Learner Modelled Environments

Learner modelled environments (LMEs) are digital environments that are capable of automatically detecting learner’s behaviours in relation to a specific knowledge domain, to reason about those behaviours in order to asses learner’s 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 – as Intelligent Tutoring Systems (ITSs)

Overcoming foreign language anxiety in an emotionally intelligent tutoring system

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning. Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct and less regulated by the learner, but it is uncomfortable and requires the learner to be in the lab. Employing self-reports is scalable because it is easy to administer in the lab and online. However, it interrupts the learning flow, and people sometimes respond inaccurately. Using sensor-free human behavioral metrics is a scalable and practical measurement because it is feasible online or in class with minimum adjustments. To overcome FLA, researchers have studied the use of robots, games, or intelligent tutoring systems (ITS). Within these technologies, they applied soothing music, difficulty reduction, or storytelling. These methods lessened FLA but had limitations such as distracting the learner, not improving performance, and producing cognitive overload. Using an animated agent that provides motivational supportive feedback could reduce FLA and increase learning. It is necessary to measure FLA effectively with minimal interruption and then successfully reduce it. In the context of an e-learning system, I investigated ways to detect FLA using sensor-free human behavioral metrics. This scalable and practical method allows us to recognize FLA without being obtrusive. To reduce FLA, I studied applying emotionally adaptive feedback that offers motivational supportive feedback by an animated agent

Chapter 35 Digital Learning for Developing Asian Countries

Education – that is, the development of knowledge, skills, and values – is an important means by which to empower individuals in a society. As both a means towards and an outcome of gaining the capabilities necessary to participate in and contribute to society, education is an essential enabler in many social aspects, such as economic growth, poverty reduction, public health, and sustainable development, especially in today’s knowledge society. At the same time, however, education can still be a social institution that reflects and reproduces the social, cultural, and economic disadvantages that prevail in the rest of society (Bourdieu & Passeron, 1990). For example, students who are discriminated against socio-culturally or who are economically poor are more likely to receive an education that is characterized by inadequate infrastructure, few qualified teachers and encouraging peers, and outmoded pedagogical practices, which often results in a lower quality of life

Some Research Questions and Results of UC3M in the E-Madrid Excellence Network

32 slides.-- Contributed to: 2010 IEEE Global Engineering Education Conference (EDUCON), Madrid, Spain, 14-16 April, 2010.-- Presented by C. Delgado Kloos.Proceedings of: 2010 IEEE Global Engineering Education Conference (EDUCON), Madrid, Spain, 14-16 April, 2010Universidad Carlos III de Madrid is one of the six main participating institutions in the eMadrid excellence network, as well as its coordinating partner. In this paper, the network is presented together with some of the main research lines carried out by UC3M. The remaining papers in this session present the work carried out by the other five universities in the consortium.The Excellence Network eMadrid, “Investigación y Desarrollo de Tecnologías para el e-Learning en la Comunidad de Madrid” is being funded by the Madrid Regional Government under grant No. S2009/TIC-1650. In addition, we acknowledge funding from the following research projects: iCoper: “Interoperable Content for Performance in a Competency-driven Society” (eContentPlus Best Practice Network No. ECP-2007-EDU-417007), Learn3: Hacia el Aprendizaje en la 3ª Fase (“Plan Nacional de I+D+I” TIN2008-05163/ TSI), Flexo: “Desarrollo de aprendizaje adaptativo y accesible en sistemas de código abierto” (AVANZA I+D, TSI-020301- 2008-19), España Virtual (CDTI, Ingenio 2010, CENIT, Deimos Space), SOLITE (CYTED 508AC0341), and “Integración vertical de servicios telemáticos de apoyo al aprendizaje en entornos residenciales” (Programa de creación y consolidación de grupos de investigación de la Universidad Carlos III de Madrid).Publicad

Towards the Use of Dialog Systems to Facilitate Inclusive Education

Continuous advances in the development of information technologies have currently led to the possibility of accessing learning contents from anywhere, at anytime, and almost instantaneously. However, accessibility is not always the main objective in the design of educative applications, specifically to facilitate their adoption by disabled people. Different technologies have recently emerged to foster the accessibility of computers and new mobile devices, favoring a more natural communication between the student and the developed educative systems. This chapter describes innovative uses of multimodal dialog systems in education, with special emphasis in the advantages that they provide for creating inclusive applications and learning activities

Adapting Progress Feedback and Emotional Support to Learner Personality

Peer reviewedPostprin

Modes and Mechanisms of Game-like Interventions in Intelligent Tutoring Systems

While games can be an innovative and a highly promising approach to education, creating effective educational games is a challenge. It requires effectively integrating educational content with game attributes and aligning cognitive and affective outcomes, which can be in conflict with each other. Intelligent Tutoring Systems (ITS), on the other hand, have proven to be effective learning environments that are conducive to strong learning outcomes. Direct comparisons between tutoring systems and educational games have found digital tutors to be more effective at producing learning gains. However, tutoring systems have had difficulties in maintaining students€™ interest and engagement for long periods of time, which limits their ability to generate learning in the long-term. Given the complementary benefits of games and digital tutors, there has been considerable effort to combine these two fields. This dissertation undertakes and analyzes three different ways of integrating Intelligent Tutoring Systems and digital games. We created three game-like systems with cognition, metacognition and affect as their primary target and mode of intervention. Monkey\u27s Revenge is a game-like math tutor that offers cognitive tutoring in a game-like environment. The Learning Dashboard is a game-like metacognitive support tool for students using Mathspring, an ITS. Mosaic comprises a series of mini-math games that pop-up within Mathspring to enhance students\u27 affect. The methodology consisted of multiple randomized controlled studies ran to evaluate each of these three interventions, attempting to understand their effect on students€™ performance, affect and perception of the intervention and the system that embeds it. Further, we used causal modeling to further explore mechanisms of action, the inter-relationships between student€™s incoming characteristics and predispositions, their mechanisms of interaction with the tutor, and the ultimate learning outcomes and perceptions of the learning experience

Assessment of Learners’ Motivation during Interactions with Serious Games: A Study of Some Motivational Strategies in Food-Force

This study investigated motivational strategies and the assessment of learners’ motivation during serious gameplay. Identifying and intelligently assessing the effects that these strategies may have on learners are particularly relevant for educational computer-based systems. We proposed, therefore, the use of physiological sensors, namely, heart rate, skin conductance, and electroencephalogram (EEG), as well as a theoretical model of motivation (Keller’s ARCS model) to evaluate six motivational strategies selected from a serious game called Food-Force. Results from nonparametric tests and logistic regressions supported the hypothesis that physiological patterns and their evolution are suitable tools to directly and reliably assess the effects of selected strategies on learners’ motivation. They showed that specific EEG “attention ratio” was a significant predictor of learners’ motivation and could relevantly evaluate motivational strategies, especially those associated with the Attention and Confidence categories of the ARCS model of motivation. Serious games and intelligent systems can greatly benefit from using these results to enhance and adapt their interventions

Knowledge Elicitation Methods for Affect Modelling in Education

Research on the relationship between affect and cognition in Artificial Intelligence in Education (AIEd) brings an important dimension to our understanding of how learning occurs and how it can be facilitated. Emotions are crucial to learning, but their nature, the conditions under which they occur, and their exact impact on learning for different learners in diverse contexts still needs to be mapped out. The study of affect during learning can be challenging, because emotions are subjective, fleeting phenomena that are often difficult for learners to report accurately and for observers to perceive reliably. Context forms an integral part of learners’ affect and the study thereof. This review provides a synthesis of the current knowledge elicitation methods that are used to aid the study of learners’ affect and to inform the design of intelligent technologies for learning. Advantages and disadvantages of the specific methods are discussed along with their respective potential for enhancing research in this area, and issues related to the interpretation of data that emerges as the result of their use. References to related research are also provided together with illustrative examples of where the individual methods have been used in the past. Therefore, this review is intended as a resource for methodological decision making for those who want to study emotions and their antecedents in AIEd contexts, i.e. where the aim is to inform the design and implementation of an intelligent learning environment or to evaluate its use and educational efficacy