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

Automating Exersises Validation in e-Learning Environments

E-learning is supposing an innovation in teaching, raising from the development of new technologies. It is based in a set of educational resources, including, among others, multimedia or interactive contents accessible through Internet or Intranet networks. A whole spectrum of tools and services support e-learning, some of them include auto-evaluation and automated correction of test-like exercises, however, this sort of exercises are very constrained because of its nature: fixed contents and correct answers suppose a limit in the way teachers may evaluation students. In this paper we propose a new engine that allows validating complex exercises in the area of Data Structures and Algorithms. Correct solutions to exercises do not rely only in how good the execution of the code is, or if the results are same as expected. A set of criteria on algorithm complexity or correctness in the use of the data structures are required. The engine presented in this work covers a wide set of exercises with these characteristics allowing teachers to establish the set of requirements for a solution, and students to obtain a measure on the quality of their solution in the same terms that are later required for exams.

An eMathTeacher Tool for Active Learning Fleury’s Algorithm

An eMathTeacher [Sánchez-Torrubia 2007a] is an eLearning on line self assessment tool that help students to active learning math algorithms by themselves, correcting their mistakes and providing them with clues to find the right solution. The tool presented in this paper is an example of this new concept on Computer Aided Instruction (CAI) resources and has been implemented as a Java applet and designed as an auxiliary instrument for both classroom teaching and individual practicing of Fleury’s algorithm. This tool, included within a set of eMathTeacher tools, has been designed as educational complement of Graph Algorithm active learning for first course students. Its characteristics of visualization, simplicity and interactivity, make this tutorial a great value pedagogical instrument.

Teaching Strategies and Ontologies for E-Learning

* The work is partially suported by Russian Foundation for Basic Studies (grant 02-01-00466).

Using Problems to Introduce some Tools and Technologies for Implementation of Graphics and Animation in C#

This paper is devoted to the learning of event programming by using Visual C# in specialized training in Informatics in high schools. Some basic tools and technologies for the implementation of graphics and animation in C# are discussed. Two example problems are proposed.

Development of a Refactoring Learning Environment

This paper describes a Refactoring Learning Environment, which is intended to analyze and assess programming code, based on refactoring rules. The Refactoring Learning Environment architecture includes an intelligent assistant – Refactoring Agent, which is responsible for analysis and assessment of the code, written by students in real time by using a set of refactoring methods. According to the situation and based on the refactoring method, which should be applied, the agent could react in different ways. Its goal is to show the student, as much as possible, the weak places of his programming code and the possible ways to makes it better.

Modern Training Tools for Binding Design and Paper Conservation of Antique Books, Albums and Documents (BBINDING)

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.

Guidelines for Creating ICT-based Adaptable Learning Resources

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