Introducing Undergraduate Students to Science

Understanding the scientific method fosters the development of critical thinking and logical analysis of information. Additionally, proposing and testing a hypothesis is applicable not only to science, but also to ordinary facts of daily life. Knowing the way science is done and how its results are published is useful for all citizens and mandatory for science students. A 60-h course was created to offer undergraduate students a framework in which to learn the procedures of scientific production and publication. The course`s main focus was biochemistry, and it was comprised of two modules. Module I dealt with scientific articles, and Module II with research project writing. Module I covered the topics: 1) the difference between scientific knowledge and common sense, 2) different conceptions of science, 3) scientific methodology, 4) scientific publishing categories, 5) logical principles, 6) deductive and inductive approaches, and 7) critical reading of scientific articles. Module II dealt with 1) selection of an experimental problem for investigation, 2) bibliographic revision, 3) materials and methods, 4) project writing and presentation, 5) funding agencies, and 6) critical analysis of experimental results. The course adopted a collaborative learning strategy, and each topic was studied through activities performed by the students. Qualitative and quantitative course evaluations with Likert questionnaires were carried out at each stage, and the results showed the students` high approval of the course. The staff responsible for course planning and development also evaluated it positively. The Biochemistry Department of the Chemistry Institute of the University of Sao Paulo has offered the course four times.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.