While advances in game-based learning are already transforming educative practices globally, with tech giants like Microsoft, Apple and Google taking notice and investing in educational game initiatives, there is a concurrent and critically important development around ‘game construction’ pedagogy as a vehicle for enhancing computational literacy in middle and high school students.
Essentially, game construction-based curriculum takes the central question “do children learn from playing games” to the next stage by asking “(what) can children learn from constructing games?” Founded on Seymour Papert’s constructionist learning model, recent works by Carbonaro et al (2010) as well as Denner, Werner and Ortiz (2012), among others, offer compelling evidence that game construction can increase confidence and build capacity towards ongoing computing science involvement and other STEM subjects.
Situated at the intersection of ‘maker’ pedagogies and inquiry-based learning on one hand, and game-based learning on the other, this field of educational research is still at its early stages of theorization and implementation. There is still debate as to the utility of different software tools for game construction, models of scaffolding knowledge, and evaluation of learning outcomes and knowledge transfer.
In this paper, we present a study we conducted in a classroom environment with three groups of grade 6 students (60+ students) using Game Maker to construct their own games. Our study adds to the growing body of literature on school-based game construction through comprehensive empirical methodology and evidence-based guidelines for curriculum design. We also discuss preliminary results related to computational literacy, in addition to a theorization of game construction as an educational tool that directly engages foundational literacy and numeracy and connects to wider STEM-oriented learning objectives.
Wing (2006) defines CT as “reformulating a seemingly difficult problem into one we know how to solve, perhaps by reduction, embedding, transformation, or simulation.” Yadav et al. (2014) define CT as a “mental activity for abstracting problems and formulating solutions that can be automated”. Cuny et al. (2010) define it 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”. According to Denner, Werner and Ortiz (2011), “algorithmic thinking involves defining a problem, breaking it into smaller yet solvable parts, and identifying the steps for solving the problem.” As part of this, students must model the essential characteristics of the problem while suppressing unnecessary details. In the process, “finite sequences of instructions are coded to operationalize the modeled abstractions.” From a review of the field in Grover and Pea (2013), the following is a standard list of learning objectives or computational constructs that ought to be covered in some form in instructional designs of entry-level computing:
- Abstractions and pattern generalizations (including models and simulations)
- Systematic processing of information (proceduralization)
- Symbol systems and representations
- Structured problem decomposition (modularizing)
- Iterative, recursive, and parallel thinking
- Conditional logic
- Debugging and systematic error detection
The first phase of this study took place in two large elementary schools (with over 750 children) in Ontario, Canada in the Spring of 2015. Ontario does not currently have any mandatory computer science related curricula at the grade 6 level. We chose to work with Grade 6 students as much of the work done previously (see Carbonaro, et al., 2010; Denner, 2011) suggests that grade 6 and 7 is the point when many students begin to make choices about what courses they will or will not take at the high school level and beyond. Previous iterations of this research span over 4 years and consist of game making after-school clubs. The next phase of the research is underway at the same schools and at the same grade levels.
Based on preliminary discussion of the data gathered thus far, there are three primary conclusions that are worth emphasizing. First, as others have pointed out, claims that today’s students are defacto ‘digitally native’ is not the case for all students, nor does it indicate that students have familiarity or even facility with basic computer programming skills and competencies. Second, there are still gender differences in attitude and confidence with computers that in an instructional study such as this can and did affect performance on programming related tasks, not only on the post-test, but also in our many observations of girls during the time we spent with them. In general, they were less willing to participate in public displays of knowledge (like answering questions to the whole group) and were more likely than their male counterparts to ‘disavow’ their skills with speech acts such as: “I always break the computer” and “I am not good at computers”. Such differences in attidues, we show, can and do affect performance. Finally, our model of a structured curriculum that combines applied work with direct follow-along instruction is encouraging and we hope replicable in eventually, a school district-wide instructional programme. This preliminary analysis shows that using a commericallly available game design software that permits a variety of scalable programming actions in the process of coding and testing a game, is not only a viable way of introducing a middle-school demographic to computational literacy but is one other means for fostering and supporting STEM related competencies, vocabularies and skills.
Further reading and foundational resources:
Carbonaro, M., Szafron, D., Cutumisu, M., & Schaeffer, J. (2010) Computer-game construction: A gender-neutral attractor to Computing Science. Computers & Education, 55(3), 1098–1111. doi:10.1016/j.compedu.2010.05.007
Denner, J., Werner, L., & Ortiz, E. (2012) Computer games created by middle school girls: Can they be used to measure understanding of computer science concepts? Computers & Education, 58(1), 240–249. doi:10.1016/j.compedu.2011.08.006
diSessa, A. (2000) Changing Minds: Computers, Learning, and Literacy, MIT Press.
Gee, J. P. (2003). What video games have to teach us about learning and literacy. Palgrave Macmillan.
Hoegh, A. & B. Moskal (2009). Examining Science and Engineering Students’ Attitudes Toward Computer Science, SEE/IEEE Frontiers in Education Conference, October 18 - 21, 2009, San Antonio, TX.
Jenson, J., & de Castell, S. (2005). Her own Boss: Gender and the pursuit of incompetent play. Presented at the Changing Views: Worlds in Play, DIGRA, DIGRA. Kafai, Y. B. (2006) “Playing and making games for learning: Instructionist and constructionist perspectives for game studies,” Games and Culture, Vol 1, No. 1, pp. 34–40.
Klahr, D. & Carver, S. (1988). Cognitive Objectives in a LOGO Debugging Curriculum: Instruction, Learning and Transfer. Cognitive Psychology 20, pp.362-404.
Papert, S. (1980) Mindstorms: Children, computers, and powerful ideas, Basic Books New York.
Papert, S. (1993) The children’s machine: rethinking school in the age of the computer. New York: BasicBooks.
Prensky, M. (2005). Computer Games and Learning: Digital Game-Based Learning. In J. Raessens & J. Goldstein (Eds.), Handbook of Computer Game Studies (pp. 97–122). Cambridge, MA: MIT Press.
Wing, J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33–35.