Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Corroborating Emotion Theory with Role Theory and Agent Technology: a Framework for Designing Emotional Agents as Motivational Tutoring Entities

Nowadays, more and more applications require systems that can interact with humans. Agents can be perceived as computing services that humans, or even other agents, can request in order to accomplish their tasks. Some services may be simple and others rather complex. A way to determine the best agents (services) to be implemented is to identify who the actors are in the object of study, which roles they play, and (if possible) what kind of knowledge they use. Socially Intelligent Agents (SIAs) are agent systems that are able to connect and interface with humans, i.e. robotic or computational systems that show aspects of human-style social intelligence. In addition to their relevance in application areas such as e-commerce and entertainment, building artefacts in software and hardware has been recognized as a powerful tool for establishing a science of social minds which is a constructive approach toward understanding social intelligence in humans and other animals. Social intelligence in humans and other animals has a number of fascinating facets and implications for the design of SIAs. Human beings are biological agents that are embodied members of a social environment and are autobiographic agents who have a unique personality. They are situated in time and space and interpret new experiences based on reconstructions of previous experiences. Due to their physical embodiment, they have a unique perspective on the world and a unique history: an autobiography. Also, humans are able to express and recognize emotions, that are important in regulating individual survival and problem-solving as well as social interactions. Like artificial intelligence research trend, SIA research trend can be pursued with different goals in mind. A deep AI approach seeks to simulate real social intelligence and processes. A shallow AI approach, which will be highlighted also within this thesis, aims to create artefacts that are not socially intelligent per se, but rather appear socially intelligent to a given user. The shallow approach does not seek to create social intelligence unless it is meaningful social intelligence vis-à-vis some user situation In order to develop believable SIAs we do not have to know how beliefs-desires and intentions actually relate to each other in the real minds of the people. If one wants to create the impression of an artificial social agent driven by beliefs and desires, it is enough to draw on investigations on how people with different cultural background, develop and use theories of mind to understand the behaviours of others. Therefore, SIA technology needs to model the folk-theory reasoning rather than the real thing. To a shallow AI approach, a model of mind based on folk-psychology is as valid as one based on cognitive theory. Distance education is understood as online learning that is technology-based training which encompasses both computer-assisted and Web-based training. These systems, which appear to offer something for everyone at any time, in any place, do not always live up to the great promise they offer. The usage of social intelligent agents in online learning environments can enable the design of “enhanced-learning environments” that allow for the development and the assessment of social competences as well as the common professional competences. Within this thesis it is shown how to corroborate affective theory with role theory with agent technology in a synchronous virtual environment in order to overcome several inconveniences of distance education systems. This research embraces also the shallow approach of SIA and aims to provide the first steps of a method for creating a believable life-like tutor agent which can partially replace human-teachers and assist the students in the process of learning. The starting point for this research came from the fact: anxious, angry or depressed students do not learn; people in these conditions do not absorb information efficiently, consequentially it is an illusion to think that learning environments that do not consider motivational and emotional factors are adequate

ROSAPL: towards a heterogeneous multi‐robot system and Human interaction framework

The appearance of numerous robotic frameworks and middleware has provided researchers with reliable hardware and software units avoiding the need of developing ad-hoc platforms and focus their work on how improve the robots' high-level capabilities and behaviours. Despite this none of these are facilitating frameworks considering social capabilities as a factor in robots design. In a world that everyday seems more and more connected, with the slow but steady advance of the Internet of Things to many aspects of our daily lifes, the lack of social capabilities in a robot limits developers and researchers on areas where robots are seen as part of a solution, and not the solution. This thesis states that a social layer should be accessible in any robotic platform in order to ease the development of systems where such platforms are just a piece in the whole socio-technical system. As result of this e ort we present the ROSAPL framework to develop social robots on top of ROS middleware. We tested our approach in a real scenario at IBEC's Robotics group in the context of the InHANDS, which project tries to assist a handicapped persons in the kitchen. For them we designed and implemented a prototype to proof ROSAPL applicability. This latter will be fully implemented to o er real functionalities for the kitchen

Cost evaluation during decision making in patients at early stages of psychosis

Jumping to conclusions during probabilistic reasoning is a cognitive bias reliably observed in psychosis, and linked to delusion formation. Although the reasons for this cognitive bias are unknown, one suggestion is that psychosis patients may view sampling information as more costly. However, previous computational modelling has provided evidence that patients with chronic schizophrenia jump to conclusion because of noisy decision making. We developed a novel version of the classical beads-task, systematically manipulating the cost of information gathering in four blocks. For 31 individuals with early symptoms of psychosis and 31 healthy volunteers, we examined the numbers of ‘draws to decision’ when information sampling had no, a fixed, or an escalating cost. Computational modelling involved estimating a cost of information sampling parameter and a cognitive noise parameter. Overall patients sampled less information than controls. However, group differences in numbers of draws became less prominent at higher cost trials, where less information was sampled. The attenuation of group difference was not due to floor effects, as in the most costly block participants sampled more information than an ideal Bayesian agent. Computational modelling showed that, in the condition with no objective cost to information sampling, patients attributed higher costs to information sampling than controls (Mann-Whiney U=289, p=0.007), with marginal evidence of differences in noise parameter estimates (t=1.86 df=60, p=0.07). In patients, individual differences in severity of psychotic symptoms were statistically significantly associated with higher cost of information sampling (rho=0.6, p=0.001) but not with more cognitive noise (rho=0.27, p=0.14); in controls cognitive noise predicted aspects of schizotypy (preoccupation and distress associated with delusion-like ideation on the Peters Delusion Inventory). Using a psychological manipulation and computational modelling, we provide evidence that early psychosis patients jump to conclusions because of attributing higher costs to sampling information, not because of being primarily noisy decision makers

A Computational Framework for Planning Therapeutical Sessions aimed to Support the Prevention and Treatment of Mental Health Disorders using Emotional Virtual Agents

[EN] Interaction is defined as the realization of a reciprocal action between two or more people or things. Particularly in computer science, the term interaction refers to the discipline that studies the exchange of information between people and computers, and is generally known by the term Human-Computer Interaction (HCI). Good design decisions and an adequate development of the software is required for efficient HCI to facilitate the acceptability of computer-based applications by the users. In clinical settings it is essential to eliminate any barrier and facilitate the interaction between patients and the system. A smooth communication between the user and the computer-based application is fundamental to maximise the advantages and functionalities offered by the system. The design of these applications must consider the personal and current needs of the user by applying a User-Centered Design methodology. The main purpose of this research work is to contribute in the improvement of HCI-based applications addressed to the clinical context, particularly to enhance computer-based interactive sessions to support people suffering from a mental disorder such as Major Depression (MD). Thanks to the advances in Artificial Intelligence techniques, it is now possible to partially automate complex tasks such as the continuous provision of Cognitive-Behavioural Therapies (CBTs) to patients. These CBTs require good levels of adaptability and variability during the interaction with the patient that facilitates the acceptability in the user, an optimal usability and good level of engagement for a successful mid/long term use of the application and treatment adherence. The modelling of complex deliberative and affective processes in artificial systems can be applied to support the prevention and treatment of mental health related issues, enhancing the continuous and remote assistance of patients, saving some economical and clinical resources and reducing the waiting lists in the health services. In this regard, the efforts of this Thesis have been concentrated on the research of two main lines: (1) the generation and planning of adequate contents in an interactive system to support the prevention and treatment of MD based on characteristics of the user; and (2) the modelling of relevant affective processes able to communicate the contents in an emotional effective way taking into account the importance of the affective conditions associated with the MD in the users. Rule Based Systems and the appraisal theory of emotions have been the roots used to develop the main two modules of the computational Framework presented: the Contents Management and the Emotional Modules. Finally, the obtained Framework was integrated into two interactive systems to evaluate the achievement of the research objectives. The first system has been developed in the context of the Help4Mood European research project and its main aim was to support the remote treatment of patients with MD. The second scenario was a system developed to prevent MD and suicidal thoughts in the University community, which was developed in the context of the local PrevenDep research project. These evaluations have indicated that the proposed Framework has reached good levels of usability and acceptability in the target users thanks to the personalizations and adaptation capabilities of the contents and in the way how these contents are communicated to the user. The research work and the obtained results in this Thesis has contributed to the state of the art in HCI-based systems used as support in therapeutic interventions for the prevention and treatment of MD. This was obtained by the combination of a personalized content management to the patient, and the management of the affective processes associated to these pathologies. The developed work also identifies some research lines that need to be addressed in future works to get better HCI systems used for therapeutic purposes.[ES] Interactuar se define como la realización de una acción recíproca entre dos o más personas o cosas. Particularmente en informática, el término interacción se refiere a la disciplina que estudia el intercambio de información entre las personas y computadoras, y suele conocerse por el término anglosajón Human-Computer Interaction (HCI). Un buen diseño y un adecuado desarrollo del software es necesario para lograr una HCI eficiente que facilite la aceptabilidad del sistema por el usuario. En entornos clínicos es fundamental eliminar cualquier tipo de barrera y facilitar la interacción entre los pacientes y el computador. Es de vital importancia que haya una buena comunicación entre usuario y computador, por este motivo el sistema debe de estar diseñado pensando en las necesidades actuales, cambiantes y personales del usuario, basándose en la metodología de diseño centrado en el usuario. El propósito principal de esta investigación es la identificación de mejoras en HCI aplicada en entornos clínicos, en concreto para dar soporte a personas con trastornos mentales como la Depresión Mayor (DM) y que precisan de terapias psicológicas adecuadas y continuas. Gracias a técnicas de Inteligencia Artificial, es posible automatizar eficientemente ciertas acciones asociadas a los procesos de las terapias cognitivo-conductuales (CBTs, del inglés Cognitive-Behavioural Therapies). Los sistemas de ayuda a la CBT, requieren de una adaptabilidad y variabilidad en la interacción para favorecer la usabilidad del sistema y asegurar la continuidad de la motivación del paciente. Una buena gestión de esta automatización influiría en la aceptabilidad de los pacientes y podría mejorar su adherencia a los tratamientos y por consiguiente mejorar su estado de salud. Adicionalmente, la unión de procesos deliberativos dinámicos pueden liberar recursos clínicos, mejorando el control de los pacientes, y reduciendo los tiempos de espera y los costes económicos. En este sentido, los esfuerzos de esta Tesis se han centrado en la investigación de dos líneas diferentes: (1) la selección y planificación adecuada de los contenidos presentados durante la interacción a través de una planificación dinámica y personalizada, y (2) la adecuación de la comunicación de los contenidos hacia el paciente tomando en cuenta la importancia de los procesos afectivos asociados a estas patologías. Los Sistemas Basados en Reglas (SBR) han sido la herramienta utilizada para dar soporte a los dos módulos principales que componen el Framework presentado en esta Tesis: el módulo de gestión de los contenidos y el módulo emocional. Concluida la fase de diseño, desarrollo y testeo, el Framework fue adaptado e integrado en sistemas reales, para validar la viabilidad y la adecuación del marco de trabajo de esta Tesis. En primer lugar, el sistema se aplicó durante tres años en el tratamiento de la DM en varios centros clínicos europeos en el contexto del Proyecto Europeo de investigación Help4Mood. Finalmente, el sistema fue evaluado en la tarea de prevención de la DM y del suicidio en el Proyecto Local de investigación PrevenDep, de un año de duración. El feedback de estas evaluaciones demostraron que el HCI del Framework tiene unos niveles altos de usabilidad y aceptación, gracias a la personalización, variabilidad y adaptación de los contenidos y de la comunicación de los mismos. Los experimentos computacionales llevados a cabo en esta Tesis han permitido avanzar el estado del arte de sistemas computacionales emocionales aplicados en entornos terapéuticos para la prevención y tratamiento de la DM. Principalmente, gracias a la combinación de una gestión personalizada de los contenidos hacia el paciente tomando en cuenta la importancia de los procesos afectivos asociados a estas patologías. Este trabajo abre nuevas líneas de investigación, como la aplicación de este sistema en otras patologías de salud mental en las qu[CA] Interactuar es defineix com la realització d'una acció recíproca entre dos o més persones o coses. Particularment en informàtica, el terme interacció es refereix a la disciplina que estudia l'intercanvi d'informació entre les persones i computadores, i es sol conèixer pel terme anglosaxó Human-Computer Interaction (HCI). Un bon disseny i un adequat desenvolupament del software és necessari per aconseguir una HCI eficient que faciliti l'acceptabilitat del sistema per l'usuari. En entorns clínics és fonamental eliminar qualsevol tipus de barrera i facilitar la interacció entre els pacients i el computador. És de vital importància que hi hagi una bona comunicació entre l'usuari (o pacient) i el computador, per aquest motiu el sistema ha d'estar dissenyat pensant en les necessitats actuals, cambiants i personals de l'usuari, basant-se en la metodologia de disseny centrat en l'usuari. El propòsit principal d'aquesta investigació és la identificació de millores en HCI aplicada en entorns clínics, en concret per donar suport a persones amb trastorns mentals com la Depressió Major (DM) i que precisen de teràpies psicològiques adequades i contínues. Gràcies a tècniques d'Intel·ligència Artificial, és possible automatitzar eficientment certes accions asociades al processos de les teràpies cognitiu-conductuals. Els sistemes computacionals de ajuda a la CBT, requereixen d'una adaptabilitat i variabilitat en la interacció per afavorir la usabilitat del sistema i assegurar la continuïtat de la motiviació del pacient. Una bona gestió d'aquesta automatització influiria en l'acceptabilitat dels pacients i podria millorar la seva adherència als tractaments i per tant millorar el seu estat de salut. Addicionalment, la unió de processos deliberatius dinàmics poden alliberar recursos clínics, millorant el control dels pacients, i reduint els temps d'espera i els costos econòmics. En aquest sentit, els esforços d'aquesta Tesi s'han centrat en la investigació de dues línies diferents: (1) la selecció i planificació adequada dels continguts presentats durant la interacció a través d'una planificació dinàmica i personalitzada, i (2) l'adequació de la comunicació dels continguts cap al pacient tenint en compte la importància dels processos afectius associats a aquestes patologies. Els Sistemes Basats en Regles (SBR) han estat la eina utilitzada per donar suport als dos mòduls principals que componen el Framework presentat en aquesta Tesi: el mòdul de gestió dels continguts oferits a l'usuari; i el mòdul emocional. Conclosa la fase de disseny, desenvolupament i testeig, el Framework va ser adaptat als dominis corresponents i integrat en sistemes madurs per ser avaluat en dos escenaris reals, per validar la viabilitat i l'adequació del Framework d'aquesta tesi. Primerament, el sistema es va aplicar durant tres anys en el tractament de la DM major en diversos centres clínics europeus en el context del Projecte Europeu d'investigació Help4Mood. Finalment, el sistema va ser avaluat en la tasca de prevenció de la DM i del suïcidi al Projecte Local d'investigació PrevenDep, d'un any de durada. El feedback de les avaluacions han demostrat que el HCI del Framework obté uns nivells alts d'usabilitat i acceptació, gràcies a la personalització, variabilitat i adaptació dels continguts i de la comunicació. Els experiments computacionals duts a terme en aquesta Tesi han permès avançar l'estat de l'art de sistemes computacionals emocionals aplicats en entorns terapèutics per a la prevenció i tractament de la DM. Principalment, gracies a la combinació d'una gestió personalitzada dels continguts cap al pacient tenint en compte la importància dels processos afectius associats a aquestes patologies. Aquest treball obre noves línies d'investigació, com l'aplicació d'aquest sistema en altres patologies de salut mental en què sigui recomanable l'aplicació de sessions terapèutiques.Bresó Guardado, A. (2016). A Computational Framework for Planning Therapeutical Sessions aimed to Support the Prevention and Treatment of Mental Health Disorders using Emotional Virtual Agents [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/64082TESI

Cost Evaluation During Decision-Making in Patients at Early Stages of Psychosis

Jumping to conclusions during probabilistic reasoning is a cognitive bias reliably observed in psychosis and linked to delusion formation. Although the reasons for this cognitive bias are unknown, one suggestion is that psychosis patients may view sampling information as more costly. However, previous computational modeling has provided evidence that patients with chronic schizophrenia jump to conclusions because of noisy decision-making. We developed a novel version of the classical beads task, systematically manipulating the cost of information gathering in four blocks. For 31 individuals with early symptoms of psychosis and 31 healthy volunteers, we examined the numbers of "draws to decision" when information sampling had no, a fixed, or an escalating cost. Computational modeling involved estimating a cost of information sampling parameter and a cognitive noise parameter. Overall, patients sampled less information than controls. However, group differences in numbers of draws became less prominent at higher cost trials, where less information was sampled. The attenuation of group difference was not due to floor effects, as in the most costly block, participants sampled more information than an ideal Bayesian agent. Computational modeling showed that, in the condition with no objective cost to information sampling, patients attributed higher costs to information sampling than controls did, Mann-Whitney U = 289, p = 0.007, with marginal evidence of differences in noise parameter estimates, t(60) = 1.86, p = 0.07. In patients, individual differences in severity of psychotic symptoms were statistically significantly associated with higher cost of information sampling, ρ = 0.6, p = 0.001, but not with more cognitive noise, ρ = 0.27, p = 0.14; in controls, cognitive noise predicted aspects of schizotypy (preoccupation and distress associated with delusion-like ideation on the Peters Delusion Inventory). Using a psychological manipulation and computational modeling, we provide evidence that early-psychosis patients jump to conclusions because of attributing higher costs to sampling information, not because of being primarily noisy decision makers

Collaborative water-resource governance in the UK: Understanding network structure and functionality of a catchment-based approach to water-quality management

Since 2011 water resource governance in the UK has begun to integrate a collaborative multi-stakeholder approach to water-quality management. The Catchment-Based Approach (CaBA) facilitates local partnerships of stakeholders to co-create plans, align actions, and make collective decisions about efforts to improve and protect local river and stream environments. The approach offers potential for the enactment of effective, equitable and sustainable water management, but it is often unclear how such efforts are characterised practically. The multiplicity of stakeholders and complexity of issues and influences contribute to difficulty in discerning how governance change is functioning. This thesis uses a case study of the River Wear Catchment, North East England, where stakeholders have been operating CaBA, to begin to explore the patterns and drivers of actions and interactions that facilitate collaborative water-resource governance at the stakeholder level. Drawing on the concept of the catchment as a complex, social-environmental system, this research utilises insights from stakeholders and a combination of analytical methods, including a network approach and agent-based modelling, to provide new perspectives on the network structure and functioning of multi-stakeholder water management. A network approach is used to build a picture of interactions amongst stakeholders and to reveal the nature of the new relationships built through CaBA. Qualitative analysis of interview data identifies key influences on the decision-making of stakeholders and the functionality of new and existing networks of relations at three levels; the interactional, individual and contextual. Agent-based modelling is then used as a heuristic research tool to combine knowledge of relational structures with influences on stakeholder behaviour to experiment with potential dynamics of the system through a specific water-quality, problem-based scenario. The combination of these analytical methods allows a more in-depth and dynamic understanding of the patterns and processes of CaBA than has been revealed previously. The thesis ultimately comments on the utility of such methods for creating new understandings of the operationalisation of water governance processes, and for the utility of those new understandings to inform and question the facilitation of effective and satisfactory delivery of collaborative multi-stakeholder water-quality management at the catchment-scale