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Affordances of External Representations in Instructional Design: The Effect of Narrative and Imagery in Learning.
Consisting of both theoretical and empirical inquires, this study examines the primary functions of narrative and the relationship between narrative and mental imagery. The study proposes a new framework to interpret semiotic resources. Combining this with the linguistic functional theory of Halliday (1978), a functional method to empirically investigate semiotic representations was also developed. In the empirical inquiry, the study developed a latent construct method to empirically test the effects of narrative in a real learning situation. This study is the first to investigate the functional relationship between narrative and mental imagery, and among the first to suggest a theory and empirically investigate representations of a multimodal nature. The study is also among the first to use latent constructs to investigate the learning experience in a real educational setting. Data were collected from 190 library professionals who enrolled in three sections (two in narrative and one in plain text) of an online course administered through Vista 4.0 and who completed the course and responded to several instruments. Essay data (n = 82 x 2) were analyzed using content analysis based on the narrative analysis framework developed. Quantitative data analysis methods include univariate data analysis, factor analysis, and structural equation modeling that tests the proposed model and verifies the relationships between the latent variables. Overall, the findings support the hypotheses about the functional effects of narrative identified, and narrative is found to provide a favorable and positive learning context which is tested by the proposed model of learning experience measured by several latent constructs (X2 = 31.67, df = 47, p = .9577, RMSEA = .00, SRMR = .047, NNFI = 1.05, CFI = 1.00, and GFI = .94). The results indicate that participants who enrolled in the narrative sections of the course gained higher creative scores and showed better results in performance-based and attribution-based experiences. The model testing results indicate that even though more time spent during learning led to better outcome and performance in both groups, more time spent means more satisfaction for the individuals in the narrative group, but led to less satisfaction for the individuals in the non-narrative group
Recognition of mathematical handwriting on whiteboards
Automatic recognition of handwritten mathematics has enjoyed significant improvements in the past decades. In particular, online recognition of mathematical formulae has seen a number of important advancements. However, in reality most mathematics is still taught and developed on regular whiteboards and offline recognition remains an open and challenging task in this area. In this thesis we develop methods to recognise mathematics from static images of handwritten expressions on whiteboards, while leveraging the strength of online recognition systems by transforming offline data into online information. Our approach is based on trajectory recovery techniques, that allow us to reconstruct the actual stroke information necessary for online recognition. To this end we develop a novel recognition process especially designed to deal with whiteboards by prudently extracting information from colour images. To evaluate our methods we use an online recogniser for the recognition task, which is specifically trained for recognition of maths symbols. We present our experiments with varying quality and sources of images. In particular, we have used our approach successfully in a set of experiments using Google Glass for capturing images from whiteboards, in which we achieve highest accuracies of 88.03% and 84.54% for segmentation and recognition of mathematical symbols respectively
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