Knowledge, Volume 3, Issue 3 (September 2023) – 11 articles

Lifelong learning is crucial for equipping the workforce to navigate a volatile, uncertain, complex, and ambiguous (VUCA) world. Despite its importance, resistance to enrolling in lifelong learning courses persists. This exploratory study examines the exposure to and engagement with government-sponsored courses among two distinct groups: individuals who opt for these courses and those who select alternative courses. We employed comparative statistical analysis to identify the primary factors influencing course awareness and selection. Our findings underscore the enduring influence of traditional media in promoting course awareness. Additionally, personal interest and availability of subsidies emerged as significant determinants of course selection. Based on these insights, we propose policy recommendations to enhance the effectiveness of these courses. This empirical study contributes to the understanding of the dynamics of lifelong learning in Singapore, providing valuable insights for policy and practice. Full article

Purpose—The purpose of this study is to investigate the impact of spiritual leadership on knowledge-hiding behavior in agriculture research institutes of Khyber Pakhtunkhwa, Pakistan. The study aims to analyze theoretical and empirical evidence regarding the mediation pathway, specifically professional commitment, in order to clarify the significant association between spiritual leadership and subordinates’ knowledge-hiding behavior. Design/methodology—This survey-based study used cross-sectional data and a five-point Likert scale to investigate the given hypotheses. In order to address the primacy effect and mitigate any potential for common method bias, data were collected at two distinct time points, with a four-week interval between them. Smart PLS4 was used to assess a sample of 298 complete and valid responses for hypothesis testing. Findings—The results show that spiritual leadership has a negative impact on employees’ knowledge-hiding behavior. Additionally, this relationship is mediated by professional commitment. Originality/value—First, in contrast to the majority of previous studies, which focused on the factors influencing knowledge sharing, the present study investigates the influence of spiritual leadership on employees’ knowledge-hiding behaviors, which are two contrasting concepts. Secondly, the study empirically examined the mediation effect of professional commitment. These three variables have not previously been studied together. Full article

Backtesting represents a set of techniques that aim to evaluate trading strategies on historical data in order to verify their effectiveness before applying them to a market in real time. This requires processing large amounts of data from different periods and applying different simulation techniques to them. In general, these types of tools are not very popular for reasons such as the amount of data that must be evaluated and maintained, the computational resources that are required, and the need to have a deep conceptual understanding of these techniques in order to use them. This article presents a web application that implements a set of backtesting functionalities that allow evaluating different trading strategies, managing portfolios, representing the results of simulations, and optimizing a stock portfolio, all from an intuitive and visual interface that makes these techniques accessible to new investors in this field. Full article

The main objective of environmental education is to promote pro-environmental behaviors; increasing knowledge and understanding are the first steps. Active learning plays a crucial role in increasing engagement levels and achieving positive behavioral development. We aimed to evaluate the effectiveness of a wildlife-friendly farming curriculum, including active learning, presented to 223 students aged 13–15 years from ten middle schools in Garut Regency, Indonesia, from June to September 2019. Using pre- and post-questionnaires, we found that knowledge retention and understanding increased if students completed an exercise that involved an active discussion with parents and if the class was engaged (monitored via WhatsApp groups) in an active learning experiment. Key concepts regarding wildlife-friendly farming, such as mutual benefits for wildlife and humans, the provision of ecosystem services by animals, and the use of organic farming, were more frequent if students discussed the program with parents or if they were engaged during the experiment. We found evidence that student engagement via active learning increased knowledge retention and understanding of wildlife-friendly farming. Similar approaches should be used to promote wildlife-friendly farming approaches from even younger ages and should be tested with other projects aimed at producing pro-environmental behaviors. Full article

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