Gaming to LearnComputational Thinking

Jacqueline Leonard, Ph.D.Ruben Gamboa, Ph.D.Joy B. Johnson, Ph.D.

Background

uGame-iCompute• Funded by the National Science Foundation’s

Innovative Technology Experiences for Students and Teachers (ITEST) from 2013-2016

• Combines game design and culture to improve middle school students’ computational thinking (CT)

• Implemented in Rocky Mountain West

Project Goals

• To develop implement and study four components of an iterative intervention– Enhancing Students’ Attitudes toward STEM– Enhancing Students’ Computational Thinking– Professional Development for Teachers– Culturally Responsive Pedagogy

Rationale • Need for mathematicians and computer

scientists will grow by 22% and 24%, respectively

• Engineering growing faster than other professions

• Limited information about engineering and what the field entails

• Gaming and game design is a pathway to STEM careers

Messaging in the media…

Definition of CT

• According to the International Society for Technology in Education (ISTE), CT is a problem solving process that involves:– formulating problems– logical organization of data– representation of data through abstractions– Identifying and automating solutions through algorithmic

thinking– analyzing and implementing possible solutions– generalizing and transferring the problem solving process

The Research Study

• Pilot study on combined gaming/robotics conducted from 8-12 weeks

• Total of 133 students (35 females and 98 males; 35 underrepresented students) from seven schools in Wyoming

• AgentSheets (Scalable Game Design) used to learn programming and computational thinking

Theoretical Framework

• Saxe Model– Goals for learning

structured by common cultural practices

– Cognitive forms and functions created and used to reach goals

– Interplay across learning in different contexts

• Edelson (Learning for Use (LfU)) Model– Knowledge is

incremental– Learning is directed– Knowledge is situated– Procedural knowledge

supports conceptual knowlegde

Review of Literature

Computational Thinking• CT is a cognitive skill that all students can use

across disciplines in multiple contexts (Yadav et al., 2011)

• Digital game playing linked to development of CT (Barr et al., 2011; Repenning et al., 2010)

• Gaming linked to success in problem solving and social practices that support strategic thinking and data handling (Chang et al, 2012; Li, 2010)

Research Questions

1. How did student participants’ self-efficacy and STEM attitudes, and 21st Century Skills change as a result of the pilot study?

2. What computational thinking strategies did student participants demonstrate during computer game design?

3. What elements of culture and/or place did student participants exhibit in their game design?

Research Methodology

Mixed Methods Design• Quantitative and qualitative methods used to

collect data during the pilot study• Data Sources– Self-Efficacy in Technology and Science instrument

(SETS, Ketelhut, 2010) – Student Attitudes toward STEM survey (Friday

Institute for Educational Innovation, 2012)– 21st Century Skills – Student artifacts

Data Analysis

• Descriptive statistics used to show trends in student efficacy, STEM attitudes, and 21st Century skills

• Threshold analysis used to determine growth trends on above skills

• Rubric used to measure CT• Games analyzed for elements of culture and

place

Results

Findings: Year 1 Pilot Study

• Student self-efficacy on video gaming and computer use increased

• Student self-efficacy on computer gaming decreased slightly

• STEM attitudes were fairly stable• Measures for 21st Century Skills decreased

slightly

Self-Efficacy Constructs

Pre-Score Std. Dev Post-Score Std. Dev.

Video gaming (n=61) 3.93 0.36 3.91 0.95

Computer Gaming (n=67) 3.82 0.76 3.74 0.90

Computer Use (n=68) 4.01 0.74 3.79* 0.94

Table 1: Comparison of Pre-Post Self-Efficacy

Self-Efficacy in Technology and Science

STEM Domain Pre-Score Std. Dev Post-Score Std. Dev.

Mathematics (n=69) 3.76 0.83 3.66 0.91

Science (n=62) 3.20 0.70 3.25 0.74

Engineering/Technology

(n=67)3.74 0.79 3.70 0.90

21st Century Skills (n=64) 4.04 0.53 3.91 0.66

Table 2: Pre-Post Student Attitudes & 21st Century Skills

STEM Attitudes and 21st Century Skills

Evaluating CT

• Rubric developed using definition of CT using a 3-point scale: emerging (1), moderate (2), substantive (3)– Students who used AgentSheet tutorial scored

lower in 1.6-2.0 range– Students who learned to program using

Agentsheets scored higher at 3.0

Student Identifier

Problem Identification & Decomposition

Abstraction Logical Thinking

Algorithms Debugging & Connections

Average CT Rating

A 2 2 1 1 2 1.6

B 1 3 2 1 3 2.0

C 3 3 3 3 3 3.0

D 3 3 3 3 3 3.0

Focal Students’ Computational Thinking

Evidence of Place

Evidence of Culture

Discussion

UGIC Program• Short time frame may not have been enough

to create significant change in student efficacy, attitude and 21st century skills

• Students may have realized difficulty in programming language that influenced efficacy and attitudes

• Teachers need more professional development on AgentSheets tool

References CitedBarr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age. Learning & Leading with Technology, 20-23.Chang, K., Wu, L., Weng, S., & Sung, Y. (2012). Embedding game-based problem-solving phase into problem-posing system for mathematics learning. Computers & Education, 58(2), 775-786. Edelson, D. C. (2001). Learning-for-Use: A framework for the design of technology-supported inquiry activities. Journal of Research in Science Teaching, 38(3), 335–385.Li, Q. (2010). Digital game building: Learning in a participatory culture. Educational Reserch, 52(4), 427-443.Repenning, A., Webb, D., & Ioannidou, A. (2010). Scalable game design and the development of a checklist for getting computational thinking into public schools. Proceedings of the 41st ACM technical symposium on computer science education (pp. 265-269). Milwaukee, WI. Saxe, G. (1999). Cognition, development, and cultural practices. In E. Turiel (Ed.), Culture and development: New directions in child psychology (pp. 19–36). San Francisco, CA: Jossey-Bass.Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S., & Korb, J. T. (2011, March). Introducing computational thinking in education courses. Proceedings of the 42nd ACM technical symposium on computer science education (pp. 465-470). Dallas, Texas.

ITEST RESEARCH TEAMPrincipal Investigator: Jacqueline Leonard jleona12@uwyyo.edu

Co-Principal Investigator: Ruben Gamboa ruben@uwyo.eduProject Manager, Joy B. Johnson JoyB.Johnson@uwyo.edu

THANK YOU