Problem solving requires comprehension and a strategic plan for designing and testing a solution. Simulation games might promote learning and enhance computational problem-solving skills. The design and realization of effective simulation learning games is not easy. How should operational games be designed for learning computational problem-solving skills? What learning strategies should be embedded into simulated learning games?

Liu et al.’s paper sheds light on the effects of simulated learning games on computational problem-solving skills. They created a simulated railway construction environment in which students can create rail systems and model the actions of the trains on the rails. The game’s activities were designed to reinforce one’s mastery of object-oriented concepts, conditional and iterative statements, and object communications. Students use straight, curved, and branch tracks and manipulative materials to plan and build rail systems. The game allows students to probe the effects of acceleration, speed, gravity, and friction on the behaviors of rail systems.

First-year students in a beginning programming class were surveyed about their perceived learning experiences with traditional lectures. The students received a tutorial on computational problem-solving skills and the building blocks of the railway models, and then participated in simulated learning game activities for two weeks. The students developed solutions to computational problems by typing and modifying code; experimenting with the simulation function to verify the actions of the programs; reviewing and reusing solutions; and fetching examples from tutorials. The simulated railway construction environment logged the programs and processes the students used to solve computational problems.

The participants also completed a survey about their learning experiences with the simulated learning game. The survey results reveal that the students perceived a higher level of challenge but a lower level of problem-solving skills in the traditional lecture environment than in the simulated railway construction environment. Moreover, the intrinsic motivation to learn was higher in the simulated learning game than in the traditional lecture environment. In the simulated railway construction environment, students used this inherent motivation to proceed with solving computational problems. Students who were less nervous about learning in the simulated railway construction environment recognized and applied existing solutions to solve new computational problems--they learned through examples, trial and error, and methodical reasoning. The students who were edgy or bored in the simulated railway construction environment only exhibited trial-and-error and analytical problem-solving strategies. The simulated railway construction environment’s design--with its focus on physics--limits its applicability to a variety of computational problems [1,2]. Nevertheless, the authors present resounding results to advocate simulated learning games as valuable tools for learning computational problem-solving skills.