Game designers commonly use paper prototyping to evaluate educational effectiveness, enjoyment, flow, and usability, while also reducing costs and exploring alternative implementations. However, creating a paper prototype that yields actionable feedback can be challenging due to the wide range of methods available, from low-fidelity sketches to high-fidelity mockups. Most paper prototypes are static and progress discretely, making it difficult to prototype physics-based games effectively, unless focusing on interfaces, narrative, or underlying systems. This paper details the creation of three prototypes of varying fidelity and metaphor for a physics-based educational game on concurrency and parallel programming. Each prototype undergoes playtesting to assess construction methods and their effectiveness in gathering player feedback.

For citizen science games (CSGs) to be successful in advancing scientific research, they must effectively train players. Designing tutorials for training can be aided through developing a skill chain of required skills and their dependencies, but skill chain development is an intensive process. In this work, we hypothesized that free recall may be a simpler yet effective method of directly eliciting skill chains. We elicited 23 skill chains from players and developers and augmented our reflexive thematic analysis with 11 semi-structured interviews in order to determine how players and developers conceptualize skill trees and whether free recall can be used as an alternative to more resource-intensive cognitive task analyses. We provide three main contributions: (1) a comparison of skill chain conceptualizations between players and developers and across prior literature; (2) insights to the process of free recall in eliciting CSG skill chains; and (3) a preliminary toolkit of CSG skill-based design recommendations based on our findings. We conclude CSG developers should: give the big picture up front; embrace social learning and paratext use; reinforce the intended structure of knowledge; situate learning within applicable, meaningful contexts; design for discovery and self-reflection; and encourage practice and learning beyond the tutorial. Free recall was ineffective for determining a traditional skill chain but was able to elicit the core gameplay loops, tutorial overviews, and some expert insights.

Presenting levels commensurate with players' current understanding of game mechanics and level design is a significant challenge in designing games. Often game designers create levels by hand intending for the levels to increase in difficulty over the the course of the game while relying on their intuition or extensive user feedback, reiteration, and testing. Instead, this study starts from a number of procedurally generated levels originally generated by parameters expected to encourage a good difficulty progression and then presented to players during playtests. A number of AI-bots with different characteristics were then designed to assess the difficulty of each level. These findings are then compared with player data. Our findings show that bots encapsulating idealized player strategies can help us create a richer model of level difficulty that then reveals useful information about player struggles and learning across level progressions.

Computer Science (CS) education at the middle school level using educational games has seen recent growth and shown promising results. Typically these games teach the craft of programming and not the perspectives required for computational thinking, such as abstraction and algorithm design, characteristic of a CS curriculum. This research presents a game designed to teach computational thinking via the problem of minimum spanning trees to middle school students, a set of evaluation instruments, and the results of an experimental pilot study. Results show a moderate increase in minimum spanning tree performance; however, differences between gender, collaboration method, and game genre preference are apparent. Based on these results, we discuss design considerations for future CS educational games focused on computational thinking.