http://orcid.org/0000-0003-0121-4572
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ABSTRACT
Building Computational Thinkers, a three-year research study, explored how educators and designers can most effectively support the development of computational thinking capacity, and how these learning experiences could be customized to meet the needs of different learners. This research study focused on three specific exhibit design approaches that conveyed problem decomposition content in The Science Behind Pixar (Pixar), a 13,000 square foot traveling exhibition about the computer science, mathematics, and science behind Pixar’s innovative films. Phase One investigated how novice learners could be supported to interact with exhibits and understand problem solving strategies that tackle complex, creative challenges in computer programming. Phase Two investigated the affordances of these exhibits to build capacity, feelings of efficacy, and interest in problem decomposition content in middle and high school youth. The findings in this paper describe the types of scaffolds that can be used to support computational thinking in novice youth, as well as how a combination of exhibit approaches were found to increase youth perceptions, understanding, and beliefs of computer programming. It will also discuss how two exhibit approaches worked particularly well for engaging girls in problem decomposition content.
Acknowledgements
“Building Computational Thinkers through Informal Exhibit Experiences” would not be possible without the leadership of PI Christine Reich and Co-PI Ben Wilson. A special thanks also goes to “The Science Behind Pixar” exhibition development team who designed and created the exhibits for this project, as well as the R&E research assistants who spent many hours helping with data collection and entry for this project.
Disclosure statement
No potential conflict of interest was reported by the authors.
About the authors
Leigh Ann Mesiti (M.Ed.) is the Assistant Manager of Research and Evaluation at the Museum of Science, Boston (MOS), where she has worked for the past eight years. During that time, she conducted research and evaluation for a range of MOS exhibitions and programming, as well as multi-institutional studies. At MOS, Leigh Ann collaborateed with in-house exhibit teams to design and prototype new interactive exhibits. Her most recent work has focused on The Science Behind Pixar traveling exhibition and the associated research project, Building Computational Thinkers through Informal Exhibit Experiences.
Alana Parkes (M.Ed.) is the Supervisor of Exhibit Content Development at the Museum of Science, Boston. She is responsible ensuring the work of three content developers aligns with the goals of the Museum and each specific exhibit and for implementing the pedagogical approach for developing exhibits. She has worked as an Exhibit Content Developer for 18 years. Her skills include defining the overall scope of an exhibit, creating exhibit messages and goals, developing interactives, and writing label copy. Her most recent project is the 13,000-square foot traveling exhibit, The Science Behind Pixar.
Sunewan C. Paneto is a Research and Evaluation Assistant at the Museum of Science, Boston, where she has worked for the past seven years. She has assisted on various research and evaluation projects for both exhibitions and museum programs. She worked on the summative evaluation for the Science Behind Pixar exhibition and was also part of the Building Computational Thinkers through Informal Exhibit Experiences project.
Clara Cahill was the originating Principal Investigator of the Building Computational Thinkers through Informal Exhibit Experiences research study. At the Museum of Science, Boston, she worked on the summative evaluation for the Science Behind Pixar exhibition and was also part of the Building Computational Thinkers through Informal Exhibit Experiences project.
ORCID
Alana Parkes http://orcid.org/0000-0003-0121-4572
Notes
1 Wing, “Research Notebook: Computational Thinking.”
2 Ibid.
3 NSF project number: CNS 1339244.
4 The Science Behind Pixar first opened at MOS in June 2015 and is scheduled to tour nationally and internationally through 2027.
5 Brennan and Resnick, “Using Artifact-based Interviews.”
6 Reiser, “Scaffolding Complex Learning,” 274.
7 Quintana, Zhang, and Krajcik, “A Framework for Supporting Metacognitive Aspects,” 237.
8 Schunn and Nelson, “Expert-novice Studies,” 3.
9 Hmelo-Silver, Marathe, and Liu, “Fish Swim, Rocks Sit, and Lungs Breathe,” 319; Schunn and Nelson, “Expert-novice Studies,” 3.
10 Ibid.
11 Quintana, Zhang, and Krajcik, “A Framework for Supporting,” 236.
12 Reiser, “Scaffolding Complex Learning,” 277.
13 Schunn and Nelson, “Expert-novice Studies,” 1.
14 See note 11 above.
15 Reiser, “Scaffolding Complex Learning,” 278.
16 Quintana, Zhang, and Krajcik, “A Framework for Supporting,” 240; Reiser, “Scaffolding Complex Learning,” 284.
17 Reiser, “Scaffolding Complex Learning,” 280.
18 Reiser, “Scaffolding Complex Learning,” 284.
19 Grover and Pea, “Computational Thinking in K-12,” 40.
20 Novice groups were primarily recruited through posts to the museum’s social media platforms and experts were recruited through local college and tech professional listervs.
21 Demographics included gender, age, and race/ethnicity.
22 Differences in their interpretive approaches were identified through inductive, open-coding coding techniques using NVivo coding software; Patton, Qualitative Research and Evaluation Methods; Corbin and Strauss, Basics of Qualitative Research.
23 Exhibit combinations included: (1) two multimedia narratives & two solution explorations, (2) two multimedia narratives & two creative design activities, (3) two solution explorations & two creative design activities, or (4) all six exhibits.
24 Data were analyzed through inductive, open-coding in Excel, while quantitative data were analyzed in SPSS; Patton, Qualitative Research and Evaluation Methods; Corbin and Strauss, Basics of Qualitative Research.
25 Grover and Pea, “Computational Thinking in K-12,” 39; Barr and Stephenson, “Bringing Computational Thinking to K-12,” 50.
26 Immediate post-qualitative problem decomposition task; Extended post-survey-paired t-test: t = −1.189; p = 0.032; effect size (cohen’s d) = 0.23; n = 81).
27 Extended post-survey; girls: (B = .201; p < 0.024); (B = .594; p < 0.001; overall model adjusted R2 = .421; p < .001).
28 Extended post-survey; Of the 13-item measure that captured youths’ beliefs and preferences for computer programming, three of these items pertained to creativity in computer programming; (B = .201; p < 0.024); controlling for pre-visit beliefs (B = .594; p < 0.001; overall model adjusted R2 = .421; p < .001).
29 Timing: Girls spent an average of 10 min, 39 s (SD = 4:18) at these exhibits, while boys spent 8 min, 39 s (SD = 5:03). Interest: (81.5% of girls and 59.3% of boys).
30 (B = 0.13; p < 0.05); Adjusted R2 = .57).