Advanced ICT based Training Methods

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

Bloom's Taxonomy and Training in Programming Style

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

Training Primary School Teachers to use E-Learning Technologies – A Pilot Study

In this paper we present a blended learning scenario for training of students in master program “ICT in primary school” carried out in South-West University “Neofit Rilski”. Our approach is based on “face to face” lectures and seminars, SCORM compatible e-learning content with a lot of simulation demonstrations, trainings and self assessment, group problem based learning. Also we discuss the results of the course and attitude of the participants in the course towards used methods and possibilities of application of e-learning in primary schools.

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.