Programming Paradigms in Computer Science Education

Main styles, or paradigms of programming – imperative, functional, logic, and object-oriented – are shortly described and compared, and corresponding programming techniques are outlined. Programming languages are classified in accordance with the main style and techniques supported. It is argued that profound education in computer science should include learning base programming techniques of all main programming paradigms.

How to Elaborate a Demonstrator in Inspiring Science Education Platform

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

Achievements and Problems in the In-service Teacher Education in Inquiry Based Style

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

Two-Language, Two-Paradigm Introductory Computing Curriculum Model and its Implementation

This paper analyzes difficulties with the introduction of object-oriented concepts in introductory computing education and then proposes a two-language, two-paradigm curriculum model that alleviates such difficulties. Our two-language, two-paradigm curriculum model begins with teaching imperative programming using Python programming language, continues with teaching object-oriented computing using Java, and concludes with teaching object-oriented data structures with Java.

Large Distinct Part Sizes in a Random Integer Partition

A partition of a positive integer n is a way of writing it as the sum of positive integers without regard to order; the summands are called parts. The number of partitions of n, usually denoted by p(n), is determined asymptotically by the famous partition formula of Hardy and Ramanujan [5]. We shall introduce the uniform probability measure P on the set of all partitions of n assuming that the probability 1/p(n) is assigned to each n-partition. The symbols E and V ar will be further used to denote the expectation and variance with respect to the measure P . Thus, each conceivable numerical characteristic of the parts in a partition can be regarded as a random variable.