Search Results (76)

The successful implementation of infrastructure projects through public–private partnerships (PPPs) significantly relies on a well-designed procurement scheme; however, there is currently no established systematic decision-making model to identify the most optimal one. This paper explores the factors affecting the selection of public–private partnership schemes in infrastructure projects, with a particular focus on the differences between developed and emerging economies. The study opted for a comprehensive literature review and open-ended interviews to validate 25 critical factors affecting the optimum selection of PPP procurement for infrastructure projects. Then, a questionnaire survey was adopted to evaluate the selected factors and empirically examine the differences and commonalities between developed and emerging economies. The results highlighted the “financial attraction of projects to investors” and “financial viability based on the net present value and risk-adjusted present value” as the two most important factors. While the importance of most selection factors was agreed upon, nine selection factors were ranked unanimously higher for developed economies than for emerging economies. The findings of this study will aid in comprehending the factors that impact the choice of PPP schemes and provide insights for policymakers and project managers in both developed and emerging economies. These factors serve as inputs in developing a decision-making framework that aids both public and private stakeholders in selecting the most appropriate PPP procurement schemes for infrastructure projects. Full article

This study proposes some rules for performing a function-oriented search (providing function and object) to extract technical systems from patents, using syntax and dependency patterns to analyse multiple sentences. Unlike the most common inter-sentence analysis methods, the proposed method does not use context information or distance to link the elements of several sentences, but generic terms from patent ontology. The content provided by the rules was entirely derived from a statistical analysis of many patents from different domains, in order to provide a general validity for the rules. The application of the method in two case studies, related to metal cutting and manure processing, highlighted its main advantages. Its degree of automation is such that the expert is almost exclusively excluded, except in the definition of the function on which to build the document pool. The precision and the recall of the results during the tests exceeded 90%. The current limitation concerns the manual control of some results, about 25%, which derive from an additional set of dependency patterns that are difficult to automate and deserve further investigation. The technical systems are many more in number and are more detailed with regard to structural aspects than those obtainable by analysing only single sentences and/or syntax. Full article

The remedial teaching policy is a flagship education policy of the Greek Ministry of Education that aims to create a school of equal opportunities by providing additional support to students from disadvantaged social backgrounds. In this work we utilised a data set provided by the Ministry of Education, followed a black box approach and built on previous results in order to achieve the first ever evaluation, based on data, of the remedial teaching policy. Our findings indicate that remedial teaching is very effective in supporting very weak students, helping 70% of them achieve better academic performance and one out of three of them to sustain this enhanced academic performance in the future, long after they have stopped receiving remedial teaching. On the other hand, and contrary to what is widely believed, our results show that remedial teaching has the opposite impact to what it was designed for, as it is primarily the privileged students that receive the benefits. Consequently, in the way it is currently implemented, remedial teaching widens the gap between privileged and disadvantaged students rather than reduces it. The implications of the work are wide and far reaching, including the establishment of the need to revisit the way remedial teaching is implemented, the highlighting of the value in the data gathered by the Ministry of Education and the proof that individual educational policies can be objectively assessed despite being part of a complex system in which multiple education policies are implemented concurrently. Full article

This article discusses the importance of open-ended problems in mathematics education. The traditional approach to teaching mathematics focuses on the repetitive practice of well-defined problems with a clear solution, leaving little room for students to develop critical thinking and problem-solving skills. Open-ended problems, on the other hand, open-ended problems require students to apply their knowledge creatively and flexibly, often with multiple solutions. We herein present a case study of a high school mathematics class that incorporated open-ended problems into its curriculum. The students were given challenging problems requiring them to think beyond what they had learned in class and develop their problem-solving methods. The study results showed that students exposed to open-ended problems significantly improved their problem-solving abilities and ability to communicate and collaborate with their peers. The article also highlights the benefits of open-ended problems in preparing students for real-world situations. By encouraging students to develop their problem-solving strategies, they are better equipped to face the unpredictable challenges of the future. Additionally, open-ended problems promote a growth mindset and a love for learning, as students are encouraged to take risks and explore new ideas. Overall, the article argues that incorporating open-ended problems into mathematics education is a necessary step towards developing students’ critical thinking skills and preparing them for success in the real world. Full article

In the early days of the COVID-19 pandemic of 2020–2022, public uncertainty about the nature of the virus, and in particular its symptoms and mode of transmission, was met by the daily briefings issued by public health departments and political leaders. They were ill-equipped to respond to emerging knowledge management demands in an agile fashion. As this paper will show, this gap was filled on a volunteer basis by personal initiative. Examples for this are contact tracing register applications, an archive of daily COVID-19 incidence numbers at local government levels and a crowdsourced site that allowed the public find rapid antigen test kits during a time of extreme shortages. Once government and professional bodies eventually caught up and supplanted these volunteer endeavours, they become obsolete and by and large forgotten. Yet it can be posited that societal angst would have been much greater without them. Full article

We propose a methodology for training neural networks in which ensembles of under-trained neural networks are used to obtain broadly repeatable predictions, and we augment their performance by disrupting their training, with each neural network in the ensemble being trained on a potentially different data set generated from the base data by a method that we call randomization with full range sampling. Sleep habits in animals are a function of innate and environmental factors that determine the species’ place in the ecosystem and, thus, its requirement for sleep and opportunity to sleep. We apply the proposed methodology to train neural networks to predict hours of sleep from only seven correlated observations in only 39 species (one set of observations per species). The result was an ensemble of neural networks making more accurate predictions (lower mean squared error) and predictions that are more robust against variations in any one input parameter. The methodology presented here can be extended to other problems in which the data available for training are limited, or the neural network is to be applied, post-training, on a problem with substantial variation in the values of inputs (independent variables). Full article

Deep learning is widely used in many real-life applications. Despite their remarkable performance accuracies, deep learning networks are often poorly calibrated, which could be harmful in risk-sensitive scenarios. Uncertainty quantification offers a way to evaluate the reliability and trustworthiness of deep-learning-based model predictions. In this work, we introduced uncertainty quantification to our virtual research assistant recommender platform through both Monte Carlo dropout ensemble techniques. We also proposed a new formula to incorporate the uncertainty estimates into our recommendation models. The experiments were carried out on two different components of the recommender platform (i.e., a BERT-based grant recommender and a temporal graph network (TGN)-based collaborator recommender) using real-life datasets. The recommendation results were compared in terms of both recommender metrics (AUC, AP, etc.) and the calibration/reliability metric (ECE). With uncertainty quantification, we were able to better understand the behavior of our regular recommender outputs; while our BERT-based grant recommender tends to be overconfident with its outputs, our TGN-based collaborator recommender tends to be underconfident in producing matching probabilities. Initial case studies also showed that our proposed model with uncertainty quantification adjustment from ensemble gave the best-calibrated results together with the desirable recommender performance. Full article

Self-regulated learning (SRL) requires learners’ active participation, i.e., they need to activate cognitive and metacognitive learning strategies. These strategies can be activated and supported by using cognitive and metacognitive prompts. Extensive research concerning the effects of prompts on SRL is necessary to determine connections between these two concepts. Our study investigates the effects of cognitive and metacognitive activities—i.e., prompts—on learning performance during SRL. Therefore, we developed three types of learning environments that use different types of prompts—cognitive or metacognitive prompts—or no prompts. Moreover, we also used a questionnaire to examine prior knowledge and post-knowledge. Pre- and post-tests show that self-confidence in prior knowledge has a significant effect on self-confidence in post-knowledge, cognitive prompts reduce extrinsic motivation, and knowing how to use cognitive learning strategies enables using cognitive prompts more effectively. These results are partially in line with existing research findings on the effects of prompts in SRL. Full article

Our purpose is to advance a reasoned perspective on the scientific validity of computer simulation, using an example—integrated assessment modeling of climate change and its projected impacts—that is itself of great and urgent interest to policy in the real world. The spirited and continuing debate on the scientific status of integrated assessment models (IAMs) of global climate change has been conducted mostly among climate change modelers and users seeking guidance for climate policy. However, it raises a number and variety of issues that have been addressed, with various degrees of success, in other literature. The literature on methodology of simulation was mostly skeptical at the outset but has become more nuanced, casting light on some key issues relating to the validity and evidentiary standing of climate change IAMs (CC-IAMs). We argue that the goal of validation is credence, i.e., confidence or justified belief in model projections, and that validation is a matter of degree: (perfect) validity is best viewed as aspirational and, other things equal, it makes sense to seek more rather than less validation. We offer several conclusions. The literature on computer simulation has become less skeptical and more inclined to recognize that simulations are capable of providing evidence, albeit a different kind of evidence than, say, observation and experiments. CC-IAMs model an enormously complex system of systems and must respond to several challenges that include building more transparent models and addressing deep uncertainty credibly. Drawing on the contributions of philosophers of science and introspective practitioners, we offer guidance for enhancing the credibility of CC-IAMs and computer simulation more generally. Full article

The purpose of this paper is to explore how the social position of functional managers, as defined by their stocks of intellectual and social capital, influences their attitude towards cooperation for the integration of distributed knowledge in the conceptualization of bottom-up (autonomous) strategic initiatives. Bourdieu’s social practice theory was employed for integrating the organizational conditions in the initiative conceptualization-as-knowledge-creation process. By developing and analyzing two case studies on strategic operations, it was found that the degree of engagement in productive cooperation, and hence the potential and effectiveness of functional managers as knowledge-creating agents promoting their particular interests, are influenced by their social position which in turn depends on the path of accumulation of their intellectual and social capital resources. Full article

The phenomenon of school coexistence has gained special relevance in recent years, mainly due to the increase in conflict situations among students. The main objective of this work has been to carry out a systematic review of the scientific literature on the impact of the application of technologies as a didactic resource for the improvement of school coexistence, as well as to find out the current and future lines of research in this field of investigation. For this purpose, a total of 14 scientific articles indexed in the Scopus, Web of Science and Google Scholar databases were selected following the principles of the PRISMA Declaration. The results show that, although the scientific literature on the implementation of technologies for school coexistence is limited, didactic strategies measured with technologies reduce cases of school conflict. Among the conclusions are that technologies are tools to be taken into account for the improvement of school coexistence; however, their misuse due to a lack of digital skills can lead to violent behaviour on the part of students. Full article

In recent literature, alternative models for handling missing item responses in large-scale assessments have been proposed. Based on simulations and arguments based on psychometric test theory, it is argued in this literature that missing item responses should never be scored as incorrect in scaling models but rather treated as ignorable or handled based on a model. The present article shows that these arguments have limited validity and illustrates the consequences in a country comparison using the PIRLS 2011 study. It is argued that students omit (constructed response) items because they do not know the correct item answer. A different treatment of missing item responses than scoring them as incorrect leads to significant changes in country rankings, which induces nonignorable consequences regarding the validity of the results. Additionally, two alternative item response models are proposed based on different assumptions for missing item responses. In the first pseudo-likelihood approach, missing item responses for a particular student are replaced by a score that ranges between zero and a model-implied probability computed based on the non-missing items. In the second approach, the probability of a missing item response is predicted by a latent response propensity variable and the item response itself. The models were applied to the PIRLS 2011 study, demonstrating that country comparisons change under different modeling assumptions for missing item responses. Full article

Although there is a long history of searching for the road from knowledge to wisdom, there is no final and clear result. In fact, there are multiple ways of starting from knowledge and reaching wisdom due to the complexity of the semantic domains of both concepts. In addition, there are different perspectives on interpreting these conceptual maps, ranging from philosophy to psychology or management. We are interested in understanding the connecting ideas between knowledge and wisdom from the management perspective, where decision making is the key driving force for transforming knowledge into efficient actions for creating value for customers through products and services. The well-known knowledge pyramid or wisdom pyramid is a good metaphor to start with in understanding the basic concepts of data, information, knowledge, and wisdom (DIKW) and their transformations. We analyze different interpretations of these four basic concepts and focus on the transition from knowledge to wisdom, looking beyond the DIKW pyramid. Additionally, to get a larger view of the multiple connections between knowledge and wisdom, we perform a bibliometric analysis using VOSviewer as a specialized software tool. The contribution of the present paper comes from this enlarged framework of searching for links between knowledge and wisdom and analyzing their relevance to business management. The results are relevant to anyone who would like to understand how to manage efficiently knowledge in their organizations. We explain the semantic differences in interpreting the concepts of “information” and “knowledge” in philosophy, information science, and knowledge management, which can be useful both in theory and in practice. Full article

The combined effects of decarbonization and digitalization have had a significant impact on the German automotive industry, with different business models emerging that often involve new business alliances with other automotive companies and technology companies. This rapid and dramatic change momentum has resulted in a “knowledge deficit” in the industry, as regards the skills and know-how required to operate successfully in the digital economy. Using an inductive, qualitative research methodology, based on in-depth interviews with industry experts and practitioners, this article identifies the main areas in which skills, knowledge and competencies are lacking, and assesses the main ways in which the industry is trying to address the problem. A number of emergent issues are also discussed. The article finds that many years of technology outsourcing have left the industry deficient in core technology skills for software development, data management and architecture design, and that new competencies in cybersecurity, platforms and ecosystems, and sourcing management are also urgently needed. The industry is addressing this challenge through a combination of strategies, including major partnership arrangements with the big tech companies. The article concludes that entrepreneurial innovation and radical digital leadership will be required to adequately address the knowledge deficit in the digital era. Full article

Electronic waste generation is increasing dramatically throughout the world. Consequently, this increase in E-waste harms the environment, health, and other aspects of human life. Moreover, hazardous substances and the informal disposal of E-waste severely threaten human health and the environment. Saudi Arabia is the largest Arab country in terms of electronic waste generation and is the Arab country that generates the most E-waste. Over the past few decades, several initiatives and policy implementations have been undertaken in the country. However, the management of E-waste is still a source of distress and an unresolved issue. Sustainable development requires much more effort, primarily efficient E-waste management, which can only be achieved by establishing a formal collection system, early forecasting, and accurate estimations. The purpose of this study is to provide an overview of the fundamental and emerging trends in E-waste production worldwide and in Saudi Arabia. This paper aims to summarize the hazardous elements present in E-waste, their dangerous effects, and the economic potential for recycling E-waste as a means of developing sustainable communities. This study explores the practices regarding efficient E-waste management and E-waste estimation and prediction globally, specifically in Saudi Arabia and other Arab countries. This study suggests that more than the use of a single management probe will be needed to achieve significant results. Instead, a complementary approach can be used to achieve the most effective results. Additionally, people should be aware of the importance of handling and recycling E-waste. This study emphasizes the importance of formal collection and documentation to ensure effective monitoring and sustainable development in any developed country. Full article