‘Immersed in Art’: Engaged learning in art and design history

2021 ◽  
Vol 17 (2) ◽  
pp. 235-252
Author(s):  
Lisa Chandler ◽  
Alistair Ward ◽  
Lisa Ward

Established approaches to art history pedagogy typically involve a primarily passive form of instruction incorporating the viewing of works projected on screens. While such approaches can convey valuable information, they can also contribute to student disengagement and do not necessarily support deep learning. This article examines three learning initiatives incorporating an immersive teaching space to determine how these forms of technology-enhanced active learning might enhance student comprehension and engagement. The article considers how learning design incorporating the affordances of such immersive environments can provide multimodal learning experiences that stimulate student imaginations and support learning and engagement in a manner that complements rather than replaces traditional modes of instruction.

Author(s):  
Christopher Totten

This chapter explores art history to establish parallels between the current state of the game art field and historical art and architectural periods. In doing so, it proposes methods for both making and studying games that subvert the popular analysis trends of game art that are typically based on the history of game graphics and technology. The chapter will then demonstrate the use of art and design history in game development by discussing the Atelier Games project, which utilizes the styles and techniques of established artists and art movements to explore the viability of classic methods for the production of game art and game mechanics.


Author(s):  
Prathmesh Madhu ◽  
Ronak Kosti ◽  
Lara Mührenberg ◽  
Peter Bell ◽  
Andreas Maier ◽  
...  
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Author(s):  
Hao Zheng ◽  
Lin Yang ◽  
Jianxu Chen ◽  
Jun Han ◽  
Yizhe Zhang ◽  
...  

Deep learning has been applied successfully to many biomedical image segmentation tasks. However, due to the diversity and complexity of biomedical image data, manual annotation for training common deep learning models is very timeconsuming and labor-intensive, especially because normally only biomedical experts can annotate image data well. Human experts are often involved in a long and iterative process of annotation, as in active learning type annotation schemes. In this paper, we propose representative annotation (RA), a new deep learning framework for reducing annotation effort in biomedical image segmentation. RA uses unsupervised networks for feature extraction and selects representative image patches for annotation in the latent space of learned feature descriptors, which implicitly characterizes the underlying data while minimizing redundancy. A fully convolutional network (FCN) is then trained using the annotated selected image patches for image segmentation. Our RA scheme offers three compelling advantages: (1) It leverages the ability of deep neural networks to learn better representations of image data; (2) it performs one-shot selection for manual annotation and frees annotators from the iterative process of common active learning based annotation schemes; (3) it can be deployed to 3D images with simple extensions. We evaluate our RA approach using three datasets (two 2D and one 3D) and show our framework yields competitive segmentation results comparing with state-of-the-art methods.


2019 ◽  
Vol 20 (2) ◽  
pp. 3-27
Author(s):  
Sharon Bratt

Educational action research bridges the gap between theory and practice; where the learning design is the proposed hypothesis and the classroom is where it is field-tested by the teacher as researcher (McKernan, 2007; Stenhouse, 1975). Through this lens we see inquiry as a deepened understanding of one’s own practice. The purpose of this study was to critically evaluate the design of an introduction to data visualization course with community-engaged learning as its core pedagogy.  Results show that many of the core elements of community-engaged learning were achieved at the exemplary level, based on the assessment matrix developed by Dahan and Seligsohn (2003). Several recommendations emerged, both situational and generalizable, which could enhance the redesign and improve the experience for practitioners who use community-engaged learning as a core pedagogy.


2021 ◽  
Author(s):  
Jinran Qie ◽  
Erfan Khoram ◽  
Dianjing Liu ◽  
Ming Zhou ◽  
Li Gao

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