15,607 research outputs found

    Adaptivity in E-learning systems

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    A Hybrid Approach for Supporting Adaptivity in E-learning Environments

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    Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the ECA model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity in any given Learning Management System based on learnersā€™ learning styles. Design/methodology/approach: This paper offers a brief review of current frameworks and systems to support adaptivity in e-learning environments. A framework to support adaptivity is designed and discussed, reflecting the hybrid approach in detail. A system prototype is developed incorporating different adaptive features based on the Felder-Silverman learning styles model. Finally, the prototype is implemented in Moodle. Findings: The system prototype supports real-time adaptivity in any given Learning Management System based on learnersā€™ learning styles. It can deal with any type of content provided by course designers and instructors in the Learning Management System. Moreover, it can support adaptivity at both course and learner levels. Research limitations/implications: Practical implications: Social implications: Originality/value: To the best of our knowledge, no previous work has been done incorporating the concept of the ECA model and intelligent agents as hybrid architecture to support adaptivity in e-learning environments. The system prototype has wider applicability and can be adapted to support different types of adaptivity

    The Anatomy of an adaptive multimedia presentation system

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    The use of multimedia presentations within learning environments is described and guidelines for the design of good E-Learning systems are identified. It is argued that a linear sequential presentation of knowledge segments is effective, but that the user is provided with optional links to relevant segments during the presentation. The synchronisation of multiple media is considered and the design of a prototype E-Learning system is discussed. The segmentation of material is then discussed and how the information can be stored in a data repository consider with respect to the requirement of accessing linked segments. Finally, the nature of adaptivity is discussed leading to a discussion of the salient parts of an adaptive multimedia presentation system

    Selection and Prioritization of Adaptivity Criteria in Intelligent and Adaptive Hypermedia e-Learning Systems

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    One of the main characteristics of Intelligent and Adaptive Hypermedia E-Learning Systems (IAHe-LS) are adaptivity criteria. Selecting adaptivity criteria is one of the main steps in developing a prototype of a System for Dynamic Generating of Learning Objects (SDGLO) that will support the individual personalised learning process. The selection of those criteria has a high impact on the quality of usage of those systems. This paper presents research into prioritisation of adaptivity criteria from the perspective of their usage and the selection of adaptivity criteria for creating the SDGLO prototype. The methods that were used in the research are: descriptive statistics, Cronbach Alpha, one-way ANOVA, and Analytic Hierarchy Process (AHP) with final qualitative analysis. In conclusion, for the development of a prototype of SDGLO, adaptivity criteria that are selected are learning style, cognitive style and learning objectives

    Setting an Agenda for Urban AI Adaptivity in Urban Planning and Architecture E-learning

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    The rapid spread of technology and learning systems have altered the viewpoint about the lack of E-learning to the human element. The intersection of AI and education is highlighted by many technologists and researchers showing the diverse possibilities and challenges of using AI in education. However, little research addresses the potential of using AI to create an adaptive e-learning experience that brings a fully personalized experience to e-learners in architecture and urban educational fields. Building on that, we postulate that adaptive AI learning could be useful for urban online teaching and urban development Massive Open Online Courses (MOOCs), specifically as urban planners need to explore different scenarios of future city making. Therefore, the aim is to explore how educators from the architecture and urban field E-Learning stakeholders perceive AI in the creation of urban Moocs as well as other online teaching activities, as well as address the ways in which adaptive learning can be created in urban e-learning MOOCs using AI. In an attempt to answer the question, what is the current perception of educators about AI adaptivity in e-learning?To achieve this, first, we review the literature available on the topic to provide a comprehensive and inclusive look at adaptive AI learning, its potential, and its challenges. This overview informed and guided the formulation of the survey questions. Then we conducted a survey on educators in Architecture and urban fields from universities in Egypt. The unfamiliarity of the participants with AI provides us with deeper insights into perceptions of educators\u27 AI adaptivity in online learning and MOOCs. The study develops a framework for adaptive e-learning using AI in an attempt to create more interactive and personalized e-learning experiences that can be used in different fields and for different types of learners

    An E-Learning Investigation into Learning Style Adaptivity

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    Making Legacy LMS adaptable using Policy and Policy templates

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    Koesling, A., Herder, E., De Coi, J., & Abel, F. (2008). Making Legacy LMS adaptable using Policy and Policy templates. In J. Baumeister & M. AtzmĆ¼ller, Proceedings of the 16th Workshop on Adaptivity and User Modeling in Interactive System, ABIS 2008 (pp. 35-40). October, 6-8, 2008, WĆ¼rzburg, Germany: University of WĆ¼rzburg. Website with link to proceedings: http://lwa08.informatik.uni-wuerzburg.de/Wiki.jsp?page=FGABIS08In this paper, we discuss how users and designers of existing learning management systems (LMSs) can make use of policies to enhance adaptivity and adaptability. Many widespread LMSs currently only use limited and proprietary rule systems defining the system behaviour. Personalization of those systems is done based on those rule systems allowing only for fairly restricted adaptation rules. Policies allow for more sophisticated and flexible adaptation rules, provided by multiple stakeholders and they can be integrated into legacy systems. We present the benefits and feasibility of our ongoing approach of extending an existing LMS with policies. We will use the LMS ILIAS as a hands-on example to allow users to make use of system personalization.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

    Setting an Agenda for Urban AI Adaptivity in Urban Planning and Architecture E-learning

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    The rapid spread of technology and learning systems have altered the viewpoint about the lack of E-learning to the human element. The intersection of AI and education is highlighted by many technologists and researchers showing the diverse possibilities and challenges of using AI in education. However, little research addresses the potential of using AI to create an adaptive e-learning experience that brings a fully personalized experience to e-learners in architecture and urban educational fields. Building on that, we postulate that adaptive AI learning could be useful for urban online teaching and urban development Massive Open Online Courses (MOOCs), specifically as urban planners need to explore different scenarios of future city making. Therefore, the aim is to explore how educators from the architecture and urban field E-Learning stakeholders perceive AI in the creation of urban Moocs as well as other online teaching activities, as well as address the ways in which adaptive learning can be created in urban e-learning MOOCs using AI. In an attempt to answer the question, what is the current perception of educators about AI adaptivity in e-learning?To achieve this, first, we review the literature available on the topic to provide a comprehensive and inclusive look at adaptive AI learning, its potential, and its challenges. This overview informed and guided the formulation of the survey questions. Then we conducted a survey on educators in Architecture and urban fields from universities in Egypt. The unfamiliarity of the participants with AI provides us with deeper insights into perceptions of educators\u27 AI adaptivity in online learning and MOOCs. The study develops a framework for adaptive e-learning using AI in an attempt to create more interactive and personalized e-learning experiences that can be used in different fields and for different types of learners

    Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity

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    Submodular maximization is a general optimization problem with a wide range of applications in machine learning (e.g., active learning, clustering, and feature selection). In large-scale optimization, the parallel running time of an algorithm is governed by its adaptivity, which measures the number of sequential rounds needed if the algorithm can execute polynomially-many independent oracle queries in parallel. While low adaptivity is ideal, it is not sufficient for an algorithm to be efficient in practice---there are many applications of distributed submodular optimization where the number of function evaluations becomes prohibitively expensive. Motivated by these applications, we study the adaptivity and query complexity of submodular maximization. In this paper, we give the first constant-factor approximation algorithm for maximizing a non-monotone submodular function subject to a cardinality constraint kk that runs in O(logā”(n))O(\log(n)) adaptive rounds and makes O(nlogā”(k))O(n \log(k)) oracle queries in expectation. In our empirical study, we use three real-world applications to compare our algorithm with several benchmarks for non-monotone submodular maximization. The results demonstrate that our algorithm finds competitive solutions using significantly fewer rounds and queries.Comment: 12 pages, 8 figure
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