Education in the Third Dimension: 3D Stereoscopics as a Cognitive Tool for Learning
Bibliographic record
Abstract
Three-dimensional (3D) stereoscopic technologies bring a whole new dimension to educational media and learning. Perception and orientation in the threedimensional world is propagated through many different object and environmental factors. The use of 3D stereoscopics as a cognitive tool for augmenting reality from the two-dimensional representations on a computer screen to one that is three-dimensional, interactive, and immersive provides researchers with a new tool to help understand how we learn. The purpose of this paper is to introduce and examine the techniques and technologies of 3D stereoscopics for learning. Implications and applications of 3D stereoscopics in education are proposed in hopes to begin a dialog for bringing 3D stereoscopic technology to the wider learning community in a meaningful and engaging way. Three-dimensional (3D) stereoscopic technologies bring a whole new dimension to educational media and learning. The ability to use inexpensive 3D glasses with regular computer monitors or media projectors makes stereoscopic images, animations, and movies accessible and affordable. The unique properties that stereoscopics provide, allows the user to gain a perspective that cannot be produced using virtual-3D or two-dimensional pictures. The distinctiveness of stereoscopic images, animation, and movies, make them an ideal media in which to explore, learn, and experience virtual three-dimensional worlds and objects. Stereoscopics, from the word stereo derived from Greek meaning relating to space and optic from the properties of sight (Howard, 2002), is a technique that has been used for over 150 years to produce images with three-dimensional depth. Used in such fields as visual data mining (E. Wegman, J. & J. Symanzik, 2002), cancer detection (Skelly, 2007), art (Layer, 1971; Sorenson & Russett, 1999), entertainment (Zone, 2007), and chemistry (Holford & Kempa, 1970), stereoscopic technologies have wide ranging applications and uses. However, given its long history and applications in research and industry, stereoscopic imaging has not a made a long or lasting impact in education. The purpose of this paper is to introduce and examine the techniques and technologies of 3D stereoscopics for learning. A brief introduction of three-dimensional perception and a history of stereoscopic techniques will be outlined to give some context of a technology that has been available for over 150 years. Second, the ranges of techniques that are available to produce stereoscopic images are reviewed. Third, applications of stereoscopics currently used in education and learning are addressed to draw some inferences of how these benefits can be realized by a larger educational audience. Fourth, a review of the literature on using graphics for learning, with specific reference to stereoscopic technologies as a cognitive tool is addressed. The unique properties of images, animations, and movies as they relate to learning, form a solid foundation in which to promote stereoscopics as an educational tool. Finally, implications and applications of 3D stereoscopics in education will be proposed to begin the dialog of bringing 3D stereoscopic technology to the wider learning community. 3D Vision, Perception, and Depth Perception and orientation in the three-dimensional world is propagated through many different object and environmental factors. Objects size, gradation of color, shadow, contrast, texture, occlusions, and movement all play a part in our perception of objects’ dimensionality in space (Mather, 2008). One of the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".