Development of a Collaborative Platform for Art Education: From Pedagogy to Technology

We report on the development of a Java-based application devised to support collaborative learning of Art concepts and ideas over the Internet. Starting from an examination of the pedagogy of both Art education and collaborative learning we propose principles which are useful for the design and construction of a “lightweight” software application which supports interactive Art learning in groups. This application makes “dynamics” of an art work explicit, and supports group interaction with simple messaging and “chat” facilities. This application may be used to facilitate learning and teaching of Art, but also as a research tool to investigate the learning of Art and also the development and dynamics of collaborating groups. Evaluation of a pilot study of the use of our system with a group of 20 school children is presented.

Applications of a Collaborative Open Object Oriented Learning Environment to Electronics, Computing and Mathematics Education

A lightweight Java application suite has been developed and deployed allowing collaborative learning between students and tutors at remote locations. Students can engage in group activities online and also collaborate with tutors. A generic Java framework has been developed and applied to electronics, computing and mathematics education. The applications are respectively: (a) a digital circuit simulator, which allows students to collaborate in building simple or complex electronic circuits; (b) a Java programming environment where the paradigm is behavioural-based robotics, and (c) a differential equation solver useful in modelling of any complex and nonlinear dynamic system. Each student sees a common shared window on which may be added text or graphical objects and which can then be shared online. A built-in chat room supports collaborative dialogue. Students can work either in collaborative groups or else in teams as directed by the tutor. This paper summarises the technical architecture of the system as well as the pedagogical implications of the suite. A report of student evaluation is also presented distilled from use over a period of twelve months. We intend this suite to facilitate learning between groups at one or many institutions and to facilitate international collaboration. We also intend to use the suite as a tool to research the establishment and behaviour of collaborative learning groups. We shall make our software freely available to interested researchers.

Scaling up online learning Curriculum design and support for online learning checklist

This checklist highlights key questions for institutions that are considering their online learning provision and relates to the Jisc Scaling up online learning Curriculum design and support for online learning guide.

Assessing a Methodological Proposal for the Design, Creation and Evaluation of Learning Objects Oriented to Educators with Diverse Educational and Technological Competencies

This paper describes a methodological proposal for the design, creation and evaluation of Learning Objects (LOs). This study arises from the compilation and analysis of several LO design methodologies currently used in Ibero-America. This proposal, which has been named DICREVOA, defines five different phases: analysis, design (instructional and multimedia), implementation (LO and metadata), evaluation (from the perspective of both the producer and the consumer of the LO), and publishing. The methodology focuses not only on the teaching inexperienced, but also on those having a basic understanding of the technological and educational aspects related to LO design; therefore, the study emphasizes LO design activities centered around the Kolb cycle and the use of the ExeLearning tool in order to implement the LO core. Additionally, DICREVOA was used in a case study, which demonstrates how it provides a feasible mechanism for LO design and implementation within different contexts. Finally, DICREVOA, the case study to which it was applied, and the results obtained are presented

Cooperative Design, Cooperative Science: Investigating Collaborative Research through Design with Floracaching

This dissertation presents a case study of collaborative research through design with Floracaching, a gamified mobile application for citizen science biodiversity data collection. One contribution of this study is the articulation of collaborative research through design (CRtD), an approach that blends cooperative design approaches with the research through design methodology (RtD). Collaborative research through design is thus defined as an iterative process of cooperative design, where the collaborative vision of an ideal state is embedded in a design. Applying collaborative research through design with Floracaching illustrates how a number of cooperative techniques—especially contextual inquiry, prototyping, and focus groups—may be applied in a research through design setting. Four suggestions for collaborative research through design (recruit from a range of relevant backgrounds; take flexibility as a goal; enable independence and agency; and, choose techniques that support agreement or consensus) are offered to help others who wish to experiment with this new approach. Applying collaborative research through design to Floracaching yielded a new prototype of the application, accompanied by design annotations in the form of framing constructs for designing to support mobile, place-based citizen science activities. The prototype and framing constructs, which may inform other designers of similar citizen science technologies, are a second contribution of this research.

Combination of Evolutionary Algorithms with Experimental Design, Traditional Optimization and Machine Learning

Evolutionary algorithms alone cannot solve optimization problems very efficiently since there are many random (not very rational) decisions in these algorithms. Combination of evolutionary algorithms and other techniques have been proven to be an efficient optimization methodology. In this talk, I will explain the basic ideas of our three algorithms along this line (1): Orthogonal genetic algorithm which treats crossover/mutation as an experimental design problem, (2) Multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses decomposition techniques from traditional mathematical programming in multiobjective optimization evolutionary algorithm, and (3) Regular model based multiobjective estimation of distribution algorithms (RM-MEDA) which uses the regular property and machine learning methods for improving multiobjective evolutionary algorithms.

Applying Platform Design to Improve Product-Service Systems Collaborative Development

Part 3: Product-Service Systems

Ergonomics as authoritarian or libertarian: Learning from colin ward's politics of design

Ergonomics is intrinsically connected to political debates about the good society, about how we should live. This article follows the ideas of Colin Ward by setting the practices of ergonomics and design along a spectrum between more libertarian approaches and more authoritarian. Within Anglo-American ergonomics, more authoritarian approaches tend to prevail, often against the wishes of designers who have had to fight with their employers for best possible design outcomes. The article draws on debates about the design and manufacturing of schoolchildren's furniture. Ergonomics would benefit from embracing these issues to stimulate a broader discourse amongst its practitioners about how to be open to new disciplines, particularly those in the social sciences.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.

How textbook design may influence learning with geography textbooks

This paper investigates how textbook design may influence students’ visual attention to graphics, photos and text in current geography textbooks. Eye tracking, a visual method of data collection and analysis, was utilised to precisely monitor students’ eye movements while observing geography textbook spreads. In an exploratory study utilising random sampling, the eye movements of 20 students (secondary school students 15–17 years of age and university students 20–24 years of age) were recorded. The research entities were double-page spreads of current German geography textbooks covering an identical topic, taken from five separate textbooks. A two-stage test was developed. Each participant was given the task of first looking at the entire textbook spread to determine what was being explained on the pages. In the second stage, participants solved one of the tasks from the exercise section. Overall, each participant studied five different textbook spreads and completed five set tasks. After the eye tracking study, each participant completed a questionnaire. The results may verify textbook design as one crucial factor for successful knowledge acquisition from textbooks. Based on the eye tracking documentation, learning-related challenges posed by images and complex image-text structures in textbooks are elucidated and related to educational psychology insights and findings from visual communication and textbook analysis.

The Proces of Curriculum and Module Design

The outcome of the inductive decision -making process of the leading project management group (PMG) was the proposal to develop three modules, Human Resource Management and Knowledge Management, Quality Management and Intercultural management, each for 10 ECTS credits. As a result of the theoretical and organisational framework and analytical phase of the project, four strategies informed the development and implemen- tation of the modules: 1. Collaboration as a principle stemming from EU collaborative policy and receiving it’s expression on all implementation levels (designing the modules, modes of learning, delivering the modules, evaluation process). 2. Building on the Bologna process masters level framework to assure ap- propriate academic level of outputs. 3. Development of value -based leadership of students through transforma- tional learning in a cross -cultural setting and continual reflection of theory in practice. 4. Continual evaluation and feedback among teachers and students as a strategy to achieve a high quality programme. In the first phase of designing the modules the collaborative strategy in particular was applied, as each module was led by one university, but members from all other universities participated in the discussions and development of the mod- ules. The Bologna process masters level framework and related standards and guidelines informed the form and method of designing the modules.

Machine Learning Unsupervised Methods in the Design of an On-board Health Monitoring System for Satellite Applications

The dissertation starts by providing a description of the phenomena related to the increasing importance recently acquired by satellite applications. The spread of such technology comes with implications, such as an increase in maintenance cost, from which derives the interest in developing advanced techniques that favor an augmented autonomy of spacecrafts in health monitoring. Machine learning techniques are widely employed to lay a foundation for effective systems specialized in fault detection by examining telemetry data. Telemetry consists of a considerable amount of information; therefore, the adopted algorithms must be able to handle multivariate data while facing the limitations imposed by on-board hardware features. In the framework of outlier detection, the dissertation addresses the topic of unsupervised machine learning methods. In the unsupervised scenario, lack of prior knowledge of the data behavior is assumed. In the specific, two models are brought to attention, namely Local Outlier Factor and One-Class Support Vector Machines. Their performances are compared in terms of both the achieved prediction accuracy and the equivalent computational cost. Both models are trained and tested upon the same sets of time series data in a variety of settings, finalized at gaining insights on the effect of the increase in dimensionality. The obtained results allow to claim that both models, combined with a proper tuning of their characteristic parameters, successfully comply with the role of outlier detectors in multivariate time series data. Nevertheless, under this specific context, Local Outlier Factor results to be outperforming One-Class SVM, in that it proves to be more stable over a wider range of input parameter values. This property is especially valuable in unsupervised learning since it suggests that the model is keen to adapting to unforeseen patterns.