Implementing process methods in learning research:targeting emotional responses in collaborative learning

Abstract. Context and aim: While interacting with peers and teachers in a collaborative learning task, students experience socio-emotional challenges and display emotional responses. These responses have two major components: arousal and valence, which influence the learning process and its outcomes. The aim of the study was twofold: first, to explore how group members’ arousal levels vary across different phases of a collaborative learning task; and second, to investigate how case students’ emotional responses are distributed in the arousal-valence space across the phases of the collaborative task. Methods: Twelve 6th graders from a school of Finland participated in a collaborative task, in groups of three students. The task was to build an energy efficient house in three distinct phases: brainstorming, planning, and building. While performing the activity, students wore Empatica E4 wristbands to measure their electrodermal activity (EDA) and were video-recorded with 360° cameras. Arousal levels were calculated in peaks per min (ppm) and classified as low, middle, and high. Emotional valence was classified from video analysis into positive, neutral, and negative. Results: The ranges for arousal levels were established between 26 and 88 ppm. Only two students displayed the same arousal level across the three phases of the experiment. Three students displayed higher arousal at first and then fell in to lower levels. Four students had the opposite experience and three students did not display a pattern. As for the case students, the student leading a poorly collaborating group experienced oscillating levels of arousal, from middle to high, and displayed a mix of negative and positive valence most of the time. The student loafing around experienced all arousal levels and positive valence most of the time. Overall conclusions and relevance: The study allowed to establish measurement thresholds for arousal as a starting point for future studies in collaborative learning and the arousal-valence space provided a quantifiable picture to help teachers understand the importance of emotional responses in classroom during collaborative learning

Being in-sync: A multimodal framework on the emotional and cognitive synchronization of collaborative learners

Collaborative learners share an experience when focusing on a task together and coevally influence each other’s emotions and motivations. Continuous emotional synchronization relates to how learners co-regulate their cognitive resources, especially regarding their joint attention and transactive discourse. “Being in-sync” then refers to multiple emotional and cognitive group states and processes, raising the question: to what extent and when is being in-sync beneficial and when is it not? In this article, we propose a framework of multi-modal learning analytics addressing synchronization of collaborative learners across emotional and cognitive dimensions and different modalities. To exemplify this framework and approach the question of how emotions and cognitions intertwine in collaborative learning, we present contrasting cases of learners in a tabletop environment that have or have not been instructed to coordinate their gaze. Qualitative analysis of multimodal data incorporating eye-tracking and electrodermal sensors shows that gaze instruction facilitated being emotionally, cognitively, and behaviorally “in-sync” during the peer collaboration. Identifying and analyzing moments of shared emotional shifts shows how learners are establishing shared understanding regarding both the learning task as well as the relationship among them when they are emotionally “in-sync.

Methods and techniques for analyzing human factors facets on drivers

Mención Internacional en el título de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident. Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces. The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed. Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn. From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models. Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart

Word Associations and the Bilateral Electrodermal Responses of High and Low Repressive Females as Measured by the MMPI R Factor Scale

On the basis of the MMPI R Factor Scale, 16 subjects were classified as high repressed and 14 as low repressed. Subjects were compared on patterns of bilateral differences in skin conductance as a function of three cognitive tasks intended to produce specific manipulations in the relative activation of the two cerebral hemispheres. Tasks 1 and 2 examined the effects of Verbal (left hemisphere) and Spatial (right hemisphere) tasks on amplitudes of electrodermal responses. Task 3 examined the effects of the presentation of double-entendre and asexual stimulus words (designed to produce an emotional stimulus) on the high and low repressed groups. Results showed no tasks were accompanied by significant bilateral differences in electrodermal activity although high repressed subjects showed a consistent tendency toward greater amplitudes in both hands to the sexual portion of the word task. These findings are in direct contradiction to research suggesting that hemisphere activation is task dependent, but support the theoretical postulation of \u27\u27hemisphericity (the individual preference for the use of one hemisphere or the other). Subsequent to the tasks, each subject completed a Sexual Activity Questionnaire to determine categories of orgasmic or non-orgasmic. These data proved to be highly related to the personality variables of high and low repression. All subjects self-reported to be orgasmic (n = 3) scored in the low repressed group. Of 16 subjects self-reported to be non-orgasmic, 11 (69%) scored in the high repressed group. These findings argue strongly that sexual conflicts in high repressors leads to psychosomatic sexual dysfunctions as postulated by traditional psychoanalytic theory. Present findings were discussed in terms of the relationships between personality, repression, and sexual conflict and how these .variables influence electrodermal functioning. Implications for future research and theoretical complexities in the interpretation of the present results suggesting support for the hemisphericity postulation were also discussed

Low-cost methodologies and devices applied to measure, model and self-regulate emotions for Human-Computer Interaction

En aquesta tesi s'exploren les diferents metodologies d'anàlisi de l'experiència UX des d'una visió centrada en usuari. Aquestes metodologies clàssiques i fonamentades només permeten extreure dades cognitives, és a dir les dades que l'usuari és capaç de comunicar de manera conscient. L'objectiu de la tesi és proposar un model basat en l'extracció de dades biomètriques per complementar amb dades emotives (i formals) la informació cognitiva abans esmentada. Aquesta tesi no és només teòrica, ja que juntament amb el model proposat (i la seva evolució) es mostren les diferents proves, validacions i investigacions en què s'han aplicat, sovint en conjunt amb grups de recerca d'altres àrees amb èxit.En esta tesis se exploran las diferentes metodologías de análisis de la experiencia UX desde una visión centrada en usuario. Estas metodologías clásicas y fundamentadas solamente permiten extraer datos cognitivos, es decir los datos que el usuario es capaz de comunicar de manera consciente. El objetivo de la tesis es proponer un modelo basado en la extracción de datos biométricos para complementar con datos emotivos (y formales) la información cognitiva antes mencionada. Esta tesis no es solamente teórica, ya que junto con el modelo propuesto (y su evolución) se muestran las diferentes pruebas, validaciones e investigaciones en la que se han aplicado, a menudo en conjunto con grupos de investigación de otras áreas con éxito.In this thesis, the different methodologies for analyzing the UX experience are explored from a user-centered perspective. These classical and well-founded methodologies only allow the extraction of cognitive data, that is, the data that the user is capable of consciously communicating. The objective of this thesis is to propose a methodology that uses the extraction of biometric data to complement the aforementioned cognitive information with emotional (and formal) data. This thesis is not only theoretical, since the proposed model (and its evolution) is complemented with the different tests, validations and investigations in which they have been applied, often in conjunction with research groups from other areas with success

Automatic Stress Classification With Pupil Diameter Analysis

This article proposes a method based on wavelet transform and neural networks for relating pupillary behavior to psychological stress. The proposed method was tested by recording pupil diameter and electrodermal activity during a simulated driving task. Self-report measures were also collected. Participants performed a baseline run with the driving task only, followed by three stress runs where they were required to perform the driving task along with sound alerts, the presence of two human evaluators, and both. Self-reports and pupil diameter successfully indexed stress manipulation, and significant correlations were found between these measures. However, electrodermal activity did not vary accordingly. After training, the four-way parallel neural network classifier could guess whether a given unknown pupil diameter signal came from one of the four experimental trials with 79.2% precision. The present study shows that pupil diameter signal has good discriminating power for stress detection

Autonomic arousal and attentional orienting to visual threat are predicted by awareness

The rapid detection and evaluation of threat is of fundamental importance for survival. Theories suggest that this evolutionary pressure has driven functional adaptations in a specialized visual pathway that evaluates threat independently of conscious awareness. This is supported by evidence that threat-relevant stimuli rendered invisible by backward masking can induce physiological fear responses and modulate spatial attention. The validity of these findings has since been questioned by research using stringent, objective measures of awareness. Here, we use a modified continuous flash suppression paradigm to ask whether threatening images induce adaptive changes in autonomic arousal, attention, or perception when presented outside of awareness. In trials where stimuli broke suppression to become visible, threatening stimuli induced a significantly larger skin conductance response than nonthreatening stimuli and attracted spatial attention over scrambled images. However, these effects were eliminated in trials where observers were unaware of the stimuli. In addition, concurrent behavioral data provided no evidence that threatening images gained prioritized access to awareness. Taken together, our data suggest that the evaluation and spatial detection of visual threat are predicted by awareness

The Application of Physiological Metrics in Validating User Experience Evaluation on Automotive Human Machine Interface Systems

Automotive in-vehicle information systems have seen an era of continuous development within the industry and are recognised as a key differentiator for prospective customers. This presents a significant challenge for designers and engineers in producing effective next generation systems which are helpful, novel, exciting, safe and easy to use. The usability of any new human machine interface (HMI) has an implicit cost in terms of the perceived aesthetic perception and associated user experience. Achieving the next engaging automotive interface, not only has to address the user requirements but also has to incorporate established safety standards whilst considering new interaction technologies. An automotive (HMI) evaluation may combine a triad of physiological, subjective and performance-based measurements which are employed to provide relevant and valuable data for product evaluation. However, there is also a growing interest and appreciation that determining real-time quantitative metrics to drivers’ affective responses provide valuable user affective feedback. The aim of this research was to explore to what extent physiological metrics such as heart rate variability could be used to quantify or validate subjective testing of automotive HMIs. This research employed both objective and subjective metrics to assess user engagement during interactions with an automotive infotainment system. The mapping of both physiological and self-report scales was examined over a series of studies in order to provide a greater understanding of users’ responses. By analysing the data collected it may provide guidance within the early stages of in-vehicle design evaluation in terms of usability and user satisfaction. This research explored these metrics as an objective, quantitative, diagnostic measure of affective response, in the assessment of HMIs. Development of a robust methodology was constructed for the application and understanding of these metrics. Findings from the three studies point towards the value of using a combination of methods when examining user interaction with an in-car HMI. For the next generation of interface systems, physiological measures, such as heart rate variability may offer an additional dimension of validity when examining the complexities of the driving task that drivers perform every day. There appears to be no boundaries on technology advancements and with this, comes extra pressure for car manufacturers to produce similar interactive and connective devices to those that are already in use in homes. A successful in-car HMI system will be intuitive to use, aesthetically pleasing and possess an element of pleasure however, the design components that are needed for a highly usable HMI have to be considered within the context of the constraints of the manufacturing process and the risks associated with interacting with an in-car HMI whilst driving. The findings from the studies conducted in this research are discussed in relation to the usability and benefits of incorporating physiological measures that can assist in our understanding of driver interaction with different automotive HMIs

Impact of Information Technology Multitasking on Hedonic Experience

This study investigates the impact of information technology (IT) multitasking on multisensory hedonic experience. Existing literature extensively studies the impact of IT multitasking on user experience in a professional context but still lacks insight regarding this influence in a hedonic context. This study contributes to the literature by examining how technology can alter pleasure induced by hedonic activities. In a context of engaged IT interaction along with multisensory music listening, we hypothesize that the multisensory factor positively influences emotional reaction. We also hypothesize that IT interaction will degrade the hedonic experience. We conducted a multi-method experiment using both explicit (questionnaires) and implicit (automatic facial analysis, and electrodermal activity) measures of emotional reactions. Results support our hypotheses and highlight the importance of avoiding multitasking with technology during passive hedonic activities for better experience. Future research may examine IT multitasking’s influence on active hedonic activities