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Recent interest in online assessment of scientific inquiry has led to several new online systems that attempt to assess these skills, but producing models that detect when students are successfully practising these skills can be challenging. In this paper, we study models that assess student inquiry in an immersive virtual environment, where a student navigates an avatar around a world, speaking to in-game characters, collecting samples and conducting scientific tests with those samples in the virtual laboratory. To this goal, we leverage log file data from nearly 2000 middle school students using virtual performance assessment (VPA), a software system where students practice inquiry skills in different virtual scenarios. We develop models of student interaction within VPA that predict whether a student will successfully conduct scientific inquiry. Specifically, we identify behaviours that lead to distinguishing causal from non-causal factors to identify a correct final conclusion and to design a causal explanation about these conclusions. We then demonstrate that these models can be adapted with minimal effort between VPA scenarios. We conclude with discussions of how these models serve as a tool for better understanding scientific inquiry in virtual environments and as a platform for the future design of evidence-based interventions. [ABSTRACT FROM AUTHOR]

Measuring stride variability and dynamics in children is useful for the quantitative study of gait maturation and neuromotor development in childhood and adolescence. In this paper, we computed the sample entropy (SampEn) and average stride interval (ASI) parameters to quantify the stride series of 50 gender-matched children participants in three age groups. We also normalized the SampEn and ASI values by leg length and body mass for each participant, respectively. Results show that the original and normalized SampEn values consistently decrease over the significance level of the Mann-Whitney U test (p<0.01) in children of 3–14 years old, which indicates the stride irregularity has been significantly ameliorated with the body growth. The original and normalized ASI values are also significantly changing when comparing between any two groups of young (aged 3–5 years), middle (aged 6–8 years), and elder (aged 10–14 years) children. Such results suggest that healthy children may better modulate their gait cadence rhythm with the development of their musculoskeletal and neurological systems. In addition, the AdaBoost.M2 and Bagging algorithms were used to effectively distinguish the children’s gait patterns. These ensemble learning algorithms both provided excellent gait classification results in terms of overall accuracy (≥90%), recall (≥0.8), and precision (≥0.8077). [ABSTRACT FROM AUTHOR]