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Game-based learning has proven to be effective for enhancing student motivation and learning outcomes. In this study, the authors first designed and then tested a 3D virtual world-based video game to support students in learning a foreign language. Two data mining clustering techniques are used to analyse the impact of the game on learning processes and outcomes. The aim is to classify students according to learning outcomes, by comparing specific language competencies, such as the grammar, vocabulary and writing, before, during and after a case study. The case study used here involved 102 undergraduate German language students from the A1.2 level (CEFR). The results obtained from the application of two different clustering techniques indicate that learners do not all benefit from game-based learning in the same way; some students might better develop language competencies through more conventional and guided learning approaches.

Video games are effective tools to engage students in foreign language learning. Research has shown that not all students benefit from video games in the same way nor at the same pace. We have created a 3D video game, based on a virtual world platform, that aims at fostering students' writing competence. It requires learners to play collaboratively a game, interacting and communicating through text chats. To analyse the game's impact on students' language learning and especially on their writing skills, we use data mining clustering techniques. The aim is firstly, to classify students according to their learning outcomes and secondly, to compare grammar, vocabulary and writing skills before and after the experience. The target group consisted of more than 100 students of an A1 level (CEFR) German language course. Results show that not all students improve to a similar degree, but instead there are students who might better develop their writing competences by using other learning approaches.

Complexity-penalization strategies are one way to decide on the most appropriate network size in order to address the trade-off between overfitted and underfitted models. In this paper we propose a new penalty term derived from the behaviour of candidate models under noisy conditions that seems to be much more robust against catastrophic overfitting errors that standard techniques. This strategy is applied to several regression problems using polynomial functions, univariate autoregressive models and RBF neural networks. The simulation study at the end of the paper will show that the proposed criterion is extremely competitive when compared to state-of-the-art criteria.

The goal of combining the outputs of multiple models is to form an improved meta-model with higher generalization capability than the best single model used in isolation. Most popular ensemble methods do specify neither the number of component models nor their complexity. However, these parameters strongly influence the generalization capability of the meta-model. In this paper we propose an ensemble method which generates a meta-model with optimal values for these parameters. The proposed method suggests using resampling techniques to generate multiple estimations of the generalization error and multiple comparison procedures to select the models that will be combined to form the meta-model. Experimental results show the performance of the model on regression and classification tasks using artificial and real databases.

Estimating Prediction Risk is important for providing a way of computing the expected error for predictions made by a model, but it is also an important tool for model selection. This paper addresses an empirical comparison of model selection techniques based on the Prediction Risk estimation, with particular reference to the structure of nonlinear regularized neural networks. To measure the performance of the different model selection criteria a large-scale small-samples simulation is conducted for feedforward neural networks.

One of the main research concern in neural networks is to find the appropriate network size in order to minimize the trade-off between overfitting and poor approximation. In this paper the choice among different competing models that fit to the same data set is faced when statistical methods for model comparison are applied. The study has been conducted to find a range of models that can work all the same as the cost of complexity varies. If they do not, then the generalization error estimates should be about the same among the set of models. If they do, then the estimates should be different and our job would consist on analyzing pairwise differences between the least generalization error estimate and each one of the range, in order to bound the set of models which might result in an equal performance. This method is illustrated applied to polynomial regression and RBF neural networks.