Identifying Learning Trajectories While Playing a Learning-to-Learn Computer Game in Different Children and Instruction Types

This research focuses on identifying learning trajectories expressed among children playing a learning-to-learn computer game and examining the relationships between the learning trajectories and individual characteristics such as developmental age, prior knowledge, and instruction type (adult- and/or computer-assisted, or no instruction). Each child in the sample (<i>N</i> = 184; ages 2.6&#8211;4.2 years) repeatedly played the game, resulting in 13 measurements. At each measurement, we registered 7 aspects of mouse behavior such as error frequency, mouse click frequency, and reaction times. Based on those data, we identified 6 types of learning trajectories with multilevel latent class growth analysis. The types appeared significantly related to developmental age, prior knowledge, and type of instruction. Furthermore, prior knowledge appeared the best predictor; specifically among children with moderate prior knowledge having received instruction was associated with better performances than no instruction. The results support the conclusion that children at risk have a high probability of showing benefits in their learning-to-learn skills while playing this educational computer game. However, more adapted games, with more variation and adult-assisted instruction, would also benefit children with uninhibited behavior