Supporting Collaborative Learning in Computer-Enhanced Environments

As computers have expanded into almost every aspect of our lives, the ever-present graphical user interface (GUI) has begun facing its limitations. Demanding its own share of attention, GUIs move some of the users\u27 focus away from the task, particularly when the task is 3D in nature or requires collaboration. Researchers are therefore exploring other means of human-computer interaction. Individually, some of these new techniques show promise, but it is the combination of multiple approaches into larger systems that will allow us to more fully replicate our natural behavior within a computing environment. As computers become more capable of understanding our varied natural behavior (speech, gesture, etc.), the less we need to adjust our behavior to conform to computers\u27 requirements. Such capabilities are particularly useful where children are involved, and make using computers in education all the more appealing. Herein are described two approaches and implementations of educational computer systems that work not by user manipulation of virtual objects, but rather, by user manipulation of physical objects within their environment. These systems demonstrate how new technologies can promote collaborative learning among students, thereby enhancing both the students\u27 knowledge and their ability to work together to achieve even greater learning. With these systems, the horizon of computer-facilitated collaborative learning has been expanded. Included among this expansion is identification of issues for general and special education students, and applications in a variety of domains, which have been suggested

Arithmetic Notation…now in 3D!

When people reason formally, they often make use of special notations—algebra and arithmetic are familiar examples. These notations are often treated as mere shorthand—a concise way of referring to meaningful mathematical concepts. Other authors have argued that people treat notations as pictures—literal diagrams of an imagined set of objects (Dörfler, 2003; Landy Goldstone, 2009). If notations depict objects that exist in space, then it makes sense to wonder how they are arranged not just in the two visible dimensions, but in depth. In four experiments, we find a consistent pattern: properties that increase mathematical precedence also tend to make objects appear closer in space. This alignment of formal pressures and informal pressures suggests that perceived depth may play a role in supporting computational reasoning processes. Although our primary focus is documenting the existence of depth illusions in notations, we also evaluate several sources of information that might guide depth judgments: availability of an object for computational actions, formal syntactic structure, relative symbol salience and voluntary attention shifts. We consider relationships between these nonexclusive possible sources of information in guiding how people judge depth in mathematics

Discrimination of orientation-defined texture edges

AbstractPreattentive texture segregation was examined using textures composed of randomly placed, oriented line segments. A difference in texture element orientation produced an illusory, or orientation-defined, texture edge. Subjects discriminated between two textures, one with a straight texture edge and one with a “wavy” texture edge. Across conditions the orientation of the texture elements and the orientation of the texture edge varied. Although the orientation difference across the texture edge (the “texture gradient”) is an important determinant of texture segregation performance, it is not the only one. Evidence from several experiments suggests that configural effects are also important. That is, orientation-defined texture edges are strongest when the texture elements (on one side of the edge) are parallel to the edge. This result is not consistent with a number of texture segregation models including feature- and filter-based models. One possible explanation is that the second-order channel used to detect a texture edge of a particular orientation gives greater weight to first-order input channels of that same orientation

A terahertz polarization insensitive dual band metamaterial absorber

Metamaterial absorbers have attracted considerable attention for applications in the terahertz range. In this Letter, we report the design, fabrication, and characterization of a terahertz dual band metamaterial absorber that shows two distinct absorption peaks with high absorption. By manipulating the periodic patterned structures as well as the dielectric layer thickness of the metal–dielectric–metal structure, significantly high absorption can be obtained at specific resonance frequencies. Finite-difference time-domain modeling is used to design the structure of the absorber. The fabricated devices have been characterized using a Fourier transform IR spectrometer. The experimental results show two distinct absorption peaks at 2.7 and 5.2 THz, which are in good agreement with the simulation. The absorption magnitudes at 2.7 and 5.2 THz are 0.68 and 0.74, respectively