Enhancing 5th graders’ science content knowledge and self-efficacy through game-based learning

Many argue that games can positively impact learning by providing an intrinsically motivating and engaging learning environment for students in ways that traditional school cannot. Recent research demonstrates that games have the potential to impact student learning in STEM content areas and that collaborative gameplay may be of particular importance for learning gains. This study investigated the effects of collaborative and single game player conditions on science content learning and science self-efficacy. Results indicated that there were no differences between the two playing conditions; however, when conditions were collapsed, science content learning and self-efficacy significantly increased. Future research should focus on the composition of collaboration interaction among game players to assess what types of collaborative tasks may yield positive learning gains.     

Comparing the impact of bentonite addition for both must clarification and wine fining on the chemical profile of wine from Chambave Muscat grapes

The impact of bentonite fining is generally studied on final wines, and few studies have evaluated it throughout the winemaking process. This work studied the fate of proteins and terpenols, the main targets of bentonite addition, during the processing of Chambave Muscat aromatic grapes. The experimental trials were performed in two vintages (2006 and 2007) and included the following processes: bentonite addition to must only (100 g hL^?1 in both vintages), fining of wine only (100 g hL^?1 in both vintages), double bentonite treatment on must and wine (200 and 150 g hL^?1 in 2006 and in 2007, respectively) and no treatment (control). The results demonstrated that the treatment allowing the best removal of protein was the addition of bentonite to must only. Moreover, a lower removal of free terpenols was observed in samples from double treatment compared with wines fined with bentonite only after alcoholic fermentation.     

Preliminary evaluation of biogenic amines content in Chilean young varietal wines by HPLC

Biogenic amines are organic nitrogenous compounds produced in wines mainly during malolactic fermentation. Its presence is a health risk for sensitive individuals and it could negatively affect the wine quality. The objective of this work was to determine the biogenic amines content in Chilean young varietal wines. For that, a liquid chromatographic method with pre-column derivatization was optimized and validated to achieve a reliable quantification of the most important biogenic amines in wines, i.e. histamine, tyramine, spermine, spermidine, putrescine, cadaverine and phenylethylamine. Using a face-centered central composite design derivatization and chromatographic conditions were optimized. Derivatization with dansyl chloride showed an optimum yield for all biogenic amines at 60 °C during 60 min. These derivatives were optimally separated with a C₁₈ YMC-Pack ODS-A column set a 40 °C using a mobile phase composed of acetonitrile and water under gradient conditions. An effective separation and quantification of the biogenic amines was achieved in 40 min using ultraviolet detection at 254 nm. Validation was established according to ICH guidelines, calibrations in matrix fitted a linear regression model with R² > 0.997. Repeatability (n = 6) and intermediate precision (n = 3) in matrix showed RSD values <2.34% and <3.16%, respectively. This method was applied to determine for the first time the biogenic amines content in Chilean wines. Twenty-seven samples of young varietal wines, i.e. Cabernet Sauvignon (n = 9), Merlot (n = 8) and Carménère (n = 10) were analyzed finding a biogenic amines content from 18.12 to 39.84 mg L^-1, where putrescine showed the highest concentration in all grape varieties. No significant difference was found between grapes varieties regarding total biogenic amines content (P = 0.7842, df = 26). Along with putrescine, histamine and tyramine were the most abundant biogenic amines but similar levels were also observed with spermidine.     

The Assessment Portfolio: A Possible Answer to the Distance Education Assessment Dilemma

ABSTRACT

This “Siteseeing” column provides reviews of Awards sites.     

Geographical Authentication of Tequila According to its Mineral Content by Means of Support Vector Machines

The elemental profile of tequila samples from the three main production areas of the State of Jalisco, namely Amatitlan, Guadalajara, and Tequila, was used to carry out their geographical characterization. With this aim, the concentration of Al, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, S, Sr, and Zn was determined by inductively coupled plasma atomic emission spectroscopy. Principal component analysis was addressed to visualize data trends. The number of input variables was reduced by means of backward stepwise linear discriminant analysis and support vector machines were used to construct an adequate classification model. The best classification performance was obtained by a linear support vector machine model with 100% of prediction ability.     

Automatic word naming recognition for an on-line aphasia treatment system

One of the most common effects among aphasia patients is the difficulty to recall names or words. Typically, word retrieval problems can be treated through word naming therapeutic exercises. In fact, the frequency and the intensity of speech therapy are key factors in the recovery of lost communication functionalities. In this sense, speech and language technology can have a relevant contribution in the development of automatic therapy methods. In this work, we present an on-line system designed to behave as a virtual therapist incorporating automatic speech recognition technology that permits aphasia patients to perform word naming training exercises. We focus on the study of the automatic word naming detector module and on its utility for both global evaluation and treatment. For that purpose, a database consisting of word naming therapy sessions of aphasic Portuguese native speakers has been collected. In spite of the different patient characteristics and speech quality conditions of the collected data, encouraging results have been obtained thanks to a calibration method that makes use of the patients’ word naming ability to automatically adapt to the patients’ speech particularities.     

Understanding the diffusion of mobile digital content: a growth curve modelling approach

While the sales of mobile digital content have been growing exponentially, the understanding of its diffusion remains limited in the literature. In this study, we set out to enrich this understanding using a growth curve modelling approach. We applied four widely used growth curve models of innovation diffusion and compared their performance in explaining the diffusion of mobile digital content empirically. Analysis based on the data collected on a product adopted by nearly 30 million mobile phone users over a 149-week period in 31 regions in China revealed that the Gompertz model was the most effective model in depicting the diffusion process, with more than 99 % of the variance explained. Moreover, we found that population, urbanization, education level, and mobile technology usage were significant determinants of various parameters such as the diffusion rate and inflection point of the diffusion process, respectively. Implications for both researchers and practitioners are discussed.     

The multiphysics modeling of heat and moisture induced stress and strain of historic building materials and artefacts

The basic structure of historic sites and their associated interior artefacts can be damaged or even destroyed by climate change. The evaluation of combined heat and moisture induced stress and strain (HMSS) can predict possible damage-related processes. In this paper, the development of one- and two-dimensional HMSS models of building materials and artefacts in COMSOL Multiphysics Version 4, a commercial finite element software, is presented. The validation of the numerical models is revealed using analytical, numerical and experimental solutions. As a result, the HMSS model was shown to be an adequate predictive tool to determine possible damage-related processes in building assemblies and artefacts.