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Airway management is one of the main situations that required nursing skills. Actual airway management experience on live patients is limited due to few opportunities to perform endotracheal intubation for students and issues related to students' liability. Therefore, this study evaluated the effect of High Fidelity Simulation airway management training program on nursing students' performance, satisfaction, and self-confidence in Palestine. A pre-post-test control group was applied through the recruitment of a 154 nursing students from Arab American University Palestine. The sample distributed randomly to experimental and control groups. Data were collected using the American Heart Association airway management performance checklist and the learner satisfaction and self-confidence for Learning (LSSCL) Questionnaires. Findings showed that there were no significant differences of airway management performance, satisfaction, and self-confidence scores between both groups at pre-test. On the contrary, there were significant differences between both groups of airway management performance, satisfaction, and self-confidence at post-test. Thus, High-fidelity simulation (HFS) was found to be an effective tool to provide a safe and effective learning environment for nursing students, consequently improving their airway management performance and increasing their satisfaction and self-confidence. This study supports the need to integrate HFS into the nursing curriculum.

Background: The research interview is a valuable tool for collecting data in qualitative studies. All education researchers, whether novice or experienced, need to prepare carefully when planning to conduct interviews. Thus, how best to assist newer researchers to acquire interviewing skills for the purpose of collecting research data remains an important question. Mock, or 'practice', interviews can offer opportunities for such researchers to become more familiar with the methodological processes involved in interviewing, so that they can start to build necessary skills prior to carrying out interviews in the field. Purpose: Set in the context of nursing education, the research sought to describe the use of an online mock interview protocol as a researcher's training tool and consider key lessons learnt from this process. Method: In this qualitative study, seven purposively selected participants from a university in Australia were involved in a mock interview process. A video conferencing platform was utilised as an online environment for the interviews. Findings: Analysis of the procedure indicated the ways in which carrying out the mock interviews supported the researcher's preparation for qualitative interviewing and facilitated the development of good practice for conducting interviews in a virtual environment. Processes included undertaking a rigorous training needs analysis, optimising technical preparedness for the mock interviews, and practising how to ask interview questions in a suitable way. The experience involved learning lessons around self-evaluation, mental readiness, retrieving rich data and troubleshooting technical issues. Conclusion: The research highlights the extent to which successful interviewing depends on the quality of pre-interview preparation on the part of the researcher. Conducting mock interviews prior to online qualitative research could help support less experienced researchers in areas of research including nursing education. Integration of mock interview protocols into curricula for educational research purposes could help provide research students with opportunities to strengthen their skills.

Simulation has an ever-increasing role in health professions' education, however there is limited evidence on the transferability of learning to practice. In this study, a pragmatic approach using a convergent mixed methods design explored the use of a virtual high-fidelity Computed Tomography (CT) simulator in preparing students for clinical placement. Specifically, it aimed to describe radiography students' perceptions of their confidence and ability to transfer their learning from a virtual CT simulator into clinical practice. Radiography students (n = 60) were surveyed on their perceptions of how using the CT simulator (5 hours) prepared them for clinical placement. Four themes emerged from the data analysis: Simulation platform, Teaching and learning activities, Confidence and Transferability. Key findings demonstrated a significant link between the students' perceptions of their ability to transfer their learning into practice and the CT scanner platform they used on clinical placement. Students who used a CT scanner on placement that was similar to the simulator were more likely to believe their learning was transferable to practice compared with those who did not use a similar scanner on placement. However, confidence gained was not dependent on the scanner used on clinical placement. Discussion on the metacognitive processes linked to these findings are included.

Learner agency, often understood in terms of self-direction and negotiated engagement, is considered important in technical and vocational education and training (TVET). Yet nurturing and supporting agency is resource-intensive and difficult. In this paper, we consider learner agency for online TVET--a setting where content delivery models can be experienced as stultifying. We document the design and evaluation of specific task designs using the method and principle of double-stimulation, where prompts help participants to reconceptualise problematic situations and break out of conflicting motives. We draw on data from a research-intervention with adult learners undertaking a facilities management diploma online, while working. We explore how, across nine online workshops, task designs engendered transformative agency: the ability to collaboratively diverge from instructional intent, question dilemmatic conditions, and propose and enact change. We claim (1) that specific double-stimulation tasks encouraged participants to engage in understanding institutional practice, exposing conflict, and enacting change; (2) that participants came to view their own problematic conditions as stimuli for resistance, criticism, and development; and (3) that online resources were crucial for highlighting evidence of failure and learners' potential roles in change. Our findings are of importance to help learners set and evaluate their own learning agendas.

With recent advances in artificial intelligence (AI), machine learning (ML) has been identified as particularly useful for organizations seeking to create value from data. However, as ML is commonly associated with technical professions, such as computer science and engineering, incorporating training in the use of ML into non-technical educational programs, such as social sciences courses, is challenging. Here, we present an approach to address this challenge by using no-code AI in a course for university students with diverse educational backgrounds. This approach was tested in an empirical, case-based educational setting, in which students engaged in data collection and trained ML models using a no-code AI platform. In addition, a framework consisting of five principles of instruction (problem-centered learning, activation, demonstration, application, and integration) was applied. This paper contributes to the literature on IS education by providing information for instructors on how to incorporate no-code AI in their courses and insights into the benefits and challenges of using no-code AI tools to support the ML workflow in educational settings.

This article reports upon roleplaying simulations used for teaching and learning subjects in the field of international relations. It is based upon five years of experience gained by the author while teaching in RUDN University in Moscow, Russia, and Webster University in Tashkent, Uzbekistan. The article aims to unravel both the value of roleplaying simulations in teaching international relations subjects, and how this practice correlates with the regional specificity of students in post-Soviet space. The main hypothesis advanced is that due to the peculiarities of traditional higher education processes in the countries of post-Soviet space, and the unique national traits of the students involved, the application of roleplaying simulations has provided additional motivation for the students. It has raised their level of immersion in their education, partially alleviating concerns about under-performance and low interest in the classroom activities among most of the students. The author argues that besides such scenario-based learning being in general a more pro-active way of learning, leading to better performance and overall facilitating competences formation process, it is specifically effective in learning environments that can be characterised as conservative and reactive, which for the most part is the case for post-Soviet space higher education.

Employment interviews can be challenging for autistic people. In addition to standard interview procedures often being inaccessible, features associated with autism (including atypical emotional expression, eye-contact, and gestures) may lead to negative first impressions. Research using vignettes and 'thin slices' of communication indicate that perceptions of an autistic individual can be improved when their autism diagnosis is disclosed. Here, we examined the impact of diagnostic disclosure on judgements of autistic adults undergoing an entire mock employment interview. A total of 119 participants (known as 'raters') watched a video of an autistic candidate's mock employment interview, after which they provided overall impressions of the candidate on factors such as confidence, motivation, and knowledgeability. Raters were either (1) unaware of the interviewee's diagnosis, (2) aware of their diagnosis, or (3) aware of their diagnosis and provided with information about autism. Results indicate that diagnostic disclosure improved perceptions of autistic candidates across all dimensions compared to when raters were unaware of their diagnosis. The provision of additional information about the diagnosis did not further improve ratings. The findings have important implications for employers and autistic people, who should consider the potential impact of diagnostic disclosure prior to interviewing for a role.

We present a bias-adjusted three-step estimation approach for multilevel latent class models (LC) with covariates. The proposed approach involves (1) fitting a single-level measurement model while ignoring the multilevel structure, (2) assigning units to latent classes, and (3) fitting the multilevel model with the covariates while controlling for measurement error introduced in the second step. Simulation studies and an empirical example show that the three-step method is a legitimate modeling option compared to the existing one-step and two-step methods.

Active learning approaches to biology teaching, including simulation-based activities, are known to enhance student learning, especially of higher-order skills; nonetheless, there are still many open questions about what features of an activity promote optimal learning. Here we designed three versions of a simulation-based tutorial called Understanding Experimental Design that asks students to design experiments and collect data to test their hypotheses. The three versions vary the experimental design task along the axes of feedback and constraint, where constraint measures how much choice students have in performing a task. Using a variety of assessments, we ask whether each of those features affects student learning of experimental design. We find that feedback has a direct positive effect on learning. We further find that small changes in constraint have only subtle and mostly indirect effects on learning. This work suggests that designers of tools for teaching higher-order skills should strive to include feedback to increase impact and may feel freer to vary the degree of constraint within a range to optimize for other features such as the ability to provide immediate feedback and time-on-task.

A frequent criticism of exploratory factor analysis (EFA) is that it does not allow correlated residuals to be modelled, while they can be routinely specified in the confirmatory (CFA) model. In this article, we propose an EFA approach in which both the common factor solution and the residual matrix are unrestricted (i.e., the correlated residuals need not be specified a priori). The estimation procedures are two-stage and based on the unweighted least squares principle. Procedures for judging the solution appropriateness (including goodness of fit) are also proposed. The simulation studies and illustrative example suggest that the approach works quite well Although the proposal is based on existing results, most of the developments can be considered to be new contributions, and are expected to be particularly useful in the earlier stages of item calibration. The whole procedure has been implemented in both R language and a well-known non-commercial EFA program.

Penalized structural equation models (PSEM) is a new powerful estimation technique that can be used to tackle a variety of difficult structural estimation problems that can not be handled with previously developed methods. In this paper we describe the PSEM framework and illustrate the quality of the method with simulation studies. Maximum-likelihood and weighted least squares PSEM estimation is discussed for SEM models with continuous and categorical variables. We show that traditional EFA, multiple group alignment (MGA), and Bayesian SEM (BSEM) are examples of PSEM. The PSEM framework also extends standard SEM models with the possibility to structurally align various model parameters. Exploratory latent growth models, also referred to as Tuckerized curve models, can also be estimated in the PSEM framework and are illustrated here with simulation studies and an empirical example.

Measurement invariance (MI) is required for validly comparing latent constructs measured by multiple ordinal self-report items. Non-invariances may occur when disregarding (group differences in) an acquiescence response style (ARS; an agreeing tendency regardless of item content). If non-invariance results solely from neglecting ARS, one should not worry about scale inequivalences but model the ARS instead. In a simulation study, we investigated the effect of ARS on MI testing, both when including ARS as a factor in the measurement model or not. For (semi-) balanced scales, disregarding a large ARS resulted in non-invariance already at the configural level. This was resolved by including an ARS factor for all groups. For unbalanced scales, disregarding ARS did not affect MI testing, and including an ARS factor often resulted in non-convergence. Implications and recommendations for applied research are discussed.

Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets effectively contain missing values for all participants in those datasets. Furthermore, data pooling typically leads to a mix of continuous and categorical items with nonnormal multivariate distributions. We investigated two popular approaches to handle missing data in this context: (1) applying direct maximum likelihood by treating data as continuous (con-ML), and (2) applying categorical least squares using a polychoric correlation matrix computed from pairwise deletion (cat-LS). These approaches are available for free and relatively straightforward for empirical researchers to implement. Through simulation studies with confirmatory factor analysis and latent mediation analysis, we found cat-LS to be unsuitable for pooled data analysis, whereas con-ML yielded acceptable performance for the estimation of latent path coefficients barring severe nonnormality.

The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters' opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average temporal flexibility features at 21 Brodmann areas (BA) and four band frequencies. Twelve eye-tracking features were extracted and used to develop linear random intercept models for performance evaluation and multivariate linear regression models for the evaluation of the learning rate. Results showed that subject-wise standardization of features improved the R[superscript 2] of the models. Average pupil diameter and rate of saccade were associated with performance in the Tubes task (multivariate analysis; p-value = 0.01 and p-value = 0.04, respectively). Entropy of pupil diameter was associated with performance in Dots and Needles task (multivariate analysis; p-value = 0.01). Average temporal flexibility and search information in several BAs and band frequencies were associated with performance and rate of learning. The models may be used to objectify performance and learning rate evaluation in RAS once validated with a broader sample size and tasks.

Sensemaking is an important way of learning and engaging in science. Research has shown that sensemaking activities, such as questioning, hypothesizing, and model building, are pivotal in developing critical thinking and problem-solving skills in science education. This paper investigates the role of computational simulations in facilitating sensemaking in chemistry education, specifically examining how these simulations can sustain the sensemaking process. Through a detailed case study in a physical chemistry course, we explore the interplay between students' real-world experiences, theoretical knowledge, and computational simulations. This analysis reveals that computational simulations, by providing interactive and visual representations of chemical phenomena, can create a conducive environment for sensemaking, where students actively engage in exploring and resolving conceptual uncertainties. Based on these results, we argue that computational tools, when effectively integrated into educational settings, can facilitate sensemaking in science education.

Social work education has increasingly integrated simulated learning, yet there is a scarcity of quantitative syntheses on its effects. This systematic review and meta-analysis examines the effectiveness of simulated learning in social work education based on outcomes from using either live standardized clients (SCs) or virtual clients (VCs). The study indicates a significant positive effect of simulated learning, regardless of client type (SMD = 0.95; 95% CI = 0.61 to 1.29). Subgroup analyses suggest that such learning is more effective with Masters of Social Work students, when utilizing VCs, for mental health assessment learning, in smaller classes, and in recent studies. These findings enhance the evidence base supporting the use of simulated learning in social work education.

The all-encompassing term "Medical Spanish" references the Spanish used by healthcare professionals. Medical Spanish courses have gained traction as the Spanish-speaking population in the U.S. continues to grow. Consequently, demand for courses in Medical Spanish and other areas of Spanish for Specific Purposes (SSP), a subfield of Languages for Specific Purposes (LSP), has grown. As SSP discipline grows, there is a need to understand curricular decisions and implementation, assessment, and learning activities for Medical Spanish courses. One area of Medical Spanish that has not been widely explored is that of assessment. This article discusses assessment challenges in teaching Medical Spanish and offers tips for instructors of these courses, including two learning activities--community service-learning and role playing--that can serve as useful assessment tools.

Engaging children in argumentation-focused discussions is essential to helping them collaboratively make sense of scientific phenomena. To support this effort, teachers must listen and be responsive to students' ideas to move the discussion forward with the goal of reaching consensus. Given the complexity of this ambitious science teaching practice, in lieu of traditional field experiences, online simulated teaching experiences provide opportunities for preservice teachers to practice implementing these strategies in a low-risk, high-support environment. Limited research has explored elementary preservice teachers' responsiveness while navigating an argumentation-focused discussion, particularly in an online simulated teaching experience. The purpose of this study was to examine preservice teachers' responsiveness to students' ideas while eliciting students' initial constructed arguments and encouraging argument critique in two online simulated teaching experiences. Findings showed that preservice teachers' responsiveness to students' ideas was high in both online simulated teaching experiences when asking students to share evidence as well as engage in critique. However, their responsiveness varied when prompting for reasoning and was often low when eliciting students' claims. These findings provide empirical evidence that such online simulated teaching experiences can be used as productive spaces for PSTs to practice being responsive to students' ideas during argumentation-focused discussions.

Generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexibility honed over the decade, GSCA always defines every component as a linear function of observed variables, which can be less optimal when observed variables for a component are nonlinearly related, often reducing the component's predictive power. To address this issue, we combine deep learning and GSCA into a single framework to allow a component to be a nonlinear function of observed variables without specifying the exact functional form in advance. This new method, termed deep learning generalized structured component analysis (DL-GSCA), aims to maximize the predictive power of components while their directed or undirected network remains interpretable. Our real and simulated data analyses show that DL-GSCA produces components with greater predictive power than those from GSCA in the presence of nonlinear associations between observed variables per component.

A standard assumption of latent class (LC) analysis is conditional independence, that is the items of the LC are independent of the covariates given the LCs. Several approaches have been proposed for identifying violations of this assumption. The recently proposed likelihood ratio approach is compared to residual statistics (bivariate residuals [BVR] and expected parameter change [EPC] statistics) for identifying nonuniform direct effect of covariates on the items of the LC model. The simulation study results show that the likelihood ratio (LR) test correctly identifies direct effects more often than the BVR statistics, showing comparable results to the EPC statistic in many situations- this at the price of having also a higher false positive rate than BVR. A real data example illustrates the use of the three procedures. Overall the combined use of residual statistics and LR testing is recommended for applied research.

Within the frequentist structural equation modeling (SEM) framework, adjudicating model quality through measures of fit has been an active area of methodological research. Complicating this conversation is research revealing that a higher quality measurement portion of a SEM can result in poorer estimates of overall model fit than lower quality measurement models, given the same structural misspecifications. Through population analysis and Monte Carlo simulation, we extend the earlier research to recently developed Bayesian SEM measures of fit to evaluate whether these indices are susceptible to the same reliability paradox, in the context of using both uninformative and informative priors. Our results show that the reliability paradox occurs for RMSEA, and to some extent, gamma-hat and PPP (measures of absolute fit); but not CFI or TLI (measures of relative fit), across Bayesian (MCMC) and frequentist (maximum likelihood) SEM frameworks alike. Taken together, these findings indicate that the behavior of these newly adapted Bayesian fit indices map closely to their frequentist analogs. Implications for their utility in identifying incorrectly specified models are discussed.

Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact of temporal misalignment on parameter estimation were investigated in a simulation study, which showed that temporal misalignment led to incomparable cross-lagged effects between variables. Then, two solutions, model adjustment and data interpolation, were proposed, and their performance was compared with those of the naive estimation which blindly treating temporally misaligned data as aligned. The simulation results supported the effectiveness of the model adjustment method over the other two methods. Finally, all three methods were applied to two empirical data collected by daily diaries and empirical sampling method, and recommendations were made for collecting and analyzing intensive longitudinal data.

Precisely estimating factor scores is challenging, especially when models are mis-specified. Stemming from network analysis, centrality measures offer an alternative approach to estimating the scores. Using a two-fold simulation design with varying availability of a priori theoretical knowledge, this study implemented hybrid centrality to estimate factor scores and compared the performance with traditional methods, where both proper and improper specifications were considered. In supervised scenarios, network scores using hybrid centrality performed similarly to CFA scores for correctly specified models. The network scores were more accurate when sample sizes were small or test reliability was low, and also demonstrated higher robustness under misspecification. In the second fold, network scores performed better than EFA scores when structure knowledge was unavailable, as the LoGo algorithm of network analysis demonstrated the best performance in retaining the correct factor structure. The results suggest that network analysis can be a better choice for factor score estimation under various conditions.

Factor Score Regression (FSR) is increasingly employed as an alternative to structural equation modeling (SEM) in small samples. Despite its popularity in psychology, the performance of FSR in multigroup models with small samples remains relatively unknown. The goal of this study was to examine the performance of FSR, namely Croon's correction and the bias avoiding method, for multigroup models with small samples and compare the methods to SEM. We conducted two simulation studies to evaluate how the sample size, proportion of invariant items, reliability, number of indicators, and measurement model misspecifications affect conclusions about the structural relationships in multigroup models. Additionally, we extended the methods to a multigroup actor-partner interdependence model. Results suggest that Croon's correction generally outperforms conventional SEM and the bias avoiding method in terms of bias, efficiency, Type I error, and coverage, especially in more complex multigroup models and under difficult estimation conditions.

Causal mediation analysis is a popular approach for investigating whether the effect of an exposure on an outcome is through a mediator to better understand the underlying causal mechanism. In recent literature, mediation analysis with multiple mediators has been proposed for continuous and dichotomous outcomes. In contrast, methods for mediation analysis for an ordinal outcome are still underdeveloped. In this paper, we first review mediation analysis methods with a continuous mediator for an ordinal outcome and then develop mediation analysis with a binary mediator for an ordinal outcome. We further consider multiple mediators for an ordinal outcome in the counterfactual framework and provide identification assumptions for identifying the mediation effects. Under the identification assumptions, we propose a regression-based method to estimate the mediation effects through multiple mediators while allowing the presence of exposure-mediator interactions. The closed-form expressions of mediation effects are also obtained for three scenarios: multiple continuous mediators, multiple binary mediators, and multiple mixed mediators. We conduct simulation studies to assess the finite sample performance of our new methods and present the biases, standard errors, and confidence intervals to demonstrate that our proposed estimators perform well in a wide range of practical settings. Finally, we apply our proposed methods to assess the mediation effects of candidate DNA methylation CpG sites in the causal pathway from socioeconomic index to body mass index.

A recent review found that 11% of published factor models are hierarchical models with second-order factors. However, dedicated recommendations for evaluating hierarchical model fit have yet to emerge. Traditional benchmarks like RMSEA <0.06 or CFI >0.95 are often consulted, but they were never intended to generalize to hierarchical models. Through simulation, we show that traditional benchmarks perform poorly at identifying misspecification in hierarchical models. This corroborates previous studies showing that traditional benchmarks do not maintain optimal sensitivity to misspecification as model characteristics deviate from those used to derive the benchmarks. Instead, we propose a hierarchical extension to the dynamic fit index (DFI) framework, which automates custom simulations to derive cutoffs with optimal sensitivity for specific model characteristics. In simulations to evaluate performance, results showed that the hierarchical DFI extension routinely exceeded 95% classification accuracy and 90% sensitivity to misspecification whereas traditional benchmarks applied to hierarchical models rarely exceeded 50% classification accuracy and 20% sensitivity.

In the post-pandemic era, distance education and training modes without space constraints are gradually becoming a new trend in training. Game-based training combined with situational simulations is a potential approach. This study used a simulation company scenario and a decision-making analysis task of promotion assessment as the game context, and proposed an innovative game mechanism integrating the simulation situation scenario and the interactive contextual clue scaffolding as a guide to enhance learning effectiveness. This study used the "Gather Town" platform to design a simulation office scenario, and "Google Forms" to design interactive contextual clue scaffolding. This study conducted empirical research to evaluate this mechanism, with a total of 60 participants divided into an experimental group that conducted game activities and a control group that was only provided with document files as scaffolding for comparative analysis. The results of the study revealed that the proposed exploration guidance mechanism of integrating immersive scenes and interactive contextual clue scaffolding could allow learners to experience a high level of game fidelity and increase the discovery rate and usefulness of the scaffolding. It also reduced learning anxiety, maintained learning motivation and high flow, and resulted in significantly higher learning effectiveness than the control group approach.

Regression factor score predictors have the maximum factor score determinacy, that is, the maximum correlation with the corresponding factor, but they do not have the same inter-correlations as the factors. As it might be useful to compute factor score predictors that have the same inter-correlations as the factors, correlation-preserving factor score predictors have been proposed. However, correlation-preserving factor score predictors have smaller correlations with the corresponding factors (factor score determinacy) than regression factor score predictors. Thus, higher factor score determinacy goes along with bias of the inter-correlations and unbiased inter-correlations go along with lower factor score determinacy. The aim of the present study was therefore to investigate the size and conditions of the trade-off between factor score determinacy and bias of inter-correlations by means of algebraic considerations and a simulation study. It turns out that under several conditions very small gains of factor score determinacy of the regression factor score predictor go along with a large bias of inter-correlations. Instead of using the regression factor score predictor by default, it is proposed to check whether substantial bias of inter-correlations can be avoided without substantial loss of factor score determinacy using a correlation-preserving factor score predictor. A syntax that allows to compute correlation-preserving factor score predictors from regression factor score predictors, and to compare factor score determinacy and inter-correlations of the factor score predictors is given in the Appendix.

Although the generalized deterministic inputs, noisy "and" gate model (G-DINA; de la Torre, 2011) is a general cognitive diagnosis model (CDM), it does not account for the heterogeneity that is rooted from the existing latent groups in the population of examinees. To address this, this study proposes the mixture G-DINA model, a CDM that incorporates the G-DINA model within the finite mixture modeling framework. An expectation--maximization algorithm is developed to estimate the mixture G-DINA model. To determine the viability of the proposed model, an extensive simulation study is conducted to examine the parameter recovery performance, model fit, and correct classification rates. Responses to a reading comprehension assessment were analyzed to further demonstrate the capability of the proposed model.

The questionnaire method has always been an important research method in psychology. The increasing prevalence of multidimensional trait measures in psychological research has led researchers to use longer questionnaires. However, questionnaires that are too long will inevitably reduce the quality of the completed questionnaires and the efficiency of collection. Computer adaptive testing (CAT) can be used to reduce the test length while preserving the measurement accuracy. However, it is more often used in aptitude testing and involves a large number of parametric assumptions. Applying CAT to psychological questionnaires often requires question-specific model design and preexperimentation. The present article proposes a nonparametric and item response theory (IRT)-independent CAT algorithm. The new algorithm is simple and highly generalizable. It can be quickly used in a variety of questionnaires and tests without being limited by theoretical assumptions in different research areas. Simulation and empirical studies were conducted to demonstrate the validity of the new algorithm in aptitude tests and personality measures.

Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter--a parameter akin to the correlation root mean squared residual. The misspecification parameter can be interpreted on its own terms as a measure of absolute model fit and allows for comparing different models fit to the same data. By estimating the degree of misspecification simultaneously with structural parameters, the uncertainty about structural parameters reflects the degree of model misspecification. This results in a model that produces more reliable inference than extant Bayesian structural equation modeling. In addition, the approach estimates the residual covariance matrix that can be the basis for diagnosing misspecifications and updating a hypothesized model. These features are confirmed using simulation studies. Demonstrations with a variety of real-world examples show additional properties of the approach.

Social media is rapidly growing and evolving from the introduction of new platforms to platform-specific innovations. The dynamic nature of social media makes it difficult for even practitioners to keep up; therefore, the gap between academia and industry continues to grow. This gap presents significant challenges for marketing educators to keep course content current so students are ready for the workforce. To identify which types of social media pedagogical assessments best meet industry needs and make recommendations for bridging this gap, this article uses a three-study approach consisting of content analysis of job descriptions, practitioner interviews, and student perceptions of two commonly used pedagogical assessments: simulations and client-based projects. Results show the critical need for social media meta-skills and technical skills, with client-based projects significantly improving meta-skill acquisition by undergraduate students compared with simulations alone. Practical implications for helping educators improve market-ready graduates for entry-level jobs with a social media component are discussed.

There is substantial evidence that incorporating interactive environments for game-based instruction has a significant potential to support the development of computational thinking and programming skills in primary education students. However, it is not clear whether a simulation game (SG) with different user interface elements, created via three-dimensional (3D) virtual worlds and visual programming environments to project various problem-solving exercises in a simulated reality, can significantly influence students to think and practice "computationally" their solution plans into code. The current study aims to identify any possible added value of each instructional approach by measuring students' game experience and learning performance. This quasi-experimental study involved ninety participants (n = 90) aged between 10 and 11 years that consisted of two comparison conditions. The experimental group (n = 45) received training using OpenSimulator in combination with Scratch4SL, whereas the control group (n = 45) was trained using Scratch. The findings indicate considerable differences in students' game experience and satisfaction, but no statistically significant difference in their learning performance and knowledge gain was identified. This study provides several design implications for user interface and game elements to inform educational practitioners and instructors about the benefits that each approach can offer students for better knowledge acquisition and deeper disciplinary understanding.

Purpose: The previous coronavirus disease 2019 (COVID-19) pandemic has forced pre-service English teachers to conduct their teaching simulation virtually. In the context of online teaching, social presence is vital for effective learning. This skill is perceived as a way to humanize the online learning process. This article explores how social presence is represented in the pre-service teacher's synchronous online classroom and how the development of social presence enables the pre-service teachers to develop their students' well-being and thus humanize online classrooms. Design/methodology/approach: The current study draws upon case study research. The data were collected from (1) synchronous online teaching simulation recordings of pre-service English teachers from the Language Classroom Management course and (2) interview transcripts from three participants. Both data sources were transcribed, coded and analyzed. A two-step coding scheme for pre-service English teachers' social presence was adapted from the Community of Inquiry (CoI) coding template. Findings: This study reports that all the three social presence categories (i.e. cohesive, affective and interactive) emerged from their online teaching simulation. In addition, the development of social presence to humanize online classrooms from the pre-service teachers was found in these stages: (1) having a role model for teaching, (2) showing respect and being friendly and (3) adjusting the language of instructions to the students' level. Research limitations/implications: This study has been limited to the pre-service teachers in one university only. Therefore, this needs to be tested in wider settings and contexts. Practical implications: Theoretically, this study has added students' well-being as the element that connects social presence to the humanizing online classroom. Meanwhile, practically, in relation to teacher education, this paper also recommends the teaching stages leading to a more humanized online learning. Originality/value: Current studies tend to describe social presence in experienced teachers' asynchronous online classrooms. Rarely did the studies explore social presence as practiced by pre-service teachers in the context of synchronous videoconferencing class. Furthermore, there has also been limited research connecting social presence with humanized classrooms.

Unintentional injuries are one of the leading causes of morbidity and mortality among children with intellectual and developmental disabilities (IDD). First aid training involves teaching critical first aid skills, some of which are designed to treat unintentional injuries. To date, no study has (a) evaluated the effects of a video-prompting procedure to teach first aid skills to children with IDD or (b) attempted to teach these skills to children by using a telehealth delivery format. We used a concurrent multiple-baseline-across-skills design to evaluate the efficacy of a video-prompting procedure via telehealth to teach five children with IDD to perform first aid on themselves for insect stings, minor cuts, and minor burns under simulated conditions. For all participants, our procedure produced large improvements that maintained for a minimum of 4 weeks. Furthermore, the effects of the training generalized to novel confederates for all participants, and these effects maintained for a minimum of 4 weeks.

Many universities sponsor student-oriented transit services that could reduce alcohol-induced risks but only if services adequately anticipate and adapt to student needs. Human choice data offer an optimal foundation for planning and executing late-night transit services. In this simulated choice experiment, respondents opted to either (a) wait an escalating delay for a free university-sponsored "safe" option, (b) pay an escalating fee for an on-demand rideshare service, or (c) pick a free, immediately available "unsafe" option (e.g., ride with an alcohol-impaired driver). Behavioral-economic nonlinear models of averaged-choice data describe preference across arrangements. Best-fit metrics indicate adequate sensitivity to contextual factors (i.e., wait time, preceding late-night activity). At short delays, students preferred the free transit option. As delays extend beyond 30 min, most students preferred competing alternatives. These data depict a policy-relevant delay threshold to better safeguard undergraduate student safety.

Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time, which overlooks the dynamic nature of mediation effect. To address this issue, we propose dynamic mediation models that can capture the dynamic nature of the mediation effect. Specifically, we model the path parameters of mediation models as auto-regressive (AR) processes of time that can vary over time. Additionally, we define the mediation effect under the potential outcome framework, and examine its identification and causal interpretation. Bayesian methods utilizing Gibbs sampling are adopted to estimate unknown parameters in the proposed dynamic mediation models. We further evaluate our proposed models and methods through extensive simulations and illustrate their application through a real data application.

This simulation provides public administration, public policy, and management students and practitioners with an immersive disaster scenario. The table-top exercise provides an example of how municipal leaders can prepare for disaster scenarios and the importance of embedding social equity into disaster response. Additionally, it provides an example of how to embed social equity in the planning and development of disaster response planning and preparation. This classroom-based group simulation takes approximately 90 minutes (20 minutes for preparation, 50 minutes for the simulation case study, and 20 minutes for discussion) and also includes barriers to disaster relief experienced by minority populations. The case is organized into three sections and concludes with teaching notes and discussion questions.

A number of familiarization trials are needed for reliable measurement, particularly for inexperienced subjects. Researchers have studied and developed familiarization protocols that vary by exercise and study population. The pace of familiarization and fatigue may be an individual-level characteristic, so a population-level protocol may not fit all subjects. In this article, the authors view this practical challenge as a statistical problem. We apply piecewise linear regression and model averaging for estimating the true performance level of a subject after familiarization and before fatigue. This statistical method does not require an experimenter to determine when a study participant is familiarized and fatigued. Simulation studies demonstrate that this statistical approach provides a more reliable measurement than the best-fit model. An online interactive applet is provided for those who are not familiar with statistical programming. Detailed instructions for the applet and case study are provided for demonstration.

Scholars of teaching and learning frequently examine whether simulations promote content knowledge and engagement with course material. But many educators use simulations to promote additional goals. This article suggests that designers of political simulations often pursue four ends: "knowledge, engagement, skills, and empathy (KESE)." The article discusses the popularity of simulations and the reasons that political science educators use them. It establishes the KESE framework and pays particular attention to the ways that simulations might promote soft skills (e.g., public speaking, negotiation) and/or empathy. Then, to investigate the extent to which simulations promote KESE goals, the article examines several years of pre- and post-surveys of students who did and did not participate in the Mid-Atlantic European Union Simulation. It finds that the KESE framework and intentional, systematic assessment of all four KESE components can help to capture the full array of simulations' potential benefits.

The purpose of this study is to demonstrate clustering methods within a functional data analysis (FDA) framework for identifying subgroups of individuals that may be exhibiting categories of misfit. Person response functions (PRFs) estimated within a FDA framework (FDA-PRFs) provide graphical displays that can aid in the identification of persons that have responded unexpectedly to items comprising an achievement test. Typical person fit statistics are also useful for detecting unexpected response patterns, but they do not provide insight into the underlying behaviors responsible for those responses. However, different responding behaviors tend to produce FDA-PRFs of different shapes, and may provide additional information regarding the reasons for misfit. Functional clustering methods are useful for categorizing respondents into subgroups based on the shapes of their FDA-PRFs. In this study, a small simulation illustrates the potential of clustering FDA-PRFs for identifying persons displaying common types of responding behaviors. The methodology is also applied to data from a high school biology assessment and a mathematics achievement test. Clustering FDA-PRFs offers a promising methodology for operationalizing person fit evaluations in large-scale assessments, and may be a valuable step in person fit assessment when used in conjunction with traditional indices of psychometric quality.

Background: Most people with stroke exhibit a variety of impairments that need to be addressed by a multidisciplinary team. Communication and swallowing disorders are common and should be screened very early. To guarantee a patient-centred approach, all patients, even those with speech and language disorders, must be actively engaged in the healthcare process. Effective communication is essential to success in many of the needed interventions. However, healthcare professionals often do not receive formal training in communicating with these patients, thus increasing the risk of preventable adverse events. Aims: To describe the design, implementation and evaluation of a post-acute stroke multidisciplinary team training using patient actors in a simulation approach. Methods & Procedures: A 2-day course focused on the transdisciplinary knowledge related to communication and swallowing that all members of the multidisciplinary stroke team should acquire was implemented. A case-based learning methodology used simulation and resorting to patients' actors. Learning outcomes were evaluated by comparing the results obtained in two knowledge tests, one for each topic, which participants performed before and after each day course. Reaction to the training was gathered concerning the content, teacher quality and course organization. The follow-up was performed 6 months later to assess training skills transfer to the workplace environment. Outcomes & Results: All the participants considered that the programme objectives were relevant or truly relevant and revealed that the programme's dynamic, rhythm and scenarios set were excellent. After the end of the programme, both communication and swallowing knowledge increased. Most participants had the opportunity to employ the acquired training skills in their work environment. The main barriers identified to implementing these skills were the 'need for additional training', the 'lack of time' or 'the lack of opportunities'. Conclusions & Implications: Simulation is a central method to increase and improve health professionals' skills when intervening with stroke patients. Using simulation with patient actors allows flexibility and diversification of clinical situations under analysis, which can provide a multiplier effect of reflection and learning. The implemented training achieved its objectives.

Purpose: This paper aims to investigate the effects of four types of cyber entrepreneurship courses on entrepreneurial self-efficacy (ESE) and intention. It is based on Social Cognitive Theory and Regulatory Focus Theory, which takes Chinese college students as the research objects. Design/methodology/approach: Approximately 101 senior business school students who had participated in all cyber entrepreneurship courses were selected to complete the horizontal analysis. Approximately 317 students from different grades who had participated in different cyber entrepreneurship courses were selected for the multi-group analysis (MGA) for the longitudinal comparison. Findings: The results show that different cyber entrepreneurship courses may trigger Chinese students' positive or defensive mechanisms for cyber entrepreneurship and affect their ESE and intentions. The impact of cyber entrepreneurship theoretical courses on Chinese students' entrepreneurial intentions is not significant, and self-efficacy has no mediating effect between cyber entrepreneurial theory courses and intentions. Originality/value: This study helps teachers and policymakers to better understand the impacts of cyber entrepreneurship courses and to adopt proper teaching contents and methods for diversity goals. It also has reference value in theoretical and practical perspectives.

Low test-taking effort of examinees has been considered a source of construct-irrelevant variance in item response modeling, leading to serious consequences on parameter estimation. This study aims to investigate how non-effortful response (NER) influences the estimation of item and person parameters in item-pool scale linking (IPSL) and whether differential NER for two groups has a different effect on parameter estimation under the equivalent and non-equivalent group designs. Three simulation studies and an empirical study were conducted to answer these research questions. Results show that NER on items in a certain item set (e.g. common new items, exclusive new items) have a dominant negative effect on parameter estimation of items from the same item set, while NER on items from the item pool would seriously impact the accuracy of ability parameters. Discussions and recommendations for item set orders are provided.

Researchers who use measurement models for evaluation purposes often select models with stringent requirements, such as Rasch models, which are parametric. Mokken Scale Analysis (MSA) offers a theory-driven nonparametric modeling approach that may be more appropriate for some measurement applications. Researchers have discussed using MSA as a preliminary procedure with which to evaluate data quality before applying a parametric model. However, the literature includes only a few examples in which researchers have integrated MSA techniques with parametric models throughout the analytic procedure. We consider a systematic approach for integrating results from nonparametric MSA techniques with parametric measurement models to evaluate measurement quality and construct scales with useful measurement properties. We use real-data illustrations and a simulation study to demonstrate and systematically explore our approach. We discuss implications for research and practice.

This qualitative study aims to examine foundation year Master of Social Work (MSW) students' experiences with the objective structured clinical examination (OSCE) using a holistic framework. Study participants included 64 MSW students who completed a 15-minute virtual OSCE with standardized patients and expert observers followed by a five minute debrief. In-depth student narratives about the experience were transcribed and analyzed thematically. Key themes emerged highlighting the value of practicing clinical skills, receiving immediate constructive feedback and honing skills in managing nervousness and developing confidence. Study implications include the critical need for MSW students as developing clinicians to rehearse clinical skills and receive feedback and the benefits of using the OSCE to expand field- and classroom-based learning.

Background: Objective Structured Clinical Examinations (OSCEs) are a simulation-based assessment tool used extensively in medical education for evaluating clinical competence. OSCEs are widely regarded as more valid, reliable, and valuable compared to traditional assessment measures, and are now emerging within professional psychology training programs. While there is a lack of findings related to the quality of OSCEs in published psychology literature, psychometric properties can be inferred by investigating implementation. Accordingly, the current review assessed implementation of OSCEs within psychology programs against a set of Quality Assurance Guidelines (QAGs). Methods: A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) recommendations. Electronic databases including ProQuest Psychology, PsycArticles, Psychology and Behavioural Sciences Collection, PsycInfo and key indexing databases such as Scopus, ProQuest, and Web of Science were used to identify relevant articles. Twelve full-text articles met all inclusion criteria and were included in the review. Results: There was considerable heterogeneity in the quality of studies and reporting of OSCE data. Implementation of OSCEs against QAGs revealed overall adherence to be "Fair." Conclusion: The current review consolidated what is known on psychometric quality of OSCEs within psychology programs. A further need for quantitative evidence on psychometric soundness of OSCEs within psychology training is highlighted. Furthermore, it is recommended that future training programs implement and report OSCEs in accordance with standardized guidelines.

A pretest-posttest control group design with switching replications was used to examine the effectiveness of a one-time, 75-minute behavioral skills training (BST) package. Fifty pre-service elementary general educators enrolled in a classroom management course were shown a video depicting challenging behaviors and were asked to collect antecedent-behavior-consequence (ABC) data. Using the data collected, participants from two instructional groups were asked to hypothesize the function of the problem behaviors. Results suggest a statistically significant increase in correct responses for both instructional groups from pretest to posttest following BST. A series of ANCOVAS suggested that, when controlling for initial pretest performance, BST significantly improved correct responding in posttest when compared to performance on the second pretest. However, participants did not reach an optimal level of performance following a one-time administration of BST. Practical implications for teacher education contexts, limitations, and future research directions are thoroughly addressed.