A quiz-based game for addressing growing population issues:linking learning mechanics to the GROWTH serious game

This paper discusses the integration of quiz mechanism into digital game-based learning platform addressing environmental and social issues caused by population growth. 50 participants' learning outcomes were compared before and after the session. Semi-structured interview was used to gather participants' viewpoints regarding of issues presented in the game. Phenomenography was used as a methodology for data collection and analysis. Preliminary outcomes have shown that the current game implementation and quiz mechanism can be used to: (1) promote learning and awareness on environmental and social issues and (2) sustain players' attention and engagements.

Semi-supervised Gaussian process latent variable model with pairwise constraints

In machine learning, Gaussian process latent variable model (GP-LVM) has been extensively applied in the field of unsupervised dimensionality reduction. When some supervised information, e.g., pairwise constraints or labels of the data, is available, the traditional GP-LVM cannot directly utilize such supervised information to improve the performance of dimensionality reduction. In this case, it is necessary to modify the traditional GP-LVM to make it capable of handing the supervised or semi-supervised learning tasks. For this purpose, we propose a new semi-supervised GP-LVM framework under the pairwise constraints. Through transferring the pairwise constraints in the observed space to the latent space, the constrained priori information on the latent variables can be obtained. Under this constrained priori, the latent variables are optimized by the maximum a posteriori (MAP) algorithm. The effectiveness of the proposed algorithm is demonstrated with experiments on a variety of data sets. © 2010 Elsevier B.V.