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The purpose of this study is to highlight and demonstrate how the study of stress and related responses in construction can best be measured and benchmarked effectively.

A range of perceptual and physiological measures are obtained across different time periods and during different activities in a fieldwork setting. Differences in the empirical results are analysed and implications for future studies of stress discussed.

The results of this study strongly support the use of multiple psychometrics and biosensors whenever biometrics are included in the study of stress. Perceptual, physiological and environmental factors are all shown to act in concert to impact stress. Strong conclusions on the potential drivers of stress should then only be considered when consistent results apply across multiple metrics, time periods and activities.

Stress is an incredibly complex condition. This study demonstrates why many current applications of biosensors to study stress in construction are not up to the task and provides empirical evidence on how future studies can be significantly improved.

To the best of the author’s knowledge, this is the first study to focus explicitly on demonstrating the need for multiple research instruments and settings when studying stress or related conditions in construction.

This compact case provides a relatively large data set that students explore using visualization and a Tableau dynamic dashboard that they create. Students were asked to describe what the data set contained in relation to employee attrition experience of Baca Beverage Distributors (BBD). The application and managerial questions are set in human resources and a company that is facing high attrition during the pandemic.

BBD shared their data and problem scenario for this compact case. The protagonist, Morgan Matthews, was the authors’ contact and provided significant clarification and guidance about the data. Both the company and the protagonist have been disguised. Some of the job positions have been rephrased. All names of employees, supervisors and managers have been replaced with codes.

During the 2020–2022 pandemic years, BBD experienced, like many companies, a higher than usual employee turnover rate and Morgan Matthews, Director of People, was concerned. Not only was it time-consuming, expensive and disruptive but the company had prided itself on being a good place to work. Were they hiring the right people, people that fit the company culture and people that fit the positions for which they were hired? The company had been using the Predictive Index [1] when on-boarding employees. In addition, there were results from self-reviews and manager reviews that could be used. Morgan wondered if data visualization and visual analytics would be useful in describing their employees and whether it would reveal any opportunities to improve the turnover rate. Before seeking a solution for the high turnover, it was important to step back and learn what the data said about who was leaving and the reasons they gave for leaving.

This compact case can be used in courses that include visualization using Tableau and dashboards. As it is a compact case, it requires less preparation time from the students and less class time for discussion. The case is for students who have been recently introduced to business analytics, specifically visualization and data storytelling with Tableau. For this reason, significant guidance has been provided in the case assignment. The level of the case can be adjusted by the amount of guidance provided in the case assignment. Courses include introduction to business analytics, descriptive analytics and visualization, communication through data storytelling. The case can be used for all modalities – in person, hybrid, online. The authors use it here for visualization and dynamic dashboards but using the same data set and compact case description, exploratory data analysis could be assigned.

Supplementary material for this article can be found online.

The number of students from culturally and linguistically diverse (CLD) backgrounds continue to increase in classrooms across the United States. These students have complex needs as they experience more barriers to success when compared to their peers. These barriers are further compounded when CLD students are also identified as having disabilities. To address the barriers and meet the needs of CLD students with disabilities, teaching professionals should move away from the traditional American educational values of individual freedom and self-reliance, equal opportunity and competition, and material wealth and hard work. Conversely, schools and teaching professionals should incorporate the modern values of social justice, diversity, equity, inclusion, accessibility, and belonging when working with students from CLD backgrounds who have disabilities. This chapter presents these values and provides recommendations for teaching professionals and schools.

This chapter provides an overview on the traditions and values of teaching students with traumatic brain injury (TBI). First, we discuss the prevalence, identification, and characteristics associated with TBI and how those characteristics affect learning, behavior, and daily life functioning. Next, we focus on instructional and behavioral interventions used in maintaining the traditions in classrooms for working with students with TBI. Findings from a review of the literature conclude that there are no specific academic curriculums designed specifically for teaching students with TBI; however, direct instruction and strategy instruction have been shown to be effective educational interventions. Current research on students with TBI is predominately being conducted in medical centers and clinics focusing on area of impairments (e.g., memory, attention, processing speed) rather than academic achievement and classroom interventions. Finally, we conclude with a list of accommodations and a discussion of recommendations for future work in teaching students with TBI.

The study explores diverse and critical understandings of “international” in a higher education curriculum context, situated in a curriculum review of a postgraduate taught programme entitled “International Education” at a university located in England. Our study problematises and decentres some dominant, normalised notions of “international”, exploring critical possibilities of engaging with the term for higher education internationalisation.

We examined a set of programme curriculum documents and conducted a survey exploring teaching staff’s uses and interpretations of “international” in their design and delivery of course units. Through a thematic analysis of the dataset, we identify what “international” might mean or how it may be missing across the curriculum.

Our findings suggest a locally-developed conceptualisation of “international” beyond the normalised interpretation of “international” as the inclusion or comparison of multiple nations, and different, other countries around the global world. More diverse, critical understandings of the term have been considered, including international as intercultural, competences, ethics, languages and methods. The study provides an example approach to reflective scholarship that programmes can undergo in order to develop clarity, depth and purposefulness into internationalisation as enacted in a local curriculum context.

The study provides a first step towards establishing clearer guidelines on internationalising the curriculum by higher education institutions and individual programmes in order to challenge a superficial engagement of “international” within internationalisation. It exemplifies a starting point for making purposeful steps away from normalised notions and assumptions of international education and facilitates development towards its critical, ethically-grounded opportunities.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

To support the advancement of an underrepresented category of research in the field of entrepreneurial teams, this study proposes and tests a novel empirical model that connects two team emergent states, namely team entrepreneurial passion (TEP) and transactive memory systems (TMSs), and their influence on team performance.

The data were gathered using an online questionnaire distributed to undergraduate students who had formed entrepreneurial teams as part of a course assignment. Two methods were executed on the obtained data, namely partial least-square structural equation modelling (PLS-SEM) and necessary condition analysis (NCA).

The results uphold the hypothesised mediation role of TMSs between TEP and team performance. Of the two direct relations in the model, only the necessary conditions were present for the effect of TEP on TMSs.

The issue of the small sample size, a common feature in entrepreneurial team research, as discussed in the methodical section of the paper, is sidestepped with the use of PLS-SEM tools. Nonetheless, a larger sample size could have increased confidence in the results' validity. In addition, a longitudinal approach to data collection and analysis could have been used to augment that confidence further.

Three practical implications stem from the empirical findings. First, it lends support for implementing teaching approaches and task designs that are envisaged to improve team functioning in university classrooms. Making a business plan boosts students' desire to exploit the received knowledge and find a venture, so the teaching effort in entrepreneurship courses can have real-world consequences.

By testing the mediation model, new insights are made into the associations between team emerging states and, subsequently, team performance. In addition, this study responds to recent calls in the literature to incorporate NCA in an entrepreneurial setting.

Higher education institutions and their lecturers are strategic agents and main drivers that contribute to circular economy transition. This requires them to understand the key circular economy competencies and how to integrate circular economy holistically into their curricula with the suitable teaching and learning approaches. This study aims to support them by providing an overview on the characteristics of education for the circular economy (ECE) and suggestions to lecturers to further develop their curricula.

The data consisted of scientific articles (n = 22) describing circular economy courses in higher education. Qualitative content analysis with quantitative features was performed on the selected articles to answer the research question.

The findings confirm that the system’s focus is the key issue in ECE. However, to integrate circular economy holistically into the curricula, ECE should be implemented more widely in the context of different industries and market contexts to find innovative teaching and learning approaches. The demand side needs to be incorporated in the courses, as systemic transformation is also about transforming consumption. All levels of implementation and circular economy objectives should be included in courses to promote systems thinking. In addition, innovative forms of real workplace interaction should be increased.

As ECE has started to emerge as a new field of study, this article provides the first integrated overview of the topic.

In the wake of the COVID-19 pandemic, remote working (RW) has emerged as a viable alternative to working employees in general and knowledge workers in particular. However, previous researchers have worked on the concept, development and facilitation of RW since the 1970s. Therefore, this study aims to review the existing literature on RW to ascertain the evolution of the concept in the business and management domain and provide for requisite arguments to extend the settings for future research agendas.

The authors based this study on a bibliometric analysis of articles (n = 349) retrieved from the Web of Science database published between January 1990 and October 2021. The authors have used a bibliometric toolbox comprising performance analysis, science mapping and network analysis in various software namely, VOSviewer, Gephi and Biblioshiny package in R.

The study’s results accentuated important themes like work–life balance, strengthening digital infrastructure, performance and productivity, hybrid work models and well-being and clustered them under four heads with proposed future research questions.

The study is based on a single database; the authors have used an extensive but not exhaustive list of keywords to retrieve the articles. The analysis employs certain threshold limits while using the science mapping technique.

This study would enable managers and academics to comprehensively understand remote work and offer logical implications to appreciate its nuances.

This study is unique as it recognizes the intellectual structure in the existing literature on RW and traces the advancements and exponential growth post-COVID-19. The authors recapitulated the literature as network analysis of the RW facilitation model comprising the antecedents, outcomes, mediators and moderators.

Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions.

The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data.

The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs.

The proposed approach explores the possibility of using machine learning for SDG classifications in scale.