Learning by Doing in the Context of Modern Information and Media Literacy of Students in “Library and Information Management” in ULSIT Using the Digital Collection “Bulgarian Revival”

The purpose of this article is to evaluate the effectiveness of learning by doing as a practical tool for managing the training of students in "Library Management" at the ULSIT, Sofia, Bulgaria, by using the creation of project 'Data Base “Bulgarian Revival Towns” (CD), financed by Bulgarian Ministry of Education, Youth and Science (1/D002/144/13.10.2011) headed by Prof. DSc Ivanka Yankova, which aims to create new information resource for the towns which will serve the needs of scientific researches. By participating in generating the an array in the database through searching, selection and digitization of documents from these period, at the same time students get an opportunity to expand their skills to work effectively in a team, finding the interdisciplinary, a causal connection between the studied items, objects and subjects and foremost – practical experience in the field of digitization, information behavior, strategies for information search, etc. This method achieves good results for the accumulation of sustainable knowledge and it generates motivation to work in the field of library and information professions.

Discover the Thracians - An Approach for Use of 2D and 3D Technologies for Digitization of Cultural Heritage in the Field of E-learning

Information and communication technologies (ICT) offer an easier access to and a multi-perspective view of cultural heritage artifacts and may also enrich and improve cultural heritage education through the adoption of innovative learning/teaching methods. This paper examines the different practices and opportunities for digitization of cultural artifacts with historical significance and describes the work on a pilot project concerning the development of e-learning materials in the Thracian cultural and historical heritage. The proposed method presents an approach based on a combination of 2D and 3D technologies to facilitate the overall process of digitization of individual objects. This approach not only provides greater opportunities for presenting the Thracian heritage but also new perspectives for studying it - students, scientists, PhD students will have the opportunity to work with the materials without having access to them.

Approaches to Organization of Teaching for Creating Digital Learning Materials

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

Group Work as a Strategy to Improve the Effectiveness of the Learning Process

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

Intercultural Learning and Entrepreneurship

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

A Multi-View Learning Algorithm

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Applying Data Mining Tools in E-Learning

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Developing Financial Expertise Through Mathematics and IT School Curricula

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

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.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.

Automated Data Retrieval in Distance Learning Quality

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

The Positive Effect of Using the Integral Approach for Improving Quality of Learning and Students' motivation

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

Teaching Statistics to Engineers: Learning from Experiential Data

The purpose of the work is to claim that engineers can be motivated to study statistical concepts by using the applications in their experience connected with Statistical ideas. The main idea is to choose a data from the manufacturing factility (for example, output from CMM machine) and explain that even if the parts used do not meet exact specifications they are used in production. By graphing the data one can show that the error is random but follows a distribution, that is, there is regularily in the data in statistical sense. As the error distribution is continuous, we advocate that the concept of randomness be introducted starting with continuous random variables with probabilities connected with areas under the density. The discrete random variables are then introduced in terms of decision connected with size of the errors before generalizing to abstract concept of probability. Using software, they can then be motivated to study statistical analysis of the data they encounter and the use of this analysis to make engineering and management decisions.

Study of the Self-Assessment as а Component of the Model of Self-Regulated Learning for Engineering Students

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

How Students Study? An Analysis of Learning Strategies in Engineering and Technology Courses

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014