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

Distinguishing and Improving Mouse Behavior With Educational Computer Games in Young Children With Autistic Spectrum Disorder or Attention Deficit/Hyperactivity Disorder: An Executive Function-Based Interpretation

In this exploratory multiple case study, it is examined how a computer game focused on improving ineffective learning behavior can be used as a tool to assess, improve, and study real-time mouse behavior (MB) in different types of children: 18 children (3.86.3 years) with Autistic Spectrum Disorder (ASD), Attention Deficit/Hyperactivity Disorder (ADHD), or comorbid ASD and ADHD, and 5 effectively learning (EL) children (3.53.8 years). The children's MB processes, for example Errors and Reaction times, were interpreted in terms of executive functions (EFs). Trajectories of averaged MB were compared among the groups of ASD, ADHD, comorbid, and EL children. Clinical groups showed differences in their MB, which were similar to the expected differences based on EF tests. In addition, a case study of a typical ASD, ADHD, and EL child was included in order to demonstrate typical individual MB patterns across time. MB processes might therefore provide a window into the processes of EF (dys)functioning

Learning through movement:A comparison of learning fraction skills on a digital playful learning environment with a sedentary computer-task

Effects of physical exercise during educational tasks on learning are not obvious. This study examines the effects of movement on learning fraction skills at a physically active Playful Learning Environment (PLE). Employing a mixed-method approach, we investigated whether differences in motivational and verbal helping behaviour processes (underlying learning mechanisms) impacted learning gains. Results from 32 4th and 5th graders (n = 16 with the PLE, n = 16 in an equal sedentary computer task) approached significance, indicating that the PLE group showed higher learning gains compared to the SE group. Motivation was initially significantly higher for the PLE group. However, results indicated decreasing differences between the PLE and the sedentary group. There were no clear differences in the quality of verbal helping behaviour between the groups. Furthermore, subsequent utterances of verbal helping behaviour of two dyads selected in a case-study showed that their helping behaviour was sub-optimal for learningEffects of physical exercise during educational tasks on learning are not obvious. This study examines the effects of movement on learning fraction skills at a physically active Playful Learning Environment (PLE). Employing a mixed-method approach, we investigated whether differences in motivational and verbal helping behaviour processes (underlying learning mechanisms) impacted learning gains. Results from 32 4th and 5th graders (n = 16 with the PLE, n = 16 in an equal sedentary computer task) approached significance, indicating that the PLE group showed higher learning gains compared to the SE group. Motivation was initially significantly higher for the PLE group. However, results indicated decreasing differences between the PLE and the sedentary group. There were no clear differences in the quality of verbal helping behaviour between the groups. Furthermore, subsequent utterances of verbal helping behaviour of two dyads selected in a case-study showed that their helping behaviour was sub-optimal for learnin