Building an adaptive E-learning system

© 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Research in adaptive learning is mainly focused on improving learners' learning achievements based mainly on personalization information, such as learning style, cognitive style or learning achievement. In this paper, an innovative adaptive learning approach is proposed based upon two main sources of personalization information that is, learning behaviour and personal learning style. To determine the initial learning styles of the learner, an initial assigned test is employed in our approach. In order to more precisely reflect the learning behaviours of each learner, the interactions and learning results of each learner are thoroughly recorded and in depth analysed, based on advanced machine learning techniques, when adjusting the subject materials. Based on this rather innovative approach, an adaptive learning prototype system has been developed

Towards automatic construction of adaptable courseware storyboards

In last twenty years, researchers have conducted intensive research in the area of principal models, software architectures and practical system development of adaptive e-learning platforms. Brains are fascinated by great opportunities for radical improvement of the teaching process by means of applying adaptability at different levels. There are two general issues of adaptive e-learning – enabling different educational content delivered to different individuals or groups and, as well, differently formed sequencing and presentation of that content delivery. This paper presents two approaches for creating and delivering training courses adaptable to learners with different learning styles. The first one is implemented within a platform for building edutainment (education plus entertainment) services called ADOPTA (ADaptive technOlogy-enhanced Platform for eduTAinment). By means of ADOPTA, e-learning courses can be created manually by an instructor as directed storyboard graphs. Another feasible approach is to generate them automatically on-the-fly by the adaptive engine. The article discusses advantages and drawbacks of these two approaches for adaptive e-learning course constructio

Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations

Data availability has dramatically increased in recent years, driving model-based control methods to exploit learning techniques for improving the system description, and thus control performance. Two key factors that hinder the practical applicability of learning methods in control are their high computational complexity and limited generalization capabilities to unseen conditions. Meta-learning is a powerful tool that enables efficient learning across a finite set of related tasks, easing adaptation to new unseen tasks. This paper makes use of a meta-learning approach for adaptive model predictive control, by learning a system model that leverages data from previous related tasks, while enabling fast fine-tuning to the current task during closed-loop operation. The dynamics is modeled via Gaussian process regression and, building on the Karhunen-Lo{\`e}ve expansion, can be approximately reformulated as a finite linear combination of kernel eigenfunctions. Using data collected over a set of tasks, the eigenfunction hyperparameters are optimized in a meta-training phase by maximizing a variational bound for the log-marginal likelihood. During meta-testing, the eigenfunctions are fixed, so that only the linear parameters are adapted to the new unseen task in an online adaptive fashion via Bayesian linear regression, providing a simple and efficient inference scheme. Simulation results are provided for autonomous racing with miniature race cars adapting to unseen road conditions

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

Diagnosis and the management constituency of small-scale fisheries

Diagnosis and adaptive management can help improve the ability of small-scale fisheries (SSF) in the developing world to better cope with and adapt to both external drivers and internal sources of uncertainty. This paper presents a framework for diagnosis and adaptive management and discusses ways of implementing the first two phases of learning: diagnosis and mobilising an appropriate management constituency. The discussion addresses key issues and suggests suitable approaches and tools as well as numerous sources of further information. Diagnosis of a SSF defines the system to be managed, outlines the scope of the management problem in terms of threats and opportunities, and aims to construct realistic and desired future projections for the fishery. These steps can clarify objectives and lead to development of indicators necessary for adaptive management. Before management, however, it is important to mobilize a management constituency to enact change. Ways of identifying stakeholders and understanding both enabling and obstructive interactions and management structures are outlined. These preliminary learning phases for adaptive SSF management are expected to work best if legitimised by collaborative discussion among fishery stakeholders drawing on multiple knowledge systems and participatory approaches to assessment. (PDF contains 33 pages

How Dutch Institutions Enhance the Adaptive Capacity of Society

This report examines the adaptive capacity of the institutional framework of the Netherlands to cope with the impacts of climate change. Historically, institutions have evolved incrementally to deal with existing social problems. They provide norms and rules for collective action and create continuity rather than change. However, the nature of societal problems is changing as a result of the processes of globalization and development. With the progress made in the natural sciences, we are able to predict in advance, to a certain extent, the potential environmental impacts of various human actions on society, for example, climate change. This raises some key questions: Are our institutions capable of dealing with this new knowledge about future impacts and, more importantly, with the impacts themselves? Are our institutions capable of dealing with the inherent uncertainty of the predictions

Citizen science and natural resource governance: program design for vernal pool policy innovation

Effective natural resource policy depends on knowing what is needed to sustain a resource and building the capacity to identify, develop, and implement flexible policies. This retrospective case study applies resilience concepts to a 16-year citizen science program and vernal pool regulatory development process in Maine, USA. We describe how citizen science improved adaptive capacities for innovative and effective policies to regulate vernal pools. We identified two core program elements that allowed people to act within narrow windows of opportunity for policy transformation, including (1) the simultaneous generation of useful, credible scientific knowledge and construction of networks among diverse institutions, and (2) the formation of diverse leadership that promoted individual and collective abilities to identify problems and propose policy solutions. If citizen science program leaders want to promote social-ecological systems resilience and natural resource policies as outcomes, we recommend they create a system for internal project evaluation, publish scientific studies using citizen science data, pursue resources for program sustainability, and plan for leadership diversity and informal networks to foster adaptive governance. Effective natural resource policy depends on knowing what is needed to sustain a resource and building the capacity to identify, develop, and implement flexible policies. This retrospective case study applies resilience concepts to a 16-year citizen science program and vernal pool regulatory development process in Maine, USA. We describe how citizen science improved adaptive capacities for innovative and effective policies to regulate vernal pools. We identified two core program elements that allowed people to act within narrow windows of opportunity for policy transformation, including (1) the simultaneous generation of useful, credible scientific knowledge and construction of networks among diverse institutions, and (2) the formation of diverse leadership that promoted individual and collective abilities to identify problems and propose policy solutions. If citizen science program leaders want to promote social-ecological systems resilience and natural resource policies as outcomes, we recommend they create a system for internal project evaluation, publish scientific studies using citizen science data, pursue resources for program sustainability, and plan for leadership diversity and informal networks to foster adaptive governance