Transactions on Edutainment VIII

The traditional E-learning often offers the online examination to assess the learning effect of a student after completion of the online learning. Basically, this traditional learning assessment mechanism is a passive and negative assessment mechanism, which cannot provide an real-time learning warning mechanism for teachers or students to find out problems as early as possible (including such learning conditions as “absence of mind” resulting from poor learning stage or physical or psychological factor), and the post-assessment mechanism also cannot assess the learning effectiveness provided by the online learning system. This research paper attempts to acquire the electroencephalogram to analyze the characteristic frequency band of the brainwave related to learning and formulate the learning energy index (LEI) for the learner at the time when the learner is reasoning logically via the brain-wave detector based on the cognitive neuroscience. With the established LEI, the physical and psychological conditions of an online leaner can be provided instantly for teachers for assessment. Given that the learning system is integrated into the brainwave analytic sensing component, the system not only can provide learners an instant learning warming mechanism, but also help teachers and learning partners to further understand the causes of learning disorder of learners, and can also provide relevant learning members with timely care and encouragement. Besides, this research also would prove that the game-based learning has not only the energy distribution of the characteristic frequency band the same as that by using professional textbooks, but also the way of game design can enhance the LEI of learners more in the aspect of training logical reasoning.

[1]  Duc Quang Nguyen,et al.  Physically based modeling and animation of fire , 2002, ACM Trans. Graph..

[2]  Matthias Teschner,et al.  Corotated SPH for Deformable Solids , 2009, NPH.

[3]  Guoping Wang,et al.  Meshless simulation of brittle fracture , 2011, Comput. Animat. Virtual Worlds.

[4]  Namrata Vaswani,et al.  Particle Filtering for Large-Dimensional State Spaces With Multimodal Observation Likelihoods , 2008, IEEE Transactions on Signal Processing.

[5]  Jovan Popović,et al.  Real-time hand-tracking with a color glove , 2009, SIGGRAPH 2009.

[6]  Andrew Witkin,et al.  Fast and Controllable Simulation of the Shattering of Brittle Objects , 2001 .

[7]  Marc Alexa,et al.  Point based animation of elastic, plastic and melting objects , 2004, SCA '04.

[8]  James F. O'Brien,et al.  Graphical modeling and animation of ductile fracture , 2002, SIGGRAPH '02.

[9]  Baoxin Li,et al.  Rao-Blackwellised particle filter for tracking with application in visual surveillance , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[10]  Ronald Fedkiw,et al.  A virtual node algorithm for changing mesh topology during simulation , 2004, SIGGRAPH 2004.

[11]  Yoichi Sato,et al.  Real-time tracking of multiple fingertips and gesture recognition for augmented desk interface systems , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[12]  Hongbin Zha,et al.  Riemannian Manifold Learning for Nonlinear Dimensionality Reduction , 2006, ECCV.

[13]  Ying Wu,et al.  Capturing human hand motion in image sequences , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[14]  E. Rivlin,et al.  Dimensionality Reduction for Articulated Body Tracking , 2007, 2007 3DTV Conference.

[15]  Björn Stenger,et al.  Model-based hand tracking using a hierarchical Bayesian filter , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  John C. Platt,et al.  Elastically deformable models , 1987, SIGGRAPH.

[17]  Ronald Fedkiw,et al.  Fracturing Rigid Materials , 2007, IEEE Transactions on Visualization and Computer Graphics.

[18]  L. Guibas,et al.  Meshless animation of fracturing solids , 2005, ACM Trans. Graph..

[19]  N.D. Georganas,et al.  3D Hand Tracking and Motion Analysis with a Combination Approach of Statistical and Syntactic Analysis , 2007, 2007 IEEE International Workshop on Haptic, Audio and Visual Environments and Games.

[20]  Ronald Fedkiw,et al.  Visual simulation of smoke , 2001, SIGGRAPH.

[21]  R. Schaback,et al.  Characterization and construction of radial basis functions , 2001 .

[22]  Jessica K. Hodgins,et al.  Graphical modeling and animation of brittle fracture , 1999, SIGGRAPH.

[23]  Roger J. Hubbold,et al.  A real-time hand tracker using variable-length Markov models of behaviour , 2007, Comput. Vis. Image Underst..

[24]  Demetri Terzopoulos,et al.  Modeling inelastic deformation: viscolelasticity, plasticity, fracture , 1988, SIGGRAPH.

[25]  Mathieu Desbrun,et al.  Smoothed particles: a new paradigm for animating highly deformable bodies , 1996 .

[26]  Mathieu Desbrun,et al.  Animating soft substances with implicit surfaces , 1995, SIGGRAPH.

[27]  Stephen J. McKenna,et al.  Hand tracking for behaviour understanding , 2002, Image Vis. Comput..

[28]  Wei Liang,et al.  Tracking articulated objects by learning intrinsic structure of motion , 2009, Pattern Recognit. Lett..

[29]  Gui-Rong Liu,et al.  An Introduction to Meshfree Methods and Their Programming , 2005 .

[30]  James F. O'Brien,et al.  Animating suspended particle explosions , 2003, ACM Trans. Graph..