Virual world users evaluated according to environment design, task based adn affective attention measures

This paper presents research that engages with virtual worlds for education users to understand design of these applications for their needs. An in-depth multi-method investigation from 12 virtual worlds participants was undertaken in three stages; initially a small scale within-subjects eye-tracking comparison was made between the role playing game 'RuneScape' and the virtual social world 'Second Life', secondly an in-depth evaluation of eye-tracking data for Second Life tasks (i.e. avatar, object and world based) was conducted, finally a qualitative evaluation of Second Life tutorials in comparative 3D situations (i.e. environments that are; realistic to surreal, enclosed to open, formal to informal) was conducted. Initial findings identified increased users attention within comparable gaming and social world interactions. Further analysis identified that 3D world focused interactions increased participants' attention more than object and avatar tasks. Finally different 3D situation designs altered levels of task engagement and distraction through perceptions of comfort, fun and fear. Ultimately goal based and environment interaction tasks can increase attention and potentially immersion. However, affective perceptions of 3D situations can negatively impact on attention. An objective discussion of the limitations and benefits of virtual world immersion for student learning is presented

Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment

This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs. online discussion) in computer-supported collaborative learning (CSCL) on student knowledge construction. Students (N = 87) were first randomly assigned to the two information presentation conditions to work individually on a case-based assignment in IDLM. Students who received information during learning task clusters tended to show better results on knowledge construction than those who received information only before each cluster. The students within the two separate information presentation conditions were then randomly assigned to pairs to discuss the outcomes of their assignments under either the personal discussion or online discussion condition in CSCL. When supportive information had been presented before each learning task cluster, online discussion led to better results than personal discussion. When supportive information had been presented during the learning task clusters, however, the online and personal discussion conditions had no differential effect on knowledge construction. Online discussion in CSCL appeared to compensate for suboptimal timing of presentation of supportive information before the learning task clusters in IDLM

Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories

With the explosion in the availability of spatio-temporal tracking data in modern sports, there is an enormous opportunity to better analyse, learn and predict important events in adversarial group environments. In this paper, we propose a deep decision tree architecture for discriminative dictionary learning from adversarial multi-agent trajectories. We first build up a hierarchy for the tree structure by adding each layer and performing feature weight based clustering in the forward pass. We then fine tune the player role weights using back propagation. The hierarchical architecture ensures the interpretability and the integrity of the group representation. The resulting architecture is a decision tree, with leaf-nodes capturing a dictionary of multi-agent group interactions. Due to the ample volume of data available, we focus on soccer tracking data, although our approach can be used in any adversarial multi-agent domain. We present applications of proposed method for simulating soccer games as well as evaluating and quantifying team strategies.Comment: To appear in 4th International Workshop on Computer Vision in Sports (CVsports) at CVPR 201

Nose Heat: Exploring Stress-induced Nasal Thermal Variability through Mobile Thermal Imaging

Automatically monitoring and quantifying stress-induced thermal dynamic information in real-world settings is an extremely important but challenging problem. In this paper, we explore whether we can use mobile thermal imaging to measure the rich physiological cues of mental stress that can be deduced from a person's nose temperature. To answer this question we build i) a framework for monitoring nasal thermal variable patterns continuously and ii) a novel set of thermal variability metrics to capture a richness of the dynamic information. We evaluated our approach in a series of studies including laboratory-based psychosocial stress-induction tasks and real-world factory settings. We demonstrate our approach has the potential for assessing stress responses beyond controlled laboratory settings

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 359)

This bibliography lists 164 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jan. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Brainatwork: Logging Cognitive Engagement and Tasks in the Workplace Using Electroencephalography

Today's workplaces are dynamic and complex. Digital data sources such as email and video conferencing aim to support workers but also add to their burden of multitasking. Psychophysiological sensors such as Electroencephalography (EEG) can provide users with cues about their cognitive state. We introduce BrainAtWork, a workplace engagement and task logger which shows users their cognitive state while working on different tasks. In a lab study with eleven participants working on their own real-world tasks, we gathered 16 hours of EEG and PC logs which were labeled into three classes: central, peripheral and meta work. We evaluated the usability of BrainAtWork via questionnaires and interviews. We investigated the correlations between measured cognitive engagement from EEG and subjective responses from experience sampling probes. Using random forests classification, we show the feasibility of automatically labeling work tasks into work classes. We discuss how BrainAtWork can support workers on the long term through encouraging reflection and helping in task scheduling