187 research outputs found
Biocybernetic Adaptation Strategies: Machine awareness of human state for improved operational performance
Human operators interacting with machines or computers continually adapt to the needs of the system ideally resulting in optimal performance. In some cases, however, deteriorated performance is an outcome. Adaptation to the situation is a strength expected of the human operator which is often accomplished by the human through self-regulation of mental state. Adaptation is at the core of the human operator’s activity, and research has demonstrated that the implementation of a feedback loop can enhance this natural skill to improve training and human/machine interaction. Biocybernetic adaptation involves a “loop upon a loop,” which may be visualized as a superimposed loop which senses a physiological signal and influences the operator’s task at some point. Biocybernetic adaptation in, for example, physiologically adaptive automation employs the “steering” sense of “cybernetic,” and serves a transitory adaptive purpose – to better serve the human operator by more fully representing their responses to the system. The adaptation process usually makes use of an assessment of transient cognitive state to steer a functional aspect of a system that is external to the operator’s physiology from which the state assessment is derived. Therefore, the objective of this paper is to detail the structure of biocybernetic systems regarding the level of engagement of interest for adaptive systems, their processing pipeline, and the adaptation strategies employed for training purposes, in an effort to pave the way towards machine awareness of human state for self-regulation and improved operational performance
Método para la evaluación de usabilidad de sitios web transaccionales basado en el proceso de inspección heurística
La usabilidad es considerada uno de los factores más importantes en el desarrollo de productos
de software. Este atributo de calidad está referido al grado en que, usuarios específicos de un
determinado aplicativo, pueden fácilmente hacer uso del software para lograr su propósito. Dada
la importancia de este aspecto en el éxito de las aplicaciones informáticas, múltiples métodos de
evaluación han surgido como instrumentos de medición que permiten determinar si la propuesta
de diseño de la interfaz de un sistema de software es entendible, fácil de usar, atractiva y agradable
al usuario. El método de evaluación heurística es uno de los métodos más utilizados en el área de
Interacción Humano-Computador (HCI) para este propósito debido al bajo costo de su ejecución
en comparación otras técnicas existentes. Sin embargo, a pesar de su amplio uso extensivo durante
los últimos años, no existe un procedimiento formal para llevar a cabo este proceso de evaluación.
Jakob Nielsen, el autor de esta técnica de inspección, ofrece únicamente lineamientos generales
que, según la investigación realizada, tienden a ser interpretados de diferentes maneras por los
especialistas. Por tal motivo, se ha desarrollado el presente proyecto de investigación que tiene
como objetivo establecer un proceso sistemático, estructurado, organizado y formal para llevar a
cabo evaluaciones heurísticas a productos de software. En base a un análisis exhaustivo realizado
a aquellos estudios que reportan en la literatura el uso del método de evaluación heurística como
parte del proceso de desarrollo de software, se ha formulado un nuevo método de evaluación
basado en cinco fases: (1) planificación, (2) entrenamiento, (3) evaluación, (4) discusión y (5)
reporte. Cada una de las fases propuestas que componen el protocolo de inspección contiene un
conjunto de actividades bien definidas a ser realizadas por el equipo de evaluación como parte
del proceso de inspección. Asimismo, se han establecido ciertos roles que deberán desempeñar
los integrantes del equipo de inspectores para asegurar la calidad de los resultados y un apropiado
desarrollo de la evaluación heurística. La nueva propuesta ha sido validada en dos escenarios
académicos distintos (en Colombia, en una universidad pública, y en Perú, en dos universidades
tanto en una pública como en una privada) demostrando en todos casos que es posible identificar
más problemas de usabilidad altamente severos y críticos cuando un proceso estructurado de
inspección es adoptado por los evaluadores. Otro aspecto favorable que muestran los resultados
es que los evaluadores tienden a cometer menos errores de asociación (entre heurística que es
incumplida y problemas de usabilidad identificados) y que la propuesta es percibida como fácil
de usar y útil. Al validarse la nueva propuesta desarrollada por el autor de este estudio se consolida
un nuevo conocimiento que aporta al bagaje cultural de la ciencia.Tesi
A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
People increasingly use videos on the Web as a source for learning. To
support this way of learning, researchers and developers are continuously
developing tools, proposing guidelines, analyzing data, and conducting
experiments. However, it is still not clear what characteristics a video should
have to be an effective learning medium. In this paper, we present a
comprehensive review of 257 articles on video-based learning for the period
from 2016 to 2021. One of the aims of the review is to identify the video
characteristics that have been explored by previous work. Based on our
analysis, we suggest a taxonomy which organizes the video characteristics and
contextual aspects into eight categories: (1) audio features, (2) visual
features, (3) textual features, (4) instructor behavior, (5) learners
activities, (6) interactive features (quizzes, etc.), (7) production style, and
(8) instructional design. Also, we identify four representative research
directions: (1) proposals of tools to support video-based learning, (2) studies
with controlled experiments, (3) data analysis studies, and (4) proposals of
design guidelines for learning videos. We find that the most explored
characteristics are textual features followed by visual features, learner
activities, and interactive features. Text of transcripts, video frames, and
images (figures and illustrations) are most frequently used by tools that
support learning through videos. The learner activity is heavily explored
through log files in data analysis studies, and interactive features have been
frequently scrutinized in controlled experiments. We complement our review by
contrasting research findings that investigate the impact of video
characteristics on the learning effectiveness, report on tasks and technologies
used to develop tools that support learning, and summarize trends of design
guidelines to produce learning video
On Data-driven systems analyzing, supporting and enhancing users’ interaction and experience
Tesis doctoral en inglés y resumen extendido en español[EN] The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions.
Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data.
This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task.
Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human-Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs.
As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience.
The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and data-driven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques
On data-driven systems analyzing, supporting and enhancing users’ interaction and experience
[EN]The research areas of Human-Computer Interaction and Software Architectures have
been traditionally treated separately, but in the literature, many authors made efforts to
merge them to build better software systems. One of the common gaps between software
engineering and usability is the lack of strategies to apply usability principles in the initial
design of software architectures. Including these principles since the early phases of software
design would help to avoid later architectural changes to include user experience
requirements. The combination of both fields (software architectures and Human-Computer
Interaction) would contribute to building better interactive software that should include the
best from both the systems and user-centered designs. In that combination, the software
architectures should enclose the fundamental structure and ideas of the system to offer the
desired quality based on sound design decisions.
Moreover, the information kept within a system is an opportunity to extract knowledge
about the system itself, its components, the software included, the users or the interaction
occurring inside. The knowledge gained from the information generated in a software
environment can be used to improve the system itself, its software, the users’ experience, and
the results. So, the combination of the areas of Knowledge Discovery and Human-Computer
Interaction offers ideal conditions to address Human-Computer-Interaction-related
challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge
Discovery in computational intelligence, and the combination of both can raise the support
of human intelligence with machine intelligence to discover new insights in a world crowded
of data.
This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven
software architectures (using Knowledge Discovery techniques) can help to improve the users'
interaction and experience within an interactive system. Specifically, it deals with how to
improve the human-computer interaction processes of different kind of stakeholders to
improve different aspects such as the user experience or the easiness to accomplish a specific
task.
Several research actions and experiments support this investigation. These research
actions included performing a systematic literature review and mapping of the literature that
was aimed at finding how the software architectures in the literature have been used to
support, analyze or enhance the human-computer interaction. Also, the actions included work
on four different research scenarios that presented common challenges in the Human-
Computer Interaction knowledge area. The case studies that fit into the scenarios selected
were chosen based on the Human-Computer Interaction challenges they present, and on the
authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss
and learn, a system that includes very large web forms, and an environment where
programmers develop code in the context of quantum computing. The development of the
experiences involved the review of more than 2700 papers (only in the literature review
phase), the analysis of the interaction of 6000 users in four different contexts or the analysis
of 500,000 quantum computing programs.
As outcomes from the experiences, some solutions are presented regarding the minimal
software artifacts to include in software architectures, the behavior they should exhibit, the
features desired in the extended software architecture, some analytic workflows and
approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction
and experience.
The results achieved led to the conclusion that, despite this is not a standard practice in
the literature, the software environments should embrace Knowledge Discovery and datadriven
principles to analyze and respond appropriately to the users’ needs and improve or
support the interaction. To adopt Knowledge Discovery and data-driven principles, the
software environments need to extend their software architectures to cover also the challenges
related to Human-Computer Interaction. Finally, to tackle the current challenges related to
the users’ interaction and experience and aiming to automate the software response to users’
actions, desires, and behaviors, the interactive systems should also include intelligent
behaviors through embracing the Artificial Intelligence procedures and techniques
On driver behavior recognition for increased safety:A roadmap
Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced
Artificial Intelligence for Multimedia Signal Processing
Artificial intelligence technologies are also actively applied to broadcasting and multimedia processing technologies. A lot of research has been conducted in a wide variety of fields, such as content creation, transmission, and security, and these attempts have been made in the past two to three years to improve image, video, speech, and other data compression efficiency in areas related to MPEG media processing technology. Additionally, technologies such as media creation, processing, editing, and creating scenarios are very important areas of research in multimedia processing and engineering. This book contains a collection of some topics broadly across advanced computational intelligence algorithms and technologies for emerging multimedia signal processing as: Computer vision field, speech/sound/text processing, and content analysis/information mining
A UX model for the evaluation of learners' experience on lms platforms over time
Although user experience (UX) is dynamic and evolves over time, prior research reported that the learners' experience models developed so far were only for the static evaluation of learners' experiences. So far, no model has been developed for the dynamic summative evaluation of the UX of LMS platforms over time. The objective of this study is to build a UX model that will be used to evaluate learners' experience on LMS over time. The study reviewed relevant literature with the goal of conceptualizing a theoretical model. The Stimuli-Organism-Response (SOR) framework was deployed to model the experience engineering process. To verify the model, 6 UX experts were involved. The model was also validated using a quasi-experimental design involving 900 students. The evaluation was conducted in four time points, once a week for four weeks. From the review, a conceptual UX model was developed for the evaluation of learners' experience with LMS design over time. The outcome of the model verification shows that the experts agreed that the model is adequate for the evaluation of learners' experience on LMS. The results of the model validation indicate that the model was highly statistically significant over time (Week 1: x2(276) = 273 I 9.339, Week2: x2(276) = 23419.626, Week3: x2(276) =18941.900, Week4: x2(276) = 27580.397, p=000<0.01). Each design quality had strong positive effects on the learners' cognitive, sensorimotor and affective states respectively. Furthermore, each of the three organismic states: cognitive, sensorimotor, and affective, had strong positive influence on learners' overall learning experience. These results imply that the experience engineering process was successful. The study fills a significant gap in knowledge by contributing a novel UX model for the evaluation of learners' experience on LMS platforms over time. UX quality assurance practitioners can also utilize the model in the verification and validation of learner experience over tim
- …