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This study aims to focus on providing a computerized classification testing (CCT) system that can easily be embedded as a self-assessment feature into the existing legacy environment of a higher education institution, empowering students with self-assessments to monitor their learning progress and following strict data protection regulations. The purpose of this study is to investigate the use of two different versions (without dashboard vs with dashboard) of the CCT system during the course of a semester; to examine changes in the intended use and perceived usefulness of two different versions (without dashboard vs with dashboard) of the CCT system; and to compare the self-reported confidence levels of two different versions (without dashboard vs with dashboard) of the CCT system.

A total of N = 194 students from a higher education institution in the area of economic and business education participated in the study. The participants were provided access to the CCT system as an opportunity to self-assess their domain knowledge in five areas throughout the semester. An algorithm was implemented to classify learners into master and nonmaster. A total of nine metrics were implemented for classifying the performance of learners. Instruments for collecting co-variates included the study interest questionnaire (Cronbach’s a = 0. 90), the achievement motivation inventory (Cronbach’s a = 0. 94), measures focusing on perceived usefulness and demographic data.

The findings indicate that the students used the CCT system intensively throughout the semester. Students in a cohort with a dashboard available interacted more with the CCT system than students in a cohort without a dashboard. Further, findings showed that students with a dashboard available reported significantly higher confidence levels in the CCT system than participants without a dashboard.

The design of digitally supported learning environments requires valid formative (self-)assessment data to better support the current needs of the learner. While the findings of the current study are limited concerning one study cohort and a limited number of self-assessment areas, the CCT system is being further developed for seamless integration of self-assessment and related feedback to further reveal unforeseen opportunities for future student cohorts.

Current Army doctrine stresses a need for military leaders to have the capability to make flexible and adaptive decisions based on a future unknown environment, location and enemy. To assess a military decision maker’s ability in this context, this paper aims to modify the Wisconsin Card Sorting Test which assesses cognitive flexibility, into a military relevant map task. Thirty-four military officers from all service branches completed the map task.

The purpose of this study was to modify a current psychological task that measures cognitive flexibility into a military relevant task that includes the challenge of overcoming experiential bias, and understand underlying causes of individual variability in the decision-making and cognitive flexibility behavior of active duty military officers on this task.

Results indicated that non-perseverative errors were a strong predictor of cognitive flexibility performance on the map task. Decomposition of non-perseverative error into efficient errors and random errors revealed that participants who did not complete the map task changed their sorting strategy too soon within a series, resulting in a high quantity of random errors.

This study serves as the first step in customizing cognitive psychological tests for a military purpose and understanding why some military participants show poor cognitive flexibility.

Cognitive dysfunctions are a common clinical feature of schizophrenia and represent important indicators of outcome among patients who are affected. Therefore, a randomized, controlled, monocentric, single-blind trial was carried out to compare two different rehabilitation strategies adopted for the restoration and recovery of cognitive functioning of residential patients with schizophrenia. A sample of 110 residential patients were selected and, during the experimental period, a group of 55 patients was treated with sets of domain-specific exercises (SRT+CRT), whereas an equal control group was treated with sets of non-domain-specific exercises (SRT+PBO) belonging to the Cogpack® software. The effects on the scores (between T0 and T1) of the variables treatment and time and of the interaction time X treatment were analyzed: for the total BACS, the main effect of the between-factors variable treatment is statistically significant (F=201.562 P=0.000), as well as the effect of the within-factors variable “time” (F=496.68 P=0.000).The interaction of these two factors is also statistically significant (F=299.594 P=0.000). The addition of cognitive remediation therapy (CRT) to a standard treatment of metacognitive training (MCT) resulted in a significant improvement in global neurocognitive functioning and has reported positive effects with regard to the strengthening of verbal and working memory, selective and sustained attention at T1. A relevant result is the statistically significance of “time X treatment” for all the tests administered: we can assume that the domain-specific cognitive training amplifies the effects of SRT, as the primary and secondary goals of the present study were achieved.

The term “agent-based modelling” (ABM) is a buzzword which is widely used in the scientific literature even though it refers to a variety of methodologies implemented in different disciplinary contexts. The numerous works dealing with ABM require a clarification to better understand the lines of thinking paved by this approach in economics. All modelling tasks are a means and a source of knowledge, and this epistemic function can vary depending on the methodology. this paper is to present four major ways (deductive, abductive, metaphorical and phenomenological) of implementing an agent-based framework to describe economic systems. ABM generates numerous debates in economics and opens the room for epistemological questions about the micro-foundations of macroeconomics; before dealing with this issue, the purpose of this paper is to identify the kind of ABM the author can find in economics.

The profusion of works dealing with ABM requires a clarification to understand better the lines of thinking paved by this approach in economics. This paper offers a conceptual classification outlining the major trends of ABM in economics.

There are four categories of ABM in economics.

This paper suggests a methodological categorization of ABM works in economics.

The hospitality and tourism industry faces a number of workforce challenges, especially the high turnover rates and associated replacement costs associated with continually identifying and hiring new employees. The purpose of this paper is to discuss how hospitality and tourism organizations can use electronic human resource management (eHRM) and artificial intelligence (AI) to help recruit and select qualified employees, increase individual retention rates and decrease the time needed to replace employees. Specifically, it discusses how e-recruiting and e-selection and AI tools can help hospitality and tourism organizations improve recruiting and selection outcomes.

Research on eHRM, AI, employee recruitment and employee selection are applied to the hospitality and tourism industry and insights for how eHRM and AI can be applied to the industry are discussed.

eHRM and AI have the potential to transform how the hospitality and tourism industry recruit and select employees. However, care must be taken to ensure that the insights gained and the decisions made are well received by employees and lead to better employee and organizational outcomes.

This paper represents the first research that integrates research from eHRM and AI and applies it to the hospitality and tourism industry.

This paper represents the first research that integrates research from eHRM and AI and applies it to the hospitality and tourism industry.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

Entrepreneurship and the development of new business continue to be the forefront of socioeconomic development in virtually all economies today. Despite evidence of increasing research into entrepreneurial growth, the existing research is limited by the fact that most studies define entrepreneurial growth as a unidimensional construct and operationalize it as “realized” growth relying on financially based measures. Consequently, this article has two objectives: (1) to develop a set of accurate and comprehensive entrepreneurial growth measures; and (2) to test a series of hypotheses regarding precursors of growth intentions‐more specifically, to what extent, infrastructure factors affect entrepreneurial growth intentions. These two questions were examined using Entrepreneurial Profile Questionnaire (EPQ) in the context of Romania.

Results from factor analysis revealed refined patterns of entrepreneurial growth, including resource aggregation, market expansion, and technological improvement. The relationships between infrastructure and entrepreneurial growth were tested using a multiple regression model. Overall, it was posited that infrastructure is positively related to entrepreneurial growth. However, in most of the cases, the opposite proved to be true. These findings suggest that the Romanian entrepreneurs would pursue expansion plans in spite of the obstacles thrown into their path. Perhaps they have already developed strategies about overcoming those obstacles and in that process have developed the strength, ingenuity, and confidence to grow their new business ventures. Perhaps the many years that Romanians were confronted with numerous political and economical obstacles have prepared them to be much more flexible and adaptive.These counter-intuitive findings reflect on the hardiness and perseverance of the Romanian entrepreneurs.