Matematikos mokymosi objektų daugkartinio panaudojamumo kokybės vertinimas eQNet projekte

The paper is aimed to analyse the application of several scientific approaches, methods, and principles for evaluation of quality of digital learning objects (LOs) for Mathematics subject. The authors analyse the following approaches to minimise subjectivity level in expert evaluation of LOs quality, namely: (1) principles of multiple criteria decision analysis for identification of quality criteria, (2) technological quality criteria classification principle, (3) fuzzy group decision making theory toobtain evaluation measures, (4) normalisation requirement for criteria weights, and (5) scalarisation method for LOs quality evaluation. The applied approaches have been used practically for evaluation of LOs while implementing European Lifelong Learning programme’s eQNet project in Lithuanian comprehensive schools in winter and spring 2010.Darbe siekiama išanalizuoti kelis mokslinius metodus ir taikymo principus skaitmeninių matematikos mokymosi objektų (MO) kokybei vertinti. Šie metodai taikomi, siekiant sumažinti ekspertų MO kokybės vertinimo subjektyvumo lygį. Darbe nagrinėjami šie metodai ir principai: (1) daugiakriterinio sprendimo analizės principai MO kokybės kriterijams identifikuoti, (2) technologinių kokybės vertinimo kriterijų klasifikavimo principas, (3) sprendimų priėmimo teorijos neraiškiųjų (angl.  fuzzy) elementų vertinimo priemonės, (4) kriterijų svorių normalizavimas, ir (5) skaliarizacijos metodas MO kokybės vertinimui. Taikomi metodai yra praktiškai naudojami MO kokybės vertinimui, įgyvendinant Europos mokymosi visą gyvenimą programos eQNet projektą Lietuvos bendrojo lavinimo mokyklose 2010 m. žiemą ir pavasarį

New MCEQLS TFN method for evaluating quality and reusability of learning objects

The aim of the paper is to present a new simple to use and efficient MCEQLS (Multiple Criteria Evaluation of the Quality of Learning Software) TFN (Trapezoidal Fuzzy Numbers) method for the expert evaluation of the quality and reusability of learning objects (LOs). MCEQLS and TFN methods are analysed, improved, and practically applied to present the decision analysis process for selecting LOs suitable to reuse in different pedagogical situations and in different education systems. The research results are implemented in eQNet – a three-year strategic pan-European project focused on reusability of LOs. A novel method of consecutive application of Fuzzy Numbers theory to establish the weights of LOs quality criteria and MCEQLS approach to establish final evaluation results are explored in more detail. A number of multiple criteria decision analysis principles are applied to create a comprehensive quality model (criteria system) for evaluating the quality and reusability of LOs. Several practical examples of LOs evaluated against the proposed MCEQLS TFN method are presented in the paper. The research results have shown that the proposed method is quite objective, exact, simple to use, and efficient for selecting qualitative reusable LOs alternatives in the market

New MCEQLS AHP method for evaluating quality of learning scenarios

The aim of the paper is to present a new MCEQLS AHP method for the expert evaluation of quality of learning scenarios. A special attention is paid to suitability of scenarios to particular learner groups (styles). Solution of learning scenarios quality evaluation and optimisation problem could help educational institutions to select suitable scenarios for particular learning styles. The research results will be implemented in iTEC – a four-year, largest pan-European e-learning R&D project focused on the design of the future classroom funded by 7th Framework Programme. A novel method of consecutive triple application of AHP is explored in more detail. Suitability of several iTEC scenarios to particular learner groups is also analysed in the paper. A number of multiple criteria decision analysis principles are applied to create a comprehensive quality model (criteria tree) for evaluating quality of scenarios. Several optimisation methods are explored and applied to optimise learning scenarios in conformity with particular learning style. Several practical examples of iTEC learning scenarios alternatives have been evaluated against the proposed MCEQLS AHP method. The research results have shown that the proposed MCEQLS AHP method is quite objective, exact and simple to use for selecting qualitative scenarios alternatives for particular learner groups

Learning natural coding conventions

Coding conventions are ubiquitous in software engineering practice. Maintaining a uniform coding style allows software development teams to communicate through code by making the code clear and, thus, readable and maintainable—two important properties of good code since developers spend the majority of their time maintaining software systems. This dissertation introduces a set of probabilistic machine learning models of source code that learn coding conventions directly from source code written in a mostly conventional style. This alleviates the coding convention enforcement problem, where conventions need to first be formulated clearly into unambiguous rules and then be coded in order to be enforced; a tedious and costly process. First, we introduce the problem of inferring a variable’s name given its usage context and address this problem by creating Naturalize — a machine learning framework that learns to suggest conventional variable names. Two machine learning models, a simple n-gram language model and a specialized neural log-bilinear context model are trained to understand the role and function of each variable and suggest new stylistically consistent variable names. The neural log-bilinear model can even suggest previously unseen names by composing them from subtokens (i.e. sub-components of code identifiers). The suggestions of the models achieve 90% accuracy when suggesting variable names at the top 20% most confident locations, rendering the suggestion system usable in practice. We then turn our attention to the significantly harder method naming problem. Learning to name methods, by looking only at the code tokens within their body, requires a good understating of the semantics of the code contained in a single method. To achieve this, we introduce a novel neural convolutional attention network that learns to generate the name of a method by sequentially predicting its subtokens. This is achieved by focusing on different parts of the code and potentially directly using body (sub)tokens even when they have never been seen before. This model achieves an F1 score of 51% on the top five suggestions when naming methods of real-world open-source projects. Learning about naming code conventions uses the syntactic structure of the code to infer names that implicitly relate to code semantics. However, syntactic similarities and differences obscure code semantics. Therefore, to capture features of semantic operations with machine learning, we need methods that learn semantic continuous logical representations. To achieve this ambitious goal, we focus our investigation on logic and algebraic symbolic expressions and design a neural equivalence network architecture that learns semantic vector representations of expressions in a syntax-driven way, while solely retaining semantics. We show that equivalence networks learn significantly better semantic vector representations compared to other, existing, neural network architectures. Finally, we present an unsupervised machine learning model for mining syntactic and semantic code idioms. Code idioms are conventional “mental chunks” of code that serve a single semantic purpose and are commonly used by practitioners. To achieve this, we employ Bayesian nonparametric inference on tree substitution grammars. We present a wide range of evidence that the resulting syntactic idioms are meaningful, demonstrating that they do indeed recur across software projects and that they occur more frequently in illustrative code examples collected from a Q&A site. These syntactic idioms can be used as a form of automatic documentation of coding practices of a programming language or an API. We also mine semantic loop idioms, i.e. highly abstracted but semantic-preserving idioms of loop operations. We show that semantic idioms provide data-driven guidance during the creation of software engineering tools by mining common semantic patterns, such as candidate refactoring locations. This gives data-based evidence to tool, API and language designers about general, domain and project-specific coding patterns, who instead of relying solely on their intuition, can use semantic idioms to achieve greater coverage of their tool or new API or language feature. We demonstrate this by creating a tool that suggests loop refactorings into functional constructs in LINQ. Semantic loop idioms also provide data-driven evidence for introducing new APIs or programming language features

Horizon Report Europe - 2014 Schools Edition

The NMC Horizon Project from the New Media Consortium is a long-term investigation launched in 2002 that identifies and describes emerging technologies likely to have a large impact over the coming five years in education around the globe. The NMC Horizon Report Europe: 2014 Schools Edition, the first of its kind for Europe, examines six key trends, six significant challenges and six important developments in educational technology that are very likely to impact educational change processes in European schools over the next five years (2014-2018). The topics within each section were carefully selected by the Horizon Project Europe Expert Panel, a body of 53 experts in European education, technology, and other fields. They come from 22 European countries, as well as international organisations and European networks. Throughout the report, references and links are made to more than 150 European publications (reports, articles, policy documents, blog posts etc.), projects (both EU-funded and national initiatives) and various policy initiatives from all over Europe. The Creative Classrooms multidimensional framework, developed by European Commission’s JRC-IPTS on behalf of DG EAC, was used for analysing the trends, challenges and technologies impacting European schools over the next five years. The analysis reveals that a systemic approach is needed for integrating new technologies in European schools and impacting educational change over the next five years.JRC.J.3-Information Societ

Deep Understanding of Technical Documents : Automated Generation of Pseudocode from Digital Diagrams & Analysis/Synthesis of Mathematical Formulas

The technical document is an entity that consists of several essential and interconnected parts, often referred to as modalities. Despite the extensive attention that certain parts have already received, per say the textual information, there are several aspects that severely under researched. Two such modalities are the utility of diagram images and the deep automated understanding of mathematical formulas. Inspired by existing holistic approaches to the deep understanding of technical documents, we develop a novel formal scheme for the modelling of digital diagram images. This extends to a generative framework that allows for the creation of artificial images and their annotation. We contribute on the field with the creation of a novel synthetic dataset and its generation mechanism. We propose the conversion of the pseudocode generation problem to an image captioning task and provide a family of techniques based on adaptive image partitioning. We address the mathematical formulas’ semantic understanding by conducting an evaluating survey on the field, published in May 2021. We then propose a formal synthesis framework that utilized formula graphs as metadata, reaching for novel valuable formulas. The synthesis framework is validated by a deep geometric learning mechanism, that outsources formula data to simulate the missing a priori knowledge. We close with the proof of concept, the description of the overall pipeline and our future aims

ISO 9000 QMS: a strategic business development agent or a production quality assurance and marketing tool: the Greek agro-coops' experience

A serious business issue, recurring in the public debate held in Greece and concerning the future of the Agricultural sector, is the stated need of the Agro-coops’ sector’s organisational and operational reform and improvement (Karamichas, 2008; Tolios, 2003). This identified and/or suggested required strategic business development and change is considered to be a product and at the same time the source of the agro-coops’ required organisational performance improvement, as Kamenidis (2008), Arvanitoyiannis (2001) and Parnell (2000) suggest. Furthermore, they relate this strategic business development and change, and performance improvement process with the agro-coops’ business processes’ reconfiguration and resulting improvement. According to the majority of the proposed economic and business models, the major mechanism to accomplish the aforementioned aim - that is the strategic choice of the Agro-coops’ sector for operating effectively and efficiently - is and/or should be the adoption of the privately owned businesses’ sector organisational framework (Karamichas, 2008; Parnell, 2000). Furthermore, the agro-coops have to combine these adopted elements and practices with the particularities of the agricultural business sector, as Ian McPherson (1995) emphatically states. On the other hand, the distinct ownership structure of the agro-coops which is more democratic than hierarchical makes the implementation of change more difficult

XVIII Simposio Internacional de Informática Educativa, SIIE 2016

El Simposio Internacional de Informática Educativa (SIIE) ofrece un foro internacional para la presentación y debate de los últimos avances en investigación sobre las tecnologías para el aprendizaje y su aplicación práctica en los procesos educativos. También pretende poner en contacto a investigadores, desarrolladores, representantes institucionales y profesores para compartir puntos de vista, conocimientos y experiencias

Flood Management in a Complex River Basin with a Real-Time Decision Support System Based on Hydrological Forecasts

During the last decades, the Upper Rhone River basin has been hit by several flood events causing significant damages in excess of 500 million Swiss Francs. From this situation, the 3rd Rhône river training project was planned in order to improve the flood protection in the Upper Rhone River basin in Vaud and Valais Cantons. In this framework, the MINERVE forecast system aims to contribute to a better flow control during flood events in this catchment area, taking advantage of the existing hydropower multi-reservoir network. This system also fits into the OWARNA national project of the Swiss Federal Office of Environment by establishing a national platform on natural hazards alarms. The Upper Rhone River basin has a catchment area with high mountains and large glaciers. The surface of the basin is 5521 km2 and its elevation varies between 400 and 4634 m a.s.l. Numerous hydropower schemes with large dams and reservoirs are located in the catchment area, influencing the hydrological regime. Their impact during floods can be significant as appropriate preventive operations can decrease the peak discharges in the Rhone River and its main tributaries, thus reducing the damages. The MINERVE forecast system exploits flow measurements, data from reservoirs and hydropower plants as well as probabilistic (COSMO-LEPS) and deterministic (COSMO-2 and COSMO-7) numerical weather predictions from MeteoSwiss. The MINERVE hydrological model of the catchment area follows a semi-distributed approach. The basin is split into 239 sub-catchments which are further sub-divided into 500 m elevation bands, for a total of 1050 bands. For each elevation band, precipitation, temperature and potential evapotranspiration are calculated. They are considered in order to describe the temperature-driven processes accurately, such as snow and glaciers melt. The hydrological model was implemented in the Routing System software. The object oriented programming environment allows a user-friendly modelling of the hydrological, hydraulic and operating processes. Numerical meteorological data (observed or predicted) are introduced as input in the model. Over the calibration and validation periods of the model, only observed data (precipitation, temperature and flows) was used. For operational flood forecast, the observed measurements are used to update the initial conditions of the hydrological model and the weather forecasts for the hydrological simulations. Routing System provides then hydrological predictions in the whole catchment area. Subsequently, a warning system was developed especially for the basin to provide a flood warning report. The warning system predicts the evolution of the hydrological situation at selected main check points in the catchment area. It displays three warning levels during a flood event depending on respective critical discharge thresholds. Furthermore, the multi-reservoir system is managed in an optimal way in order to limit or avoid damages during floods. A decision support tool called MINDS (MINERVE Interactive Decision Support System) has been developed for real-time decision making based on the hydrological forecasts. This tool defines preventive operation measures for the hydropower plants such as turbine and bottom outlet releases able to provide an optimal water storage during the flood peak. The overall goal of MINDS is then to retain the inflowing floods in reservoirs and to avoid spillway and turbine operations during the peak flow, taking into account all restrictions and current conditions of the network. Such a reservoir management system can therefore significantly decrease flood damages in the catchment area. The reservoir management optimisation during floods is achieved with deterministic and probabilistic forecasts. The definition of the objective function to optimise is realised with a multi-attribute decision making approach. Then, the optimisation is performed with an iterative Greedy algorithm or a SCE-UA (Shuffled Complex Evolution – University of Arizona) algorithm. The developed decision support system combines the high-quality optimisation system with its user-friendly interface. The purpose is to help decision makers by being directly involve in main steps of the decision making process as well as by understanding the measures undertaken and their consequences

Primary school pupils environmental literacy development applying interactive animation

Collective and individual human actions have deformed life on the planet. Human society is clearly to promote an environmental degradation. Therefore it is important to take care of this problem. In addition, education is the key to helping people to understand environmental problems and to make necessary actions. Development of human being environmental literacy is a long term process, that is why it is important to start educate people from childhood. Thus, human environmental literacy can be developed in different ways. One of the methods to develop children’s Environmental literacy can be interactive animation. The aim of the research is to show how primary school pupils Environmental literacy can be developed by applying Interactive animation. The methods of the research were: • Data collecting methods (Educational experiment and partially-structured interview) • Data analysis methods (Qualitative content analysis) The data of the research is relevant to the primary school teachers and creators of the Interactive animation, because they can help to improve education of primary school pupils Environmental literacy and creation process of Interactive animation for an effective primary school pupils Environmental education