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2015 » Papers » Volume 2 » Generative models for computer programming disciplines 1. GENERATIVE MODELS FOR COMPUTER PROGRAMMING DISCIPLINES Authors: Chirila Ciprian-Bogdan Volume 2 | DOI: 10.12753/2066-026X-15-176 | Pages: 556-562 | Download PDF | Abstract
Nowadays the IT industry is more and more present in several areas like: automotive,
telecommunications, finance, medicine, pharmaceutics etc. One of the main problems in
the development of these domains is the lack of highly qualified IT specialists, thus
loosing million euros projects and afferent taxes. Among the IT specialists there is
a significant segment allocated to programmers and testers. In order to train more
IT specialists universities introduced several programs including distance learning and
evening courses. The problem with these students is that they get weak results because of
the limited in class face to face learning amount of time. The use learning management
systems based on learning objects (LO) can ameliorate this problem.
Generative learning objects (GLO) are instantiable generic templates that generate concrete
learning objects. They are considered to be the second generation of learning objects (LO).
They can be used in several learning areas like: economy - accounting, medical science -
nursing, computer science - understanding programming with the help of robots.
In this paper we will present a set of generative models designed for the e-learning of
computer programming disciplines. In order to enable accessibility, enhanced understanding,
creativity stimulation of computer programming we designed a set of generative models which
are based on the GLO paradigm obtaining automatically concrete LOs.
Our focus is set on providing these models to GLO authors with less programming skills together
with the feature of automatic instantiation based on random number generators.
Our models were designed for data structure disciplines in the context of developing programming
competences, namely how to program using data structures like: arrays, lists, trees and graphs. | Keywords
generative learning objects, generative models, computer science disciplines |
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