Blending Video Games Into Language Learning

Around 2 billion people worldwide engage in video games and a similar number of English language learners are anticipated by the year 2020. It can be assumed that many language learners are also ‘gamers', and that a language learner may play a video game to learn English. This article focuses on the language learning affordances in offline video games. General game-based learning principles identified by Gee are used as the method to identify and classify the learning affordances in a selection of video games. These learning principles are explained and then used to detail general learning opportunities inherent in a variety of video games. It suggests that language learning opportunities on video-games are too varied and that the scaffolding guidance of a teacher might be needed. It concludes by proposing that contextualized live video-game-like immersive experiences could also be conducive to language learning

SLR - Análisis del Aprendizaje Basado en Juegos Serios en las Prácticas de los Estudios de Ingeniería

Este trabajo se trata de un Análisis Sistemático de la Literatura del uso de los juegos serios en los estudios de ingeniería.15 página

A Mimetic Strategy to Engage Voluntary Physical Activity In Interactive Entertainment

We describe the design and implementation of a vision based interactive entertainment system that makes use of both involuntary and voluntary control paradigms. Unintentional input to the system from a potential viewer is used to drive attention-getting output and encourage the transition to voluntary interactive behaviour. The iMime system consists of a character animation engine based on the interaction metaphor of a mime performer that simulates non-verbal communication strategies, without spoken dialogue, to capture and hold the attention of a viewer. The system was developed in the context of a project studying care of dementia sufferers. Care for a dementia sufferer can place unreasonable demands on the time and attentional resources of their caregivers or family members. Our study contributes to the eventual development of a system aimed at providing relief to dementia caregivers, while at the same time serving as a source of pleasant interactive entertainment for viewers. The work reported here is also aimed at a more general study of the design of interactive entertainment systems involving a mixture of voluntary and involuntary control.Comment: 6 pages, 7 figures, ECAG08 worksho

ContextVP: Fully Context-Aware Video Prediction

Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions. We identify an important contributing factor for imprecise predictions that has not been studied adequately in the literature: blind spots, i.e., lack of access to all relevant past information for accurately predicting the future. To address this issue, we introduce a fully context-aware architecture that captures the entire available past context for each pixel using Parallel Multi-Dimensional LSTM units and aggregates it using blending units. Our model outperforms a strong baseline network of 20 recurrent convolutional layers and yields state-of-the-art performance for next step prediction on three challenging real-world video datasets: Human 3.6M, Caltech Pedestrian, and UCF-101. Moreover, it does so with fewer parameters than several recently proposed models, and does not rely on deep convolutional networks, multi-scale architectures, separation of background and foreground modeling, motion flow learning, or adversarial training. These results highlight that full awareness of past context is of crucial importance for video prediction.Comment: 19 pages. ECCV 2018 oral presentation. Project webpage is at https://wonmin-byeon.github.io/publication/2018-ecc