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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]

Voice disorders are associated with irregular vibrations of vocal folds. Based on the source filter theory of speech production, these irregular vibrations can be detected in a non-invasive way by analyzing the speech signal. In this paper we present a multiband approach for the detection of voice disorders given that the voice source generally interacts with the vocal tract in a non-linear way. In normal phonation, and assuming sustained phonation of a vowel, the lower frequencies of speech are heavily source dependent due to the low frequency glottal formant, while the higher frequencies are less dependent on the source signal. During abnormal phonation, this is still a valid, but turbulent noise of source, because of the irregular vibration, affects also higher frequencies. Motivated by such a model, we suggest a multiband approach based on a three-level discrete wavelet transformation (DWT) and in each band the fractal dimension (FD) of the estimated power spectrum is estimated. The experiments suggest that frequency band 1-1562 Hz, lower frequencies after level 3, exhibits a significant difference in the spectrum of a normal and pathological subject. With this band, a detection rate of 91.28 % is obtained with one feature, and the obtained result is higher than all other frequency bands. Moreover, an accuracy of 92.45 % and an area under receiver operating characteristic curve (AUC) of 95.06 % is acquired when the FD of all levels is fused. Likewise, when the FD of all levels is combined with 22 Multi-Dimensional Voice Program (MDVP) parameters, an improvement of 2.26 % in accuracy and 1.45 % in AUC is observed. [ABSTRACT FROM AUTHOR]

An algorithm for determining the intersections between fractures is presented in this paper. The algorithm is implemented in GEOFRAC, a stochastic fracture pattern-modeling program. Previously, GEOFRAC allowed one to subdivide a fracture network into isolated subnetworks to assess connectivity. A new GEOFRAC feature can be used to analyze length and orientation distributions of the fracture intersections. Connectivity parameters such as the number of intersections per unit volume, , and the intersection length per unit volume can be calculated. Intersections and connectivity are important because they govern the fluid flow and stability behavior of rock masses. The program is used to model fracture intersections in the Boston area. The simulations show that for fractures in the Boston area, the mean fracture intersection length ranges from 0.5 to 0.7 m. The results also show that the relative frequency of long intersections (>2.0 m) increases with increasing modeling volume size. In contrast to these effects of volume, it appears that intersection orientation is not affected by the volume being considered. [ABSTRACT FROM AUTHOR]

This paper studies genetic algorithms as a means of estimating the number of changepoints and their locations in a climatic time series. Such methods bypass classic subsegmentation algorithms, which sometimes yield suboptimal conclusions. Minimum description length techniques are introduced. These techniques require optimizing an objective function over all possible changepoint numbers and location times. The general objective functions allow for correlated data, reference station aspects, and/or nonnormal marginal distributions, all common features of climate time series. As an exhaustive evaluation of all changepoint configurations is not possible, the optimization is accomplished via a genetic algorithm that randomly walks through a subset of good models in an intelligent manner. The methods are applied in the analysis of 173 yr of annual precipitation measurements from New Bedford, Massachusetts, and the North Atlantic basin tropical cyclone record. [ABSTRACT FROM AUTHOR]