| Physiological Synchronization Is Associated with Narrative Emotionality and Subsequent Behavioral Response | | BIBAK | Full-Text | 3-13 | |
| Bethany K. Bracken; Veronika Alexander; Paul J. Zak; Victoria Romero; Jorge A. Barraza | |||
| Neurophysiological compliance is a correlation of neurophysiological
measures (synchronicity) between individuals. Higher compliance among team
members is related to better performance, and higher synchronicity occurs
during emotional moments of a stimulus. The aim of the current study is to
examine whether synchrony may be observable via peripheral nervous system (PNS)
activity. We used inter-subject correlation (ISC) analysis to assess whether
synchronicity of PNS measures are related to stimulus emotionality or
similarity in behavioral responses. Participants viewed a 100-second emotional
video, followed by an appeal to donate experimental earnings to a related
charity. We found high ISC for cardiac and electrodermal activity (EDA) between
donors versus non-donors. For both groups, we found an association between ISC
of cardiac activity and emotional moments in the stimulus. For non-donors we
found an association between ISC of EDA and emotional moments. Our findings
indicate that PNS measures yield similar results to neurophysiological
measures. Keywords: Cognitive Modeling; Perception; Emotion; and Interaction; Physiological
Synchronization; Inter-Subject Correlation Analysis; RR-Interval; Skin
Conductance Level; Narrative | |||
| A Social-Cognitive Prediction of the Perceived Threat of Terrorism and Behavioral Responses of Terrorist Activities | | BIBAK | Full-Text | 14-25 | |
| Lisa A. Cave | |||
| This paper applies a social-cognitive model to the situation in Israel
following the second intifada. In the model cognitive and social-contextual
factors directly influence behavioral responses to terrorism as well as
indirectly through affective factors. The findings suggest that the perceived
risk of a terrorist attack influenced both preparedness and anxiety and
concern. However, in some cases the influence of anxiety and concern on
behavioral responses was greater than the cognitive or social-contextual
factors i.e. gas mask preparedness. In other cases, the Iranian nuclear threat,
the perceived risk did not influence the level of preparedness indirectly
through anxiety and concern. The divergence in these findings reflects
overconfidence in the state's ability to cope with the nuclear threat and the
hypothetical nature of the responses. Keywords: risk perception; social-cognition; terrorism | |||
| Untangling Operator Monitoring Approaches When Designing Intelligent Adaptive Systems for Operational Environments | | BIBAK | Full-Text | 26-34 | |
| Ming Hou; Cali M. Fidopiastis | |||
| An Intelligent Adaptive System (IAS) is a synergy between an intelligent
interface and adaptive automation technologies capable of context sensitive
interaction with operators. A well-designed IAS should enable flexible task
allocation between the operator and the machine. Research suggests that the
integration of real-time operator state assessment (e.g., performance,
psychophysiology) can create a true 'human-in-the-loop' system, thereby
minimizing deleterious performance effects such as overlooking automation
failures and slowly reorienting to tasks. However, these research approaches
apply a variety of methodologies to determine sensors, metrics, and overall
system design when applied to real world tasks. This paper seeks to untangle
these issues such that a more comprehensive framework for systematically
evaluating the utility of cognitive state detection methods is attainable. Keywords: Intelligent tutoring systems; adaptive automation; augmented cognition;
psychophysiological measures; cognitive state | |||
| Semantic Representation Analysis: A General Framework for Individualized, Domain-Specific and Context-Sensitive Semantic Processing | | BIBAK | Full-Text | 35-46 | |
| Xiangen Hu; Benjamin D. Nye; Chuang Gao; Xudong Huang; Jun Xie; Keith Shubeck | |||
| Language agnostic methods for semantic extraction, encoding, and
applications are an increasingly active research area in computational
linguistics. This paper introduces an analytic framework for vector-based
semantic representation called semantic representation analysis (SRA). The
rationale for this framework is considered, as well as some successes and
future challenges that must be addressed. A cloud-based implementation of SRA
as a domain-specific semantic processing portal has been developed.
Applications of SRA in three different areas are discussed: analysis of online
text streams, analysis of the impression formation over time, and a virtual
learning environment called V-CAEST that is enhanced by a conversation-based
intelligent tutoring system. These use-cases show the flexibility of this
approach across domains, applications, and languages. Keywords: Semantic analysis; language agnostic; domain vocabulary; intelligent
tutoring systems | |||
| Toward Multi-brain Communication: Collaborative Spelling with a P300 BCI | | BIBAK | Full-Text | 47-54 | |
| Christoph Kapeller; Rupert Ortner; Gunther Krausz; Markus Bruckner; Brendan Z. Allison; Christoph Guger; Günter Edlinger | |||
| In a brain-computer interface (BCI), users perform specific mental tasks to
convey messages or commands through direct measures of brain activity.
Typically, users must perform each mental task for two or more seconds before
their brain activity is distinct enough for accurate classification. In P300
BCIs, this usually entails silently counting specific flashes three or more
times. Although numerous articles have explored the prospect of a P300 BCI that
relies on only one flash, results consistently show that the resulting accuracy
would be too low for effective communication. The goal of this article was to
introduce a new way to reduce the time to identify a message or command.
Instead of relying on brain activity from one subject, our system utilized
brain activity from eight subjects performing a single trial. Hence, the system
could rely on an average based on eight trials, which is more than sufficient
for adequate classification, even though each subject contributed only one
trial. Results confirmed that all eight subjects could not have attained
effective control with a single trial, but could attain 100% accuracy when the
other seven subjects' data were also used. This is the first time that people
worked together to accomplish a goal with a BCI, and could encourage future
research into collaborative brain-based communication and control. Keywords: brain-computer interface (BCI); brain-machine interface (BMI); multi-brain
computing; multi-brain gaming; EEG; ERP; P300; spelling | |||
| Deducing User States of Engagement in Real Time by Using a Purpose Built Unobtrusive Physiological Measurement Device: An Empirical Study and HCI Design Challenges | | BIBAK | Full-Text | 55-66 | |
| Anthony Psaltis; Charalampos Rizopoulos; Zacharias Lekkas; Constantinos Mourlas | |||
| Human emotion is a psycho-physiological state in most cases not obvious to
the subject. Different permutations of emotional constituents sometimes cause
similar outward expressions; therefore facial expression methods cannot achieve
reliable estimates. Sensing physiological manifestations of hormonal and neural
stimulations instigated by emotion and affect is widely accepted as a credible
method of detecting psycho-physiological states. A major impediment in
interactive environments employing physiological sensing affecting the
credibility of measurements is the physical and psychological impairment caused
by electrodes and wiring used for the acquisition of signals. In the system
described in this paper, the above obstacle has been overcome. Physiological
signals acquired via an in-house developed computer mouse and coinciding
physiological patterns were investigated as reactions to emotion raising
events. A classification algorithm analyzed herein produced a real time
allocation model of states of engagement. Experiments have revealed strong
correlations between events and respective emotional states. Keywords: HCI; Biofeedback Measurements; Affective Interactions; User Evaluation;
Stress Loading | |||
| Ubiquitous Augmented Cognition | | BIBAK | Full-Text | 67-77 | |
| Anna Skinner; Clementina Russo; Lisa Baraniecki; Molly Maloof | |||
| The paradigm shift in pervasive computing drives human-computer interaction
(HCI) toward complex domains, where novel approaches to support human cognition
in mobile and dynamic environments are necessary. The field of Augmented
Cognition (AugCog) provides a scientifically-grounded approach toward intrinsic
human information processing and challenges associated with data-intensive
systems, leveraging empirically-based HCI solutions that account for human
cognitive limitations. Applying this to ubiquitous computing demands
unobtrusive technologies capable of time-dependant user assessment in various
environments. Technological advances and utilization of personal activity
reporting at a consumer level have made ubiquitous AugCog a necessary
implementation. Such technologies (i.e., head-mounted displays) produce a
deluge of data, generating a need for experimentally-based metrics, algorithms,
and adaptive interfaces for closed-loop, synergistic human-technology
performance systems. A Ubiquitous AugCog framework is proposed along with an
essential use case to guide the design of multi-modal human performance
assessment and optimization tools beyond laboratory settings. Keywords: HCI; Augmented Cognition; Ubiquitous Computing; Pervasive Computing | |||
| Our Emotions as Seen through a Webcam | | BIBA | Full-Text | 78-89 | |
| Natalie Sommer; Leanne Hirshfield; Senem Velipasalar | |||
| Humanity's desire to enable machines to "understand" us drives research that seeks to uncover the mysteries of human beings and of their reactions. That is because a computer's ability to correctly classify our emotions will lead to an enhanced experience for a user. Making use of the eye of the computer, a webcam, we can acquire human reaction data through the acquisition of facial images in response to stimuli. The data of interest in this research are changes in pupil size and gaze patterns in conjunction with classification of facial expression. Although fusion of these measurements has been considered in the past by Xiang and Kankanhalli [14] as well as Valverde et al. [15], their approach was quite different from ours. Both groups used a multimodal set-up: an eye tracker alongside a webcam and the stimulus was visual. A novel approach is to avoid costly eye trackers and rely on images acquired only from a standard webcam to measure changes in pupil size, gaze patterns and facial expression in response to auditory stimuli. The auditory mode is often preferred since luminance does not need to be accounted for, unlike visual stimulation from a monitor. The fusion of the information from these features is then used to distinguish between negative, neutral and positive emotional states. In this paper we discuss an experiment (n = 15) where the stimuli from the auditory version of the international affective picture system (IAPS) are used to elicit these three main emotions in participants. Webcam data is recorded during the experiments and advanced signal processing and feature extraction techniques are used on the resulting image files to achieve a model capable of predicting neutral, positive, and negative emotional states. | |||
| Neural Network Estimation of Atmospheric Thermodynamic State for Weather Forecasting Applications | | BIBAK | Full-Text | 93-103 | |
| William J. Blackwell; Adam B. Milstein; Bradley Zavodsky; Clay B. Blankenship | |||
| We present recent work using neural network estimation techniques to process
satellite observation of the Earth's atmosphere to improve weather forecasting
performance. A novel statistical method for the retrieval of atmospheric
temperature and moisture (relative humidity) profiles has been developed and
evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and
the Advanced Microwave Sounding Unit (AMSU) on the NASA Aqua satellite and the
Infrared Atmospheric Sounding Interferometer (IASI) and AMSU on the EUMETAT
MetOp-A satellite. The present work focuses on the cloud impact on the AIRS and
IASI radiances and explores the use of stochastic cloud clearing mechanisms
together with neural network estimation. The algorithm outputs are ingested
into a numerical model, and forecast information and decision support tools are
then presented to a meteorologist. We discuss the underlying physical problem,
the algorithmic framework, and the interaction with forecaster. Keywords: Neural networks; numerical weather prediction; weather forecasting | |||
| Augmenting Human Cognition with Adaptive Augmented Reality | | BIBAK | Full-Text | 104-113 | |
| Jayfus T. Doswell; Anna Skinner | |||
| Wearable Augmented Reality (AR) combines research in AR, mobile/ubiquitous
computing, and human ergonomics in which a video or optical see-through head
mounted display (HMD) facilitates multi-modal delivery of contextually relevant
and computer generated visual and auditory data over a physical, real-world
environment. Wearable AR has the capability of delivering on-demand assistance
and training across a variety of domains. A primary challenge presented by such
advanced HCI technologies is the development of scientifically-grounded methods
for identifying appropriate information presentation, user input, and feedback
modalities in order to optimize performance and mitigate cognitive overload. A
proposed framework and research methodology are described to support
instantiation of physiologically-driven, adaptive AR to assess and contextually
adapt to an individual's environmental and cognitive state in real time.
Additionally a use case within the medical domain is presented, and future
research is discussed. Keywords: augmented cognition; augmented reality; context-awareness; cognitive
display; mobile computing; mixed-reality; wearable computing | |||
| Two Steps Back for One Step Forward: Revisiting Augmented Cognition Principles from a Perspective of (Social) System Theory | | BIBAK | Full-Text | 114-124 | |
| Sven Fuchs; Jessica Schwarz; Frank O. Flemisch | |||
| One decade after the Augmented Cognition concept validation experiments, the
field may have passed the hype phases of inflated expectations and
disillusionment but seems to have lost traction before reaching the maturity
necessary for broad acceptance and widespread application. The authors suggest
that the engineering-driven approach to the field's challenges may have been
necessary in the beginning but should be complemented by looking into
implications from the social sciences. The goal of this paper is to raise
awareness for the field of (social) system theory by taking two steps back: One
to revisit the foundational concepts of AugCog from an alternative perspective,
another to illustrate the relevance and importance of social systems principles
to the AugCog field. We outline practical implications that (social) system
theory holds for the design of adaptation management and conclude our review by
suggesting that an approach to human-system cooperation may be able to address
the challenges. Keywords: adaptive systems; adaptation management; augmented cognition; cooperative
human-machine systems; human-systems integration; systems engineering; system
theory | |||
| Mining and Modeling the Phenomenology of Situational Awareness | | BIBAK | Full-Text | 125-135 | |
| Monte Hancock; Michael Higley | |||
| One expert has said "Most simply put, situational awareness (SA) is knowing
what is going on around you." [1] "Knowing" is not just having a checklist of
facts. Functionally, SA is about possessing information of sufficient scope and
accuracy to support decision making that facilitates effective action.
Augmented Cognition research shows that presenting too much data, even of high
quality, can be as harmful to effective cognition as presenting little or no
data [2]. Research has shows that in 35% of aviation errors in SA, all needed
information was present, but not attended to by decision makers. [3] This work
presents a formal but simple mathematical SA Model, and describes the
application of data mining and modeling to SA errors resulting from inattention
to the most salient facts. The model is applied to two data sets to demonstrate
a general approach to automating the detection and diagnosis of SA errors. Keywords: situational awareness; decision support; coincidental correctness; context
error | |||
| Team Behaviors and Cognitive Cohesion in Complex Situations | | BIBAK | Full-Text | 136-147 | |
| Cynthia Lamb; Jerry Lamb; Ronald Stevens; Abaigeal Caras | |||
| Our research has indicated that, in addition to technical skills, deliberate
and effective team practices are necessary to manage the wide variety of
simultaneous and increasingly complex problems that occur during tactical
operations. This paper looks at team performance from two aspects, the first is
observable team behaviors and the second is from two objective measures of team
interactions. The combined results point toward a fuller understanding of team
dynamics that can be applied to assessment, improvement, and prediction of team
performance in operational situations. Keywords: Team; Team Performance; Cognitive Cohesion; Team Behaviors; Submarines | |||
| Architecture for Machine Learning Techniques to Enable Augmented Cognition in the Context of Decision Support Systems | | BIBAK | Full-Text | 148-156 | |
| David Martinez | |||
| For a wide range of applications, one of the key challenges is to identify
an architecture that is suitable for machine learning techniques to enable
important augmented cognition capabilities in the context of complex decision
support systems. This overview paper presents an architecture framework. The
elements of the architecture are described starting with data formatting, a
machine learning algorithm taxonomy, components of courses of action, resource
management, and finally the role of augmented cognition within the
architecture. The paper includes one cyber security example where the
architecture framework is employed. The paper concludes with future work in the
development of a recommender system. Keywords: Machine learning; decision support systems; human-machine interfaces;
recommender system | |||
| Teams Reorganize Neurodynamically When They Sense Loss of Control | | BIBAK | Full-Text | 157-166 | |
| Ronald Stevens; Trysha Galloway | |||
| Perturbations to the normal flow of teamwork arise externally through
changes in the environment or internally as a result of the team's processes /
decisions. We used quantitative neurophysiologic models of the rhythms and
organizations of teams to examine the effects of these two classes of
perturbations on team neurodynamics. Electroencephalographic (EEG) signals from
dyads were transformed into cognitive workload estimates and then into
neurodynamic symbols (NS) showing the second-by-second workload of each
individual as well as the team. Periods of changing cognitive organizations
were identified by a moving average smoothing of the Shannon entropy of the NS
data stream and related to team speech, actions and responses to external and
internal task changes. Dyads performing an unscripted map navigation (HCRC Map
Task) developed fluctuating NS dynamics around the construct of workload which
were disrupted by external task perturbations or when the team became confused
or uncertain of their progress. Importantly, we detected no significant
neurodynamic fluctuations associated with periods when the team made mistakes
and did not realize they made the mistake. These results indicated that
neurodynamics reorganizations occurred in teams in response to multiple types
of perturbations, but primarily when the team perceived difficulties. Keywords: team neurodynamics; entropy; coordination dynamics; rhythms | |||
| Assessing Neural Synchrony in Tutoring Dyads | | BIBAK | Full-Text | 167-178 | |
| Bradly Stone; Anna Skinner; Maja Stikic; Robin Johnson | |||
| The current study examined synchronous psychophysiological monitoring across
a tutor and tutee during a spatial reasoning video game, Tetris®. We
hypothesized that increased synchrony across tutor-tutee would correlate with
increased performance (i.e. increased learning. A teaming platform enabled
simultaneous electroencephalogram (EEG) and electrocardiogram (ECG) acquisition
for the tutor-tutee dyad throughout the gaming sessions, using the B-Alert®
X10 EEG system (Advanced Brain Monitoring, Inc, Carlsbad, CA). A sample of n =
15 healthy participants as tutees with a single tutor across all dyads
completed the protocol with each tutee playing 3 rounds of Tetris®. Initial
results indicate small, significant, correlations in psychophysiological
metrics that increased with experience. Exploratory stepwise regressions found
the correlations explained more variance in performance than individual
tutee/tutor psychophysiological metrics. These data imply that synchrony on a
psychophysiological level between tutor and tutee impact tutee performance.
Further examination of more complex synchrony metrics is required. Keywords: Neurophysiology; EEG; ECG; Neural Synchrony | |||
| Insights into User Personality and Learning Styles through Cross Subject fNIRS Classification | | BIBAK | Full-Text | 181-189 | |
| Danushka Bandara; Leanne Hirshfield; Senem Velipasalar | |||
| There is limited literature on classifying user personality/learning style
and other cross subject traits using brain activity patterns. In this paper we
describe an experiment to classify a computer user's' personality type and
learning style using their brain data acquired while they were conducting
spatial/verbal tasks in front of a computer. The brain activity in the left and
right hemispheres were measured by an fNIRS device and the resulting data was
analyzed using the participant's personality/learning style as the label
(Obtained through established survey instruments). We obtained promising
results for all of the traits we strived to classify providing paths for future
research into this area. Keywords: fNIRS; Personality; Learning styles; Cross subject Classification;
Visual/Verbal tasks | |||
| Using a Cognitive/Metacognitive Task Model to Analyze Students Learning Behaviors | | BIBAK | Full-Text | 190-201 | |
| Gautam Biswas; John S. Kinnebrew; James R. Segedy | |||
| Adapting to learners' needs and providing useful, individualized feedback to
help them succeed has been a hallmark of most intelligent tutoring systems.
More recently, to promote deep learning and critical thinking skills in STEM
disciplines, researchers have begun developing open-ended learning environments
that present learners with complex problems and a set of tools for learning and
problem solving. To be successful in such environments, learners must employ a
variety of cognitive skills and metacognitive strategies. This paper discusses
a framework that combines a theory-driven, top-down approach with a bottom-up,
pattern-discovery approach for analyzing learning activity data in these
environments. Combining these approaches allows for more complex qualitative
and quantitative interpretation of a student's cognitive and metacognitive
abilities. The results of this analysis provide a foundation for developing
performance- and behavior-based learner models in conjunction with adaptive
scaffolding mechanisms to promote effective, personalized learning experiences. Keywords: metacognition; theory-driven top-down analysis; pattern-driven bottom-up
analysis; effectiveness measures; pattern mining; adaptivity; tutoring | |||
| Just Enough Fidelity in Student and Expert Modeling for ITS | | BIBAK | Full-Text | 202-211 | |
| Brandt Dargue; Elizabeth Biddle | |||
| Intelligent Tutoring Systems (ITSs) are usually comprised of three primary
models -- an expert model, a domain or system model, and a student model. Many
of these models are quite complex to enable just about any learner to get the
optimum tailored experience possible. These systems have shown great results,
typically at least one standard deviation (a letter grade) better than
traditional training (e.g. [1] [2]). This complexity not only ensured that ITSs
were successful, it also prohibited their widespread use (e.g. [3]). Results of
studies in which the expert and system models were simplified show similar
gains in effectiveness (e.g. [4] [5]), suggesting that lower-cost ITSs can be
just as effective as those developed at higher costs. This paper compares the
results of effectiveness studies in which the ITSs had various levels of
fidelity and presents some recommended guidelines in determining the level of
fidelity for student, expert, and system models of the ITS. Keywords: Cognitive Modeling; Perception; Emotion and Interaction; Machine Learning;
Neural Networks Techniques for Data Processing; Adaptive User Interfaces; Human
performance improvement; Intelligent Tutoring; Adaptive training | |||
| How the Granularity of Evaluation Affects Reliability of Peer-Assessment Modelization in the OpenAnswer System | | BIBAK | Full-Text | 212-223 | |
| Maria De Marsico; Andrea Sterbini; Marco Temperini | |||
| The OpenAnswer system has the goal of exploiting teacher mediated
peer-assessment for the evaluation of answers to open ended questions. The
system models both the learning state of each student and their choices during
peer-assessment. In OpenAnswer, each student is represented as a Bayesian
network made of a triple of finite-domain variables: K for student's Knowledge
about a topic, J for the estimated ability to evaluate ("Judge") the answer of
another peer, C for Correctness of the answer to a given question. The
student's individual sub-networks are connected through further Bayesian
variables which model each peer-assessment choice, depending on the type of
peer-assessment performed: (G for grading, B for choosing the best, W for
choosing the worst). During an assessment session, each student grades a fixed
number of peers' answers. The final result for a given session is a full set of
grades for all students' answers, although the teacher had actually graded only
a part of them. The student's assessments are instantiated in the network as
evidence, together with the teacher's (perhaps partially complete) grades, so
that OpenAnswer deduces the remaining grades. In the former OpenAnswer
implementation, all variables were represented through a probability
distribution over three values (Good/Fair/Bad for K and J, correct/fair/wrong
for C). We present experiments and simulations showing that, by increasing the
domain granularity for all variables from 3 to 6 values (A to F), the
information obtained from the Bayesian network achieves higher reliability. Keywords: assessment; peer-assessment; social collaborative e-learning | |||
| What Is Adaptivity? Does It Improve Performance? | | BIBAK | Full-Text | 224-235 | |
| Jacqueline A. Haynes; Jody S. Underwood; Robert Pokorny; Amit Spinrad | |||
| Asking whether adaptivity improves performance is the wrong question. The
right question is what kinds of adaptivity should be used to tailor the
interactions between learner, context, objective, and instructional approach to
maximize learning and performance. Most research on adaptive learning has
focused on learning in intelligent tutoring systems and other digital learning
environments. However, there is a lack of research that focuses on retention
and deeper learning. This paper will define adaptivity, review different types
of adaptivity used for instruction and their effects within the learning
environment and longitudinally, and give some examples of how we have used
adaptivity for short- and long-term improvement in performance and learning. We
conclude that adaptivity in learning environments should be used to focus on
deep conceptual learning promoting long term results. Keywords: adaptivity; intelligent tutoring systems; digital learning environments;
learning; pedagogy; individualized instruction | |||
| Visualizing Adaptive Learning Effects on Clinical Skill Acquisition and Decay | | BIBAK | Full-Text | 236-244 | |
| Phillip M. Mangos; Ari Bodaghee | |||
| The purpose of this paper is to present a visualization tool, grounded in
modern psychometric theory, for optimizing the parameters of adaptive training
systems. The tool can be used in a research capacity to rapidly visualize the
course, trajectory, and shape of one's learning curves resulting from different
adaptive training conditions and how these are affected by conditions intrinsic
to both the task domain and the learner. Keywords: Adaptive training; skill acquisition; data visualization; medical simulation | |||
| How Real Is Good Enough? Assessing Realism of Presence in Simulations and Its Effects on Decision Making | | BIBAK | Full-Text | 245-256 | |
| Debbie Patton | |||
| Simulations are frequently used for experimentation and training in many
domains as a safe and cost-effective stand-in for real world experiences.
However, research about which factors (trait and state) affect how real a
simulation is to a person and how this "realness" affects cognition is lacking.
Presence research lacks the assessment of interactions between psychological
and physiological responses in interactive immersive environments. This paper
provides empirical results from both trait and state psychological and
physiological measures to identify and provide operationally relevant
information regarding the realism of presence in a 300° immersive
simulation and its effects on a Shoot-Don't-Shoot decision-making task.
Participants engaged in a Shoot-Don't-Shoot simulation under three types of
feedback conditions: (1) small shock, (2) life bar, and (3) no feedback.
Results indicate that (1) trait uncertainty mitigates the stress experience and
(2) both immersion and errors were significantly greater in the shock
condition. Recommendations for future research are discussed. Keywords: stress; cognition; performance; decision making; simulation; presence;
military | |||
| Utilizing the Generalized Intelligent Framework for Tutoring to Encourage Self-Regulated Learning | | BIBAK | Full-Text | 257-264 | |
| Anne M. Sinatra | |||
| Self-regulated learning is of particular importance to computer-based and
online instruction, as students need to manage their own time and their
interactions with the system. Elements of self-regulatory learning
traditionally include the metacognitive strategies of the students (e.g., their
knowledge of their planning, and assessment of their own progress), their
management of educational goals (e.g., what information is most important to
them, and should receive their primary attention), and the strategies that
students use in order to study and retain the provided information [1, 2]. By
incorporating feedback and guidance within computer-based learning activities
it can encourage students to engage in successful self-regulated learning with
a better awareness of their own cognition, and strategies. Intelligent tutoring
systems can utilize adaptive scaffolding and guidance in order to support
self-regulated student learning [3]. The Generalized Intelligent Framework for
Tutoring (GIFT) [4] is an open-source adaptive tutoring system framework. The
included tools within GIFT can be used to structure courses which guide
individuals through the learning environment and are consistent with
self-regulated best practices. The current paper includes a brief review of
research into self-regulated learning in the context of computer-based and
adaptive instruction. Further, the authoring capabilities of GIFT are
discussed, and recommendations are given for future feature additions to GIFT
which will benefit instructors who wish to develop courses that facilitate
self-regulated learning. Keywords: Strategy assessment and individual differences; Self-Regulated Learning;
Adaptive Tutoring; Intelligent Tutoring Systems | |||
| Using Learner Data to Influence Performance during Adaptive Tutoring Experiences | | BIBAK | Full-Text | 265-275 | |
| Robert A. Sottilare | |||
| During computer-based tutoring sessions, Intelligent Tutoring Systems (ITSs)
adapt planning and manage real-time instructional decisions. The link between
learner data and enhanced performance is the adaptive tutoring learning effect
chain through which learner data informs learner state classification which in
turn informs optimal instructional decisions to enhance performance. This paper
examines the roles and influence of learner data in both short-term (also
called run-time or session) and long-term (also called persistent) learner
models used to support adaptive tutoring decisions within the Generalized
Intelligent Framework for Tutoring (GIFT). To enhance the usability of tutoring
systems and learner performance, recommendations for the design of future
learner models are also presented. Keywords: adaptive tutoring; learner modeling; Intelligent Tutoring Systems | |||
| An Alternative Design Perspective for Technology Supporting Youngsters with Autism | | BIBAK | Full-Text | 279-287 | |
| Priscilla Braz; Viviane Felipe David; Alberto Raposo; Simone Diniz Junqueira Barbosa; Clarisse Sieckenius de Souza | |||
| People with autism present several disabilities in communication, social
interaction and behavioral fields. There is a wide variation among these
individuals and it is essential to develop therapies and materials customized
for them. There are many design approaches in Human-Computer Interaction, but
most of them present some limitations for designing to this audience. We
conducted a study using paper prototyping with children with autism in order to
contribute to the design of software for them. In this paper, we report some
limitations in using this technique and the need for customizing applications
for the individual who will use them. Reflecting on these needs and analyzing
approaches to interface design, we present and discuss a proposal for a design
methodology that combines Meta-design and Semiotic Engineering. Keywords: Autism; Prototyping; Semiotic Engineering; Meta-design | |||
| The Predictability of Pharm-EEG in Patients with Long Unconscious Status | | BIBAK | Full-Text | 288-295 | |
| Sergey Lytaev; Mikhail Aleksandrov; Sergey Vasilyev; Anna Arutunyan | |||
| The main syndrome of severe poisoning is coma. An option of coma outcome is
a vegetative state. EEG reactivity due to intravenous benzodiazepines estimates
the prognosis for such patients. However, a positive benzodiazepines test has
the predictability of about 50-60%. The aim of the work is to assess the role
of interaction between gamma amino butyric acid (GABA) and cholinergic systems
of the brain. The consequent injections of benzodiazepine and atropine lead to
a 20% increase in predictability. The results obtained confirm the following
hypothesis. Abnormality of GABA-cholinergic interaction is one of the
mechanisms of forming a stable pathological system resulting in the
pathogenesis of the vegetative state. Keywords: acute poisoning; neurotoxicity; acute cerebral insufficiency; EEG;
bioelectric activity | |||
| Somneo: Device with Thermal Stimulation for Modulating Sleep Architecture and Enhancing Neuro-Cognitive Function | | BIBAK | Full-Text | 296-304 | |
| Catherine McConnell; Djordje Popovic; Chris Berka; Dan Levendowski; Gene Davis | |||
| Sleep deprivation and inefficiency can have a crucial impact on performance.
One potential method for ameliorating the impact of bad sleep, or eliminating
it all-together, is with sleep stage-dependent sensory stimulation. The effect
of superficial facial heating on sleep architecture was studied in 10 subjects.
A 20% decrease in sleep onset latency (p < 0.05) was measured when the face
was heated by 1oC. These results are support for further development of the
patented Somneo, a ubiquitous feedback device for optimizing sleep architecture
and duration; through the real-time assessment of sleep stage, and delivery of
stage-dependent thermal, visual and/or auditory sensory stimulation. Keywords: Sleep; Sleep Deprivation; Sensory Stimulation; Performance Optimization;
Wearable Devices | |||
| Contribution of Biosensors to Enhancing Performance for Users with Special Needs | | BIBAK | Full-Text | 305-314 | |
| Thanh Truc T. Nguyen; Martha E. Crosby; Marie Iding; Neil G. Scott | |||
| This paper describes two lines of research addressing the use of biosensors
for populations with disabilities. The first line of research focused on
deriving changing cognitive state information from the patterns of data
acquired from users with the goal of improving presentation of multimedia
computer information. Detecting individual differences via performance and
psychometric tools can be supplemented by using real-time physiological sensors
such as eye tracking and pressure applied to a computer mouse. We describe a
computer task that demonstrates how to identify cognitive state and discuss
types of physiological and cognitive state measures and associated advantages
and disadvantages. Adaptive information filtering is discussed as a model for
using the physiological information to improve individual performance. In the
second line of research we interviewed participants with disabilities in an
engineering vocational training program about their needs and suggestions for
assistive devices that incorporate biosensors. Keywords: biosensors; special needs; cognition; physiological sensors; adaptive
information filtering | |||
| Finding Keys for People with Mild Dementia -- Not Just a Matter of Beeping and Flashing | | BIBA | Full-Text | 315-324 | |
| Lars Oestreicher | |||
| Searching for everyday objects are a frequent activity for most people.
Misplaced keys, mobiles and other devices are a source of annoyance, and even
more so, if people are affected by memory problems. Searching for objects is a
frustrating activity, especially if this is a frequently recurring phenomenon.
There are several existing techniques for retrieving objects, but many of them
do not use the available technology to the full extent, providing solutions
that are almost "good-enough" but not necessarily useable practice.
In this paper we present a solution that is intense to be more than good-enough, and simultaneously argue that there is a need for solutions that don't only facilitate a good life for people with impairments, but that also does so with the user's emotional experience (UEX?) in focus. | |||
| Patients Initiated Timeline Marking of Events in Parkinson's Disease: Visualization of Time Correlation between Patients Marked Events and Acquired Data from Sensors | | BIBAK | Full-Text | 325-334 | |
| J. Artur Serrano; Andrea Thoms; Peter Weber | |||
| The reality of a Parkinson's Disease patient involves coping with the
condition 24 hours a day for the rest of her or his life. A continuous decay of
physical and sometimes cognitive functions makes activities of daily life
progressively more difficult to accomplish. Many keep a diary where they take
note of feelings, relevant events related to the daily routines, reaction to
the medication, etc. Such diaries may prove extremely useful for a better
understanding of the disease progression, both by the patient and by the
doctor. The SENSE-PARK project went a step forward: it combines the patient
diary notes (self-reported) with information gathered from movement sensors
(automatic measurement) and a visualization mechanism combining the two. A
system has been designed, prototyped and tested. Parkinson's medical
specialists, user experience experts, technologists and most important the
patients themselves, were involved in this process. Keywords: Parkinson's Disease (PD); People with Parkinson's (PwP's); patient diaries;
marking of events; symptom; timeline; chronic disease management;
self-management; second level prevention; activities of daily living (ADL);
movement sensor; accelerometer; monitoring; degenerative disease; time
correlation; User Centred Design (UCD); participatory design | |||
| Biomarker and Biometric Indices of Cognitive Decrements due to Physical Exhaustion | | BIBAK | Full-Text | 335-346 | |
| Regina M. Shia; Kyle Traver; Lindsey K. McIntire; Josh A. Hagen; Chuck D. Goodyear; Leanne N. Dykstra; Andrea R. Myers | |||
| State of the art sensors and diagnostic tools are continuously being
researched, tested, and procured for every piece of high tech equipment in the
Air Force while the most critical asset, the Airman, lacks diagnostics to
analyze physiological well being and cognitive performance. Eighteen active
duty Air Force males completed a physical exhaustion task on a treadmill while
performing two cognitive tasks and while having various biometrics and
biomarkers collected. We found that while physiological variables exhibited
reliable indices of physical exertion, biological changes were found to be more
related to cognitive changes. Keywords: Biomarkers; Physical Exhaustion; Cognitive Performance; Biometrics | |||
| Smartphones, Smart Seniors, But Not-So-Smart Apps: A Heuristic Evaluation of Fitness Apps | | BIBAK | Full-Text | 347-358 | |
| Paula Alexandra Silva; Kelly Holden; Aska Nii | |||
| This paper reports on the results of a heuristic evaluation of Nike+ and
RunKeeper, two of the most popular health and fitness mobile apps found in both
Google Play and the iTunes stores for Android and iOS platforms respectively.
Given the potential benefit of practicing physical exercise in living a
healthier and longer life, this study aimed at understanding whether or not
these apps are ready to accommodate the needs of older adult users. The study
concludes that the inspected apps are not ready to accommodate older adults
needs. Small target sizes, insufficient contrast and reduced font sizes, are
some of the common violations found in the user interfaces; these are also
impeditive of the use of the apps by this target user population. It is thus
necessary to highlight these issues in order to eliminate the barrier of access
to these apps by this population by also encouraging careful observation of
design guidelines. Keywords: Older adults; gamification; health and fitness; heuristics; heuristic
evaluation; active aging | |||
| ERmed -- Towards Medical Multimodal Cyber-Physical Environments | | BIBA | Full-Text | 359-370 | |
| Daniel Sonntag | |||
| With new technologies towards medical cyber-physical systems, such as networked head-mounted displays (HMDs) and eye trackers, new interaction opportunities arise for real-time interaction between cyber-physical systems and users. This leads to cyber-physical environments in which the user has an active role to play inside the cyber-physical system. With our medical application in the context of a cancer screening programme, we are combining active speech based input, passive/active eye tracker user input, and HMD output (all devices are on-body and hands-free) in a convenient way for both the patient and the doctor inside such a medical cyber-physical system. In this paper, we discuss the design and implementation of our resulting Medical Multimodal Cyber-Physical Environment and focus on how situation awareness provided by the environmental sensors effectively leads to an augmented cognition application for the doctor. | |||