The social construction of a Teacher Support Team: an experience of university lecturers’ professional development in STEM

Abstract

This paper focuses on understanding and exploring how a group of university engineering and science tutor educators learn and assimilate new conceptions about their role in the face of the forces of globalisation that are transforming the system of higher education. This research paper adopts the notion of the Teacher Support Team (TST) as developed by Daniels and grounded in Vygotsky’s sociocultural account of the social formation of mind. These structures of meaning provide insight into the role played by the context, the interactions, the needs and the demands of actual activities, agreements and learning processes that this group of STEM lecturers undertook as they sought to transform their usual teaching methods that were focused on individual and isolated work in order to create more innovative practices and impact on their students’ performance. The analysis of this experience, which was based on the epistemological principles of the sociocultural approach, focused on the educational model that emerges from needs that are perceived and shared through the group’s interactions, as well as the transitions that such a team undergoes in its actions and decisions.     

Children’s Models about Colours in Nahuatl-Speaking Communities

This paper presents the development and structure of indigenous children’s ideas about mixing colours as well as their ideas about each colour, derived from their traditions. The children were interviewed both at school and outside it, and an educational proposal was implemented. Ideas expressed in the school context were analysed using the partial possible model, which states that the inferences and explanations used to describe a subject consist of constricting ideas, rules of correspondence, and a set of phenomenological inferences about processes. After identifying these components in the children’s ideas, we developed models to describe their conceptions about mixing colours. We employed a different approach to analyse children’s ideas related to their cultural context. The results showed that children change from a conception that focuses on colours as entities that do not change and as properties of objects (model 1) to the idea that colour represents a quality of substances or objects that can be modified by mixing colours (model 2). Cultural context analysis showed that stories are independent from one another and that they are not connected to colour mixing processes, only to the actions of colour on people. We concluded that students generate independent constructions between school and cultural knowledge.     

Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms

In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.