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During the past decade, cognitive diagnostic models (CDMs) have become prevalent in providing diagnostic information for learning. Cognitive diagnostic models have generally focused on single cross-sectional time points. However, longitudinal assessments have been commonly used in education to assess students' learning progress as well as evaluating intervention effects. Thus, it becomes natural to identify longitudinal growth in skills profiles mastery, which can yield meaningful inferences on learning. This study proposes longitudinal CDMs that incorporate latent growth curve modeling and covariate extensions, with the aim to measure the growth of skills mastery and to evaluate attribute-level intervention effects over time. Using real-world data, this study demonstrates applications of unconditional and conditional latent growth CDMs. Simulation studies show stable parameter recovery and classification of latent classes for different sample sizes. These findings suggest that building on the well-established growth modeling frameworks, applications of covariate-based longitudinal CDM can help understand the effect of explanatory factors and intervention on the change of attribute mastery.

Bone defects are a global public health problem. However, the available methods for inducing bone regeneration are limited. The application of traditional Chinese herbs for bone regeneration has gained popularity in recent years. β-ecdysterone is a plant sterol similar to estrogen, that promotes protein synthesis in cells; however, its function in bone regeneration remains unclear. In this study, we investigated the function of β-ecdysterone on osteoblast differentiation and bone regeneration in vitro and in vivo. MC3T3-E1 cells were used to test the function of β-ecdysterone on osteoblast differentiation and bone regeneration in vitro. The results of the Cell Counting Kit-8 assay suggested that the proliferation of MC3T3-E1 cells was promoted by β-ecdysterone. Furthermore, β-ecdysterone influenced the expression of osteogenesis-related genes, and the bone regeneration capacity of MC3T3-E1 cells was detected by polymerase chain reaction, the alkaline phosphatase (ALP) test, and the alizarin red test. β-ecdysterone could upregulate the expression of osteoblastic-related genes, and promoted ALP activity and the formation of calcium nodules. We also determined that β-ecdysterone increased the mRNA and protein levels of components of the BMP-2/Smad/Runx2/Osterix pathway. DNA sequencing further confirmed these target effects. β-ecdysterone promoted bone formation by enhancing gene expression of the BMP-2/Smad/Runx2/Osterix signaling pathway and by enrichment biological processes. For in vivo experiments, a femoral condyle defect model was constructed by drilling a bone defect measuring 3 mm in diameter and 4 mm in depth in the femoral condyle of 8-week-old Sprague Dawley male rats. This model was used to further assess the bone regenerative functions of β-ecdysterone. The results of micro-computed tomography showed that β-ecdysterone could accelerate bone regeneration, exhibiting higher bone volume, bone surface, and bone mineral density at each observation time point. Immunohistochemistry confirmed that the β-ecdysterone also increased the expression of collagen, osteocalcin, and bone morphogenetic protein-2 in the experiment group at 4 and 8 weeks. In conclusion, β-ecdysterone is a new bone regeneration regulator that can stimulate MC3T3-E1 cell proliferation and induce bone regeneration through the BMP-2/Smad/Runx2/Osterix pathway. This newly discovered function of β-ecdysterone has revealed a new direction of osteogenic differentiation and has provided novel therapeutic strategies for treating bone defects.

Recent changes to the patient note (PN) format of the United States Medical Licensing Examination have challenged medical schools to improve the instruction and assessment of students taking the Step-2 clinical skills examination. The purpose of this study was to gather validity evidence regarding response process and internal structure, focusing on inter-rater reliability and generalizability, to determine whether a locally-developed PN scoring rubric and scoring guidelines could yield reproducible PN scores. A randomly selected subsample of historical data (post-encounter PN from 55 of 177 medical students) was rescored by six trained faculty raters in November-December 2014. Inter-rater reliability (% exact agreement and kappa) was calculated for five standardized patient cases administered in a local graduation competency examination. Generalizability studies were conducted to examine the overall reliability. Qualitative data were collected through surveys and a rater-debriefing meeting. The overall inter-rater reliability (weighted kappa) was 0.79 (Documentation = 0.63, Differential Diagnosis = 0.90, Justification = 0.48, and Workup = 0.54). The majority of score variance was due to case specificity (13%) and case-task specificity (31%), indicating differences in student performance by case and by case-task interactions. Variance associated with raters and its interactions were modest (<5%). Raters felt that justification was the most difficult task to score and that having case and level-specific scoring guidelines during training was most helpful for calibration. The overall inter-rater reliability indicates high level of confidence in the consistency of note scores. Designs for scoring notes may optimize reliability by balancing the number of raters and cases.

Despite the critical need for effective programs for older youth exiting from foster care, little is known about the outcomes of transition support programs or how they are perceived by [...]

Scoring of performance can be based on different modes of observation, which can include live and videotaped observations. Although live observations have been the traditional format of measuring examinee performance in education and in healthcare, videotaped observations provide educators and testing agencies the promise of unbiased and standardized evaluations, offering practical solutions to limitations of real-time scoring. This study proposes a measurement model taking into account different modes of observation, using an extension of the hierarchical rater model based on signal detection theory (HRM-SDT). A hierarchical rater model is motivated by the nested structure of scores assigned by raters – scores assigned by raters become indicators of performance quality, which in turn become indicators of examinee ability. This study extends the hierarchical structure of the scoring process to include modes of observation, which serves as an intermediary level between the latent categorical indicator of performance quality and examinee ability, forming a three-level HRM-SDT. Analyses based on real-world data showed differences in the quality of scores from live observations and videotaped recordings. Compared to the traditional HRM-SDT, the overall model fit improved when including modes of observation. Simulations using different sample sizes and conditions provide implications for uses of this model. [ABSTRACT FROM AUTHOR]

Background: The relationship between learning modalities and nursing students' sleep quality during the coronavirus disease (COVID-19) pandemic is unknown. Purpose: This study examined the relationships between remote learning and the sleep quality of nursing students during the COVID-19 pandemic. Methods: Using a cross-sectional descriptive design, 890 nursing students were surveyed online to identify sleeping habits and learning modalities. Results: There were no significant correlations between remote learning hours, self-reported sleep hours, and sleep quality. The asynchronous-only group reported better sleep quality than the in-person and online hybrid group after controlling for health conditions and grade point average. Students who reported that remote learning had impacted their sleep had poorer sleep quality. Conclusions: This study provides insight into how different learning modalities impact nursing students' sleep quality during the pandemic. [ABSTRACT FROM AUTHOR]

Differential rater functioning (DRF) occurs when raters show evidence of exercising differential severity or leniency when scoring examinees within different subgroups. Previous studies of DRF have examined rater bias using manifest variables (e.g., use of covariates) to determine the subgroups. These manifest variables include gender and the ethnicity of the examinee. For example, a rater may score males more severely. Ideally, each rater's severity should be invariant across subgroups. This study examines DRF in the context of latent subgroups that classify possible sources of DRF based on raters' scoring behavior rather than manifest factors. An extension of the latent class signal detection theory (LC-SDT) model for identifying DRF is proposed and examined using real-world data and simulations. Results from real-world data show that the signal detection approach leads to an effective method to identify latent DRF. Simulations with varying sample sizes and conditions of rater precision were shown to recover parameters at an adequate level, supporting its use to identify latent DRF in large-scale data. These findings suggest that the DRF extension of the LC-SDT can be a useful model to examine characteristics of raters and add information that can aid rater training. [ABSTRACT FROM AUTHOR]

Large-scale educational testing data often contain vast amounts of variables associated with information pertaining to test takers, schools, or access to educational resources—information that can help explain relationships between test taker performance and their learning environment. This study examines approaches to incorporate latent and observed explanatory variables as predictors for cognitive diagnostic models (CDMs). Methods to specify and simultaneously estimate observed and latent variables (estimated using item response theory) as predictors affecting attribute mastery were examined. Real-world data analyses were conducted to demonstrate the application using large-scale international testing data. Simulation studies were conducted to examine the recovery and classification for simultaneously estimating multiple latent (using dichotomous and polytomous items as indicators for the latent construct) and observed predictors for varying sample sizes and number of attributes. Results showed stable parameter recovery and consistency in attribute classification. Implications for latent predictors and attribute specifications are discussed. [ABSTRACT FROM AUTHOR]