Learning with MOOCs: promises and problems

Massive Open Online Courses (MOOCs) may be considered to be a new form of virtual technology enhanced learning environments. Since their first appearance in 2008, the increase in the number of MOOCs has been dramatic. The hype about MOOCs was accompanied by great expectations: 2012 was named the Year of the MOOCs and it was expected that MOOCs would revolutionise higher education. Two types of MOOCs may be distinguished: cMOOCs as proposed by Siemens, based on his ideas of connectivism, and xMOOCs developed in institutions such as Stanford and MIT. Although MOOCs have received a great deal of attention, they have also met with criticism. The time has therefore come to critically reflect upon this phenomenon.

Possibilities and problems of teaching the history of the present time through media and information literacy.

Le système éducatif encourage une histoire positiviste, ordonnée, unilatérale et universelle; par l´incorporation de le découpage chronologique de l´histoire en quatre étapes. Mais, est-ce qu´il serait posible que les élèves puissent étudier leur propre présent? Mon commuication poursuit d´exposer, comme Saab affirmait, le présent est “le point de départ et d´arrivée de l´enseignement de l´histoire détermine les allers et les retours au passé”. La façon d´approcher l´enseignement de l´histoire est confortable. Il n´y a pas de questions, il n´y a pas de discussions. Cette vision de l´histoire interprétée par l´homme blancoccidental-hétérosexuel s´inscrit dans le projet de la modernité du Siècle des Lumières. Par conséquent, cette histoire obvie que nous vivons dans una société postmoderne de la suspicion, de la pensée débile. En ce qui concerne la problématique autour de la pollution audiovisuelle et la façon dont les enseignants et les élèves sont quotidiennement confrontés à ce problème. Par conséquent, il est nécessaire de réfléchir à la question de l´enseignement de l´histoire quadripartite. Actuellement, les médias et les nouvelles technologies sont en train de changer la vie de l´humanité. Il est indispensable que l´élève connaisse son histoire presente et les scénarioshistoriques dans l´avenir. Je pense en la nécessité d´adopter une didactique de l’histoire presente et par conséquent, nous devons utiliser la maîtrise des médias et de l´information. Il faut une formation des enseignants que pose, comme Gadamer a dit: “le passé y le présent se trouvent par une négociation permanente”. Una formation des enseignants qui permette de comprendre et penser l´histoire future / les histoires futures. À mon avis, si les élèves comprennent la complexité de leur monde et leurs multiples visions, les élèves seront plus tolérantes et empathiques.

Combination of Evolutionary Algorithms with Experimental Design, Traditional Optimization and Machine Learning

Evolutionary algorithms alone cannot solve optimization problems very efficiently since there are many random (not very rational) decisions in these algorithms. Combination of evolutionary algorithms and other techniques have been proven to be an efficient optimization methodology. In this talk, I will explain the basic ideas of our three algorithms along this line (1): Orthogonal genetic algorithm which treats crossover/mutation as an experimental design problem, (2) Multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses decomposition techniques from traditional mathematical programming in multiobjective optimization evolutionary algorithm, and (3) Regular model based multiobjective estimation of distribution algorithms (RM-MEDA) which uses the regular property and machine learning methods for improving multiobjective evolutionary algorithms.

Phylogenetic inference's algorithms

Phylogenetic inference consist in the search of an evolutionary tree to explain the best way possible genealogical relationships of a set of species. Phylogenetic analysis has a large number of applications in areas such as biology, ecology, paleontology, etc. There are several criterias which has been defined in order to infer phylogenies, among which are the maximum parsimony and maximum likelihood. The first one tries to find the phylogenetic tree that minimizes the number of evolutionary steps needed to describe the evolutionary history among species, while the second tries to find the tree that has the highest probability of produce the observed data according to an evolutionary model. The search of a phylogenetic tree can be formulated as a multi-objective optimization problem, which aims to find trees which satisfy simultaneously (and as much as possible) both criteria of parsimony and likelihood. Due to the fact that these criteria are different there won't be a single optimal solution (a single tree), but a set of compromise solutions. The solutions of this set are called "Pareto Optimal". To find this solutions, evolutionary algorithms are being used with success nowadays.This algorithms are a family of techniques, which aren’t exact, inspired by the process of natural selection. They usually find great quality solutions in order to resolve convoluted optimization problems. The way this algorithms works is based on the handling of a set of trial solutions (trees in the phylogeny case) using operators, some of them exchanges information between solutions, simulating DNA crossing, and others apply aleatory modifications, simulating a mutation. The result of this algorithms is an approximation to the set of the “Pareto Optimal” which can be shown in a graph with in order that the expert in the problem (the biologist when we talk about inference) can choose the solution of the commitment which produces the higher interest. In the case of optimization multi-objective applied to phylogenetic inference, there is open source software tool, called MO-Phylogenetics, which is designed for the purpose of resolving inference problems with classic evolutionary algorithms and last generation algorithms. REFERENCES [1] C.A. Coello Coello, G.B. Lamont, D.A. van Veldhuizen. Evolutionary algorithms for solving multi-objective problems. Spring. Agosto 2007 [2] C. Zambrano-Vega, A.J. Nebro, J.F Aldana-Montes. MO-Phylogenetics: a phylogenetic inference software tool with multi-objective evolutionary metaheuristics. Methods in Ecology and Evolution. En prensa. Febrero 2016.