-solving process that can involve technology, but technology is not necessary for CT to occur.  Its roots are in computing (using computers) and people often use the terms interchangeably, creating a giant misconception about what CT actually is.  Although we are in an information-based society and people are becoming more tech-savvy (and gadget-savvy), CT goes much deeper than the smart devices that have become necessary appendages.    

     The general consensus in the literature defines CT as “the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can  be effectively carried out by an information processing agent…these agents can be computers or humans, or a combination of both” (Yadav, Mayfield, Zhou, Hambrusch, & Korb, 2014).  Even though this definition is accepted and agreed upon, the bulk of the literature revolves around computing and CT.  CT is a problem-solving process and problems are solved across the curriculum, beyond what occurs in the computer lab within the computer sciences curriculum.  “…computational-thinking concepts have been used in other disciplines via problem solving techniques, and that the ability to think computationally is essential to every discipline” (2014).

     Problem-solving is a key area of focus in content standards across curriculums.  When unpacking the components of what problem-solving is, CT is present.  When observing the major tenets of CT, problem-solving is present.  One does not exist without the other.  No one disagrees that CT skills are important in K-12 education.  In fact, there is a push to familiarize students with CT as early as possible.  “To reading, writing, and arithmetic, we should add computational thinking to every child’s analytical ability” (Yadav, Mayfield, Zhou, Hambrusch, & Korb, 2014).”  This will require a teacher professional development on CT and an overall shift in the current understanding of CT. 

Statement of the Problem

     Currently, few studies exist on embedding CT across the curriculum.  “…in order to maximize the…benefits of computational thinking and get students interested in computing, we need to integrate CT in core content areas at the K-12 level” (Yadav, Mayfield, Zhou, Hambrusch, & Korb, 2014).  To meet this challenge at its apex, the study aimed to prepare preservice teachers to “present CT ideas in explicit ways” to their future K-12 students.  Prior research indicated that “introducing teachers to computational thinking can change their attitudes towards computing as well as raise their understanding of CT as an approach to solving problems…these efforts, however, need to involve content-area teachers and not just computer science teachers” (2014).
Significance of the Problem

     The misconception that CT solely deals with the use and understanding of computers deters teachers from understanding its importance and implementing it in their classrooms.  Many teachers are unfamiliar with CT, solely because they think it belongs in the computer sciences curriculum.  It is important to redirect CT misconceptions into tangible classroom experiences that facilitate the strengthening of students’ CT skills.  “…the goal of teaching computational thinking is to teach [students] how to think like an economist, a physicist, an artist, and to understand how to use computation to solve their problems, to create, and to discover new questions that can fruitfully be explored and not for everyone to think like a computer scientist” (Yadav, Mayfield, Zhou, Hambrusch, & Korb, 2014).
Conceptual Framework

     The authors behind this study did not discuss a particular theory or concept that guided their research, but Problem-based Learning Theory (PBL) supports CT.  In PBL, the teacher facilitates student work through an authentic problem that is relevant to them. 

Basic PBL Model

1.      Identify, clarify, and describe the problem.

2.      Identify what is already known about the problem.

3.      Identify what is unknown at this point in the process.

4.      Identify possible solutions with the information that has been generated thus far.  Examine these solutions to determine if the answers seem correct.

5.      Identify the solution that is the best answer for the problem presented.  If there is not one, continue to search and develop possible solutions.

6.      Identify the solution to be presented and assess this solution (Fredrickson, McMahan, & Dunlap, 2013).  

     CT is present in the steps of a basic PBL model.  In the 2014 Horizon Report, key skills of CT are identified.  These skills include:

·         “Logical analysis and organization of data;

·         modeling, abstractions, and simulations;

·         identifying, testing, and implementing possible solutions; and

·         making complex ideas understandable with data visualization, imagery, succinct narrative, and other communication techniques” (Johnson, Adams Becker, Estrada, & Freeman, 2014).

Research Questions

1.       What is the influence of computational-thinking modules on preservice teachers’ understanding of computational thinking?

2.       What is the influence of computational-thinking modules on preservice teachers’ attitudes towards computing?

Methodological Approach

     Three hundred and fifty-seven preservice teachers from a Midwestern university participated in this study.  The control group had a total of 200 students enrolled in the introductory educational psychology course during Fall 2011, while the treatment group had 157 students enrolled in the introductory educational psychology course during Spring 2012.  A one-week module on CT was introduced in the course for the treatment group.  The module was developed jointly by faculty and graduate students from education and computer science.  Participants completed the Computational-Thinking Quiz, which was composed of three open-ended questions to assess students’ understanding of computational thinking.  Participants also completed the Computing Attitude Questionnaire in order to examine their attitudes towards computing.  The survey consisted of 21 Likert-type scale questions on the following scale: Strongly Agree, Agree, Disagree, and Strongly Disagree.  “The control group received the content typical for the course, which included lectures on higher-level cognitive processes, such as problem solving, transfer, critical thinking, and creativity. The treatment group…received the computational-thinking module.  Both groups completed the same quiz and computing attitude survey during the class one week later” (Yadav, Mayfield, Zhou, Hambrusch, & Korb, 2014).

Findings

     Aligned with the common misconception that CT and computing are interchangeable, control group participants “tended to include the use of computers as a necessary component in computational thinking” (Yadav, Mayfield, Zhou, Hambrusch, & Korb, 2014).  In contrast, the treatment group demonstrated an understanding of CT as a “cognitive tool that involved using computing concepts to solve complex problems with or without the use of computers” (2014).  On integrating CT into their future classrooms, the control group generally focused more on the technology-aspects of CT, while the treatment group focused on “critical thinking skills and how to use algorithms and heuristics” (2014).  The treatment group was more likely to answer “yes” as compared to the control group when asked if CT relates to other content areas, aside from computer science.  Although the findings highlighted the misconceptions of CT, it also showed that “there were no statistically significant differences between the control and the [treatment] groups with regards to their comfort and interest in computing” (2014).  Technology is already such an integral part of the lives of the preservice teachers who participated in the study, which consisted of mostly sophomores and juniors.

Conclusions and Implications  

     CT is a core 21^st century skill and it is important to go beyond the push to expose students in the K-12 realm as early as possible.  It is critical to foster intentional, explicit experiences with CT to give students the necessary time to understand CT and develop all of the skills it encompasses.  For this to happen, teachers need to go through this process.  “It is important that we develop teachers’ understanding of computational thinking in the context of the subject matter they teach. Unless their knowledge is developed in that context, teachers may only gain an abstract understanding of CT. As a result, their knowledge will remain inert and they will be unable incorporate it into their teaching” (Yadav, Mayfield, Zhou, Hambrusch, & Korb, 2014). 

     The authors of this study recommend that future work be collaborative between educators and computer scientists.  Concrete examples in literacy, mathematics, the sciences and the arts need to be developed.  Time must be invested in the development of CT pedagogy as well.  For CT to become an integral part of the K-12 realm, all stakeholders (policymakers, educators, and computer scientists) must be involved.  Because the study did not focus on the actual implementation of CT and there is a gap in the current literature, it is recommended that “future research should examine how teachers from a variety of disciplines incorporate [CT] practices in their own teaching” (2014). 

Resources

Fredrickson, R., McMahan, S., & Dunlap, K. (2013). Problem-based learning theory in the handbook of educational theories (pp.211-217). Charlotte, NC: Information Age Pub.

Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2014). NMC Horizon Report > 2014 K-12 Edition. Retrieved February 26, 2017, from http://www.nmc.org/publication/nmc-horizon-report-2014-k-12-edition/

Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. (2014). Computational thinking in elementary and secondary teacher education. Acm Transactions On Computing Education, 14(1).