Modeling Student Affective State Patterns during Self-Regulated Learning in Physics Playground

This dissertation research focuses on investigating the incidence of student self-regulated learning behavior, and examines patterns in student affective states that accompany such self-regulated behavior. This dissertation leverages prediction models of student affective states in the Physics Playground educational game platform to identify common patterns in student affective states during use of self-regulated learning behavior. In Study 1, prediction models of student affective states are developed in the context of the educational game environment Physics Playground, using affective state observations and computer log data that had already been collected as part of a larger project. The performances of student affective state prediction models generated using a combination of the computer log and observational data are then compared against those of similar prediction models generated using video data collected at the same time. In Study 2, I apply these affective state prediction models to generate predictions of student affective states on a broader set of data collected from students participants playing Physics Playground. In parallel, I define aggregated behavioral features that represent the self-observation and strategic planning components of self-regulated learning. Affective state predictions are then mapped to playground level attempts that contain these self-regulated learning behavioral features, and sequential pattern mining is applied to the affective state predictions to identify the most common patterns in student emotions. Findings from Study 1 demonstrate that both video data and interaction log data can be used to predict student affective states with significant accuracy. Since the video data is a direct measure of student emotions, it shows better performance across most affective states. However, the interaction log data can be collected natively by Physics Playground and is able to be generalized more easily to other learning environments. Findings from Study 2 suggest that self-regulatory behavior is closely associated with sustained periods of engaged concentration and .self-regulated learning behaviors are associated with transitions from negative affective states (confusion, frustration, and boredom) to the positive engaged concentration state. The results of this dissertation project demonstrate the power of measuring student affective states in real time and examining the temporal relationship to self-regulated learning behavior within an unstructured educational game platform. These results thus provide a building block for future research on the real-time assessment of student emotions and its relationship with self-regulated learning behaviors, particularly within online student-centered and self-directed learning contexts

Genome sequences reveal global dispersal routes and suggest convergent genetic adaptations in seahorse evolution

Seahorses have a circum-global distribution in tropical to temperate coastal waters. Yet, seahorses show many adaptations for a sedentary, cryptic lifestyle: they require specific habitats, such as seagrass, kelp or coral reefs, lack pelvic and caudal fins, and give birth to directly developed offspring without pronounced pelagic larval stage, rendering long-range dispersal by conventional means inefficient. Here we investigate seahorses’ worldwide dispersal and biogeographic patterns based on a de novo genome assembly of Hippocampus erectus as well as 358 re-sequenced genomes from 21 species. Seahorses evolved in the late Oligocene and subsequent circum-global colonization routes are identified and linked to changing dynamics in ocean currents and paleo-temporal seaway openings. Furthermore, the genetic basis of the recurring “bony spines” adaptive phenotype is linked to independent substitutions in a key developmental gene. Analyses thus suggest that rafting via ocean currents compensates for poor dispersal and rapid adaptation facilitates colonizing new habitats.Fil: Chunyan, Li. Southern Marine Science and Engineering Guangdong Laboratory; China. Pilot National Laboratory for Marine Science and Technology; China. Chinese Academy of Sciences; República de ChinaFil: Olave, Melisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina. University of Konstanz; AlemaniaFil: Hou, Yali. Chinese Academy of Sciences; República de ChinaFil: Geng, Qi. Chinese Academy of Sciences; República de China. Southern Marine Science and Engineering Guangdong Laboratory; ChinaFil: Schneider, Ralf. University Of Konstanz; Alemania. Helmholtz Centre for Ocean Research Kie; AlemaniaFil: Zeixa, Gao. Huazhong Agricultural University; ChinaFil: Xiaolong, Tu. Allwegene Technologies ; ChinaFil: Xin, Wang. Chinese Academy of Sciences; República de ChinaFil: Furong, Qi. China National Center for Bioinformation; China. University of Chinese Academy of Sciences; ChinaFil: Nater, Alexander. University of Konstanz; AlemaniaFil: Kautt, Andreas F.. University of Konstanz; Alemania. Harvard University; Estados UnidosFil: Wan, Shiming. Chinese Academy of Sciences; República de ChinaFil: Yanhong, Zhang. Chinese Academy of Sciences; República de ChinaFil: Yali, Liu. Chinese Academy of Sciences; República de ChinaFil: Huixian, Zhang. Chinese Academy of Sciences; República de ChinaFil: Bo, Zhang. Chinese Academy of Sciences; República de ChinaFil: Hao, Zhang. Chinese Academy of Sciences; República de ChinaFil: Meng, Qu ,. Chinese Academy of Sciences; República de ChinaFil: Shuaishuai, Liu. Chinese Academy of Sciences; República de ChinaFil: Zeyu, Chen. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Zhong, Jia. Chinese Academy of Sciences; República de ChinaFil: Zhang, He. BGI-Shenzhen; ChinaFil: Meng, Lingfeng. BGI-Shenzhen; ChinaFil: Wang, Kai. Ludong University; ChinaFil: Yin, Jianping. Chinese Academy of Sciences; República de ChinaFil: Huang, Liangmin. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Venkatesh, Byrappa. Institute of Molecular and Cell Biology; SingapurFil: Meyer, Axel. University of Konstanz; AlemaniaFil: Lu, Xuemei. Chinese Academy of Sciences; República de ChinaFil: Lin, Qiang. Chinese Academy of Sciences; República de China. Southern Marine Science and Engineering Guangdong Laboratory; China. Pilot National Laboratory for Marine Science and Technology; China. University of Chinese Academy of Sciences; Chin

Molecular Dynamics Study of Interfacial Micromechanical Behaviors of 6H-SiC/Al Composites under Uniaxial Tensile Deformation

This paper investigated the micromechanical behavior of different 6H-SiC/Al systems during the uniaxial tensile loading by using molecular dynamics simulations. The results showed that the interface models responded diversely to the tensile stress when the four low-index surfaces of the Al were used as the variables of the joint surfaces. In terms of their stress–strain properties, the SiC(0001)/Al(001) models exhibited the highest tensile strength and the smallest elongation, while the other models produced certain deformations to relieve the excessive strain, thus increasing the elongation. The SiC(0001)/Al(110) models exhibited the largest elongations among all the models. From the aspect of their deformation characteristics, the SiC(0001)/Al(001) model performed almost no plastic deformation and dislocations during the tensile process. The deformation of the SiC(0001)/Al(110) model was dominated by the slip of the 1/6 Shockley partial dislocations, which contributed to the intersecting stacking faults in the model. The SiC(0001)/Al(111) model produced a large number of dislocations under the tensile loading. Dislocation entanglement was also found in the model. Meanwhile, a unique defect structure consisting of three 1/6 stair-rod dislocations and three stacking faults were found in the model. The plastic deformation in the SiC(0001)/Al(112) interface model was restricted by the L-C lock and was carried out along the 1/6 stair-rod dislocations’ direction. These results reveal the interfacial micromechanical behaviors of the 6H-SiC/Al composites and demonstrate the complexity of the deformation systems of the interfaces under stress

Molecular Dynamics Study of Interfacial Micromechanical Behaviors of 6H-SiC/Al Composites under Uniaxial Tensile Deformation

This paper investigated the micromechanical behavior of different 6H-SiC/Al systems during the uniaxial tensile loading by using molecular dynamics simulations. The results showed that the interface models responded diversely to the tensile stress when the four low-index surfaces of the Al were used as the variables of the joint surfaces. In terms of their stress–strain properties, the SiC(0001)/Al(001) models exhibited the highest tensile strength and the smallest elongation, while the other models produced certain deformations to relieve the excessive strain, thus increasing the elongation. The SiC(0001)/Al(110) models exhibited the largest elongations among all the models. From the aspect of their deformation characteristics, the SiC(0001)/Al(001) model performed almost no plastic deformation and dislocations during the tensile process. The deformation of the SiC(0001)/Al(110) model was dominated by the slip of the 1/6 <112> Shockley partial dislocations, which contributed to the intersecting stacking faults in the model. The SiC(0001)/Al(111) model produced a large number of dislocations under the tensile loading. Dislocation entanglement was also found in the model. Meanwhile, a unique defect structure consisting of three 1/6 <110> stair-rod dislocations and three stacking faults were found in the model. The plastic deformation in the SiC(0001)/Al(112) interface model was restricted by the L-C lock and was carried out along the 1/6 <110> stair-rod dislocations’ direction. These results reveal the interfacial micromechanical behaviors of the 6H-SiC/Al composites and demonstrate the complexity of the deformation systems of the interfaces under stress

Deep learning assisted sparse array ultrasound imaging.

This study aims to restore grating lobe artifacts and improve the image resolution of sparse array ultrasonography via a deep learning predictive model. A deep learning assisted sparse array was developed using only 64 or 16 channels out of the 128 channels in which the pitch is two or eight times the original array. The deep learning assisted sparse array imaging system was demonstrated on ex vivo porcine teeth. 64- and 16-channel sparse array images were used as the input and corresponding 128-channel dense array images were used as the ground truth. The structural similarity index measure, mean squared error, and peak signal-to-noise ratio of predicted images improved significantly (p < 0.0001). The resolution of predicted images presented close values to ground truth images (0.18 mm and 0.15 mm versus 0.15 mm). The gingival thickness measurement showed a high level of agreement between the predicted sparse array images and the ground truth images, as indicated with a bias of -0.01 mm and 0.02 mm for the 64- and 16-channel predicted images, respectively, and a Pearson's r = 0.99 (p < 0.0001) for both. The gingival thickness bias measured by deep learning assisted sparse array imaging and clinical probing needle was found to be <0.05 mm. Additionally, the deep learning model showed capability of generalization. To conclude, the deep learning assisted sparse array can reconstruct high-resolution ultrasound image using only 16 channels of 128 channels. The deep learning model performed generalization capability for the 64-channel array, while the 16-channel array generalization would require further optimization