Combining Instructional Activities for Sense-Making Processes and Perceptual-Induction Processes Involved in Connection-Making Among Multiple Visual Representations

Abstract

Connection-making among multiple representations is a crucial but difficult competence in STEM learning. Prior research has focused on one type of learning process involved in connection-making: sense-making processes leading to conceptual understanding of connections. Yet, other research suggests that a second type of learning process is important: inductive learning processes leading to perceptual intuitions about connections. We investigate whether combining instructional activities designed to support sense-making processes for understanding of connections (understanding activities) and instructional activities that support inductive processes for perceptual intuitions about connections (perception activities) enhances students’ learning of chemistry knowledge. A laboratory-based experiment with 117 undergraduate students compared students in (a) a control condition that received only conventional activities that did not require connection-making; (b) a condition that received conventional and understanding-activities; (c) a condition that received conventional and perception-activities; and (d) a combined condition that received conventional, understanding-activities, and perception-activities. Results show that only the combined condition outperformed the control condition on a test of chemistry knowledge. Eye-gaze data and verbal reports show that understanding-activities and perception-activities have complementary effects on how students integrate information from multiple representations during the learning phase. Finally, we found that students’ spatial skills moderate their benefit from understanding-activities and perception-activities.     

A Computationally Efficient State Space Approach to Estimating Multilevel Regression Models and Multilevel Confirmatory Factor Models

Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multilevel factor models. A brief outline of the state space formulation is provided and then state space forms for univariate and multivariate multilevel regression models, and a multilevel confirmatory factor model, are illustrated. The utility of the state space approach is demonstrated with either a simulated or real example for each multilevel model. It is concluded that the results from the state space approach are essentially identical to those from specialized multilevel regression modeling and structural equation modeling software. More importantly, the state space approach offers researchers a computationally more efficient alternative to fit multilevel regression models with a large number of Level 1 units within each Level 2 unit or a large number of observations on each subject in a longitudinal study.     

Sensitivity of Fit Indices to Misspecification in Growth Curve Models

This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of approximation, standardized root mean square residual, comparative fit index, and Tucker-Lewis Index. The fit indices were found to have differential sensitivity to different types of misspecification in either the mean or covariance structures with severity of misspecification controlled. No fit index was always more (or less) sensitive to misspecification in the marginal mean structure relative to those in the covariance structure. Specifying the covariance structure to be saturated can substantially improve the sensitivity of fit indices to misspecification in the marginal mean structure; this result might help identify the sources of specification error in a growth curve model. An empirical example of children's growth in math achievement (Wu, West, &; Hughes, 2008 Wu, W., West, S. G. and Hughes, J. N. 2008. Effect of retention in first grade on children's achievement trajectories over four years: A piecewise growth analysis using propensity score matching.. Journal of Educational Psychology, 100: 727–740. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) was used to illustrate the results.     

Hydrogen-induced cold cracking in heat-affected zone of low-carbon high-strength steel

The Y-groove cracking test by submerged arc welding was employed to study the susceptibility of a low-carbon high-strength steel to hydrogen-induced cold cracking (HICC). The morphology of hydrogen cracks was observed using an electron probe microscope. The results showed that the heat-affected zone (HAZ) has a higher susceptibility to HICC than the weld metal and that increasing heat input can improve the HICC resistance of the weldment. The intergranular microcracking is the main HICC mode at the lowest heat input condition, accompanied with some transgranular microcracks attached to complex inclusions. In combination with phase transformation behaviour in sub-zones, the effect of the phase transformation sequence is proposed to try to illustrate the fact that the fine-grained HAZ has higher probability of hydrogen cracking than the coarse-grained HAZ owing to the occurrence of hydrogen enrichment in the fine-grained HAZ after the transformation.     

High-pressure phase transition and lattice dynamics of rock-salt and FeSi-type phases of MgS

A phase transition of MgS under high pressure is investigated using a first-principles method. It is found from energy-volume calculations that the rock-salt (B1) phase of MgS transforms into a FeSi-type (B28) phase at 143?GPa. The calculated ground-state parameters in the B1 phase are in excellent agreement with available experimental and theoretical data. Ab initio phonon calculations are also performed to investigate the structural behaviour of MgS under high pressure. An unstable transverse acoustic mode and a phase transition from B1 to B28 phase at ~143.7?GPa driven by this soft mode are predicted. The B28 structure of MgS is stable up to 350?GPa according to lattice dynamics calculations.     

Determination of the depth distribution of subsurface damage during polishing of alumina

High-resolution grazing-incidence parallel-beam powder diffraction has been used to detect the subsurface damage below alumina surfaces subjected to polishing with cerium oxide or diamond. Despite very significant evolution of the surface morphology, no changes in the subsurface strains were observed over a 20min period of polishing with ceria. For both polishing materials, the variation with angle in the full-width at half-height maximum was successfully modelled by a strain distribution that fell exponentially with increasing depth. Although the surface amplitude and depth dependence parameter are coupled, we have been able to place upper limits on the depth to which the damage extends. Under realistic assumptions, the depth of damage induced by 1µm diamond paste is comparable with that from 3µm ceria polish.     

The orientation and temperature dependences of dislocation velocity in Ni 3 Al single crystals

The average velocities of screw dislocations in Ni 3 Al single crystals have been directly measured as a function of resolved shear stress (RSS) and orientation in the temperature domain of the flow stress anomaly using the etch-pit technique. The velocity was found to be extremely sensitive to the RSS in all cases. In contrast with ordinary metals, the screw dislocation velocities in Ni 3 Al show anomalous behaviour; under a constant RSS, the velocities decrease dramatically with increasing temperature. Furthermore, the velocities and the tension-compression asymmetry of the velocities depend on the orientation of applied stress.     

Prevalence and Predictors of Posttraumatic Growth in Accidentally Injured Patients

This study examined prevalence and predictors of posttraumatic growth in 180 accidentally injured patients of mainland China in their convalescence stage, investigating its relationships with demographic and accidental injury variables, personality, posttraumatic stress disorder (PTSD) symptoms, and coping styles. Our results showed that posttraumatic growth (PTG) presented mostly in the domain of Relating to Others and indicated that PTG was significantly related to marital status, educational level, personality, coping styles, and PTSD symptoms. Avoidance of PTSD symptoms, Openness to experience, and positive coping were significant predictors of PTG. The findings emphasize that when promoting PTG of accidentally injured patients, healthcare providers should facilitate patients utilizing personal resources, understand PTG coexists with PTSD symptoms, and adjust interventions based on the coping styles the patients have adopted.     

Mind-reading without the scanner: Behavioural decoding of working memory content

ABSTRACT

Sophisticated machine learning algorithms have been successfully applied to functional neuroimaging data in order to characterize internal cognitive states. But is it possible to “mind-read” without the scanner? Capitalizing on the robust finding that the contents of working memory guide visual attention toward memory-matching objects, we trained a multivariate pattern classifier on behavioural indices of attentional guidance. Working memory representations were successfully decoded from behaviour alone, both within and between individuals. The current study provides a proof-of-concept for applying machine learning techniques to simple behavioural outputs (e.g., response times) in order to decode information about specific internal cognitive states.     

Play it again: neural responses to reunion with excluders predicted by attachment patterns

Reunion behavior following stressful separations from caregivers is often considered the single most sensitive clue to infant attachment patterns. Extending these ideas to middle childhood/early adolescence, we examined participants' neural responses to reunion with peers who had previously excluded them. We recorded event‐related potentials among nineteen 11‐ to 15‐year‐old youth previously classified on attachment interviews (11 secure and 8 insecure‐dismissing) while they played a virtual ball‐toss game (Cyberball) with peers that involved fair play, exclusion and reunion phases. Compared to secure participants, dismissing participants displayed a greater increment in the N2 during reunion relative to fair play, a neural marker commonly linked to expectancy violation. These data suggest a greater tendency toward continued expectations of rejection among dismissing children, even after cessation of social exclusion. In turn, the link between self‐reported ostracism distress and neural signs of negative expectancy at reunion was moderated by attachment, such that self‐reports were discordant with the neural index of expectancy violation for dismissing, but not for secure children. A video abstract of this article can be viewed at http://www.youtube.com/watch?v=qzoRel5c-4s .