4,440 research outputs found
The use of emotions in the implementation of various types of learning in a cognitive agent
Les tuteurs professionnels humains sont capables de prendre en considĂ©ration des Ă©vĂ©nements du passĂ© et du prĂ©sent et ont une capacitĂ© d'adaptation en fonction d'Ă©vĂ©nements sociaux. Afin d'ĂȘtre considĂ©rĂ© comme une technologie valable pour l'amĂ©lioration de l'apprentissage humain, un agent cognitif artificiel devrait pouvoir faire de mĂȘme. Puisque les environnements dynamiques sont en constante Ă©volution, un agent cognitif doit pareillement Ă©voluer et s'adapter aux modifications structurales et aux phĂ©nomĂšnes nouveaux. Par consĂ©quent, l'agent cognitif idĂ©al devrait possĂ©der des capacitĂ©s d'apprentissage similaires Ă celles que l'on retrouve chez l'ĂȘtre humain ; l'apprentissage Ă©motif, l'apprentissage Ă©pisodique, l'apprentissage procĂ©dural, et l'apprentissage causal. Cette thĂšse contribue Ă l'amĂ©lioration des architectures d'agents cognitifs. Elle propose 1) une mĂ©thode d'intĂ©gration des Ă©motions inspirĂ©e du fonctionnement du cerveau; et 2) un ensemble de mĂ©thodes d'apprentissage (Ă©pisodique, causale, etc.) qui tiennent compte de la dimension Ă©motionnelle. Le modĂšle proposĂ© que nous avons appelĂ© CELTS (Conscious Emotional Learning Tutoring System) est une extension d'un agent cognitif conscient dans le rĂŽle d'un tutoriel intelligent. Il comporte un module de gestion des Ă©motions qui permet d'attribuer des valences Ă©motionnelles positives ou nĂ©gatives Ă chaque Ă©vĂ©nement perçu par l'agent. Deux voies de traitement sont prĂ©vues : 1) une voie courte qui permet au systĂšme de rĂ©pondre immĂ©diatement Ă certains Ă©vĂ©nements sans un traitement approfondis, et 2) une voie longue qui intervient lors de tout Ă©vĂ©nement qui exige la volition. Dans cette perspective, la dimension Ă©motionnelle est considĂ©rĂ©e dans les processus cognitifs de l'agent pour la prise de dĂ©cision et l'apprentissage. L'apprentissage Ă©pisodique dans CELTS est basĂ© sur la thĂ©orie du Multiple Trace Memory consolidation qui postule que lorsque l'on perçoit un Ă©vĂ©nement, l'hippocampe fait une premiĂšre interprĂ©tation et un premier apprentissage. Ensuite, l'information acquise est distribuĂ©e aux diffĂ©rents cortex. Selon cette thĂ©orie, la reconsolidation de la mĂ©moire dĂ©pend toujours de l'hippocampe. Pour simuler de tel processus, nous avons utilisĂ© des techniques de fouille de donnĂ©es qui permettent la recherche de motifs sĂ©quentiels frĂ©quents dans les donnĂ©es gĂ©nĂ©rĂ©es durant chaque cycle cognitif. L'apprentissage causal dans CELTS se produit Ă l'aide de la mĂ©moire Ă©pisodique. Il permet de trouver les causes et les effets possibles entre diffĂ©rents Ă©vĂ©nements. Il est mise en Ćuvre grĂące Ă des algorithmes de recherche de rĂšgles d'associations. Les associations Ă©tablies sont utilisĂ©es pour piloter les interventions tutorielles de CELTS et, par le biais des rĂ©ponses de l'apprenant, pour Ă©valuer les rĂšgles causales dĂ©couvertes. \ud
______________________________________________________________________________ \ud
MOTS-CLĂS DE LâAUTEUR : agents cognitifs, Ă©motions, apprentissage Ă©pisodique, apprentissage causal
Learner Modelled Environments
Learner modelled environments (LMEs) are digital environments that are capable of
automatically detecting learnerâs behaviours in relation to a specific knowledge
domain, to reason about those behaviours in order to asses learnerâs performance,
skills, socio-emotional and cognitive needs, and to act accordingly in a pedagogically
appropriate manner. Digital environments that possess such capabilities are typically
referred to as Intelligent Learning Environments, or more traditionally â as Intelligent
Tutoring Systems (ITSs)
Interventions to Regulate Confusion during Learning
Confusion provides opportunities to learn at deeper levels. However, learners must put forth the necessary effort to resolve their confusion to convert this opportunity into actual learning gains. Learning occurs when learners engage in cognitive activities beneficial to learning (e.g., reflection, deliberation, problem solving) during the process of confusion resolution. Unfortunately, learners are not always able to resolve their confusion on their own. The inability to resolve confusion can be due to a lack of knowledge, motivation, or skills. The present dissertation explored methods to aid confusion resolution and ultimately promote learning through a multi-pronged approach. First, a survey revealed that learners prefer more information and feedback when confused and that they preferred different interventions for confusion compared to boredom and frustration. Second, expert human tutors were found to most frequently handle learner confusion by providing direct instruction and responded differently to learner confusion compared to anxiety, frustration, and happiness. Finally, two experiments were conducted to test the effectiveness of pedagogical and motivational confusion regulation interventions. Both types of interventions were investigated within a learning environment that experimentally induced confusion via the presentation of contradictory information by two animated agents (tutor and peer student agents). Results showed across both studies that learner effort during the confusion regulation task impacted confusion resolution and that learning occurred when the intervention provided the opportunity for learners to stop, think, and deliberate about the concept being discussed. Implications for building more effective affect-sensitive learning environments are discussed
Domain independent strategies in an affective tutoring system
There have been various attempts to develop an affective tutoring system (ATS) framework
that considers and reacts to a studentâs emotions while learning. However, there is a gap
between current systems and the theory underlying human appraisal models. The current
frameworks rely on a single appraisal and reaction phase. In contrast, the human appraisal
process (Lazarus, 1991) involves two phases of appraisal and reaction (i.e. primary and
secondary appraisal phases).
This thesis proposes an affective tutoring (ATS) framework that introduces two phases of appraisal and reaction (i.e. primary and secondary appraisal and reaction phases). This
proposed framework has been implemented and evaluated in a system to teach Data Structures.
In addition, the system employs both domain-dependent and domain-independent strategies for coping with studentsâ affective states. This follows the emotion regulation model (Lazarus, 1991) that underpins the ATS framework which argues that individuals use both kinds of strategies in solving daily life problems. In comparison, current affective (ITS) frameworks concentrate on the use of domain-dependent strategies to cope with studentsâ affective states.
The evaluation of the system provides some support for the idea that the ATS framework is useful both in improving studentsâ affective states (i.e. during and by the end of a learning session) and also their learning performance
Player agency in interactive narrative: audience, actor & author
The question motivating this review paper is, how can
computer-based interactive narrative be used as a constructivist learn-
ing activity? The paper proposes that player agency can be used to
link interactive narrative to learner agency in constructivist theory,
and to classify approaches to interactive narrative. The traditional
question driving research in interactive narrative is, âhow can an in-
teractive narrative deal with a high degree of player agency, while
maintaining a coherent and well-formed narrative?â This question
derives from an Aristotelian approach to interactive narrative that,
as the question shows, is inherently antagonistic to player agency.
Within this approach, player agency must be restricted and manip-
ulated to maintain the narrative. Two alternative approaches based
on Brechtâs Epic Theatre and Boalâs Theatre of the Oppressed are
reviewed. If a Boalian approach to interactive narrative is taken the
conflict between narrative and player agency dissolves. The question
that emerges from this approach is quite different from the traditional
question above, and presents a more useful approach to applying in-
teractive narrative as a constructivist learning activity
Adaptive User Interfaces for Intelligent E-Learning: Issues and Trends
Adaptive User Interfaces have a long history rooted in the emergence of such eminent technologies as Artificial Intelligence, Soft Computing, Graphical User Interface, JAVA, Internet, and Mobile Services. More specifically, the advent and advancement of the Web and Mobile Learning Services has brought forward adaptivity as an immensely important issue for both efficacy and acceptability of such services. The success of such a learning process depends on the intelligent context-oriented presentation of the domain knowledge and its adaptivity in terms of complexity and granularity consistent to the learnerâs cognitive level/progress. Researchers have always deemed adaptive user interfaces as a promising solution in this regard. However, the richness in the human behavior, technological opportunities, and contextual nature of information offers daunting challenges. These require creativity, cross-domain synergy, cross-cultural and cross-demographic understanding, and an adequate representation of mission and conception of the task. This paper provides a review of state-of-the-art in adaptive user interface research in Intelligent Multimedia Educational Systems and related areas with an emphasis on core issues and future directions
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
Learning designs incorporating animated pedagogical agents: Their potential for improving academic writing competence, writing self-efficacy, and reducing writing anxiety
Academic writing can be extremely challenging, especially for new university students. This is compounded by the mass-migration of courses to online delivery, which further increases the complexity of acquiring writing skills.
Animated pedagogical agents (APAs) have shown promise in addressing these problems, because they simulate authentic face-to-face social interactions thereby potentially increasing student engagement, motivation, and favourable emotions conducive to learning.
This studyâs first aim was to examine the impact of learning designs employing APAs on novice learnersâ academic writing, writing anxiety, and writing self-efficacy. Its second aim was to examine the influence of various delivery options (didactic delivery or scaffolded questioning) with support messages (emotional, motivational or neither) on writing competence, writing anxiety and writing self-efficacy.
These aims were achieved in a mixed-method study that included six experimental conditions tested using two multimedia academic writing lessons provided to 106 participants who were new to Australian tertiary studies. Quantitative data were collected immediately before and after the lessons (Phase 1), while qualitative data were obtained by interviews with a subset of participants after Lesson 2 (Phase 2). The impact of the independent variable combinations on the dependent variables were examined quantitatively (General Linear Modelling, t-tests) and qualitatively (thematic analysis).
The results demonstrate that completing two academic writing lessons with APAs can increase writing competence and self-efficacy, and reduce writing anxiety. However, no significant differences were found between the support and delivery groups. Despite the lack of significant inter-group differences, more participants from the emotional group reported that their negative emotions were reduced because of the lesson. Also, all the participants in the motivational group reported perceptions of writing improvement as a result of attending the lessons.
The overall positive result suggests promising possibilities for writing support delivered online to counter student under preparedness for academic writing
- âŠ