A flexible open source web platform to facilitate Learning Object evaluation

Systematic evaluation of Learning Objects is essential to make high quality Web-based education possible. For this reason, several educational repositories and e-Learning systems have developed their own evaluation models and tools. However, the differences of the context in which Learning Objects are produced and consumed suggest that no single evaluation model is sufficient for all scenarios. Besides, no much effort has been put in developing open tools to facilitate Learning Object evaluation and use the quality information for the benefit of end users. This paper presents LOEP, an open source web platform that aims to facilitate Learning Object evaluation in different scenarios and educational settings by supporting and integrating several evaluation models and quality metrics. The work exposed in this paper shows that LOEP is capable of providing Learning Object evaluation to e-Learning systems in an open, low cost, reliable and effective way. Possible scenarios where LOEP could be used to implement quality control policies and to enhance search engines are also described. Finally, we report the results of a survey conducted among reviewers that used LOEP, showing that they perceived LOEP as a powerful and easy to use tool for evaluating Learning Objects.

Curvature-driven smoothing: a learning algorithm for feed-forward networks

The performance of feed-forward neural networks in real applications can be often be improved significantly if use is made of a-priori information. For interpolation problems this prior knowledge frequently includes smoothness requirements on the network mapping, and can be imposed by the addition to the error function of suitable regularization terms. The new error function, however, now depends on the derivatives of the network mapping, and so the standard back-propagation algorithm cannot be applied. In this paper, we derive a computationally efficient learning algorithm, for a feed-forward network of arbitrary topology, which can be used to minimize the new error function. Networks having a single hidden layer, for which the learning algorithm simplifies, are treated as a special case.

Developing a Mobile Distance Learning System

This article considers the basic problems of client-server electronic learning systems based on mobile platforms. Such questions as relational learning course model and student’s transitions prediction through the learning course items are considered. Besides, technical questions of electronic learning system “E-Learning Suite” realization and questions of developing portable applications using .NET Framework are discussed.

Towards e+Learning or How to Increase the Learning into e-Learning

It is discussed some changes in the traditional e-learning notion on the point of view of R. Koper’s question 'where is the learning in e-learning?’. We put a focus on the conception of learning as a management process and present the project Bulgarian Educational Site (BEST) – a possible answer to Koper’s question. The BEST is a virtual learning environment, based on the following principles: learning is a goal-directed and didactics-managed process; learners may define their own learning objectives, monitor and regulate the learning process; collaborative e-learning is more effective; etc. The BEST is based on two famous e-learning systems (Moodle, LAMS) and Plovdiv e-University (versions 1.0 and 2.0). The paper brings up a mater about the new ‘electronic’ pedagogy and proposes an approach for pedagogical modeling and interpretation of e-learning applied in the BEST.

E-learning

E-learning means learning via electronic means and is therefore an all-embracing term covering learning via an electronic device. The "expectations" and "realities" for each of the delivery mechanisms within the electronic arena vary greatly for not just the learners themselves, but also the site providers. Because of this, each of these learning systems has vastly different design principles, which is not always understood by those unfamiliar with technology. What is appropriate for a CD-ROM off-line system is generally inappropriate for an on- line internet system. So when designing an e-learning system it is important to understand how the information is to be accessed by the learner. This paper will identify and suggest some ways to avoid e-learning's pitfalls and reap its rewards.

Generalized Nets Model of an E-Learning System Software Architecture

The paper has been presented at the International Conference Pioneers of Bulgarian Mathematics, Dedicated to Nikola Obreshko ff and Lubomir Tschakaloff , Sofi a, July, 2006.

Adaptive E-learning Content Design and Delivery Based on Learning Styles and Knowledge Level

ACM Computing Classification System (1998): K.3.1, K.3.2.

Integrating Moodle with University Information Systems

Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013

Effects of Cooperative and Individual Integrated Learning System on Attitudes and Achievement in Mathematics

The purpose of this study was to determine the effects of a computer-based Integrated Learning Systems (ILS) model used with adult high school students engaging mathematics activities. This study examined achievement, attitudinal and behavior differences between students completing ILS activities in a traditional, individualized format compared to cooperative learning groups.

Concepts and methods for efficient decentralized learning in federated settings

Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.

A deep learning model via thermodynamics and condensed matter physics

Deep Learning architectures give brilliant results in a large variety of fields, but a comprehensive theoretical description of their inner functioning is still lacking. In this work, we try to understand the behavior of neural networks by modelling in the frameworks of Thermodynamics and Condensed Matter Physics. We approach neural networks as in a real laboratory and we measure the frequency spectrum and the entropy of the weights of the trained model. The stochasticity of the training occupies a central role in the dynamics of the weights and makes it difficult to assimilate neural networks to simple physical systems. However, the analogy with Thermodynamics and the introduction of a well defined temperature leads us to an interesting result: if we eliminate from a CNN the "hottest" filters, the performance of the model remains the same, whereas, if we eliminate the "coldest" ones, the performance gets drastically worst. This result could be exploited in the realization of a training loop which eliminates the filters that do not contribute to loss reduction. In this way, the computational cost of the training will be lightened and more importantly this would be done by following a physical model. In any case, beside important practical applications, our analysis proves that a new and improved modeling of Deep Learning systems can pave the way to new and more efficient algorithms.

Real-Time Voltage Regulation in Power Distribution System Using Fuzzy Control

Two different fuzzy approaches to voltage control in electric power distribution systems are introduced in this paper. The real-time controller in each case would act on power transformers equipped with under-load tap changers. Learning systems are employed to turn the voltage-control relays into adaptive devices. The scope of this study has been limited to the power distribution substation, and the voltage measurements and control actions are carried out on the secondary bus. The capacity of fuzzy systems to handle approximate data, together with their unique ability to interpret qualitative information, make it possible to design voltage-control strategies that satisfy the requirements of the Brazilian regulatory bodies and the real concerns of the electric power distribution companies. Fuzzy control systems based on these two strategies have been implemented and the test results were highly satisfactory.

Desesenvolvimento de recursos educativos para a área de matemática em dispositivos móveis

A combinação do avanço tecnológico com o crescimento da aquisição de dispositivos móveis refletiu-­‐se na vida diária das pessoas, sendo usado nas atividades de trabalho e lazer. A transposição dessa realidade para a sala de aula, não se fez esperar, inicialmente até de uma forma marginal mas acabando por ser aceite. Confrontada com esta nova realidade as autoridades educativas começaram a apoiar e a incentivar as instituições. O desenvolvimento de tecnologias como b-­‐learning, m-­‐learning e dos sistemas de aprendizagem (Learning Management System) deram uma grande contribuição para o desenvolvimento das tecnologias móveis no ensino, no entanto ainda hoje os intervenientes da educação, especialmente professores e alunos, sentem diversas necessidades. Neste contexto procedeu-­‐se ao desenvolvimento de um recurso educativo para a disciplina de matemática. Este recurso educativo está suportado numa plataforma que permite colocar conteúdos, visualiza-­‐los, alterá-­‐los e elimina-­‐los. Numa vertente mais lúdica, foi desenvolvido um jogo didático para um dispositivo móvel, neste caso o iPhone. Desta forma o aluno aprende sem se aperceber que está a aprender e pode faze-­‐lo em qualquer lugar e em qualquer período de tempo. Explorando, assim, a interatividade e a mobilidade.

BabeLO : an extensible converter of programming exercises formats

In the last two decades, there was a proliferation of programming exercise formats that hinders interoperability in automatic assessment. In the lack of a widely accepted standard, a pragmatic solution is to convert content among the existing formats. BabeLO is a programming exercise converter providing services to a network of heterogeneous e-learning systems such as contest management systems, programming exercise authoring tools, evaluation engines and repositories of learning objects. Its main feature is the use of a pivotal format to achieve greater extensibility. This approach simplifies the extension to other formats, just requiring the conversion to and from the pivotal format. This paper starts with an analysis of programming exercise formats representative of the existing diversity. This analysis sets the context for the proposed approach to exercise conversion and to the description of the pivotal data format. The abstract service definition is the basis for the design of BabeLO, its components and web service interface. This paper includes a report on the use of BabeLO in two concrete scenarios: to relocate exercises to a different repository, and to use an evaluation engine in a network of heterogeneous systems.