This paper shows how web usage mining can be applied in e-learning systems in order to predict the marks that university students will obtain in the final exam of a course. We have also developed a specific Moodle mining tool oriented for the use of not only experts in data mining but also of newcomers like instructors and courseware authors. The performance of different data mining techniques for classifying students are compared, starting with the student's usage data in several Cordoba University Moodle courses in engineering. Several wellknown classification methods have been used, such as statistical methods, decision trees, rule and fuzzy rule induction methods, and neural networks. We have carried out several experiments using all available and filtered data to try to obtain more accuracy. Discretization and rebalance pre-processing techniques have also been used on the original numerical data to test again if better classifier models can be obtained. Finally, we show examples of some of the models discovered and explain that a classifier model appropriate for an educational environment has to be both accurate and comprehensible in order for instructors and course administrators to be able to use it for decision making.
OO-Method is an OO Methodology that blends the use of formal specification systems with conventional OO methodologies based on practice. In contrast to other approaches in this field ([Jun95,Esd93]), a set of graphical models provided by the methodology allows analysts to introduce the relevant system information to obtain the conceptual model through a requirements collection phase, so that an OO formal specification in Oasis ([Pas92, Pas95-1]), can be generated at any time. This formal specification acts as a high-level system repository. Furthermore, a software prototype which is functionally equivalent to the Oasis specification is also generated in an automated way. This is achieved by defining an execution model which gives the pattern for obtaining a concrete implementation in a declarative or an imperative software development environment (depending on the user choice). The methodology is supported by a CASE workbench.
Models and metamodels play a cornerstone role in Model-Driven Software Development. Although several notations have been proposed to specify them, the kind of formal and tool support they provide is quite limited. In this paper we explore the use of Maude as a formal notation for describing models and metamodels. Maude is an executable rewriting logic language specially well suited for the specification of object-oriented open and distributed systems. We show how Maude offers a simple, natural, and accurate way of specifying models and metamodels, and offers good tool support for reasoning about them. In particular, we show how some basic operations on models, such as model subtyping, type inference, and metric evaluation, can be easily specified and implemented in Maude, and made available in development environments such as Eclipse.
The extraction of useful information for decision making is a challenge in many different domains. Association rule mining is one of the most important techniques in this field, discovering relationships of interest among patterns. Despite the mining of association rules being an area of great interest for many researchers, the search for well-grouped continuous values is still a challenge, discovering rules that do not comprise patterns which represent unnecessary ranges of values. Existing algorithms for mining association rules in continuous domains are mainly based on a non-deterministic search, requiring a high number of parameters to be optimised. These parameters hinder the mining process, and the algorithms themselves must be known to those data mining experts that want to use them. We therefore present a grammar guided genetic programming algorithm that does not require as many parameters as other existing approaches and enables the discovery of quantitative association rules comprising small-size gaps. The algorithm is verified over a varied set of data, comparing the results to other association rule mining algorithms from several paradigms. Additionally, some resulting rules from different paradigms are analysed, demonstrating the effectiveness of our model for reducing gaps in numerical features.
This paper proposes a novel grammar-guided genetic programming algorithm for subgroup discovery. This algorithm, called comprehensible grammar-based algorithm for subgroup discovery (CGBA-SD), combines the requirements of discovering comprehensible rules with the ability to mine expressive and flexible solutions owing to the use of a context-free grammar. Each rule is represented as a derivation tree that shows a solution described using the language denoted by the grammar. The algorithm includes mechanisms to adapt the diversity of the population by self-adapting the probabilities of recombination and mutation. We compare the approach with existing evolutionary and classic subgroup discovery algorithms. CGBA-SD appears to be a very promising algorithm that discovers comprehensible subgroups and behaves better than other algorithms as measures by complexity, interest, and precision indicate. The results obtained were validated by means of a series of nonparametric tests.
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