Search results

This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms.

This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning.

Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables.

Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

This study empirically tests a model of human capital (HC) factors affecting the organisational competitiveness (OC) of automotive parts suppliers in the Industry 4.0 framework, including concepts such as Toyota Kata (TK), Kaizen and Quality 4.0, during the coronavirus disease 2019 pandemic.

An instrument was created to measure emotional intelligence (EI) and analytical skill (AS) as input variables and OC as the output variable. The instrument was distributed electronically to Tier 1 non-technical employees in Nuevo León and Querétaro, México. A total of 195 surveys were obtained. The instrument used stepwise multiple linear regression.

This study proposes a model to strengthen the OC of Tier 1 automotive parts supply industry from the perspective of HC factors. Furthermore, it is shown that EI and AS have a positive and significant impact on OC.

From an HC perspective, this study provides a useful basis to improve OC for researchers, industry experts and managers at different levels of the automotive industry, including the triple helix (academia, industry and the government).

No studies simultaneously test the relationship of EI and AS to OC; therefore, this study fills a gap in the literature. Furthermore, the study explored the literature on individual Kaizen (IK) and TK, leading to a contrast between the definitions of EI and AS. Finally, for EI, a reference to motivation was found in the IK. In the case of AS, an orientation to ability of problem solving was found in TK.

With the rise in demand and adoption of smart city initiatives, it is imperative to plan the railway infrastructure, as it will have a huge positive impact if adequately integrated into the planning process. Given the complexities involved, a whole systems thinking framework provides a useful platform for rail transport planners.

This paper proposes a simple, adoptable framework utilising systems thinking concepts and techniques taking into cognisance the key stakeholders. Milton Keynes in the United Kingdom is the adopted case study.

Selected systems thinking tools and techniques are adopted to develop a framework for mapping stakeholders and attributes when developing sustainable rail transport systems, taking note of their core functionalities and the complex systems wherein they exist.

The desire to build future (smart) cities is to effectively match infrastructural resources with a rapidly growing population, and the railway sector can play a strategic role in building a much more competitive low-carbon-emission transport system, which is a driving force for sustainable development.

The urban rail service has become vital to urban development as railway stations serve as hubs for sustainable mobility to meet local requirements. Moreover, it takes extra effort to input railway development into smart city plans, as it is a herculean task to get governments to focus on it with clarity of purpose in passing legislation.

The developed framework reduces complexities when planning and designing rail transport systems compared to many of the existing reductionist planning approaches. The simplicity of the framework would also make it easily adoptable by a wide range of users.

Construction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.

The study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.

The classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.

The findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.

The classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.

This study examined factors influencing the organisational capabilities of the public sector for building information modelling (BIM) implementation in construction projects with a view to enhancing the performance of public sector projects.

The study adopted a quantitative descriptive analysis that was based on primary data. In total, 198 valid questionnaires obtained from construction professionals within the public sector provided primary quantitative data for the assessment. The respondents provided the responses on the factors which were identified through an in-depth synthesis of literature relating to organisational capabilities of the public sector. Data collected were analysed using descriptive and inferential statistics.

The findings established that the potential of the public sector to deploy BIM in construction projects is greatly influenced by varying degree of organisational capability attributes with bureaucratic culture (mean score, MS = 3.37), structural complexity (MS = 3.17), lack of skilled and trained staff (MS = 3.12), personnel stability (MS = 3.11), staff cooperation (MS = 3.09) and political constraint (MS = 3.07) ranked highest. Through factor analysis, these and other highly influential factors were grouped into eight components, namely management-related, policy-related, technical-related, attitude-related, work structure-related, work ethic-related, decision-related and feedback-related factors. This grouping reflects the various components of organisational capability attributes which the public sector needs to efficiently develop to benefit from project management paradigm introduced by BIM.

This study provided information for improving specific capability attributes with respect to human and technical resources as well as other soft infrastructure to support BIM implementation on building projects by the public sector client. The study also serves as a guide for understanding BIM implementation by the public sector in similar socio-political and economic contexts.

This assessment indicates various degrees by which the organisational attributes of public sector have influenced the attributes' capability to implement BIM on construction projects. Thus, findings provide information on areas of improvement for better implementation of BIM by the public sector in project delivery.

Regardless of the diverse research on big data analytics (BDA) across different supply chains, little attention has been paid to exploit this information across service supply chains. The healthcare supply chains, where supply chain operations consume the second highest expenditures, have not completely attained the potential gains from data analytics. So, this paper explores the challenges of BDA at various levels of healthcare supply chains.

Drawing on the resource-based view (RBV), this research explores the various challenges of big data at organizational and operational level of different nodes in healthcare supply chains. To demonstrate the links among supply chain nodes, the authors have used a supplier-input-process-output-customer (SIPOC) chart to list healthcare suppliers, inputs (such as employees) supplied and used by the main healthcare processes, outputs (products and services) of these processes, and customers (patients and community).

Using thematic analysis, the authors were able to identify numerous challenges and commonalities among these challenges for the case of healthcare supply chains across United Arab Emirates (UAE). An applicable exploration on organizational (Socio-technical) and operational challenges to BDA can enable healthcare managers to acclimate efficient and effective strategies.

The identified common socio-technical and operational challenges could be verified, and their impacts on the sustainable performance of various supply chains should be explored using formal research methods.

This research advances the body of literature on BDA in healthcare supply chains in that (1) it presents a structured approach for exploring the challenges from various stakeholders of healthcare chain; (2) it presents the most common challenges of big data across the chain and finally (3) it uses the context of UAE where government is focusing on medical tourism in the coming years.

Originality of this work stems from the fact that most of the previous academic research in this area has focused on technology perspectives, a clear understanding of the managerial and strategic implications and challenges of big data is still missing in the literature.

The study is aimed at analyzing the perceptions of students and teachers regarding the effectiveness of massive open online courses (MOOCs) on learning efficiency of students and also evaluating MOOCs as an ideal tool for designing a blended model for education.

The analysis was carried out by using the data gathered from the students as well as teachers of University of Mysore, Karnataka, India. Two separate sets of questionnaires were developed for both the categories of respondents. Also, the respondents were required to have prior experience in MOOCs. Further, the collected data was analyzed using statistical package for social sciences (SPSS).

The study showed that MOOCs have a more positive influence on learning efficiency, as opined by both teachers and students. Negative views such as cheating during the assessment, lack of individual attention to students and low teacher-student ratio were also observed.

Many educational institutions view that the MOOCs do not influence learning efficiency and also do not support in achieving their vision. However, this study provides evidence that MOOCs are positively influencing the learning efficiency and also can be employed in a blended model of education so as to promote collaborative learning.

Technology is playing a pivotal role in all fields of life and the education sector is not an exception. It can be rightly said that the technology-based education models such as MOOCs are the need of the hour. This study may help higher education institutions to adopt MOOCs as part of their blended model of education, and, if already adopted, the outcome of the present study will help them to improve the effectiveness of the MOOCs they are offering.

Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

This study aims to investigate the role of team problem-solving creativity in new product development (NPD) in the bottom-of-the-pyramid (BoP) in business-to-business firms. This study synthesizes perspectives from NPD, creativity and leadership to examine how work-related factors such as NPD managers’ role ambiguity and individual-related factors such as CEO’s ambidextrous leadership style interact to determine team problem-solving creativity and its effect on new product performance (NPP).

The hypotheses are tested using data from a multi-informant survey of 274 middle-level managers within 137 local BoP manufacturing firms in a sub-Saharan African country.

The results show that an NPD team’s ability to solve problems creatively determines NPP in BoP markets. The findings also show that NPD managers’ role ambiguity has a negative effect on team problem-solving creativity. However, a CEO’s ambidextrous leadership neutralizes the negative impact of role ambiguity on problem-solving creativity.

This study combines three distinct streams of literature, including NPD, creativity and leadership, to explore the antecedents and outcomes of problem-solving creativity. Drawing on creativity and leadership theories, this study reports that the success of creative idea exchanges depends heavily on a supportive environment for NPD team members and minimizing the NPD manager’s role ambiguity.

We investigated the relationship between personality and managerial assessment center (AC) dimensions, emphasizing age’s moderating role within volatility, uncertainty, complexity, ambiguity (VUCA) simulations.

We analyzed 327 managers and applied the AC method, examining areas like social skills, problem-solving, management and goal striving, openness to change, employee development using the VUCA framework.

We assessed personality metatraits through a questionnaire based on the circumplex model (CPM; Strus, Cieciuch, & Rowinski, 2014), identifying four bipolar metatraits. Results highlighted passiveness and disharmony as negatively correlated with all managerial AC dimensions, with passiveness adversely affecting social skills and problem-solving.

Age’s moderating role emerged as pivotal in the relationship between personality and managerial AC dimensions, especially in specific VUCA contexts. This underscores age’s influence on the interplay between personality and managerial efficacy, suggesting varying predictive capabilities across age groups. The research illuminates the complexities of these relationships, spotlighting age’s nuanced impact.