11 research outputs found

    Recommending learning material in Intelligent Tutoring Systems

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    Nowadays, intelligent e-learning systems which can adapt to learner's needs and preferences, became very popular. Many studies have demonstrated that such systems can increase the eects of learning. However, providing adaptability requires consideration of many factors. The main problems concern user modeling and personalization, collaborative learning, determining and modifying learning senarios, analyzing learner's learning styles. Determining the optimal learning scenario adapted to students' needs is very important part of an e-learning system. According to psychological research, learning path should follow the students' needs, such as (i.a.): content, level of diculty or presentation version. Optimal learning path can allow for easier and faster gaining of knowledge. In this paper an overview of methods for recommending learning material is presented. Moreover, a method for determining a learning scenario in Intelligent Tutoring Systems is proposed. For this purpose, an Analytic Hierarchy Process (AHP) method is used

    E3: Emotions, Engagement, and Educational Digital Games

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    The use of educational digital games as a method of instruction for science, technology, engineering, and mathematics has increased in the past decade. While these games provide successfully implemented interactive and fun interfaces, they are not designed to respond or remedy students’ negative affect towards the game dynamics or their educational content. Therefore, this exploratory study investigated the frequent patterns of student emotional and behavioral response to educational digital games. To unveil the sequential occurrence of these affective states, students were assigned to play the game for nine class sessions. During these sessions, their affective and behavioral response was recorded to uncover possible underlying patterns of affect (particularly confusion, frustration, and boredom) and behavior (disengagement). In addition, these affect and behavior frequency pattern data were combined with students’ gameplay data in order to identify patterns of emotions that led to a better performance in the game. The results provide information on possible affect and behavior patterns that could be used in further research on affect and behavior detection in such open-ended digital game environments. Particularly, the findings show that students experience a considerable amount of confusion, frustration, and boredom. Another finding highlights the need for remediation via embedded help, as the students referred to peer help often during their gameplay. However, possibly because of the low quality of the received help, students seemed to become frustrated or disengaged with the environment. Finally, the findings suggest the importance of the decay rate of confusion; students’ gameplay performance was associated with the length of time students remained confused or frustrated. Overall, these findings show that there are interesting patterns related to students who experience relatively negative emotions during their gameplay

    Proceedings of the ECMLPKDD 2015 Doctoral Consortium

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    ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium

    5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

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    Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 5th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Martínez Torres, MDR.; Toral Marín, S. (2023). 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2023.2023.1700

    Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System

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    This thesis presents research that combines the benefits of intelligent tutoring systems (ITS), conversational agents (CA) and learning styles theory by constructing a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS aims to imitate a human tutor by implicitly predicting individuals’ learning style preferences and adapting its tutoring style to suit them during a tutoring conversation. ITS are computerised learning systems that intelligently personalise tutoring based on learner characteristics such as existing knowledge and learning style. ITS are traditionally student-led, hyperlink-based learning systems that adapt the presentation of learning resources by reordering or hiding links. Research suggests that students learn more effectively when instruction matches their learning style, which is typically modelled explicitly using questionnaires or implicitly based on behaviour. Learning is a social process and natural language interfaces to ITS, such as CAs, allow students to construct knowledge through discussion. Existing CITS adapt tutoring according to student knowledge, emotions and mood, however no CITS adapts to learning styles. Oscar CITS models a human tutor by directing a tutoring conversation and automatically detecting and adapting to an individual’s learning styles. Original methodologies and architectures were developed for constructing an Oscar Predictive CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured from a learning styles model to dynamically predict learning styles from an individual’s tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation algorithm to select the best tutoring style for each tutorial question. The Oscar CITS methodologies and architectures are independent of the learning styles model and subject domain. Empirical studies involving real students have validated the prediction and adaptation of learning styles in a real-world teaching/learning environment. The results show that learning styles can be successfully predicted from a natural language tutoring dialogue, and that adapting the tutoring style significantly improves learning performance
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