The Principles of Creation of Teaching Materials and Organization of Ukrainian Language Learning Process using Distance Support in Senior Classes

The article deals with the topicality and problems of using information and communication technologies in secondary education, conditions and methods for Ukrainian language learning with the distance support in senior classes. The article shows the principal similarity of distance learning to training one. The common and specific principles of creation of teaching materials for a distance learning course are described. It reveals the conditions of effective organization of Ukrainian language learning with distance support on the material of distance course “Business Ukrainian and Culture of Communication”.

Collaborative Learning and Authoring in the Frame of e-Projects

It is presented a research on the application of a collaborative learning and authoring during all delivery phases of e-learning programmes or e-courses offered by educational institutions. The possibilities for modelling of an e-project as a specific management process based on planned, dynamically changing or accidentally arising sequences of learning activities, is discussed. New approaches for project-based and collaborative learning and authoring are presented. Special types of test questions are introduced which allow test generation and authoring based on learners’ answers accumulated in the frame of given e-course. Experiments are carried out in an e-learning environment, named BEST.

Verification and Application of Conceptual Model and Security Requirements on Practical DRM Systems in E-Learning

The paper represents a verification of a previously developed conceptual model of security related processes in DRM implementation. The applicability of established security requirements in practice is checked as well by comparing these requirements to four real DRM implementations (Microsoft Media DRM, Apple's iTunes, SunnComm Technologies’s MediaMax DRM and First4Internet’s XCP DRM). The exploited weaknesses of these systems resulting from the violation of specific security requirements are explained and the possibilities to avoid the attacks by implementing the requirements in designing step are discussed.

KFTKGA: Knowledge Flow Tracer and Growth Analyzer for Community Learning

The advances in building learning technology now have to emphasize on the aspect of the individual learning besides the popular focus on the technology per se. Unlike the common research where a great deal has been on finding ways to build, manage, classify, categorize and search knowledge on the server, there is an interest in our work to look at the knowledge development at the individual’s learning. We build the technology that resides behind the knowledge sharing platform where learning and sharing activities of an individual take place. The system that we built, KFTGA (Knowledge Flow Tracer and Growth Analyzer), demonstrates the capability of identifying the topics and subjects that an individual is engaged with during the knowledge sharing session and measuring the knowledge growth of the individual learning on a specific subject on a given time space.

Automatized Generation of Metadata and Calibrated Test Item Banks for E-learning

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

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of the input space and the sample size n are usually the main ingredients in the characterization of data bigness. The specific statistical machine learning technique used to handle a particular big data set will depend on which category it falls in within the bigness taxonomy. Large p small n data sets for instance require a different set of tools from the large n small p variety. Among other tools, we discuss Preprocessing, Standardization, Imputation, Projection, Regularization, Penalization, Compression, Reduction, Selection, Kernelization, Hybridization, Parallelization, Aggregation, Randomization, Replication, Sequentialization. Indeed, it is important to emphasize right away that the so-called no free lunch theorem applies here, in the sense that there is no universally superior method that outperforms all other methods on all categories of bigness. It is also important to stress the fact that simplicity in the sense of Ockham’s razor non-plurality principle of parsimony tends to reign supreme when it comes to massive data. We conclude with a comparison of the predictive performance of some of the most commonly used methods on a few data sets.