Mobile Games for Learning

Today, pupils at the age of 15 have spent their entire life surrounded by and interacting with diverse forms of computers. It is a routine part of their day-to-day life and by now computer-literacy is common at very early age. Over the past five years, technology for teens has become predominantly mobile and ubiquitous within every aspect of their lives. To them, being online is an implicitness. In Germany, 88% of youth aged between 12-19 years own a smartphone and about 20% use the Internet via tablets. Meanwhile, more and more young learners bring their devices into the classroom and pupils increasingly demand for innovative and motivating learning scenarios that strongly respond to their habits of using media. With this development, a shift of paradigm is slowly under way with regard to the use of mobile technology in education. By now, a large body of literature exists, that reports concepts, use-cases and practical studies for effectively using technology in education. Within this field, a steadily growing body of research has developed that especially examines the use of digital games as instructional strategy. The core concern of this thesis is the design of mobile games for learning. The conditions and requirements that are vital in order to make mobile games suitable and effective for learning environments are investigated. The base for exploration is the pattern approach as an established form of templates that provide solutions for recurrent problems. Building on this acknowledged form of exchanging and re-using knowledge, patterns for game design are used to classify the many gameplay rules and mechanisms in existence. This research draws upon pattern descriptions to analyze learning game concepts and to abstract possible relationships between gameplay patterns and learning outcomes. The linkages that surface are the starting bases for a series of game design concepts and their implementations are subsequently evaluated with regard to learning outcomes. The findings and resulting knowledge from this research is made accessible by way of implications and recommendations for future design decisions.

Canine keratinocytes upregulate type I interferons and proinflammatory cytokines in response to poly(dA:dT) but not to canine papillomavirus.

Papillomaviruses (PV) are double stranded (ds) DNA viruses that infect epithelial cells within the skin or mucosa, most often causing benign neoplasms that spontaneously regress. The immune system plays a key role in the defense against PVs. Since these viruses infect keratinocytes, we wanted to investigate the role of the keratinocyte in initiating an immune response to canine papillomavirus-2 (CPV-2) in the dog. Keratinocytes express a variety of pattern recognition receptors (PRR) to distinguish different cutaneous pathogens and initiate an immune response. We examined the mRNA expression patterns for several recently described cytosolic nucleic acid sensing PRRs in canine monolayer keratinocyte cultures using quantitative reverse transcription-polymerase chain reaction. Unstimulated normal cells were found to express mRNA for melanoma differentiation associated gene 5 (MDA5), retinoic acid-inducible gene I (RIG-I), DNA-dependent activation of interferon regulatory factors, leucine rich repeat flightless interacting protein 1, and interferon inducible gene 16 (IFI16), as well as their adaptor molecules myeloid differentiation primary response gene 88, interferon-β promoter stimulator 1, and endoplasmic reticulum-resident transmembrane protein stimulator of interferon genes. When stimulated with synthetic dsDNA [poly(dA:dT)] or dsRNA [poly(I:C)], keratinocytes responded with increased mRNA expression levels for interleukin-6, tumor necrosis factor-α, interferon-β, RIG-I, IFI16, and MDA5. There was no detectable increase in mRNA expression, however, in keratinocytes infected with CPV-2. Furthermore, CPV-2-infected keratinocytes stimulated with poly(dA:dT) and poly(I:C) showed similar mRNA expression levels for these gene products when compared with expression levels in uninfected cells. These results suggest that although canine keratinocytes contain functional PRRs that can recognize and respond to dsDNA and dsRNA ligands, they do not appear to recognize or initiate a similar response to CPV-2.

Low rank subspace clustering (LRSC)

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive we mean a dictionary whose atoms can be expressed as linear combinations of themselves with low-rank coefficients. In the case of noisy data, our key contribution is to show that this non-convex matrix decomposition problem can be solved in closed form from the SVD of the noisy data matrix. The solution involves a novel polynomial thresholding operator on the singular values of the data matrix, which requires minimal shrinkage. For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage. For multiple subspaces, the low-rank coefficients obtained by our framework can be used to construct a data affinity matrix from which the clustering of the data according to the subspaces can be obtained by spectral clustering. In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator. Experiments on motion segmentation and face clustering show that our framework performs on par with state-of-the-art techniques at a reduced computational cost.

Searching for the majority: algorithms of voluntary control.

Voluntary control of information processing is crucial to allocate resources and prioritize the processes that are most important under a given situation; the algorithms underlying such control, however, are often not clear. We investigated possible algorithms of control for the performance of the majority function, in which participants searched for and identified one of two alternative categories (left or right pointing arrows) as composing the majority in each stimulus set. We manipulated the amount (set size of 1, 3, and 5) and content (ratio of left and right pointing arrows within a set) of the inputs to test competing hypotheses regarding mental operations for information processing. Using a novel measure based on computational load, we found that reaction time was best predicted by a grouping search algorithm as compared to alternative algorithms (i.e., exhaustive or self-terminating search). The grouping search algorithm involves sampling and resampling of the inputs before a decision is reached. These findings highlight the importance of investigating the implications of voluntary control via algorithms of mental operations.

Spatial modulation of primate inferotemporal responses by eye position.

BACKGROUND: A key aspect of representations for object recognition and scene analysis in the ventral visual stream is the spatial frame of reference, be it a viewer-centered, object-centered, or scene-based coordinate system. Coordinate transforms from retinocentric space to other reference frames involve combining neural visual responses with extraretinal postural information. METHODOLOGY/PRINCIPAL FINDINGS: We examined whether such spatial information is available to anterior inferotemporal (AIT) neurons in the macaque monkey by measuring the effect of eye position on responses to a set of simple 2D shapes. We report, for the first time, a significant eye position effect in over 40% of recorded neurons with small gaze angle shifts from central fixation. Although eye position modulates responses, it does not change shape selectivity. CONCLUSIONS/SIGNIFICANCE: These data demonstrate that spatial information is available in AIT for the representation of objects and scenes within a non-retinocentric frame of reference. More generally, the availability of spatial information in AIT calls into questions the classic dichotomy in visual processing that associates object shape processing with ventral structures such as AIT but places spatial processing in a separate anatomical stream projecting to dorsal structures.

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Improved cerebellar tissue classification on magnetic resonance images of brain.

PURPOSE: To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation. MATERIALS AND METHODS: Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template. The cerebellum was then separated by masking the anatomical image with individual FLAIR images. Tissues in both the cerebellum and rest of the brain were separately classified using hidden Markov random field (HMRF), a parametric method, and then combined to obtain tissue classification of the whole brain. The proposed method for tissue classification on real MR brain images was evaluated subjectively by two experts. The segmentation results on Brainweb images with varying noise and intensity nonuniformity levels were quantitatively compared with the ground truth by computing the Dice similarity indices. RESULTS: The proposed method significantly improved the cerebellar tissue classification on all normal volunteers included in this study without compromising the classification in remaining part of the brain. The average similarity indices for gray matter (GM) and white matter (WM) in the cerebellum are 89.81 (+/-2.34) and 93.04 (+/-2.41), demonstrating excellent performance of the proposed methodology. CONCLUSION: The proposed method significantly improved tissue classification in the cerebellum. The GM was overestimated when segmentation was performed on the whole brain as a single object.