First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Characterizing performance via behavior co-occurrences in a 3D collaborative virtual learning environment : an exploratory study of performance and design

The iSocial 3D CVLE is an innovative design for addressing special needs at a distance that require social and active learning. This exploratory retrospective case study explored innovative methods of analyzing co-occurrences of behavior to gain insight into understanding and evaluating student performance and 3D CVLE design. Visualization techniques were employed to model student behavior within similarly structured activities. Linear mixed models revealed that student performance significantly differed across environments. In addition, environmental design attributes were identified through qualitative memos. General behavior patterns were associated with design environment attributes, warranting further study.Includes bibliographical references (pages 258-272)

Killer Apps: Developing Novel Applications That Enhance Team Coordination, Communication, and Effectiveness

As part of the Lorentz workshop, “Interdisciplinary Insights into Group and Team Dynamics,” held in Leiden, Netherlands, this article describes how Geeks and Groupies (computer and social scientists) may benefit from interdisciplinary collaboration toward the development of killer apps in team contexts that are meaningful and challenging for both. First, we discuss interaction processes during team meetings as a research topic for both Groupies and Geeks. Second, we highlight teamwork in health care settings as an interdisciplinary research challenge. Third, we discuss how an automated solution for optimal team design could benefit team effectiveness and feed into team-based interventions. Fourth, we discuss team collaboration in massive open online courses as a challenge for both Geeks and Groupies. We argue for the necessary integration of social and computational research insights and approaches. In the hope of inspiring future interdisciplinary collaborations, we develop criteria for evaluating killer apps—including the four proposed here—and discuss future research challenges and opportunities that potentially derive from these developments

An introduction to video methods in organizational research

Video has become a methodological tool of choice for many researchers in social science, but video methods are relatively new to the field of organization studies. This article is an introduction to video methods. First, we situate video methods relative to other kinds of research, suggesting that video recordings and analyses can be used to replace or supplement other approaches, not only observational studies but also retrospective methods such as interviews and surveys. Second, we describe and discuss various features of video data in relation to ontological assumptions that researchers may bring to their research design. Video involves both opportunities and pitfalls for researchers, who ought to use video methods in ways that are consistent with their assumptions about the world and human activity. Third, we take a critical look at video methods by reporting progress that has been made while acknowledging gaps and work that remains to be done. Our critical considerations point repeatedly at articles in this special issue, which represent recent and important advances in video method

A Novel Multimodal Approach for Studying the Dynamics of Curiosity in Small Group Learning

Curiosity is a vital metacognitive skill in educational contexts, leading to creativity, and a love of learning. And while many school systems increasingly undercut curiosity by teaching to the test, teachers are increasingly interested in how to evoke curiosity in their students to prepare them for a world in which lifelong learning and reskilling will be more and more important. One aspect of curiosity that has received little attention, however, is the role of peers in eliciting curiosity. We present what we believe to be the first theoretical framework that articulates an integrated socio-cognitive account of curiosity that ties observable behaviors in peers to underlying curiosity states. We make a bipartite distinction between individual and interpersonal functions that contribute to curiosity, and multimodal behaviors that fulfill these functions. We validate the proposed framework by leveraging a longitudinal latent variable modeling approach. Findings confirm a positive predictive relationship between the latent variables of individual and interpersonal functions and curiosity, with the interpersonal functions exercising a comparatively stronger influence. Prominent behavioral realizations of these functions are also discovered in a data-driven manner. We instantiate the proposed theoretical framework in a set of strategies and tactics that can be incorporated into learning technologies to indicate, evoke, and scaffold curiosity. This work is a step towards designing learning technologies that can recognize and evoke moment-by-moment curiosity during learning in social contexts and towards a more complete multimodal learning analytics. The underlying rationale is applicable more generally for developing computer support for other metacognitive and socio-emotional skills.Comment: arXiv admin note: text overlap with arXiv:1704.0748