An exploration of secondary students' mental states when learning about acids and bases

This study explored factors of students’ mental states, including emotion, intention, internal mental representation, and external mental representation, which can affect their learning performance. In evaluating students’ mental states during the science learning process and the relationship between mental states and learning achievement, valid, reliable, and scalable measures of students’ mental states and learning achievement are needed. This paper presents the development of the Mental State Conceptual Learning Inventory (MSCLI) to identify students’ mental states before and after learning about acids and bases. This instrument is time efficient and convenient and can be administered to large student samples so that teachers and researchers can gain profound insights into their students’ learning of acids and bases in science class. The results of this study indicate that students’ mental states are highly correlated with their achievement. As a whole, low-achieving students tended to have negative emotions and low intentions, were not good at internal visualization, and were unable to interpret graphics and draw pictures. In contrast, high-achieving students had positive emotions and intentions when learning life-related topics about acids and bases, and were good at internal visualization and drawing and interpreting graphics

Dynamic Analysis of Production

A parable of economic life is that some factors can adjust rapidly while others adjust slowly in a given time scale. Focusing on production analysis in the dynamic setting leads us to emphasize the technology specification that permits the theoretical construction that can be translated and amenable to empirical implementation. A historical perspective of the framing the dynamic decision-making is reviewed. The adjustment cost model of the investment is the key conceptual feature as it can be incorporated into the formal structure of a production technology, which offers the opportunity to exploit primal-dual theory in both analysis and empirical implementation. An overview of empirical formulations in both econometric (parametric) and nonparametric settings is discussed. Dynamic production decision environment allows explicitly for the evolution of assets implying firms may not be in long-run equilibrium at a given point in time. The dynamic generalizations of modern production theory concepts measuring economic performance are reviewed given the need to properly account and value the factors that are out of equilibrium. Empirical nonparametric and parametric approaches are addressed at length. While these cases can be addressed relatively easily within a nonparametric, dynamic data envelopment analysis setting, econometric formulations are a greater challenge