CEREN OCAK

 

Domain of Knowledge- Significance  of the Study

 

The core of computational thinking is to bring solutions to problems in a process similar to computer processing (Grover & Pea, 2013). One of the reasons for that is the fact that computational thinking is historically associated with the computer science field (Denning, 2009). More recently, computational thinking (CT) has gradually been defined as a necessary skill in the age of technology and it has been broadened to include a variety of terms in subject domains such as engineering, medicine, and education (Benakli, Kostadinov, Satyanarayana, & Singh, 2017). Particularly, the recent research maps CT’s positive influence on mathematics learning (Calao et al., 2015).

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That is, computation has always engaged with mathematics; however, the introduction of computers has reframed the relationship between mathematics and computational thinking (Moursund, 2006). Particularly in mathematics education, there is an increasing trend towards adopting programming tools such as Scratch to increase computational thinking competencies, as is the mathematical thinking. Likewise, Catlin and Woollard (2014) count in educational robots as a substantial way of engaging learners in CT, which prioritizes the knowledge of programming to communicate with the robots. Accordingly, the issue to be addressed is how a technological tool, extraneous to the body, builds and frames mathematical cognition (Sfard & McClain, 2002) and if this influence would be traced.

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The change in mathematical cognition could be traced because mathematical thinking that is at the heart of robotics activity is highly embodied (Abrahamson & Lindgren, 2014). Being an embodied activity,  learning mathematics could be enhanced by the pure interaction with the environment, through the help of an external body.  Therefore, mathematical thinking, due to its embodied nature,  can be developed by engaging in robotics activities. All in all, the domain of knowledge for my study includes embodied cognition, computational thinking and how it connects to mathematics learning. 

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Goals of the Study- Research Questions

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The purpose of my study is to examine how children’s computational thinking embodied during robotics activity. Accordingly, the research questions of the study as follows:

(1) How learners reflect their abstract and automated thinking through their gesturing activity?

(2) How did learners gestures change over time as the robotics task becomes more challenging?

 

Data  Collection Plan through a Video Study

 

I plan to collect a  video data during the implementation of robotics curriculum. Here is the sketch of my video data collection plan:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fig. 1. The video set up.

 

 

References

 

Abrahamson, D., & Lindgren, R. (2014). Embodiment and embodied design. In The Cambridge handbook of the learning sciences (2nd ed., pp. 358–376).            

Cambridge University Press

Benakli, N., Kostadinov, B., Satyanarayana, A., & Singh, S. (2017). Introducing computational thinking through hands-on projects using R with applications to

calculus, probability and data analysis. International Journal of Mathematical Education in Science and Technology, 48(3), 393–427.

Calao, L. A., Moreno-León, J., Correa, H. E., & Robles, G. (2015). Developing Mathematical Thinking with Scratch. In Design for Teaching and Learning in a Networked

World (pp. 17-27). Springer International Publishing.

Catlin, D., & Woollard, J. (2014). Educational robots and computational thinking.  In 4th International Workshop Teaching Robotics, Teaching with Robotics & 5th

International Conference Robotics in Education (pp. 144–151).

Grover, S., & Pea, R. (2013). Computational Thinking in K-12: A Review of the State of the Field. Educational Researcher, 42(1), 38–43.

Moursund, D. (2006). Computational thinking and math maturity (1st ed.). Eugene, OR: David Moursund.

Sfard, A., & McClain, K. (2002). Analyzing tools: Perspective on the role of designed artifacts in mathematics learning. The Journal of the Learning            

Sciences, 11(2&3), 153–161