Implementing E-learning Specifications with Conformance Testing: Profiling for IMS Learning Design

Submitted for publication. Please contact authors for reference.Improving interoperability between e-learning systems and content has been one of the driving forces behind the adoption of e-learning specifications over recent years. A vital step towards achieving this goal is the widespread adoption of conformant implementations of e-learning specifications. A conformant implementation is one which fully complies with the conformance requirements of the specification. However, conformance testing is time consuming and expensive. The process of localising specifications to create so-called “Application Profiles” to meet individual community needs further complicates conformance testing efforts. To solve this problem, we developed the conformance testing approach presented in this article. This approach simplifies the development of Application Profiles, and the process of conformance testing against them. Using this approach, test suites can be generated to test software applications against both e-learning specifications and their derived Application Profiles. A case study based around the IMS Learning Design specification demonstrates this process

Reducing offline evaluation bias of collaborative filtering algorithms

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation). This paper presents a new application of a weighted offline evaluation to reduce this bias for collaborative filtering algorithms.Comment: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.137-142, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015

VirtualIdentity : privacy preserving user profiling

User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies. Existing systems require that the UGC is fully exposed to the module that constructs the user profiles. In this paper we show that it is possible to build user profiles without ever accessing the user's original data, and without exposing the trained machine learning models for user profiling - which are the intellectual property of the company - to the users of the social media site. We present VirtualIdentity, an application that uses secure multi-party cryptographic protocols to detect the age, gender and personality traits of users by classifying their user-generated text and personal pictures with trained support vector machine models in a privacy preserving manner

Understanding and profiling user requirements to support the conceptual design of an integrated land monitoring system

Acquiring and organizing knowledge and information elements can be essential not only to understand, but also to eliminate, reduce and control complexity and uncertainty. An integration of tools from different disciplines could systematically help in the construction of an agreed framework for problem formulation, above all when the situation is "new". An application was de-veloped in relation to an industrial project, in order to propose profiles of the potential users of an innovative system and of their requirements, and to for-mally develop models that can orient analysis, decision and action. Some ele-ments and results of this integrated application of "soft" and "hard" decision aid tools are here proposed as steps of an organizational learning cycle, which is a basic element of each innovation proces

Learn-ciam: a model-driven approach for the development of collaborative learning tools

This paper introduces Learn-CIAM, a new model-based methodological approach for the design of flows and for the semi-automatic generation of tools in order to support collaborative learning tasks. The main objective of this work is to help professors by establishing a series of steps for the specification of their learning courses and the obtaining of collaborative tools to support certain learning activities (in particular, for in-group editing, searching and modeling). This paper presents a complete methodological framework, how it is supported conceptually and technologically, and an application example. So to guarantee the validity of the proposal, we also present some validation processes with potential designers and users from different profiles such as Education and Computer Science. The results seem to demonstrate a positive reception and acceptance, concluding that its application would facilitate the design of learning courses and the generation of collaborative learning tools for professionals of both profiles

Learning roadmap studio : new approaches and strategies for efficient learning and training processes

Learning systems have emerged in a set of different information systems, oriented for different kinds of organizations and institutions, such as learning management systems, knowledge management systems and learning content management systems, which can be integrated or merged with others. From past experience, it has been denoted that strategies and pedagogical processes are tasks that can be created, enriched and boosted by actors who participate in learning and training processes: course managers, teachers and students. The challenge posed to the different actors involved also accelerates the changes that have been happening in education and training, empowering a society based on knowledge. Initiatives such as eLearning (EU Comission 2000), eLearningEurope, eTwinning and Education Observatories are an evidence of this challenge. Platforms, applications, tools and systems must respond to challenges that those actors face nowadays: heterogeneous target audiences, in terms of student profiles, number of participants, differentiated contents and schedules to achieve knowledge, outcomes and competences. Thus, a prototype application, named Learning Roadmap Studio (LRMS), has been developed and deployed at Aveiro Norte Polytechnic School of the University of Aveiro, in order to suppress gaps in learning processes and to power better learning and training. It represents a new challenge for the University of Aveiro for higher education and is already being tested. At its core is the concept of “learning roadmaps” that act upon two fundamental axes: education and learning. For the teachers, it aims at becoming a self-supporting tool that stimulates the organization and management of the course materials (lectures, presentations, multimedia content, and evaluation materials, amongst others). For the students, the learning roadmap aims at promoting self-study and supervised study, endowing the pupil with the capabilities to find the relevant information and to capture the concepts in the study materials. The outcome will be a stimulating learning process together with an organized management of those materials. It is not intended to create new learning management systems. Instead, it is presented as an application that enables the edition and creation of learning processes and strategies, giving primary relevance to teachers, instead of focusing on tools, features and contents

On control laws for discrete linear repetitive processes with dynamic boundary conditions

Repetitive processes are characterized by a series of sweeps, termed passes, through a set of dynamics defined over a finite duration known as the pass length. On each pass an output, termed the pass profile, is produced which acts as a forcing function on, and hence contributes to, the dynamics of the next pass profile. This can lead to oscillations in the sequence of pass profiles produced which increase in amplitude in the pass-to-pass direction and cannot be controlled by application of standard control laws. Here we give new results on the design of physically based control laws for so-called discrete linear repetitive processes which arise in applications areas such as iterative learning control

Space-charge distortion of transverse profiles measured by electron-based Ionization Profile Monitors and correction methods

Measurements of transverse profiles using Ionization Profile Monitors (IPMs) for high brightness beams are affected by the electromagnetic field of the beam. This interaction may cause a distortion of the measured profile shape despite strong external magnetic field applied to impose limits on the transverse movement of electrons. The mechanisms leading to this distortion are discussed in detail. The distortion itself is described by means of analytic calculations for simplified beam distributions and a full simulation model for realistic distributions. Simple relation for minimum magnetic field scaling with beam parameters for avoiding profile distortions is presented. Further, application of machine learning algorithms to the problem of reconstructing the actual beam profile from distorted measured profile is presented. The obtained results show good agreement for tests on simulation data. The performance of these algorithms indicate that they could be very useful for operations of IPMs on high brightness beams or IPMs with weak magnetic field