Search

This review explores the integration and effects of the Internet of Things (IoT) in education, highlighting its importance in transforming traditional teaching and learning techniques. It examines the early uses and historical growth of IoT, its development, and the turning points in its adoption. It explores IoT platforms, tools, and technologies in education, including wearables, smart devices, augmented and virtual reality, gamification, and collaborative learning. It discusses the role of IoT in improving campus management, including intelligent campuses with IoT-enabled infrastructure, energy-saving technologies, and safety and security improvements. The study discusses data security and privacy issues in IoT installations and the ethical and legal implications of data collection in the classroom. The study also discusses upcoming trends and prospects for IoT usage in education, including AI and machine learning integration. Finally, the review provides insights for educators, decision-makers, and stakeholders, identifying research gaps and recommending areas for future IoT implementation.

The design case details the collaborative work of a design team--three faculty members, one instructional designer, and one educational resource specialist--to create a simulation-based interprofessional education (IPE) experience for future healthcare professionals. Before the COVID-19 pandemic that caused the shutdown of campuses across the country/world, this simulation-based learning experience was always offered in a face-to-face format. Thus, this case highlights the challenges and opportunities of designing this online learning experience within a limited period of time. Further, this case documents what theories or evidence-based practices were instrumental in designing this learning experience, along with the design team's narrative regarding key design decisions and moves. It also includes a design narrative focusing on the description of the design process, such as key design judgments, decisions, and concrete examples of the design process outcome. Lastly, the design case highlights unique design features: Scalability of instruction through accessibility and usability, authenticity, interprofessional collaboration, and reflection. The design was guided by the TEACH (Team Education Advancing Collaboration in Health) core curriculum in the state of Indiana that foregrounds interprofessional practice competencies and teamwork in preparing future healthcare professionals.

Accomplishing a communication task in the real world requires the ability not only to do the task per se but also to manage aspects of the context in which it occurs. For this reason, simulations of target language use contexts have been incorporated into the design of communicative language tests as a way of enhancing the authenticity of assessment task performance. Although some contextual factors may increase extraneous cognitive load and distract learners from focusing on the task at hand (Sweller, 1994), they represent important design considerations in task-based language assessment (TBLA), where the purpose of assessment is to determine what second language (L2) learners can do with the target language in the real world. In that sense, the extraneous cognitive load might well be part of the construct we are interested in assessing. Accordingly, the current study simulated aspects of a real-world task performance context as part of an email writing task assessment. Simulated context was operationalized as (a) additional information about the task scenario, (b) a visual image to simulate the physical context, and (c) an audio to replicate the real-world experience. A total of 276 L2 English learners performed the email task, half with simulated context and the other half without it. Findings revealed that, when presented with simulated context, the tasks were perceived by participants to have induced more time pressure and to be more interesting. In terms of performance effects, the provision of simulated context negatively affected the syntactic complexity of participants' writing but positively affected their syntactic fluency. It also led to greater discrimination among learners at different proficiency levels on various measures of language performance. The paper concludes by highlighting implications for task design and validity evaluation, especially in TBLA.

Preparing special education preservice teachers (PTs) to serve students with autism includes understanding theoretical foundations of teaching strategies and developing competency in applying the resulting knowledge and strategies within teaching settings. The current pilot study explored the Internet-Based Parent-implemented Communication Strategies (i-PiCS) program, targeting evidence-based teaching practices, as an alternative training method in an autism field-experience course conducted online during the COVID-19 lockdown. The purpose of the pilot study was to explore changes in PTs' knowledge, confidence, and implementation of naturalistic communication teaching strategies in a pre- and postfield-experience course. The participants were nine females enrolled in a 3rd-year special education teacher preparation program. A multimethod assessment was utilized, including self-rating questions, knowledge quizzes, video-model analysis, and videotaped teaching simulations. Quantitative data were graphically displayed and compared. Teaching simulations were analyzed qualitatively. The results showed that all PTs improved the identification of instructional steps. Self-rating of knowledge and confidence level improved as well. Five PTs improved the accuracy of evaluating the instructional steps, albeit the level of fidelity of implementation improved only mildly. We conclude that i-PiCS could be useful for training PTs in a special education program. We discuss the limitations of the current application of i-PiCS and the appropriate implementation modalities that could be further explored.

What is the optimal penalty for errors in infant skill learning? Behavioral analyses indicate that errors are frequent but trivial as infants acquire foundational skills. In learning to walk, for example, falling is commonplace but appears to incur only a negligible penalty. Behavioral data, however, cannot reveal whether a low penalty for falling is beneficial for learning to walk. Here, we used a simulated bipedal robot as an embodied model to test the optimal penalty for errors in learning to walk. We trained the robot to walk using 12,500 independent simulations on walking paths produced by infants during free play and systematically varied the penalty for falling--a level of precision, control, and magnitude impossible with real infants. When trained with lower penalties for falling, the robot learned to walk farther and better on familiar, trained paths and better generalized its learning to novel, untrained paths. Indeed, zero penalty for errors led to the best performance for both learning and generalization. Moreover, the beneficial effects of a low penalty were stronger for generalization than for learning. Robot simulations corroborate prior behavioral data and suggest that a low penalty for errors helps infants learn foundational skills (e.g., walking, talking, and social interactions) that require immense flexibility, creativity, and adaptability.

Simulated treatment comparison (STC) is an established method for performing population adjustment for the indirect comparison of two treatments, where individual patient data (IPD) are available for one trial but only aggregate level information is available for the other. The most commonly used method is what we call 'standard STC'. Here we fit an outcome model using data from the trial with IPD, and then substitute mean covariate values from the trial where only aggregate level data are available, to predict what the first of these trial's outcomes would have been if its population had been the same as the second. However, this type of STC methodology does not involve simulation and can result in bias when the link function used in the outcome model is non-linear. An alternative approach is to use the fitted outcome model to simulate patient profiles in the trial for which IPD are available, but in the other trial's population. This stochastic alternative presents additional challenges. We examine the history of STC and propose two new simulation-based methods that resolve many of the difficulties associated with the current stochastic approach. A virtue of the simulation-based STC methods is that the marginal estimands are then clearly targeted. We illustrate all methods using a numerical example and explore their use in a simulation study.

The purpose of this paper is to discuss the role of simulations to advance students' knowledge of the business enterprise, and illustrate the attributes of a selected simulation deployed in strategic management course. Business education is a transitional phase in the lives of college students designed to empower them to occupy managerial positions that influence the long-term survival and growth of the enterprise. Professional success of fresh graduates who join the workforce could be enhanced further with the use of innovative teaching and learning approaches. Well-structured business simulations could in view of the Generative Learning Theory enable students to translate knowledge gained in their programs into practice while, at the same time, assist them to develop decision-making skills.

Contribution: This work seeks to ascertain the validity of a remote-controlled experiment of the Physics Remote Lab in the educational context, specifically among students from engineering courses. Background: In 2012, it has been started the development of the Physics Remote Lab at the Federal University of Itajubá, Brazil, a laboratory with a collection of didactical remote-controlled experiments. For a while since then, efforts have been directed mainly to technical matters and so, many questions regarding the effectiveness of these experiments as a teaching resource have not been satisfactorily answered yet. The restrictions imposed by the pandemic have provided suitable conditions to assess the effectiveness of this kind of resource. Research Questions: There are two points to be addressed. The first refers to whether remotely controlled experiments can be regarded as a valid didactical tool. The second seeks to ascertain the effectiveness of these digital objects in a very atypical scenario. Methodology: A pretest-intervention-posttest design was chosen, with three groups that totaled 145 students from engineering courses. The groups were submitted to interventions based on a remote-controlled experiment, video analysis, and simulation on kinematics. Nonparametric statistical tests were applied to check for biased groups and to compare the results before and after the intervention. Findings: The results show that all three interventions had a significant impact on the groups and the established I-index suggests that the effects were positive in all of them, despite the peculiar circumstances regarding the pandemic. Compared to other groups, the results for the remote-controlled experiment group showed that it can be as effective as a simulation or a video analysis and the students in this group achieved a better understanding of the concepts as compared to their performance on the pretest.

Clinical practice has benefited from new methodologies such as realistic simulation (RS). RS involves recreating lifelike scenarios to more accurately reflect real clinical practice, enhancing learners' skills and decision-making within controlled environments, and experiencing remarkable growth in medical education. However, RS requires substantial financial investments and infrastructure. Hence, it is essential to determine the effectiveness of RS in the development of skills among medical students, which will improve the allocation of resources while optimizing learning. This cross-sectional study was carried out in the simulation laboratory of a medical school, and the performance of students who underwent two different curriculum matrices (without RS and with RS, from 2021 to 2022) in the Advanced Cardiac Life Support (ACLS) course was compared. This test was chosen considering that the competencies involved in cardiac life support are essential, regardless of the medical specialty, and that ACLS is a set of life-saving protocols used worldwide. We observed that the impact of RS can be different for practical abilities when compared with the theoretical ones. There was no correlation between the general academic performance and students' grades reflecting the RS impact. We conclude that RS leads to less remediation and increased competence in practical skills. RS is an important learning strategy that allows repeating, reviewing, and discussing clinical practices without exposing the patient to risks.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

At the same time teachers and administrators grapple with how to develop teachers' understanding of diversity, equity, and inclusion in the classroom, recent legislative sessions have unleashed a flood of legislation that seeks to limit teachers from teaching about race, racism, oppression, and injustice in U.S. schools. Teachers are struggling with how to balance student engagement and interest while also tackling these large, complex, and difficult topics. Unfortunately, the continued practice of using classroom simulations that attempt to undertake these difficult topics does not meet the challenge. This article examines the potential dangers when using classroom simulations to teach sensitive topics and provides strategies for teachers to create engaging and innovative lessons that educate and empower, and do not marginalize students in the process. To create classrooms that are safe and affirming, the practice of using classroom simulations needs a renewed examination, particularly ones that delve into culturally and historically sensitive topics.

Bayesian methods to infer model dimensionality in factor analysis generally assume a lower triangular structure for the factor loadings matrix. Consequently, the ordering of the outcomes influences the results. Therefore, we propose a method to infer model dimensionality without imposing any prior restriction on the loadings matrix. Our approach considers a relatively large number of factors and includes auxiliary multiplicative parameters, which may render null the unnecessary columns in the loadings matrix. The underlying dimensionality is then inferred based on the number of nonnull columns in the factor loadings matrix, and the model parameters are estimated with a postprocessing scheme. The advantages of the method in selecting the correct dimensionality are illustrated via simulations and using real data sets.

Classical automated test assembly (ATA) methods assume fixed and known coefficients for the constraints and the objective function. This hypothesis is not true for the estimates of item response theory parameters, which are crucial elements in test assembly classical models. To account for uncertainty in ATA, we propose a chance-constrained version of the maximin ATA model, which allows maximizing the a-quantile of the sampling distribution of the test information function obtained by applying the bootstrap on the item parameter estimation. A heuristic inspired by the simulated annealing optimization technique is implemented to solve the ATA model. The validity of the proposed approach is empirically demonstrated by a simulation study. The applicability is proven by using the real responses to the Trends in International Mathematics and Science Study (TIMSS) 2015 science test.

Meta-analyses have become the gold standard for synthesizing evidence from multiple clinical trials, and they are especially useful when outcomes are rare or adverse since individual trials often lack sufficient power to detect a treatment effect. However, when zero events are observed in one or both treatment arms in a trial, commonly used meta-analysis methods can perform poorly. Continuity corrections (CCs), and numerical adjustments to the data to make computations feasible, have been proposed to ameliorate this issue. While the impact of various CCs on meta-analyses with rare events has been explored, how this impact varies based on the choice of pooling method and heterogeneity variance estimator is not widely understood. We compare several correction methods via a simulation study with a variety of commonly used meta-analysis methods. We consider how these method combinations impact important meta-analysis results, such as the estimated overall treatment effect, 95% confidence interval coverage, and Type I error rate. We also provide a website application of these results to aid researchers in selecting meta-analysis methods for rare-event data sets. Overall, no one-method combination can be consistently recommended, but some general trends are evident. For example, when there is no heterogeneity variance, we find that all pooling methods can perform well when paired with a specific correction method. Additionally, removing studies with zero events can work very well when there is no heterogeneity variance, while excluding single-zero studies results in poorer method performance when there is non-negligible heterogeneity variance and is not recommended.

Online laboratories have been widely used in education, research, and industrial applications. For online laboratories, various architectures have been constructed to provide good user experience and powerful capabilities for online experimentation, in which the controller is a crucial part to connect the server with a controlled test rig. Regarding the physical nature of the controllers and test rigs used in online laboratories, a specific control process can be called a simulation, hardware-in-the-loop, rapid control prototyping, or physical control. This article presents an overview of the most commonly used controllers in current online laboratories, focusing on the functionalities, use purposes, and types of different controllers, as well as their role in online laboratories for teaching and learning. In particular, the low-cost PC controller used in the Networked Control System Laboratory, which provides both remote and virtual experiments, is introduced. The limitations and potential development directions of online laboratories are identified.

Conventional random-effects models in meta-analysis rely on large sample approximations instead of exact small sample results. While random-effects methods produce efficient estimates and confidence intervals for the summary effect have correct coverage when the number of studies is sufficiently large, we demonstrate that conventional methods result in confidence intervals that are not wide enough when the number of studies is small, depending on the configuration of sample sizes across studies, the degree of true heterogeneity and number of studies. We introduce two alternative variance estimators with better small sample properties, investigate degrees of freedom adjustments for computing confidence intervals, and study their effectiveness via simulation studies.

Comprehensive evaluation of treatment effects is aided by considerations for moderated effects. In educational research, the combination of natural hierarchical structures and prevalence of group-administered or shared facilitator treatments often produces three-level partially nested data structures. Literature details planning strategies for a variety of experimental designs when moderation effects are of interest but has yet to establish power formulas for detecting moderation effects in three-level partially nested designs. To address this gap, we derive and assess the accuracy of power formulas for detecting the different types of moderation effects possible in these designs. Using Monte Carlo simulation studies, we probe power rates and adequate sample sizes for detecting the different moderation effects while varying common influential factors including variance in the outcome explained by covariates, magnitude of the moderation effect, and sample sizes. The power formulas developed improve the planning of experimental studies with partial nesting and encourage the inclusion of moderator variables to capture for whom and under what conditions a treatment is effective. Educational researchers also have some initial guidance regarding adequate sample sizes and the factors that influence detecting moderation effects in three-level partially nested designs.

Background: Speech and language therapy (SLT) education must meet the needs of the future workforce, training enough students who are competent practitioners able to meet the workforce demands. Increasing student numbers and the impact on placement providers mean students must be equipped for learning on placement. Simulation is a way of supporting students to develop their clinical skills and decision-making in a safe, supportive environment. Aims: To explore the perspectives of SLT students who were introduced to simulation during their undergraduate degree at a UK university as part of a pilot study. The aim of the pilot was to listen to the students' voices to begin to understand their lived experiences of simulation and to gather views on how simulation can support their clinical learning. Methods & Procedures: Focus groups and semi-structured interviews were carried out with second-year BSc SLT students in semester 2 following the simulated learning activities and clinical placement. Qualitative data were gathered and thematic analysis was applied to the data to identify the barriers and enablers to students' clinical learning in simulation. Outcomes & Results: A total of 11 students responded out of a cohort of 38. Three key themes were identified from the analysis: individual learning needs, facilitator skill and programme-level organization. Conclusions & Implications: Student experience of simulation was positive. One of the key elements students found to support their clinical skills was the importance of the safe space; support for learning instead of correction led them to engage in active learning. Key barriers to simulation related to having sufficient prior knowledge, the skills of the facilitator, group size and the wider learning landscape of the programme. In response to this pilot, there are plans to continue developing this model of simulation and embed simulation across the programme, led by a sound pedagogical approach with clear preparation and planning and building the necessary infrastructure. Other SLT programmes and practice educators developing simulation as part of their programmes or placement may wish to consider some of these findings to support the use of simulation in their workplace.

Background Study: The research discusses the need for a paradigm shift in engineering education current practices to accommodate the digital native students. The paper emphasizes the importance of integrating disruptive technologies, namely Virtual Reality (VR) through Head Mounted Displays VR (HMD VR) and Desktop Based VR (DB VR) and comparing it with the traditional teaching methods. The research focuses on second-year undergraduate students enrolled in a concrete structures course. Objectives: The objective of the study is to analyse the impact of DB VR and HMD VR on students' motivation, performance and workload in comparison with traditional 2D drawings. The study also aims to investigate how simulation sickness and system usability could impact students' motivation and performance when interpreting concrete structures. Methods: The study divided second-year civil engineering students into three groups, 2D, DB VR and HMD VR. The performance and motivation levels of students were assessed based on the number of mistakes identified and an intrinsic motivation survey. Furthermore, data on system usability and simulation sickness were collected. Spearman rank correlation was conducted to analyse the relationships between the variables tested. Results: The results have shown that students' motivation and performance were much higher when using VR in general and HMD VR specifically, students' efficiency and accuracy were also observed. On the other hand, usability was found to be moderately correlated with motivation, with students rating HMD VR as the most usable approach. A moderate inverse correlation was observed between simulation sickness and motivation, indicating that higher simulation sickness led to lower motivation levels. Conclusion: The study concludes that integrating VR in general, DB VR, or HMD VR into a concrete structures course for undergraduate students can significantly improve students' performance and motivation. Furthermore, enhancing usability of any of the methods used can also improve the performance and motivation. On the other hand, simulation sickness inversely affects motivation and performance, therefore, VR developers and educators should not neglect it.

The use of simulation has become a staple in social work education in North America. Concerns have been raised, however, that this pedagogy, rooted in positivist views of professional competence, might be built on educators' monolithic and stereotypical understanding of minoritized clients. In this article, I take on a position that simulation certainly advances social work education; yet there is a vital need for a critical approach that can mitigate concerns about authenticity, representation, and harm. I propose guidelines for educators, which consist of concrete steps, tasks, and reflective questions for how to develop case scenarios from a critical perspective. Finally, I outline remaining concerns and limitations that signal a need for continued advancement of simulation-based social work education.

Background: Haptic feedback has been shown to be an effective facilitator of the learning of scientific concepts in a series of studies. However, little is known about the underlying salient learning mechanisms, which are activated when learning from haptic feedback. Objectives: We investigate the learning mechanism in a higher chemistry education setting, in which the students learned about the abstract concept of potential energy in a quantum chemical context. Methods: In this work, we present a mixed-methods problem-solving prior to instruction (PS-I) study with chemistry bachelor students. In an interactive quantum chemistry learning environment, the students explored the energetic profiles of nucleophilic substitution reactions and the corresponding activation barriers experienced as repulsion and attraction. We measured cognitive and affective mechanisms which have been shown to be activated in PS-I designs, such as knowledge gap awareness, state curiosity, and positive affect as well as the learning outcome quantitatively and the learning trajectories qualitatively. Results and Conclusions: Planned contrasts revealed a small hindering effect of the haptic feedback on the learning outcome. Thematic analysis of the qualitative data lets us attribute this effect to the lack of scaffolding of the haptic feedback and the spontaneous atomic movements in the simulation, which may be interpreted as a visual representation of force in itself. This work highlights the importance of accurate mappings of the haptic feedback unto the target domain and further delivers insight into the prior conceptions of chemistry undergraduate students.

When uncertain, medical trainees often seek to co-regulate their learning with supervisors and peers. Evidence suggests they may enact self-regulated learning (SRL) strategies differently when engaged in self- versus co-regulated learning (Co-RL). We compared the impacts of SRL and Co-RL on trainees' acquisition, retention, and preparation for future learning (PFL) of cardiac auscultation skills during simulation-based training. In our two-arm, prospective, non-inferiority trial, we randomly assigned first- and second-year medical students to the SRL (N = 16) or Co-RL conditions (N = 16). Across two learning sessions separated by two-weeks, participants practiced and were assessed in diagnosing simulated cardiac murmurs. We examined diagnostic accuracy and learning trace data across sessions, and conducted semi-structured interviews to explore participants' understandings of their underlying choices and learning strategies. SRL participants' outcomes were non-inferior to Co-RL participants on the immediate post-test and retention test, but not on the PFL assessment (i.e., inconclusive). Analyzing interview transcripts (N = 31) generated three themes: perceived utility of initial learning supports for future learning; SRL strategies and sequencing of murmurs; and perceived control over learning across sessions. Co-RL participants regularly described relinquishing control of learning to supervisors and regaining it when on their own. For some trainees, Co-RL seemed to interfere with their situated and future SRL. We posit that transient clinical training sessions, typical in simulation-based and workplace-based settings, may not allow the ideal processes of Co-RL to unfold between supervisor and trainee. Future research must examine how supervisors and trainees can share accountability to develop the shared mental models that underlie effective Co-RL.

Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally-intensive analytical methods that are not widely used in practice. Instead, repurposing decisions are often based on subjective judgments from limited empirical evidence. In this article, we develop a novel Bayesian network meta-analysis (NMA) framework that can predict the efficacy of an approved treatment in a new indication and thereby identify candidate treatments for repurposing. We obtain predictions using two main steps: first, we use standard NMA modeling to estimate average relative effects from a network comprised of treatments studied in both indications in addition to one treatment studied in only one indication. Then, we model the correlation between relative effects using various strategies that differ in how they model treatments across indications and within the same drug class. We evaluate the predictive performance of each model using a simulation study and find that the model minimizing root mean squared error of the posterior median for the candidate treatment depends on the amount of available data, the level of correlation between indications, and whether treatment effects differ, on average, by drug class. We conclude by discussing an illustrative example in psoriasis and psoriatic arthritis and find that the candidate treatment has a high probability of success in a future trial.

Despite the routine use of high-fidelity patient simulation (HFPS) in health care disciplines, pharmacy residents' involvement in HFPS is limited. To evaluate pharmacy residents' self-reported confidence in their clinical skills dealing with acute medical scenarios. Three separate HFPS sessions (stroke, Advanced cardiovascular life support [ACLS], and acetaminophen poisoning) were conducted for pharmacy residents at a designated simulation center using a high-fidelity mannequin. The scenarios were facilitated by experienced physicians, nurses, and clinical pharmacists. A questionnaire that addressed 5 clinical skills was administered to the residents pre- and post-HFPS and 6 months afterward. The primary outcome was an assessment of residents' confidence in acute medical scenarios. Fourteen pharmacy residents participated (mean age of 27.7 years; 71.4% female; 57% have [greater than or equal to]3 years of work experience as a pharmacist). Around 85.6%, 78.6%, and 64.3% of the residents had encountered stroke, ACLS, and acetaminophen poisoning cases, respectively, before the HFPS. After the HFPS sessions, the residents felt significantly more confident in making decisions during an emergency in a timely manner (P = 0.006 for stroke; P = 0.02 for poisoning), providing recommendations to the health care team (P = 0.006 for stroke; P = 0.024 for ACLS), and providing optimal patient care during a stressful situation (P = 0.02 for stroke and poisoning). We found no significant difference between post-simulation and 6 months post-simulation scores in most of the domains in all scenarios, indicating residents' confidence was maintained at 6 months. HFPS is a valuable active learning tool for enhancing pharmacy residents' confidence in managing an emergency situation.

This study builds on the simulation framework of a recent paper by Stanley and Doucouliagos ("Research Synthesis Methods" 2023;14;515--519). S&D use simulations to make the argument that meta-analyses using partial correlation coefficients (PCCs) should employ a "suboptimal" estimator of the PCC standard error when constructing weights for fixed effect and random effects estimation. We address concerns that their simulations and subsequent recommendation may give meta-analysts a misleading impression. While the estimator they promote dominates the "correct" formula in their Monte Carlo framework, there are other estimators that perform even better. We conclude that more research is needed before best practice recommendations can be made for meta-analyses with PCCs.