Bayesian Networks in Educational Assessment: The State of the Field.

Bayesian networks (BN) provide a convenient and intuitive framework for specifying complex joint probability distributions and are thus well suited for modeling content domains of educational assessments at a diagnostic level. BN have been used extensively in the artificial intelligence community as student models for intelligent tutoring systems (ITS) but have received less attention among psychometricians. This critical review outlines the existing research on BN in educational assessment, providing an introduction to the ITS literature for the psychometric community, and points out several promising research paths. The online appendix lists 40 assessment systems that serve as empirical examples of the use of BN for educational assessment in a variety of domains.

Proximal Capture Dynamics for a Single Biological Nanopore Sensor.

Single nanopore sensors enable capture and analysis of molecules that are driven to the pore entry from bulk solution. However, the distance between an analyte and the nanopore opening limits the detection efficiency. A theoretical basis for predicting particle capture rate is important for designing modified nanopore sensors, especially for those with covalently tethered reaction sites. Using the finite element method, we develop a soft-walled electrostatic block (SWEB) model for the alpha-hemolysin channel that produces a vector map of drift-producing forces on particles diffusing near the pore entrance. The maps are then coupled to a single-particle diffusion simulation to probe capture statistics and to track the trajectories of individual particles on the µs to ms time scales. The investigation enables evaluation of the interplay among the electrophoretic, electroosmotic, and thermal driving forces as a function of applied potential. The findings demonstrate how the complex drift-producing forces compete with diffusion over the nanoscale dimensions of the pore. The results also demonstrate the spatial and temporal limitations associated with nanopore detection and offer a basic theoretical framework to guide both the placement and kinetics of reaction sites located on, or near, the nanopore cap.

Religious socialization of youth involved in child welfare.

Increased religiosity is associated with a variety of improved outcomes, especially for youth in disadvantaged contexts. Although youth involved in child welfare may experience protective effects of religious participation or values, little is known about the impact of maltreatment on religious development. Using the National Survey of Child and Adolescent Well-Being, a nationally representative study of child welfare involved families, correlates of religious attendance and importance of religion for youth were investigated using weighted logistic regression at two waves 18 months apart. Youth self-reports of religious attendance and their ratings of its importance were associated with religious attendance of their caregivers, whether birth-parents or foster parents. Foster parents were more likely to attend religious services than birth parents. Increases in youth attendance from Wave 1 to Wave 2 were associated with high youth religious importance at Wave 1, whereas decreases in attendance were associated with moving between home and foster placements. Increases in religious importance from Wave 1 to Wave 2 were associated with religious attendance at Wave 1 and with the youth being Black.

Information content in fluorescence correlation spectroscopy: binary mixtures and detection volume distortion.

When properly implemented, fluorescence correlation spectroscopy (FCS) reveals numerous static and dynamic properties of molecules in solution. However, complications arise whenever the measurement scenario is complex. Specific limitations occur when the detection region does not match the ideal Gaussian geometry ubiquitously assumed by FCS theory, or when properties of multiple fluorescent species are assessed simultaneously. A simple binary solution of diffusers, where both mole fraction and diffusion constants are sought, can face interpretive difficulty. In order to better understand the limits of FCS, this study systematically explores the relationship between detection-volume distortion, diffusion constants, species mole fraction, and fitting methodology in analyses that utilize a two-component autocorrelation model. FCS measurements from solution mixtures of dye-labeled protein and free dye are compared to simulations, which predict the performance of FCS under a variety of experimental circumstances. The results reveal a range of conditions necessary for performing accurate measurements and describe experimental scenarios that should be avoided. The findings also provide guidelines for obtaining meaningful measurements when grossly distorted detection volumes are utilized and generally assess the latent information contained in FCS datasets.

Accurate optical analysis of single-molecule entrapment in nanoscale vesicles.

We present a nondestructive method to accurately characterize low analyte concentrations (0-10 molecules) in nanometer-scale lipid vesicles. Our approach is based on the application of fluorescence fluctuation analysis (FFA) and multiangle laser light scattering (MALLS) in conjunction with asymmetric field flow fractionation (AFFF) to measure the entrapment efficiency (the ratio of the concentration of encapsulated dye to the initial bulk concentration) of an ensemble of liposomes with an average diameter less than 100 nm. Water-soluble sulforhodamine B (SRB) was loaded into the aqueous interior of nanoscale liposomes synthesized in a microfluidic device. A confocal microscope was used to detect a laser-induced fluorescence signal resulting from both encapsulated and unencapsulated SRB molecules. The first two cumulants of this signal along with the autocorrelation function (ACF) were used to quantify liposome entrapment efficiency. Our analysis moves beyond typical, nonphysical assumptions of equal liposome size and brightness. These advances are essential for characterizing liposomes in the single-molecule encapsulation regime. Our work has further analytical impact because it could increase the interrogation time of free-solution molecular analysis by an order of magnitude and form the basis for the development of liposome standard reference materials.

A distributed algorithm for multi-tau autocorrelation.

Network data-transfer times in distributed simulation environments can be reduced by performing data analysis at the remote source, if the analytical technique does not require the entire set of data at once. This novel multi-tau autocorrelation algorithm allows time-domain data records to be processed in discrete, distributed segments and combined at a later point in time. The new approach agrees with autocorrelation results performed by concatenating the discrete segments before correlation, but it operates with significantly shortened processing times. The multi-tau algorithm also benefits from reduced memory requirements since it does not require access to the entire data record at once, and from improved scalability since the multi-tau algorithm has order O(N), while fast Fourier transform autocorrelation algorithms have order O(N log N). This distributed algorithm has particular utility in simulations of fluorescence correlation spectroscopy or photon correlation spectroscopy.

Numerical fluorescence correlation spectroscopy for the analysis of molecular dynamics under nonstandard conditions.

The suitability of mathematical models used to extract kinetic information from correlated data constitutes a significant issue in fluorescence correlation spectroscopy (FCS). Standard FCS equations are derived from a simple Gaussian approximation of the optical detection volume, but some investigations have suggested this traditional practice can lead to inaccurate and misleading conclusions under many experimental circumstances, particularly those encountered in one-photon confocal measurements. Furthermore, analytical models cannot be derived for all measurement scenarios. We describe a novel numerical approach to FCS that circumvents conventional analytical models, enabling meaningful analyses even under extraordinarily unusual measurement conditions. Numerical fluorescence correlation spectroscopy (NFCS) involves quantitatively matching experimental correlation curves with synthetic curves generated via diffusion simulation or direct calculation based on an experimentally determined 3D map of the detection volume. Model parameters are adjusted iteratively to minimize the residual differences between synthetic and experimental correlation curves. In order to reduce analysis time, we distribute calculations across a network of processors. As an example of this new approach, we demonstrate that synthetic autocorrelation curves correspond well with experimental data and that NFCS diffusion measurements of Rhodamine B remain constant, regardless of the distortion present in a confocal detection volume.