Predicting cognitive load in future code puzzles

Abstract

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 efort necessary to complete a task, on a future puzzle. Controlling cognitive load can lead to more efective learning. Our model suggests that it is possible to predict Cognitive Load on future problems; the model could correctly distinguish the more difcult puzzle within a pair 71%-79% of the time. Further, studying the model itself provides new insights into the sources of puzzle difculty, the factors that contribute to Cognitive Load, and their interrelationships. Finally, the ability to predict Cognitive Load on a future puzzle is an important step towards the creation of adaptive code puzzle systems.