15,607 research outputs found
A Hybrid Approach for Supporting Adaptivity in E-learning Environments
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
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
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
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
Making Legacy LMS adaptable using Policy and Policy templates
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
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
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 that runs in adaptive rounds and makes
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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