La ponència explica l'experiència del canvi de llenguatge de programació, el nous plantejaments i metodologia a l'ensenyament de la programació d'ordinadors a les assignatures de Fonaments d'Informàtica i Informàtica de l'ETSEIB
Gaudioso, E.; Montero, M.; Talavera, L.; Hernandez del Olmo, F. Expert systems with applications Vol. 36, num. 2, p. 2260-2265 DOI: 10.1016/j.eswa.2007.12.035 Data de publicació: 2009-03 Article en revista
Collaborative student modeling in adaptive learning environments allows the learners
to inspect and modify their own student models. It is often considered as a
collaboration between students and the system to promote learners’ reflection and
to collaboratively assess the course. When adaptive learning environments are used
in the classroom, teachers act as a guide through the learning process. Thus, they
need to monitor students’ interactions in order to understand and evaluate their
activities. Although, the knowledge gained through this monitorization can be extremely
useful to student modeling, collaboration between teachers and the system
to achieve this goal has not been considered in the literature. In this paper we
present a framework to support teachers in this task. In order to prove the usefulness
of this framework we have implemented and evaluated it in an adaptive
web-based educational system called PDinamet.
Computers and Internet are becoming widely used in educational contexts. Particularly,
the wide availability of Learning Management Systems (LMS) allows to
easily set up virtual communities providing channels and workspaces to facilitate
communication and information sharing. Most of these systems are able to track
students interaction within the workspaces and store it in a database that can be
later analyzed to assess student behavior. In this chapter we review some experiences
using data mining to analyze data obtained from e-learning courses based
upon virtual communities. We illustrate several issues that arise in this task providing
real world examples and applications and discuss the challenges that must
be addressed in order to integrate data mining technologies in LMS.
Feature selection has received a lot of attention in the machine learning community, but mainly under the supervised paradigm. In this work we study the potential benefits of feature selection in hierarchical clustering tasks. Particularly we address this problem in the context of incremental clustering, following the basic ideas of Gennari . By using a simple implementation, we show that a feature selection scheme running in parallel with the learning process can improve the clustering task under the dimensions of accuracy, efficiency in learning, efficiency in prediction and comprehensibility.
It is widely reported in the literature that incremental clustering
systems suffer from instance ordering effects and that under some
orderings extremely poor clusterings may be obtained. In this paper we
present a new general strategy aimed to mitigate these efects, the
Not-Yet strategy, which has a general and open formulation and it is
not coupled to any particular system. In addition, we propose a
classification of strategies to avoid ordering effects which clarifies
the benefits and disadvantages we can expect from the proposal made in
the paper as well from existing ones. A particular implementation of
the Not-Yet strategy is used to conduct several experiments. Results
suggest that the strategy can improve the clustering quality and also
that performance is limited by its local nature. We also show that,
when combined with other local strategies, the Not-Yet strategy may
help the system to get high quality clusterings. The observed benefits
and limitations suggest future work under the proposed framework.
Machine Learning (ML) methods are very powerful tools to automate the knowledge acquisition (KA) task. Particularly, in illstructured domains where there is no clear idea about which concepts exist, inductive unsupervised learning systems appear to be a promising approach to help experts in the early stages of the acquisition process. In this paper we examine the concept of inductive bias, which have received great attention from the ML community, and discuss the importance of bias shift when using ML algorithms to help experts in constructing a knowledge base (KB) A simple framework for the interaction of the expert with the inductive system exploiting bias shift is shown. Also, it is suggested that under some assumptions, bias selection in unsupervised learning may be performed via parameter setting, thus allowing the user to shift the system bias externally.