Personalizing the design of computer‐based instruction to enhance learning

This paper reports two studies designed to investigate the effect on learning outcomes of matching individuals’ preferred cognitive styles to computer‐based instructional (CBI) material. Study 1 considered the styles individually as Verbalizer, Imager, Wholist and Analytic. Study 2 considered the bi‐dimensional nature of cognitive styles in order to assess the full ramification of cognitive styles on learning: Analytic/Imager, Analytic/ Verbalizer, Wholist/Imager and the Wholist/Verbalizer. The mix of images and text, the nature of the text material, use of advance organizers and proximity of information to facilitate meaningful connections between various pieces of information were some of the considerations in the design of the CBI material. In a quasi‐experimental format, students’ cognitive styles were analysed by Cognitive Style Analysis (CSA) software. On the basis of the CSA result, the system defaulted students to either matched or mismatched CBI material by alternating between the two formats. The instructional material had a learning and a test phase. Learning outcome was tested on recall, labelling, explanation and problem‐solving tasks. Comparison of the matched and mismatched instruction did not indicate significant difference between the groups, but the consistently better performance by the matched group suggests potential for further investigations where the limitations cited in this paper are eliminated. The result did indicate a significant difference between the four cognitive styles with the Wholist/Verbalizer group performing better then all other cognitive styles. Analysing the difference between cognitive styles on individual test tasks indicated significant difference on recall, labelling and explanation, suggesting that certain test tasks may suit certain cognitive styles

archiTECTONICS: Pre- and Trans-Disciplinary Reference in Beginning Design

This presentation was part of the session : Pedagogy: Procedures, Scaffolds, Strategies, Tactics24th National Conference on the Beginning Design StudentPedagogical approaches to beginning design in architecture often assume trans-disciplinary modes of exploration to filter problem parameters and sculpt perceptual outlook for iterative potential. A closer look suggests moments within the architectural design process that come before, or around, the discipline itself in the form of other disciplines accompanied by basic principles, such as Visual Literacy. Iterating and perceiving through every disciplinary dynamic, instance, and/or action in the process of designing transcends, builds, and structures its neighbor for explorative sequencing, intention, and growth of sensibilities in design resolution. An acute awareness of disciplinary state, in a maturing design process, can alleviate obscurity of ideological foundation and facilitate growth for trans-disciplinary thinking, making, and communicating in a root discipline such as architecture. How can beginning design instructors guide young designers to keep ideas and concepts for design in focus, recognizing that root disciplines transcend pre- and trans-disciplinary processes? Does recognizing variation in pace, induced by digital and analog tools, and intention of design iteration, by discipline, instill clarity by pre-disciplinary thinking, perception, and operation? Trans-disciplinary exercise provokes awareness of pre-disciplinary foundations furthering possibilities for unique root-disciplinary understandings and results. The developed exercise, archiTECTONIC, recognizes and cycles through reasoning, conceptualization, and iteration in a trans-disciplinary sequence, allowing the beginning design student to recognize pre-disciplinary ideology, pace, and purpose when processing ideas through fundamentals of architectural design. Engaging this as a strategy for seeing, thinking, and maneuvering through a dynamic process provides design liberty and clarity for processing and communicating in a root discipline, in this case architecture

Enactivism and ethnomethodological conversation analysis as tools for expanding Universal Design for Learning: the case of visually impaired mathematics students

Blind and visually impaired mathematics students must rely on accessible materials such as tactile diagrams to learn mathematics. However, these compensatory materials are frequently found to offer students inferior opportunities for engaging in mathematical practice and do not allow sensorily heterogenous students to collaborate. Such prevailing problems of access and interaction are central concerns of Universal Design for Learning (UDL), an engineering paradigm for inclusive participation in cultural praxis like mathematics. Rather than directly adapt existing artifacts for broader usage, UDL process begins by interrogating the praxis these artifacts serve and then radically re-imagining tools and ecologies to optimize usability for all learners. We argue for the utility of two additional frameworks to enhance UDL efforts: (a) enactivism, a cognitive-sciences view of learning, knowing, and reasoning as modal activity; and (b) ethnomethodological conversation analysis (EMCA), which investigates participants’ multimodal methods for coordinating action and meaning. Combined, these approaches help frame the design and evaluation of opportunities for heterogeneous students to learn mathematics collaboratively in inclusive classrooms by coordinating perceptuo-motor solutions to joint manipulation problems. We contextualize the thesis with a proposal for a pluralist design for proportions, in which a pair of students jointly operate an interactive technological device

Absorbing new subjects: holography as an analog of photography

I discuss the early history of holography and explore how perceptions, applications, and forecasts of the subject were shaped by prior experience. I focus on the work of Dennis Gabor (1900–1979) in England,Yury N. Denisyuk (b. 1924) in the Soviet Union, and Emmett N. Leith (1927–2005) and Juris Upatnieks (b. 1936) in the United States. I show that the evolution of holography was simultaneously promoted and constrained by its identification as an analog of photography, an association that influenced its assessment by successive audiences of practitioners, entrepreneurs, and consumers. One consequence is that holography can be seen as an example of a modern technical subject that has been shaped by cultural influences more powerfully than generally appreciated. Conversely, the understanding of this new science and technology in terms of an older one helps to explain why the cultural effects of holography have been more muted than anticipated by forecasters between the 1960s and 1990s

Digital Urban - The Visual City

Nothing in the city is experienced by itself for a city’s perspicacity is the sum of its surroundings. To paraphrase Lynch (1960), at every instant, there is more than we can see and hear. This is the reality of the physical city, and thus in order to replicate the visual experience of the city within digital space, the space itself must convey to the user a sense of place. This is what we term the “Visual City”, a visually recognisable city built out of the digital equivalent of bricks and mortar, polygons, textures, and most importantly data. Recently there has been a revolution in the production and distribution of digital artefacts which represent the visual city. Digital city software that was once in the domain of high powered personal computers, research labs and professional software are now in the domain of the public-at-large through both the web and low-end home computing. These developments have gone hand in hand with the re-emergence of geography and geographic location as a way of tagging information to non-proprietary web-based software such as Google Maps, Google Earth, Microsoft’s Virtual Earth, ESRI’s ArcExplorer, and NASA’s World Wind, amongst others. The move towards ‘digital earths’ for the distribution of geographic information has, without doubt, opened up a widespread demand for the visualization of our environment where the emphasis is now on the third dimension. While the third dimension is central to the development of the digital or visual city, this is not the only way the city can be visualized for a number of emerging tools and ‘mashups’ are enabling visual data to be tagged geographically using a cornucopia of multimedia systems. We explore these social, textual, geographical, and visual technologies throughout this chapter

Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery

Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists for some of the classes. ZSL aims to build a recognition model for new unseen categories by relating them to seen classes that were previously learned. We establish this relation by learning a compatibility function between image features extracted via a convolutional neural network and auxiliary information that describes the semantics of the classes of interest by using training samples from the seen classes. Then, we show how knowledge transfer can be performed for the unseen classes by maximizing this function during inference. We introduce a new data set that contains 40 different types of street trees in 1-ft spatial resolution aerial data, and evaluate the performance of this model with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information. The experiments show that the proposed model achieves 14.3% recognition accuracy for the classes with no training examples, which is significantly better than a random guess accuracy of 6.3% for 16 test classes, and three other ZSL algorithms.Comment: G. Sumbul, R. G. Cinbis, S. Aksoy, "Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery", IEEE Transactions on Geoscience and Remote Sensing (TGRS), in press, 201

Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks

Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin