An architecture to support programming algorithm learning by problem solving

Abstract

Programming learning is an important subject for the students of computer science. These students must acquire knowledge and abilities which will deal with their future programming work for solving problems. In this sense, the discipline of programming constitutes a framework where Problem Based Learning (PBL) is the base used for acquiring the knowledge and abilities needed. Computer programming is a good research field where students should be assisted by an Intelligent Tutoring System (ITS) that guides them in their learning process. Furthermore, the complexity of these eLearning environments makes indispensable the necessity of the reuse and interoperability principles among eLearning tools. In this paper we will present an architectural approach that enables PBL for programming learning, merging several techniques: from Artificial Intelligence (AI) disciplines such as Bayesian Networks (BN) and Fuzzy Logic (FL); and from eLearning standards such as IMS Learning Design (IMS-LD). © 2007 Springer-Verlag Berlin Heidelberg.