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The challenge of creating reliable technology-based resources for science learning is a perennial challenge in Philippine education, with limited learning materials accessible to all learners. This study is about the development of a videocast embedded with physics education technology (PhET) simulation that served as supplementary learning material for grade 9 science in response to the scarcity of dependable visualization materials. The study employed the developmental research design with analysisdesign-development-implementation-evaluation (ADDIE) model as the developmental framework. The vodcast evaluation tool, achievement test questionnaire, and vodcast perception survey questionnaire for students and teachers were used in the data collection, while the Kendall's W statistic, mean, percentage, and gain score were used in the data interpretation. The teacher respondents (N=64) have moderate agreement on the ranking of topic difficulty, with Kendall's W of 0.45. The researcher-made vodcast attained an overall rating of 4.78 from experts, which implies that the vodcast can be very good material for classroom implementation. The developed achievement test has acceptable difficulty and discrimination indices. The implementation stage yielded a low normalized gain, which can be accounted for by unfocused attention during the pandemic. Nevertheless, the voicecasts were found very useful in learning projectile motion, as perceived by both students and teacher-observers.

In response to the adoption of Agile practices and processes by businesses, IT/IS educators are working to add Agile content to their courses. Teaching students about Agile involves teaching them about the history, mindset, and values of Agile, along with an introduction to the practices and processes used in an Agile product. Along with this, it is essential that students gain experience using Agile in a project setting. This paper discusses an Agile practice project where students use all aspects of Agile to address a problem and build a solution using Legos. The use of Legos, along with a project that students can easily see themselves using, the practice project allows students to focus on developing their Agile skills and mindset. The project serves as a useful transition from traditional classroom instruction about Agile to a project for a real-world client.

This paper presents the results of a multi-year effort to redesign the introduction to business course at Western Michigan University. ScrimmageSIM, a business simulation that emulates commercial ERP systems, provides the core experience in the course and is a mechanism for students to develop their understanding of business with a focus on quantitative analysis. The design and implementation of the simulation experience follows the experiential learning spiral, as students run the simulation five times with increasingly complex scenarios. The overall design of the course is competency-based, as students have the opportunity to redo many of the course assignments to earn a better grade. The simulation experience, combined with the competency-based design of the course, has resulted in a more rigorous course with students earning grades that are better than they were with the previous curriculum.

Building on previous research that has demonstrated close connections between constructivism, technology, and artificial intelligence, this article investigates the constructivist underpinnings of strategically integrating GenAI experiences in higher educational contexts to catalyze student learning. This study presents a new model for leveraging GenAI tools, for individualized, formative, peer-simulated feedback in graduate-level courses in higher education. This exploratory study presents graduate student reflections about the process and product created using ChatGPT for formative feedback with an instructor-generated prompt for an organizational behavior course. An analysis of student reflections and examples of ChatGPT-generated peer-simulated feedback, as well as an examination of ethical considerations, offer insights into the learning potential of utilizing GenAI tools for peer-simulated feedback in graduate-level courses.

Cognitive dissonance is an important element in adult learning in that it challenges previously held ideas in favor of new knowledge. In-class simulations and game-based learning are used as innovative and effective pedagogical tools in challenging adult learners and enhancing the students' ability to think critically about larger societal needs. This paper will review the literature relevant to cognitive dissonance, adult learning, and game and simulation practices in higher education. The authors present the results of one simulation activity in a quasi-experimental non-random comparison group conducted across five universities within multiple undergraduate and graduate level social work policy courses. Findings of this research on the use of simulations in social work policy courses can be used to guide other social work programs with embedding this effective educational practice into their programs. Institutions of higher education, specifically within the discipline of social work, can play an important role in continuing the research and evaluation of this effective pedagogical practice through measuring outcomes on student critical thinking and changes in student beliefs and biases.

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.

The COVID-19 pandemic has had a significant impact on high-stakes testing, including the national benchmark tests in South Africa. Current linear testing formats have been criticized for their limitations, leading to a shift towards Computerized Adaptive Testing [CAT]. Assessments with CAT are more precise and take less time. Evaluation of CAT programs requires simulation studies. To assess the feasibility of implementing CAT in NBTs, SimulCAT, a simulation tool, was utilized. The SimulCAT simulation involved creating 10,000 examinees with a normal distribution characterized by a mean of 0 and a standard deviation of 1. A pool of 500 test items was employed, and specific parameters were established for the item selection algorithm, CAT administration rules, item exposure control, and termination criteria. The termination criteria required a standard error of less than 0.35 to ensure accurate abilities estimation. The findings from the simulation study demonstrated that fixed-length tests provided higher testing precision without any systematic error, as indicated by measurement statistics like CBIAS, CMAE, and CRMSE. However, fixed-length tests exhibited a higher item exposure rate, which could be mitigated by selecting items with fewer dependencies on specific item parameters (a-parameters). On the other hand, variable-length tests demonstrated increased redundancy. Based on these results, CAT is recommended as an alternative approach for conducting NBTs due to its capability to accurately measure individual abilities and reduce the testing duration. For high-stakes assessments like the NBTs, fixed-length tests are preferred as they offer superior testing precision while minimizing item exposure rates.

Educators, including English Language Teaching practitioners, acknowledge the influence of collaborative learning on students' learning experiences, as demonstrated by numerous studies conducted in diverse contexts. This research, however, keeping instructional innovation, students' collaborative engagement, language learning and classroom dynamics especially in the context of an Arab university where such studies are limited, examines the effect of a collaborative learning intervention program on EFL students' English learning and social interactions in terms of teamwork, peer learning, and social intelligence. To achieve the study objective, the quasi-experimental design with two groups (experimental and control) was employed. The study was applied to a sample of prep-year university students who were randomly divided into two groups: control and experimental. While the experimental group studied English using collaborative learning skills, the control group studied following traditional ways of teaching. Both groups were assessed using a pre-and post-test (performance test) and an assessment card (social learning). In addition, teachers were interviewed about their views on how collaborative learning can strengthen EFL students' social learning success. The result revealed statistically significant differences between the scores of the control and experimental groups in favor of the later, indicating that the intervention program highly helped students improve their English learning skills and enrich their social interaction. Finally, the interview analysis highlighted the ways to enhance EFL students' language learning and social interaction through group projects, discussion groups and debates, role-playing and simulations, diverse collaborative learning practices, and effective teacher facilitation. The findings imply that collaborative learning could be a potential treatment to help EFL learners enrich their social learning experience and success. Further research on collaborative learning's extended impacts, teacher readiness, diverse assessment methods, and gender disparities are recommended to address the limitations of the current study.

Various areas of educational research are interested in the transitions between different states--or events--in sequential data, with the goal of understanding the significance of these transitions; one notable example is affect dynamics, which aims to identify important transitions between affective states. Unfortunately, several works have uncovered issues with the metrics and procedures commonly used to analyze these transitions. As such, our goal in this work is to address these issues by outlining an alternative procedure that is based on the use of marginal models. We begin by looking at the specific mechanisms responsible for a recently discovered statistical bias with several metrics used in sequential data analysis. After giving a theoretical explanation for the issue, we show that the marginal model procedure appears to adjust for this bias. Next, a related problem is that the common practice of removing transitions to repeated states has been shown to have unintended side-effects--to account for this issue, we develop a method for extending the marginal model procedure to this specific type of analysis. Finally, in a recent study evaluating the problem of multiple comparisons and sequential data analysis, the Benjamini-Hochberg (BH) procedure, a commonly used approach to control for false discoveries, did not perform as expected. By applying a technique from the biostatistics and epidemiology literature, we show that the performance of the BH procedure, when used with the marginal model method, can be brought back to its expected level. In all of our analyses, we evaluate the proposed method by both running simulations and using actual student data. The results indicate that the marginal model procedure seemingly compensates for the problems observed with other transition metrics, thus resulting in more accurate estimates of the importance of transitions between states.

Researchers have revealed the advantages of experiential learning for students and professionals at all levels of the health care delivery system. The purpose of this study is to determine the effectiveness of the use of head simulators in dental school in acquiring proficient periodontal knowledge, dental skills, and confidence by practicing oral health professionals. Of the 117 purposive sampled participants surveyed using a 5-point Likert scale questionnaire, 60 respondents used head simulators during their dental school education. On the effect of the head simulator enhancing knowledge. The findings regarding the effect of the head simulator in dental school revealed varying perspectives. A significant majority of participants agreed the head simulator had a beneficial effect on their skills. Among these findings, a third of participants strongly agreed the use of the head simulator notably enhanced their skills. When considering the influence on knowledge, the responses were more evenly distributed. Almost 40% of participants agreed the head simulator positively affected their knowledge, while almost 20% of participants generally disagreed. Examining the effect on confidence, findings also depicted varying viewpoints among the participants with 42% acknowledging the head simulator had a positive effect on their confidence. The findings suggest that head simulators positively affect dental education, particularly in enhancing knowledge, skills, and confidence.

This study reports an investigative study with 55 English-major participants in a Chinese university about using simulation in business English correspondence teaching. The study found that participants had strong needs in practical skills development and learning business English correspondence writing. The simulation approach was perceived to be effective and had positive learning outcomes in business communication skills development, motivation, confidence and vocabulary growth, business and language knowledge improvement, and more understanding about the business practice. The constraint factors in simulation included lack of business knowledge and vocabulary, ineffective group cooperation, and lack of understanding about the real-world business background.

The network scale-up method (NSUM) is a survey-based method for estimating the number of individuals in a hidden or hard-to-reach subgroup of a general population. In NSUM surveys, sampled individuals report how many others they know in the subpopulation of interest (e.g. "How many sex workers do you know?") and how many others they know in subpopulations of the general population (e.g. "How many bus drivers do you know?"). NSUM is widely used to estimate the size of important sociological and epidemiological risk groups, including men who have sex with men, sex workers, HIV+ individuals, and drug users. Unlike several other methods for population size estimation, NSUM requires only a single random sample and the estimator has a conveniently simple form. Despite its popularity, there are no published guidelines for the minimum sample size calculation to achieve a desired statistical precision. Here, we provide a sample size formula that can be employed in any NSUM survey. We show analytically and by simulation that the sample size controls error at the nominal rate and is robust to some forms of network model mis-specification. We apply this methodology to study the minimum sample size and relative error properties of several published NSUM surveys.

Random forests are a nonparametric machine learning method, which is currently gaining popularity in the behavioral sciences. Despite random forests' potential advantages over more conventional statistical methods, a remaining question is how reliably informative predictor variables can be identified by means of random forests. The present study aims at giving a comprehensible introduction to the topic of variable selection with random forests and providing an overview of the currently proposed selection methods. Using simulation studies, the variable selection methods are examined regarding their statistical properties, and comparisons between their performances and the performance of a conventional linear model are drawn. Advantages and disadvantages of the examined methods are discussed, and practical recommendations for the use of random forests for variable selection are given.

Effective collaboration and teamwork skills are critical in high-risk sectors, as deficiencies in these areas can result in injuries and risk of death. To foster the growth of these vital skills, immersive learning spaces have been created to simulate real-world scenarios, enabling students to safely improve their teamwork abilities. In such learning environments, multiple dialogue segments can occur concurrently as students independently organise themselves to tackle tasks in parallel across diverse spatial locations. This complex situation creates challenges for educators in assessing teamwork and for students in reflecting on their performance, especially considering the importance of effective communication in embodied teamwork. To address this, we propose an automated approach for generating teamwork analytics based on spatial and speech data. We illustrate this approach within a dynamic, immersive healthcare learning environment centred on embodied teamwork. Moreover, we evaluated whether the automated approach can produce transcriptions and epistemic networks of spatially distributed dialogue segments with a quality comparable to those generated manually for research objectives. This paper makes two key contributions: (1) it proposes an approach that integrates automated speech recognition and natural language processing techniques to automate the transcription and coding of team communication and generate analytics; and (2) it provides analyses of the errors in outputs generated by those techniques, offering insights for researchers and practitioners involved in the design of similar systems.

The increasing ubiquity of gamification in everyday life normalizes it as a motivational tool. While much scholarship supports gamification, labor sociologists have long problematized the phenomenon. In this mixed-methods action research study, we explore the results of gamifying a lesson on gamification in a sociology of work course. We designed two gamified activities with varying degrees of consent that followed a lesson on gamification and consent. Students rated how problematic a series of gamified work scenarios were before and after the intervention. Our quantitative data did not show a significant increase in students' ability to identify consent after the intervention, but we did discover that students took either an employee or employer's perspective in their rating justifications. Furthermore, these findings were gendered. This article highlights the need for a more critical take on gamification in the classroom. We conclude by suggesting ways practitioners can teach about gamification in other contexts.

Simulation is accepted as an effective method of learning procedural skills. However, the translational outcomes of skills acquired through simulation still warrants investigation. We designed this study to assess if skills laboratory training in addition to bedside learning (intervention group [IG]) would provide better learning results than bedside learning alone (control group [CG]) in the context of venipuncture training. This prospective, cluster-randomized, single-blind study took place at the Faculty of Medicine, University of Kelaniya, Sri Lanka. Seventeen clusters of second-year medical students were randomly assigned to either IG or CG. The IG trained on venipuncture in the skills laboratory, receiving instruction after modified Payton's Four Step Method. Following the training, students of both IG and CG underwent bedside learning for one month. Afterward, students of both groups performed venipuncture on actual patients in a clinical setting. An independent, blinded assessor scored students' performance using the Integrated Procedural Protocol Instrument (IPPI) and a checklist. Patients assessed students' performance with the Communication Assessment Tool (CAT). Eight and nine clusters were randomized to the intervention and control groups, respectively. IG completed significantly more single steps of the procedure correctly (IG: 19.36 ± 3.87 for checklist items; CG: 15.57 ± 4.95; p < 0.001). IG also scored significantly better on IPPI ratings (median: IG: 27 (12) vs. CG: 21 (8); p < 0.001). Rated by patients, students' communication skills did not significantly differ between the two groups. Simulation-based venipuncture training enabled students to perform the procedure on actual patients with a higher technical accuracy than students who learned venipuncture at the bedside. Students were able to transfer the skills acquired through venipuncture training at the skill laboratory to the bedside.

Biology is perhaps the most complex of the sciences, given the incredible variety of chemical species that are interconnected in spatial and temporal pathways that are daunting to understand. Their interconnections lead to emergent properties such as memory, consciousness, and recognition of self and non-self. To understand how these interconnected reactions lead to cellular life characterized by activation, inhibition, regulation, homeostasis, and adaptation, computational analyses and simulations are essential, a fact recognized by the biological communities. At the same time, students struggle to understand and apply binding and kinetic analyses for the simplest reactions such as the irreversible first-order conversion of a single reactant to a product. This likely results from cognitive difficulties in combining structural, chemical, mathematical, and textual descriptions of binding and catalytic reactions. To help students better understand dynamic reactions and their analyses, we have introduced two kinds of interactive graphs and simulations into the online educational resource, Fundamentals of Biochemistry, a LibreText biochemistry book. One is available for simple binding and kinetic reactions. The other displays progress curves (concentrations vs. time) for simple reactions and complex metabolic and signal transduction pathways. Users can move sliders to change dissociation and kinetic constants as well as initial concentrations and see instantaneous changes in the graphs. They can also export data into a spreadsheet for further processing, such as producing derivative Lineweaver-Burk and traditional Michaelis-Menten graphs of initial velocity (v[subscript 0]) versus substrate concentration.

Population-adjusted indirect comparison (PAIC) is an increasingly used technique for estimating the comparative effectiveness of different treatments for the health technology assessments when head-to-head trials are unavailable. Three commonly used PAIC methods include matching-adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and multilevel network meta-regression (ML-NMR). MAIC enables researchers to achieve balanced covariate distribution across two independent trials when individual participant data are only available in one trial. In this article, we provide a comprehensive review of the MAIC methods, including their theoretical derivation, implicit assumptions, and connection to calibration estimation in survey sampling. We discuss the nuances between anchored and unanchored MAIC, as well as their required assumptions. Furthermore, we implement various MAIC methods in a user-friendly R Shiny application Shiny-MAIC. To our knowledge, it is the first Shiny application that implements various MAIC methods. The Shiny-MAIC application offers choice between anchored or unanchored MAIC, choice among different types of covariates and outcomes, and two variance estimators including bootstrap and robust standard errors. An example with simulated data is provided to demonstrate the utility of the Shiny-MAIC application, enabling a user-friendly approach conducting MAIC for healthcare decision-making. The Shiny-MAIC is freely available through the link: https://ziren.shinyapps.io/Shiny_MAIC/.

Population-adjusted indirect comparisons, developed in the 2010s, enable comparisons between two treatments in different studies by balancing patient characteristics in the case where individual patient-level data (IPD) are available for only one study. Health technology assessment (HTA) bodies increasingly rely on these methods to inform funding decisions, typically using unanchored indirect comparisons (i.e., without a common comparator), due to the need to evaluate comparative efficacy and safety for single-arm trials. Unanchored matching-adjusted indirect comparison (MAIC) and unanchored simulated treatment comparison (STC) are currently the only two approaches available for population-adjusted indirect comparisons based on single-arm trials. However, there is a notable underutilisation of unanchored STC in HTA, largely due to a lack of understanding of its implementation. We therefore develop a novel way to implement unanchored STC by incorporating standardisation/marginalisation and the NORmal To Anything (NORTA) algorithm for sampling covariates. This methodology aims to derive a suitable marginal treatment effect without aggregation bias for HTA evaluations. We use a non-parametric bootstrap and propose separately calculating the standard error for the IPD study and the comparator study to ensure the appropriate quantification of the uncertainty associated with the estimated treatment effect. The performance of our proposed unanchored STC approach is evaluated through a comprehensive simulation study focused on binary outcomes. Our findings demonstrate that the proposed approach is asymptotically unbiased. We argue that unanchored STC should be considered when conducting unanchored indirect comparisons with single-arm studies, presenting a robust approach for HTA decision-making.

The "LFK" index has been promoted as an improved method to detect bias in meta-analysis. Putatively, its performance does not depend on the number of studies in the meta-analysis. We conducted a simulation study, comparing the "LFK" index test to three standard tests for funnel plot asymmetry in settings with smaller or larger group sample sizes. In general, false positive rates of the "LFK" index test markedly depended on the number and size of studies as well as the between-study heterogeneity with values between 0% and almost 30%. Egger's test adhered well to the pre-specified significance level of 5% under homogeneity, but was too liberal (smaller groups) or conservative (larger groups) under heterogeneity. The rank test was too conservative for most simulation scenarios. The Thompson-Sharp test was too conservative under homogeneity, but adhered well to the significance level in case of heterogeneity. The true positive rate of the "LFK" index test was only larger compared with classic tests if the false positive rate was inflated. The power of classic tests was similar or larger than the "LFK" index test if the false positive rate of the "LFK" index test was used as significance level for the classic tests. Under ideal conditions, the false positive rate of the "LFK" index test markedly and unpredictably depends on the number and sample size of studies as well as the extent of between-study heterogeneity. The "LFK" index test in its current implementation should not be used to assess funnel plot asymmetry in meta-analysis.

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.

Despite the growing interest in incorporating response time data into item response models, there has been a lack of research investigating how the effect of speed on the probability of a correct response varies across different groups (e.g., experimental conditions) for various items (i.e., differential response time item analysis). Furthermore, previous research has shown a complex relationship between response time and accuracy, necessitating a functional analysis to understand the patterns that manifest from this relationship. In this study, response time data are incorporated into an item response model for two purposes: (a) to examine how individuals' speed within an experimental condition affects their response accuracy on an item, and (b) to detect the differences in individuals' speed between conditions in the presence of within-condition effects. For these two purposes, by-variable smooth functions are employed to model differential and functional response time effects by experimental condition for each item. This model is illustrated using an empirical data set to describe the effect of individuals' speed on their reading comprehension ability in two experimental conditions of reading medium (paper vs. digital) by item. A simulation study showed that the recovery of parameters and by-variable smooth functions of response time was satisfactory, and that the type I error rate and power of the test for the by-variable smooth function of response time were acceptable in conditions similar to the empirical data set. In addition, the proposed method correctly identified the range of response time where between-condition differences in the effect of response time on the probability of a correct response were accurate.

Contribution: A novel proactive and collaborative learning paradigm was proposed to engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary artificial intelligence (AI)-cybersecurity knowledge. Specifically, the proposed learning paradigm contains: 1) an immersive learning environment to motivate the students for exploring AI/ machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects and 2) a proactive education paradigm designed with the use of collaborative learning activities based on game-based learning and social constructivism. Background: Increasing evidence shows that AI techniques can be manipulated, evaded, and misled, which can result in new and profound security implications. There is an education and training gap to foster a qualified cyber-workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. Efforts have been made to incorporate a comprehensive curriculum to meet the demand. There still remain essential challenges for effectively educating students on the interaction of AI and cybersecurity. Intended Outcomes: A novel proactive and collaborative learning paradigm is proposed to educate and train a qualified cyber-workforce in this new era where security breaches, privacy violations, and AI have become commonplace. Application Design: The development of this learning paradigm is grounded in the pedagogical approaches of technology-mediated learning and social constructivism. Findings: Although the research work is still ongoing, the prototype learning paradigm has shown encouraging results in promoting the learners' engagement in applied AI learning.

Most existing diagnostic models are developed to detect whether students have mastered a set of skills of interest, but few have focused on identifying what scientific misconceptions students possess. This article developed a general dual-purpose model for simultaneously estimating students' overall ability and the presence and absence of misconceptions. The expectation-maximization algorithm was developed to estimate the model parameters. A simulation study was conducted to evaluate to what extent the parameters can be accurately recovered under varied conditions. A set of real data in science education was also analyzed to examine the viability of the proposed model in practice.

Identifying items with differential item functioning (DIF) in an assessment is a crucial step for achieving equitable measurement. One critical issue that has not been fully addressed with existing studies is how DIF items can be detected when data are multilevel. In the present study, we introduced a Lord's Wald X[superscript 2] test-based procedure for detecting both uniform and non-uniform DIF with polytomous items in the presence of the ubiquitous multilevel data structure. The proposed approach is a multilevel extension of a two-stage procedure, which identifies anchor items in its first stage and formally evaluates candidate items in the second stage. We applied the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm to estimate multilevel polytomous item response theory (IRT) models and to obtain accurate covariance matrices. To evaluate the performance of the proposed approach, we conducted a preliminary simulation study that considered various conditions to mimic real-world scenarios. The simulation results indicated that the proposed approach has great power for identifying DIF items and well controls the Type I error rate. Limitations and future research directions were also discussed.

Forced-choice (FC) measures have been widely used in many personality or attitude tests as an alternative to rating scales, which employ comparative rather than absolute judgments. Several response biases, such as social desirability, response styles, and acquiescence bias, can be reduced effectively. Another type of data linked with comparative judgments is response time (RT), which contains potential information concerning respondents' decision-making process. It would be challenging but exciting to combine RT into FC measures better to reveal respondents' behaviors or preferences in personality measurement. Given this situation, this study aims to propose a new item response theory (IRT) model that incorporates RT into FC measures to improve personality assessment. Simulation studies show that the proposed model can effectively improve the estimation accuracy of personality traits with the ancillary information contained in RT. Also, an application on a real data set reveals that the proposed model estimates similar but different parameter values compared with the conventional Thurstonian IRT model. The RT information can explain these differences.

Although parallel analysis has been found to be an accurate method for determining the number of factors in many conditions with complete data, its application under missing data is limited. The existing literature recommends that, after using an appropriate multiple imputation method, researchers either apply parallel analysis to every imputed data set and use the number of factors suggested by most of the data copies or average the correlation matrices across all data copies, followed by applying the parallel analysis to the average correlation matrix. Both approaches for pooling the results provide a single suggested number without reflecting the uncertainty introduced by missing values. The present study proposes the use of an alternative approach, which calculates the proportion of imputed data sets that result in k (k = 1, 2, 3 . . .) factors. This approach will inform applied researchers of the degree of uncertainty due to the missingness. Results from a simulation experiment show that the proposed method can more likely suggest the correct number of factors when missingness contributes to a large amount of uncertainty.

The forced-choice response format is often considered superior to the standard Likert-type format for controlling social desirability in personality inventories. We performed simulations and found that the trait information based on the two formats converges when the number of items is high and forced-choice items are mixed with regard to positively and negatively keyed items. Given that forced-choice items extract the same personality information as Likert-type items do, including socially desirable responding, other means are needed to counteract social desirability. We propose using evaluatively neutralized items in personality measurement, as they can counteract social desirability regardless of response format.

We present a case-based role-play exercise that effectively simulates a crisis students must manage. In the exercise, a farm equipment manufacturing company has decided to redirect its product line, and this change requires laying off a significant portion of its workforce. Students must grapple with the fact that a local news report will be released soon to inform the public about the company's plans, possibly before the company's employees learn about them. Students assume one of five roles in the executive team and discuss how to resolve the crisis. The objective of the exercise is to help students learn ways to lead in an organizational crisis. To meet this objective, we discuss how instructors can implement and debrief the exercise. This exercise was designed as the second in a two-part series (the first part focusing on organizational change), but it can also be used as a stand-alone exercise.

The development of mathematical reasoning skills has increasingly been of focus for the teaching and learning of mathematics. This research utilizes a teaching simulation using the methodology of scriptwriting, in which prospective teachers are asked to complete a script of a dialogue from a classroom simulation involving fraction multiplication and division with justification, assisting fictional students to work through their difficulties and helping them to justify their reasoning. Such tasks allow for the examination of the prospective teacher moves to support student reasoning through their imagined action and choice of words. Scripts from forty-one prospective primary teachers were examined for the study, and five clusters based on the type of teacher move for supporting student reasoning were found. Overall, the prospective teachers emphasized the elicitation and facilitation of students' ideas. The cluster analysis, however, provided a nuanced examination of the cohort's teacher moves. While cluster one saw the highest incident of "eliciting" teacher moves, albeit only in the low potential category, clusters two and three mostly used "facilitating" teacher moves, but varied in their use of high and low potential moves. Cluster four concentrated moves on "facilitating, eliciting," and "responding to student reasoning." Cluster five employed teacher moves from all main categories, with some instances of high potential moves in all categories except "extending student reasoning," which can better support reasoning. The prospective mathematics teachers' scripts and the five clusters that were found during analysis are discussed with implications for future teacher education and the support of building mathematical reasoning.

Business simulation games (BSG) offer a unique opportunity to provide students with hands-on experience in a simulated business environment. This study aims to explore the effectiveness of BSG in promoting entrepreneurial intentions and competencies among undergraduate students. The study was conducted with 48 students, who participated in BSG as part of their entrepreneurship education (EE). The study used a quasi-experiment to measure changes in students' cognitive and non-cognitive entrepreneurial competencies, as well as their entrepreneurial intentions. The results show that participation in the BSG significantly improved students' cognitive and non-cognitive entrepreneurial competencies, as well as their intention to start a new business. The study concludes that BSG can be an effective teaching strategy for promoting EE and developing entrepreneurial competencies among undergraduate students. However, further research with larger sample sizes and diverse populations is needed to confirm these findings and explore how combining BSG with other teaching methods or interventions can enhance the development of entrepreneurial competencies and intentions.

The early years of physiology education in medical curricula provide unique challenges. As well as inculcating concepts that are seen as difficult, modern curricula require that students learn in context in case-based learning courses. Additionally, regulating bodies stress that the soft skills of compassion, communication, and empathy are embedded throughout curricula. This has driven work in our organization involving drama and final-year medicine students during which they collaborate in realistic simulations of doctor/patient interactions. We adapted this transdisciplinary approach to second-year physiology tutorials. This emphasized the holistic importance of physiology to patient care, while also embedding "human factors" skills from the very earliest stages of the curriculum. After preparing by attending acting classes based on aspects of Konstantin Stanislavski's "System," the authors supervised tutorials in which drama students participated in a "physiology of hypofertility" session for second-year medical students, playing a 34-year-old woman with premature menopause (or their partner). Opinion (from all students) was evaluated by Likert questionnaires (which included open questions). A focus group of drama students was also interviewed, and the conversation was recorded for thematic analysis. Positive Likert scores were recorded for the authenticity of the tutorials, skills development, fostering empathy, and motivating students to improve. All participants evaluated the tutorial as highly enjoyable. These scores are reflected in positive open commentary on the questionnaires and in the focus group interviews. The results suggest that even basic science tutorials give opportunities for interdisciplinary study and enhancement of behavioral skills while gaining enthusiastic student acceptance.

Background: Electronic portfolios (e-portfolios) are valuable tools to scaffold workplace learning. Feedback is an essential element of the learning process, but it often lacks quality when incorporated in ePortfolios, while research on how to incorporate feedback into an ePortfolio design is scarce. Objectives: To compare the ease of use, usefulness and attitude among three feedback formats integrated in an ePortfolio: open-text feedback, structured-text feedback and speech-to-text feedback. Methods: In a mixed method designed experiment, we tested with 85 participants from different healthcare disciplines, three feedback formats in an ePortfolio prototype. Participants provided feedback on students' behaviour after observing video-recorded simulation scenarios. After this, participants completed a questionnaire derived from the Technology Acceptance Model (TAM). The experiment ended with a semi-structured interview. Results: Structured-text feedback received highest scores on perceived ease of use, usefulness, and attitude. This type of feedback was preferred above open-text feedback (currently the standard), and speech-to-text feedback. However, qualitative research results indicated that speech-to-text feedback is potentially valuable for feedback input on-premise. Respondents would use it to record short feedback immediately after an incident as a reminder for more expanded written feedback later or to record oral feedback to a student. Implications: Structured-text feedback was recommended over open-text feedback. The quality of the speech-to-text technology used in this experiment, was insufficient to use in a professional ePortfolio but holds the potential to improve the feedback process and should be considered when designing new versions of ePortfolios for healthcare education.

Learning engagement is an essential indication to define students' learning pacification in the class, and its automated identification technique is the foundation for exploring how to effectively explain the motive of learning impact modifications and making intelligent teaching choices. Current research have demonstrated that there is a direct link between learning engagement and emotional investment and behavioural investment, and it is appropriate and required to apply artificial intelligence to perform autonomous assessment. Unfortunately, the number of relevant research is limited, and the features of learning engagement in certain contexts have not been thoroughly examined. In this research, we highlight the features of a particular application scenario of learning engagement: the application scenario of learning engagement has to incorporate both the coarse-grained information of human body position and the fine-grained information of facial expressions. On the basis of this analysis, a fine-grained learning participation recognition model that suppresses background clutter information is presented. This model can effectively extract coarse and fine-grained information to improve the recognition of learning participation in real-world teaching situations. Particularly, the CNN-Transformer model suggested in this study employs CNN to extract fine-grained information of facial expressions and Transformer to recover coarse-grained information of human body position. Simultaneously, we gathered and categorised real teaching data based on the features of learning engagement situations and enhanced the data quality via crowdsourcing and expert verification. The experimental findings indicate that the CNN-Transformer model can accurately predict the learning engagement of unknown participants with a 92.9% rate of accuracy. Comparative trials reveal that the model's recognition impact is much greater than that of other sophisticated deep learning approaches. Our research offers a framework for future work on deep learning approaches in learning engagement settings.

There is already an abundant literature on the virtues of simulation in education. However, there is also a lack of assessment instruments to determine the impact of virtual simulation and virtual exchange on participants' self-realisation of learning in teacher education. The aim of this study is to identify the variables that best describe students' learning of current educational issues. A questionnaire consisting of Likert-type items was used as a metacognitive assessment tool to provide data for the study. An exploratory longitudinal study was conducted over three years. Data were collected using students' answers to the questionnaire. A convenience sampling procedure was used, with participating students enrolled in the Didactic Resources module of English as a Second Language (ESL) in an Official Master's Degree in Teacher Training in Compulsory Secondary Education and Baccalaureate, Vocational Training, and Language Teaching. The most strongly correlated variables that best explain the dependent variable ("I have gained deeper insights into current educational issues") addressed inclusion, special learning needs, service learning, and simulation through intercultural dialogue. Each of the variables significantly contributes to the estimation of perceived acquisition of insight into current educational issues through virtual simulation.

Recent solar eclipses provide relevant real-world contexts for learning about the scientific phenomena of the lunar phases. News coverage of the phenomenon may have raised questions such as, "Why does the Moon look different at different times, and sometimes as if it is not there?," and "What patterns can be found in the lunar phases?" Teachers can use these recent events to launch investigations into the mathematics of the phases. Connecting learning across the mathematics and science disciplines provides opportunities for students to deepen their conceptual understanding and apply what they have learned in new contexts (Vasquez et al., 2013). In this article, the authors present how they used the Moon Pie simulation in a sixth-grade science classroom to bridge the mathematics of angle measurement, fractions, covariation, and co-splitting (multiplicative/proportional covariation) with the scientific phenomenon of lunar phases.

Museum learning is beneficial for social inclusion, deepening partnerships between schools and museums, and increasing levels of pupil attainment. While there have been numerous empirical studies on the use of haptics in formal educational settings, few have explored the effect of haptic interaction on learning outcomes in museum learning. This study looks at an interactive 3D artifact simulation using a haptic interface and a non-haptic interface, with one group using 3D hand motions and receiving visual/haptic stimuli, and another group using a mouse and only receiving visual stimuli. Forty individuals majored in arts or social science courses were asked to perform four main interactive tasks about 3D collection. Using a triangulation of assessment scores, investing time, and satisfaction with interactions with the 3D artifact simulation, we explored the efficacy of haptic interaction in improving museum learning. The results showed that in general, the haptic interaction was more helpful in promoting learning performance in relation to 3D collections. However, significant differences only occurred in relation to the volume and material interactive tasks, and not in relation to the contour and color interactive tasks. The Findings reveal that the visual/haptic stimuli provided by haptic interaction in museum learning has a stronger modality effect on human information processing, and the effect of haptic interaction depends on the coupling of interactive tasks and sensorimotor experiences. Further, psychological immersion is more likely to occur when using haptic interaction, and haptically augmented 3D artifacts attract learners' attention, enhancing learner engagement and motivation. Explanations for these results are synthesized from the perceptual symbol, embodied cognition, and immersion theories.

Medical personnel often experience stress when responding to a medical emergency. A known stress-response is a measurable reduction in heart rate variability. It is currently unknown if crisis simulation can elicit the same stress response as real clinical emergencies. We aim to compare heart rate variability changes amongst medical trainees during simulated and real medical emergencies. We performed a single center prospective observational study, enrolling 19 resident physicians. Heart rate variability was measured in real time, using a 2-lead heart rate monitor (Bodyguard 2, Firstbeat Technologies Ltd) worn during 24 h critical care call shifts. Data was collected at baseline, during crisis simulation and when responding to medical emergencies. 57 observations were made to compare participant's heart rate variability. Each heart rate variability metric changed as expected in response to stress. Statistically significant differences were observed between baseline and simulated medical emergencies in Standard Deviation of the N-N interval (SDNN), Root mean square standard deviation of the N-N interval (RMSSD), Percentage of successive R-R intervals that differ by more than 50 ms (PNN50), Low Frequency (LF) and Low Frequency: High Frequency ratios (LF:HF). No statistically significant differences between simulated and real medical emergencies were identified in any heart rate variability metrics. We have shown using objective results, that simulation can elicit the same psychophysiological response as actual medical emergencies. Therefore, simulation may represent a reasonable way to practice not only essential skills in a safe environment but has the additional benefit of creating a realistic, physiological response in medical trainees.

The education faculty aims to assess departmental effectiveness by analyzing the relationship between service levels, output variables, and input variables. This objective is coupled with the formulation of faculty development strategies tailored to enhance efficiency while accommodating individual professionals' unique requirements and aspirations. Furthermore, beyond the annual University Evaluation System, faculty members conduct thorough evaluations to identify discrepancies in departmental reports detailing service levels and input components (factors of production) over a three-year period. Through descriptive statistics and hypothesis testing, reliable data guides the decision-making process regarding the optimal level for data envelopment analysis (DEA). The study employs four simulated scenarios to evaluate the overall performance of five departments using DEA. Findings reveal varying efficacy ratings across departments, with the Department of Health and Physical Education achieving a moderate rating and the Educational Technology Department exhibiting the lowest efficacy. Conversely, departments such as Art Education, Music Education, and Business Education showcase exemplary efficacy levels. Insights gleaned from quantitative analysis and questionnaires contribute significantly to faculty development, enhancing knowledge, competencies, values, attitudes, and future endeavors. Recommendations provided advocate for the widespread implementation of efficacious approaches to bolster faculty efficacy. Moreover, the study underscores the potential to enhance the efficiency and quality of service across faculties through the adoption of best practices and initiatives.

Purpose: The purpose of this article is to promote the viewpoint that simulation debriefing is a critical element of the simulation experience, which serves to facilitate students' ability to synthesize information and construct new mental models as they prepare for their future work as speech-language pathologists. The use of simulations in communication sciences and disorders (CSD) has grown significantly over the last decade. This article reviews key concepts in clinical simulation pedagogy and highlights the importance of debriefing as part of the simulation learning experience. Current guidelines and standards for simulations and debriefing in CSD are discussed, and debriefing models commonly used in nursing and medicine are described. Suggestions are provided to enhance current debriefing practices and guide future research in CSD. Conclusions: Clinical simulation, particularly the debriefing component, has the power to close the gaps in students' clinical knowledge and skills and to prepare students for robust and diverse careers. In a review of debriefing models originating from nursing and medicine, several crucial debriefing components are noted. Students need time to (a) react to and defuse emotions related to the simulation experience, (b) gather and describe key simulation details, (c) analyze simulation outcomes, and (d) summarize new knowledge and consider how it applies to future contexts. Additional research is needed to continue developing and refining simulation and debriefing best practices in CSD. Debriefing models from nursing and medicine ought to be explored and replicated in future CSD research.

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.

Statistical process control (SPC) charts have been widely used in the field of educational measurement. The cumulative sum (CUSUM) is an established SPC method to detect aberrant responses for educational assessments. There are many studies that investigated the performance of CUSUM in different test settings. This paper describes the CUSUM procedure and shows how it can be used to monitor the testtaking process and detect aberrant responses. It aims to provide an accessible guide for the CUSUM control chart.