Effects of Computer Simulation and Animation (CSA) on Students’ Problem Solving in Engineering Dynamics: What and How

The application of Computer Simulation and Animation (CSA) in the instruction of engineering dynamics has shown a significant growth in the recent years. The two foremost methods to evaluate the effectiveness of CSA tools, including student feedback and surveys and measuring student change in performance, suggest that CSA modules improve student learning in engineering dynamics. However, neither method fully demonstrates the quality of students’ cognitive changes. This study examined the quality of effects of application of CSA modules on student learning and problem solving in particle dynamics. It also compared CSA modules with textbook-style problem-solving regarding the changes they cause in students’ cognitive process. A qualitative methodology was adopted to design and implement a study to explore the changes in participants’ learning and problem-solving behavior caused by using a CSA module. Collected data were coded and analyzed using the categories of cognitive process based on the Revised Bloom’s Taxonomy. An analysis of the results revealed that the most significant effects were observed in understanding, analyzing, and evaluating. The high frequency of “inference” behavior after working with modules indicated a significant increase in participants’ understanding activity after working with computer modules. Comparing behavior changes of computer-simulation group students with those who worked with a textbook-style example demonstrated that the CSA modules ignited more analytical behavior among students than did textbook-style examples. This study illustrated that improvement in learning due to the application of CSA is not limited to conceptual understanding; CSA modules enhance students’ skills in applying, organizing, and evaluating as well. The interactive characteristics of CSA play a major role in stimulating students’ analytical reasoning and critical thinking in engineering dynamics

HYDROGEN BONDING IN WOOD-BASED MATERIALS: AN UPDATE

The contribution of hydrogen bonding to wood science and technology has been well recognized over the past century. The hydrogen bond is an important chemical characteristic contributing to wood-based material behavior and it also provides an important contribution to processing features of wood. However, the current understanding of hydrogen bond strength as a contributor to wood-based material behavior has not been updated in the wood literature. Wood-based material literature typically report hydrogen bond strengths ranging from 12.6 to 25.2 kJ/mol (3 to 6 kcal/mol) while newer data from the general chemistry field report hydrogen bond strengths up to 189 kJ/mol (45 kcal/mol), which are characteristic of covalent bond strength. In light of these new data regarding hydrogen bond strengths, it provides impetus to discuss the new understanding of hydrogen bond strength relative to wood-based material behavior. Recent developments in nanotechnology of renewable materials leading to the production and applications of cellulose nanomaterials with much higher surface areas and hydrogen bonding capacity also mandate revisiting our knowledge of the hydrogen bonding mechanism and strength

Identifying the True Revolution in Loyalty Programmes Thinking: B2B Channel Marketing Adoption and Digital Opportunities

This study identifies a paradigm shift in loyalty programme thinking and the additional benefits they may provide due to technological advancements, particularly in B2B marketing. Pull strategies like this are typical in consumer markets, but in supply chains, push tactics are usually deployed to promote brands and manage channel relationships. Data transparency and channel power from digital channel loyalty programmes have made them viable for manufacturers. We aim to identify these opportunities in detail

Effect of extrapolation on coverage accuracy of prediction intervals computed from Pareto-type data

A feature that distinguishes extreme-value contexts from more conventional statistical problems is that in the former we often wish to make predictions well beyond the range of the data. For example, one might have a 10-year sequence of observations of a phenomenon, and wish to make forecasts for the next 20 to 30 years. It is generally unclear how such long ranges of extrapolation affect prediction. In the present paper, and for extremes from a distribution with regularly varying tails at infinity, we address this problem. We approach it in two ways: first, from the viewpoint of predictive inference under a model that is admittedly only approximate, and where the errors of greatest concern are caused by the interaction of long-range extrapolation with model misspecification; second, where the model is accurate but errors arise from a combination of extrapolation and the fact that the method is only approximate. In both settings we show that, in a way which can be defined theoretically and confirmed numerically, one can make predictions exponentially far into the future without committing serious errors.Research supported by the Swedish Research Council and Foundation for International Cooperation in Research and Higher Educatio