Code puzzles are an increasingly popular way to introduce youth to programming. Yet our knowledge about how to maximize learning from puzzles is incomplete. We conducted a data collection study and trained a model that predicts cognitive load, the mental effort necessary to complete a task, on a future puzzle. Controlling cognitive load can lead to more effective learning. Our model suggests that it is possible to predict Cognitive Load on future problems; the model could correctly distinguish the more difficult puzzle within a pair 71%-79% of the time. Further, studying the model itself provides new insights into the sources of puzzle difficulty, the factors that contribute to Cognitive Load, and their inter-relationships. Finally, the ability to predict Cognitive Load on a future puzzle is an important step towards the creation of adaptive code puzzle systems.

Programmers, especially novices, often have difficulty learning new APIs (Application Programming Interfaces). Existing research has not fully addressed novice programmers' unawareness of all available API methods. To help novices discover new and appropriate uses for API methods, we designed a system called the Example Guru. The Example Guru suggests context-relevant API methods based on each programmer's code. The suggestions provide contrasting examples to demonstrate how to use the API methods. To evaluate the effectiveness of the Example Guru, we ran a study comparing novice programmers' use of the Example Guru and documentation-inspired API information. We found that twice as many participants accessed the Example Guru suggestions compared to documentation and that participants used more than twice as many new API methods after accessing suggestions than documentation.

Computer science education is still limited for middle school and high school students, while programming becomes an important skill to know. Therefore, students often have to rely on novice programming environments such as Scratch [1] and Hour of Code [2] and learn coding independently.

There are a variety of learning resources with the potential to support children in learning programming independently. While many of them have been evaluated in laboratory settings, we know little about how children choose to use these resources on their own. We conducted a study organized around a film festival to explore children's open-ended use of four different learning supports: tutorials, code puzzles, in-application documentation and code suggestions. The study began with a workshop to introduce the programming environment and available tools, continued through two weeks of home use, and culminated in a film festival. Results suggest that participants leveraged in-context forms of help most frequently, but valued documentation for question-answering and suggestions for opportunistic learning.

Many novice programmers use blocks-based programming environments outside of classrooms, due to a lack of computer science education in schools. Many solutions for supporting learning in these environments are out of the context of the programming environment and the user's project. In this poster, we present a way of expanding novice users' knowledge of a blocks-based programming environment by suggesting examples during open-ended programming.