The effects of room design on computer-supported collaborative learning in a multi-touch classroom.

While research indicates that technology can be useful for supporting learning and collaboration, there is still relatively little uptake or widespread implementation of these technologies in classrooms. In this paper, we explore one aspect of the development of a multi-touch classroom, looking at two different designs of the classroom environment to explore how classroom layout may influence group interaction and learning. Three classes of students working in groups of four were taught in the traditional forward-facing room condition, while three classes worked in a centered room condition. Our results indicate that while the outcomes on tasks were similar across conditions, groups engaged in more talk (but not more off-task talk) in a centered room layout, than in a traditional forward-facing room. These results suggest that the use of technology in the classroom may be influenced by the location of the technology, both in terms of the learning outcomes and the interaction behaviors of students. The findings highlight the importance of considering the learning environment when designing technology to support learning, and ensuring that integration of technology into formal learning environments is done with attention to how the technology may disrupt, or contribute to, the classroom interaction practices

Around the table: Are multiple-touch surfaces better than single-touch for children's collaborative interactions?

This paper presents a classroom study that investigated the potential of using touch tabletop technology to support children's collaborative learning interactions. Children aged 7-10 worked in groups of three on a collaborative planning task in which they designed a seating plan for their classroom. In the single-touch condition, the tabletop surface allowed only one child to interact with the digital content at a time. In the multiple-touch condition, the children could interact with the digital content simultaneously. Results showed that touch condition did not affect the frequency or equity of interactions, but did influence the nature of children's discussion. In the multiple-touch condition, children talked more about the task; in the single-touch condition, they talked more about turn taking. We also report age and gender differences

Region-Based Image Retrieval Revisited

Region-based image retrieval (RBIR) technique is revisited. In early attempts at RBIR in the late 90s, researchers found many ways to specify region-based queries and spatial relationships; however, the way to characterize the regions, such as by using color histograms, were very poor at that time. Here, we revisit RBIR by incorporating semantic specification of objects and intuitive specification of spatial relationships. Our contributions are the following. First, to support multiple aspects of semantic object specification (category, instance, and attribute), we propose a multitask CNN feature that allows us to use deep learning technique and to jointly handle multi-aspect object specification. Second, to help users specify spatial relationships among objects in an intuitive way, we propose recommendation techniques of spatial relationships. In particular, by mining the search results, a system can recommend feasible spatial relationships among the objects. The system also can recommend likely spatial relationships by assigned object category names based on language prior. Moreover, object-level inverted indexing supports very fast shortlist generation, and re-ranking based on spatial constraints provides users with instant RBIR experiences.Comment: To appear in ACM Multimedia 2017 (Oral

Loss Guided Activation for Action Recognition in Still Images

One significant problem of deep-learning based human action recognition is that it can be easily misled by the presence of irrelevant objects or backgrounds. Existing methods commonly address this problem by employing bounding boxes on the target humans as part of the input, in both training and testing stages. This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features. However, we consider this solution is inefficient, since the bounding boxes might not be available. Hence, instead of using a person bounding box as an input, we introduce a human-mask loss to automatically guide the activations of the feature maps to the target human who is performing the action, and hence suppress the activations of misleading contexts. We propose a multi-task deep learning method that jointly predicts the human action class and human location heatmap. Extensive experiments demonstrate our approach is more robust compared to the baseline methods under the presence of irrelevant misleading contexts. Our method achieves 94.06\% and 40.65\% (in terms of mAP) on Stanford40 and MPII dataset respectively, which are 3.14\% and 12.6\% relative improvements over the best results reported in the literature, and thus set new state-of-the-art results. Additionally, unlike some existing methods, we eliminate the requirement of using a person bounding box as an input during testing.Comment: Accepted to appear in ACCV 201