Knowledge doi: 10.3390/knowledge5030015

Authors: Bern Igoche Igoche Olumuyiwa Matthew Daniel Olabanji

Educational admission systems, particularly in developing countries, often suffer from opaque decision processes, unstructured data, and limited analytic insight. This study proposes a novel methodology that integrates structural causal models (SCMs), ontological modeling, and machine learning to uncover and apply interpretable knowledge from an admission database. Using a dataset of 12,043 records from Benue State Polytechnic, Nigeria, we demonstrate this approach as a proof of concept by constructing a domain-specific SCM ontology, validate it using conditional independence testing (CIT), and extract features for predictive modeling. Five classifiers, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were evaluated using stratified 10-fold cross-validation. SVM and KNN achieved the highest classification accuracy (92%), with precision and recall scores exceeding 95% and 100%, respectively. Feature importance analysis revealed ‘mode of entry’ and ‘current qualification’ as key causal factors influencing admission decisions. This framework provides a reproducible pipeline that combines semantic representation and empirical validation, offering actionable insights for institutional decision-makers. Comparative benchmarking, ethical considerations, and model calibration are integrated to enhance methodological transparency. Limitations, including reliance on single-institution data, are acknowledged, and directions for generalizability and explainable AI are proposed.

Knowledge doi: 10.3390/knowledge5030014

Authors: Thai Son Chu Mahfuz Ashraf

This paper shows that artificial intelligence is fundamentally transforming college curricula by enabling data-driven personalization, which enhances student outcomes and better aligns educational programs with evolving workforce demands. Specifically, predictive analytics, machine learning algorithms, and natural language processing were applied here, grounded in constructivist learning theory and Human–Computer Interaction principles, to evaluate student performance and identify at-risk students to propose personalized learning pathways. Results indicated that the AI-based curriculum achieved much higher course completion rates (89.72%) as well as retention (91.44%) and dropout rates (4.98%) compared to the traditional model. Sentiment analysis of learner feedback showed a more positive learning experience, while regression and ANOVA analyses proved the impact of AI on enhancing academic performance to be real. Therefore, the learning content delivery for each student was continuously improved based on individual learner characteristics and industry trends by AI-enabled recommender systems and adaptive learning models. Its advantages notwithstanding, the study emphasizes the need to address ethical concerns, ensure data privacy safeguards, and mitigate algorithmic bias before an equitable outcome can be claimed. These findings can inform institutions aspiring to adopt AI-driven models for curriculum innovation to build a more dynamic, responsive, and learner-centered educational ecosystem.

Knowledge doi: 10.3390/knowledge5030013

Authors: Farshad Badie Anna Rostomyan

This paper explores the concept of ‘competency’ in modern organisations. It emphasises the strategic importance of aligning organisational values, strategic goals, and employee competencies. It introduces competency mapping as a framework for ensuring such an alignment, as well as for developing a culture of continuous learning and development, where the emotions and feelings of the interactants are also taken into account based on intrapersonal and interpersonal aspects of human behaviour. The article also elucidates the interconnection among diverse human ‘intelligences’ that are of paramount importance in shaping human knowledge and guiding us in navigating through life more smoothly and efficiently. Thus, through an interdisciplinary scope, we have attempted to analyse the intrinsic value of competency mapping as a knowledge driver in modern organisational and educational settings.

Knowledge doi: 10.3390/knowledge5030012

Authors: Carmen Bueno Castellanos álvaro Fernández-Baldor

This article presents a comparative analysis of digital manufacturing laboratories (DMLs) in Mexico and Spain. It is argued that DMLs, also known as makerspaces or FabLabs, play a key role in innovation and experimentation, but that their success depends on the relationships they establish with social actors, such as local governments, universities, and firms. Key concepts of the transformative innovation approach such as “protective space” and “embeddedness” are introduced, which allow us to understand how DMLs operate within a complex system. The comparative analysis of a DML in Mexico City (Mexico) and a DML in Valencia (Spain) allows us to identify similarities and differences in their operational contexts. While the Mexican DML faces a lack of government support and dependence on the private sector, the Spanish one benefits from strong institutional support and public policies that facilitate its development. This results in greater stability and capacity for action for the Valencian FabLab VLC compared to the Mexican FabLab Finally, we reflect on how the embeddedness received from different social actors affects the autonomy and transformative capacity of DMLs, suggesting that while both labs have the potential to innovate, their contexts and relationships determine their effectiveness and sustainability in the digital sociotechnical system.

Knowledge doi: 10.3390/knowledge5020011

Authors: Terungwa James Age

This article explores the integration of “Ayatutu”, a communal philosophy from Nigeria’s Tiv people, into mathematics education frameworks. Ayatutu—embodying collective responsibility and mutual assistance—aligns with contemporary cooperative learning approaches while offering unique cultural dimensions. Through analysis of the ethnomathematics literature, indigenous knowledge systems, and cooperative learning theories this article develops a theoretical framework for Ayatutu-based mathematics instruction built on the following five core elements: collective problem-solving, resource sharing, complementary expertise, process orientation, and intergenerational knowledge transfer. The framework demonstrates significant alignment with sociocultural learning theory, communities of practice, and critical pedagogy while also offering potential benefits including enhanced mathematical engagement, positive identity development, stronger learning communities, and cultural sustainability. Implementation challenges involving teacher preparation, structural constraints, cultural translation, and balancing individual with collective learning are examined. This research contributes to decolonizing mathematics education by positioning indigenous philosophical systems as valuable resources for creating culturally responsive and mathematically powerful learning environments that serve diverse student populations while honoring cultural wisdom.

Knowledge doi: 10.3390/knowledge5020010

Authors: Shakib Sadat Shanto Akinul Islam Jony

The COVID-19 pandemic has accelerated the shift towards online education, making it a critical focus for educational institutions. Understanding students’ adaptability to this new learning environment is crucial for ensuring their academic success. This study aims to predict students’ adaptability levels in online education using a dataset of 1205 observations that incorporates sociodemographic factors and information collected across different educational levels (school, college, and university). Various machine learning (ML) and deep learning (DL) models, including decision tree (DT), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), XGBoost, and artificial neural networks (ANNs), are applied for adaptability prediction. The proposed ensemble model achieves superior performance with 95.73% accuracy, significantly outperforming traditional ML and DL models. Furthermore, explainable AI (XAI) techniques, such as LIME and SHAP, were employed to uncover the specific features that significantly impact the adaptability level predictions, with financial condition, class duration, and network type emerging as key factors. By combining robust predictive modeling and interpretable AI, this study contributes to the ongoing efforts to enhance the effectiveness of online education and foster student success in the digital age.

Knowledge doi: 10.3390/knowledge5020009

Authors: Emmanouil A. Demetroulis Ilias Papadogiannis Manolis Wallace Vassilis Poulopoulos Angeliki Antoniou

Collaboration skills are an important component of 21st century skills and a critical skill for citizens of the future. In this work, we propose collaboration-oriented robotics education (CORE), a methodology aimed at fostering the development of collaboration skills in primary school students aged 11–12 via an adjusted approach to the teaching of educational robotics. In order to assess the existence and level of collaboration skills in a student, a suitable tool is also proposed. Using a collaboration-oriented performance evaluation test (COPE) for both a pre- and post-intervention measurement and applying both the conventional and CORE approaches to teaching educational robotics to 32 students, split into control and intervention groups, we demonstrate the effectiveness of the proposed approach. Specifically, the experimental implementation shows that CORE statistically significantly increases the performance of the experimental group compared to the conventional way of teaching educational robotics. These results, in addition to validating CORE itself, demonstrate that the conventional approach to STEAM (Science, Technology, Engineering, Arts, Mathematics) education is not necessarily already optimized, thus facilitating an overall re-evaluation of the field.

Knowledge doi: 10.3390/knowledge5020008

Authors: Richard Fiene

This research article proposes the use of an uncertainty–certainty matrix (UCM) for licensing decision making in the human services, which is the decision to issue a license to operate. It is a proposed study protocol and conceptual framework; it is not an empirical study. It shows how the matrix can be used in rule decision making and how it clearly shows when decision making has gone awry when bias is introduced into the decision making. It is also proposed to be used to make decisions in differential monitoring and in validation and reliability studies. This proposal presents a potential blueprint on how the UCM can be used within human services licensing as a decision-making tool.

Knowledge doi: 10.3390/knowledge5020007

Authors: Boris M. Dolgonosov

An integral indicator of the development of society is the amount of knowledge, which can be measured by the number of accumulated publications in the form of patents, articles, and books. Knowledge production is examined on a global scale. We analyze existing econometric models and develop a generalized model that expresses the per capita knowledge production rate (called productivity) as a function of the amount of accumulated knowledge. The function interpolates two extreme cases, the first of which describes an underdeveloped society with very little knowledge and non-zero productivity, and the second, a highly developed society with a large amount of knowledge and productivity that grows according to a power law as knowledge accumulates. The model is calibrated using literature data on the number of patents, articles, and books. For comparison, we also consider the rapid growth in the global information storage capacity that has been observed since the 1980s. Based on the model developed, we can distinguish between two states of society: (1) a pre-information society, in which the knowledge amount is below a certain threshold and productivity is quite low, and (2) an information society with a super-threshold amount of knowledge and its rapid accumulation due to advanced computer technologies. An analysis shows that the transition to an information society occurred in the 1980s.

Knowledge doi: 10.3390/knowledge5010006

Authors: Isuru Udayangani Hewapathirana

Intrusion detection is a critical component of cybersecurity, enabling timely identification and mitigation of network threats. This study proposes a novel two-stage intrusion detection framework using the CSE-CIC-IDS2018 dataset, a comprehensive and realistic benchmark for network traffic analysis. The research explores two distinct approaches: the stacked autoencoder (SAE) approach and the Apache Spark-based (ASpark) approach. Each of these approaches employs a unique feature representation technique. The SAE approach leverages an autoencoder to learn non-linear, data-driven feature representations. In contrast, the ASpark approach uses principal component analysis (PCA) to reduce dimensionality and retain 95% of the data variance. In both approaches, a binary classifier first identifies benign and attack traffic, generating probability scores that are subsequently used as features alongside the reduced feature set to train a multi-class classifier for predicting specific attack types. The results demonstrate that the SAE approach achieves superior accuracy and robustness, particularly for complex attack types such as DoS attacks, including SlowHTTPTest, FTP-BruteForce, and Infilteration. The SAE approach consistently outperforms ASpark in terms of precision, recall, and F1-scores, highlighting its ability to handle overlapping feature spaces effectively. However, the ASpark approach excels in computational efficiency, completing classification tasks significantly faster than SAE, making it suitable for real-time or large-scale applications. Both methods show strong performance for distinct and well-separated attack types, such as DDOS attack-HOIC and SSH-Bruteforce. This research contributes to the field by introducing a balanced and effective two-stage framework, leveraging modern machine learning models and addressing class imbalance through a hybrid resampling strategy. The findings emphasize the complementary nature of the two approaches, suggesting that a combined model could achieve a balance between accuracy and computational efficiency. This work provides valuable insights for designing scalable, high-performance intrusion detection systems in modern network environments.

Knowledge doi: 10.3390/knowledge5010005

Authors: Moira Gundu Lorette Jacobs Modiehi Winnie Rammutloa

Sustainable knowledge sharing among mathematics and science teachers is imperative to improve the ability of such teachers to enhance the way information is transferred to learners. South Africa ranked 37th out of 42 countries in an assessment to determine the ability of high school learners to conduct mathematics and science. There is, therefore, an urgent need to investigate how teachers can be empowered to enhance their ability to transfer knowledge of mathematics and science to improve the ability of learners to engage in these subjects. A post-positivist paradigm and quantitative survey design were employed to identify ways of knowledge sharing that will enhance the ability of teachers to transfer knowledge of mathematics and science to learners. The findings identified key barriers to knowledge sharing, including the role of school management in fostering a culture of knowledge exchange, time management, and limited opportunities for professional development. Based on the findings of the research, a framework is proposed to encourage knowledge sharing, which may ultimately improve teaching practices and learner outcomes in mathematics and science.

Knowledge doi: 10.3390/knowledge5010004

Authors: Malcolm Koo

ChatGPT, or Chat Generative Pre-trained Transformer, developed by OpenAI, is a versatile chatbot known for generating human-like text responses. Since its launch in November 2022, it has sparked interest and debate. This bibliometric study aimed to explore ChatGPT-related publications using the Web of Science database from 2023 to June 2024. Original articles in English were retrieved on 24 June 2024, using the topic field “ChatGPT”. Citation records were analyzed using bibliometrix 4.1 and VOSviewer 1.6.20. Between January 2023 and 24 June 2024, 3231 original articles on ChatGPT were published in 1404 journals, with an average citation rate of 5.6 per article. The United States led with 877 articles, followed by China and India. The University of California System, Harvard University, and the State University System of Florida were the most prolific institutions. Keyword co-occurrence network analysis revealed the interdisciplinary nature of ChatGPT research, particularly contributions in healthcare, education, and technology. In conclusion, this bibliometric analysis identified critical areas of ChatGPT research focus, such as applications in educational settings and its ethical implications. These findings are crucial for fostering further advancements that leverage ChatGPT’s capabilities while mitigating its risks.

Knowledge doi: 10.3390/knowledge5010003

Authors: Jennifer Mugleston Vuong Hung Truong Cindy Kuang Lungile Sibiya Jihwan Myung

Epistemology and technology have been working in synergy throughout history. This relationship has culminated in large language models (LLMs). LLMs are rapidly becoming integral parts of our daily lives through smartphones and personal computers, and we are coming to accept the functionality of LLMs as a given. As LLMs become more entrenched in societal functioning, questions have begun to emerge: Are LLMs capable of real understanding? What is knowledge in LLMs? Can knowledge exist independently of a conscious observer? While these questions cannot be answered definitively, we can argue that modern LLMs are more than mere symbol-manipulators and that LLMs in deep neural networks should be considered capable of a form of knowledge, though it may not qualify as justified true belief (JTB) in the traditional definition. This deep neural network design may have endowed LLMs with the capacity for internal representations, basic reasoning, and the performance of seemingly cognitive tasks, possible only through a compressive but generative form of representation that can be best termed as knowledge. In addition, the non-symbolic nature of LLMs renders them incompatible with the criticism posed by Searle’s “Chinese room” argument. These insights encourage us to revisit fundamental questions of epistemology in the age of LLMs, which we believe can advance the field.

Knowledge doi: 10.3390/knowledge5010002

Authors: Natalia Chatzifoti Panos T. Chountalas Konstantina K. Agoraki Dimitrios A. Georgakellos

The significance of knowledge management in the tourism accommodation sector is increasingly vital due to rapid market changes and intense competition. Although the value of identifying and implementing critical success factors (CSFs) for knowledge management is widely recognized in the sector, there is still a lack of comprehensive understanding and practical application of these factors. This study employs the decision-making trial and evaluation laboratory (DEMATEL) methodology to systematically identify and analyze the interrelationships among these CSFs. The findings reveal a complex web of dependencies within this network. Specifically, leadership commitment and support is identified as the most influential CSF, acting as a fundamental element that enables the successful adoption and integration of knowledge management initiatives. Additionally, strategic alignment and a supportive organizational culture are crucial, working synergistically to ensure that knowledge management initiatives are aligned with overarching organizational goals and create an environment that encourages change and collaboration. Furthermore, the study highlights a mutually reinforcing relationship between knowledge processes, governance, and employee training. This relationship suggests that strong governance structures and clearly defined knowledge processes facilitate and improve the effectiveness of employee training programs while also creating a continuous improvement cycle where improved training further refines governance and knowledge processes. Moreover, the study highlights the integration of the ISO 30401:2018 standard as a systematic framework to support these CSFs, providing a structured approach to improve knowledge management systems. By mapping the cause-and-effect relationships among the identified CSFs, this research offers practical insights for industry professionals to effectively prioritize and address these factors.

Knowledge doi: 10.3390/knowledge5010001

Authors: Tri Nguyen-Quang Louis Labat Qurat Ul An Sabir

Turing’s instability has been widely introduced to explain the formation of several biological and ecological patterns, such as the skin patterning of fish or animals, wings of butterflies, pigmentation, and labyrinth patterns of the cerebral cortex of mammals. Such a mechanism may occur in the ecosystem due to the differential diffusion dispersal that happen if one of the constituent species results in the activator or the prey, showing a tendency to undergo autocatalytic growth. The diffusion of the constituent species activator is a random mobility function called passive diffusion. If the other species in the system (the predator/inhibitor) disperses sufficiently faster than the activator, then the spatially uniform distribution of species becomes unstable, and the system will settle into a stationary state. This paper introduced Turing’s mechanism in our reaction–taxis–diffusion model to simulate the harmful algal bloom (HAB) pattern. A numerical approach, the Runge–Kutta method, was used to deal with this system of reaction–taxis–diffusion equations, and the findings were qualitatively compared to the aerial patterns obtained by a drone flying over Torment Lake in Nova Scotia (Canada) during the bloom season of September 2023.

Knowledge doi: 10.3390/knowledge4040032

Authors: Zemzem Mohammed Megersa Abebe Belay Adege Faizur Rashid

Maize is one of the most widely grown crops in Ethiopia and is a staple crop around the globe; however, common rust maize disease (CRMD) is becoming a serious problem and severely impacts yields. Conventional CRMD detection and treatment methods are time-consuming, expensive, and ineffective. To address these challenges, we propose a real-time deep-learning model that provides disease detection and pesticide dosage recommendations. In the model development process, we collected 5000 maize leaf images experimentally, with permission from Haramaya University, and increased the size of the dataset to 8000 through augmentation. We applied image preprocessing techniques such as image equalization, noise removal, and enhancement to improve model performance. Additionally, during training, we utilized batch normalization, dropout, and early stopping to reduce overfitting, improve accuracy, and improve execution time. The optimal model recognizes CRMD and classifies it according to scientifically established severity levels. For pesticide recommendations, the model was integrated with the Gradio interface, which provides real-time recommendations based on the detected disease type and severity. We used a convolutional neural network (CNN), specifically the ResNet50 model, for this purpose. To evaluate its performance, ResNet50 was compared with other state-of-the-art algorithms, including VGG19, VGG16, and AlexNet, using similar parameters. ResNet50 outperformed the other CNN models in terms of accuracy, precision, recall, and F-score, achieving over 97% accuracy in CRMD classification—surpassing the other algorithms by more than 2.5% in both experimental and existing datasets. The agricultural experts verified the accuracy of the recommendation system across different stages of the disease, and the system demonstrated 100% accuracy. Additionally, ResNet50 exhibited lower time complexity during model development. This study demonstrates the potential of ResNet50 models for improving maize disease management.

Knowledge doi: 10.3390/knowledge4040031

Authors: Rafael Casta?eda Andrea Martínez-Gómez-Aldaraví Laura Mercadé Víctor Jesús Gómez Teresa Mengual Francisco Javier Díaz-Fernández Miguel Sinusia Lozano Juan Navarro Arenas ángela Barreda Maribel Gómez Elena Pinilla-Cienfuegos David Ortiz de Zárate

Education 4.0 arises to provide citizens with the technical/digital competencies and cognitive/interpersonal skills demanded by Industry 4.0. New technologies drive this change, though time-independent learning remains a challenge, because students might face a lack of support, advice and surveillance when teachers are unavailable. This study proposes complementing presential lessons with online learning driven by ChatGPT, applied as an educational tool able to mentor K-12 students learning science at home. First, ChatGPT’s performance in the field of K-12 science is evaluated, scoring A (9.3/10 in 2023, and 9.7/10 in 2024) and providing detailed, analytic, meaningful, and human-like answers. Then, an empirical interventional study is performed to assess the impact of using ChatGPT as a virtual mentor on real K-12 students. After the intervention, the grades of students in the experimental group improved by 30%, and 70% of students stated a positive perception of the AI, suggesting a positive impact of the proposed educational approach. After discussion, the study concludes ChatGPT might be a useful educational tool able to provide K-12 students learning science with the functional and social/emotional support they might require, democratizing a higher level of knowledge acquisition and promoting students’ autonomy, security and self-efficacy. The results probe ChatGPT’s remarkable capacity (and immense potential) to assist teachers in their mentoring tasks, laying the foundations of virtual mentoring and paving the way for future research aimed at extending the study to other areas and levels, obtaining a more realistic view of AI’s impact on education.

Knowledge doi: 10.3390/knowledge4040030

Authors: Marcelo Bender Perotoni Lincoln Ferreira Lucio

Autoencoders are neural networks that have applications in denoising processes. Their use is widely reported in imaging (2D), though 1D series can also benefit from this function. Here, three canonical waveforms are used to train a neural network and achieve a signal-to-noise reduction with curves whose noise energy is above that of the signals. A real-world test is carried out with the same autoencoder subjected to a set of time series corrupted by noise generated by a Zener diode, biased on the avalanche region. Results showed that, observing some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits. Results showed an average of 2.8 dB in the signal-to-noise ratio for square and triangular waveforms.

Knowledge doi: 10.3390/knowledge4040029

Authors: Elizabeth Clark Samantha Price Theresa Lucena Bailey Haberlein Abdullah Wahbeh Raed Seetan

Differentiated thyroid cancer (DTC), comprising papillary and follicular thyroid cancers, is the most prevalent type of thyroid malignancy. Accurate prediction of DTC is crucial for improving patient outcomes. Machine learning (ML) offers a promising approach to analyze risk factors and predict cancer recurrence. In this study, we aimed to develop predictive models to identify patients at an elevated risk of DTC recurrence based on 16 risk factors. We developed six ML models and applied them to a DTC dataset. We evaluated the ML models using Synthetic Minority Over-Sampling Technique (SMOTE) and with hyperparameter tuning. We measured the models’ performance using precision, recall, F1 score, and accuracy. Results showed that Random Forest consistently outperformed the other investigated models (KNN, SVM, Decision Tree, AdaBoost, and XGBoost) across all scenarios, demonstrating high accuracy and balanced precision and recall. The application of SMOTE improved model performance, and hyperparameter tuning enhanced overall model effectiveness.

Knowledge doi: 10.3390/knowledge4040028

Authors: Maria Tsiakmaki Georgios Kostopoulos Sotiris Kotsiantis

Student performance prediction is a critical research challenge in the field of educational data mining. To address this issue, various machine learning methods have been employed with significant success, including instance-based algorithms, decision trees, neural networks, and ensemble methods, among others. In this study, we introduce an innovative approach that leverages the Regularized Greedy Forest (RGF) algorithm within an active learning framework to enhance student performance prediction. Active learning is a powerful paradigm that utilizes both labeled and unlabeled data, while RGF serves as an effective decision forest learning algorithm acting as the base learner. This synergy aims to improve the predictive performance of the model while minimizing the labeling effort, making the approach both efficient and scalable. Moreover, applying the active learning framework for predicting student performance focuses on the early and accurate identification of students at risk of failure. This enables targeted interventions and personalized learning strategies to support low-performing students and improve their outcomes. The experimental results demonstrate the potential of our proposed approach as it outperforms well-established supervised methods using a limited pool of labeled examples, achieving an accuracy of 81.60%.

Knowledge doi: 10.3390/knowledge4040027

Authors: Joseph Vidal Spriha Jha Zhenyuan Liang Ethan Delgado Bereket Siraw Deneke Dennis Shasha

Knowledge comes in various forms: scientific, artistic, legal, and many others. For most non-computer scientists, it is far easier to express their knowledge in text than in programming code. The dynamic decision tree system is a system for supporting the authoring of expertise in text form and navigation via an interface that limits the cognitive load on the reader. Specifically, as the reader answers questions, relevant tree nodes appear and irrelevant ones disappear. Searching by a keyword can help to navigate the tree. Database calls bring in information from external datasets. Links bring in other decision trees as well as websites. This paper describes the reader interface, the authoring interface, the related state-of-the-art work, the implementation, and case studies.

Knowledge doi: 10.3390/knowledge4040026

Authors: Joaquín del-Río Fernández Daniel-Mihai Toma Matias Carandell-Widmer Enoc Martinez-Padró Marc Nogueras-Cervera Pablo Bou Antoni Mànuel-Làzaro

In higher education institutions, there is a strong interaction between research and teaching activities. This paper presents a case study on the research–teaching nexus based on an analysis of academic results related to the course “Instrumentation and Data Analyses in Marine Sciences” within the Marine Sciences and Technologies Bachelor’s Degree at the Universitat Politècnica de Catalunya (UPC), taught at the Vilanova i la Geltrú campus (Barcelona, Spain). The start of this degree in the academic year 2018–2019 allowed the assignment of technological subjects in the degree to a research group with extensive experience in the research and development of marine technologies. The first section of this paper aims to provide a justification for establishing the Marine Sciences and Technologies Bachelor’s Degree. It highlights the necessity of this program and delves into the suitability of the profiles of the professors responsible for teaching marine technology subjects. Their entrepreneurial research trajectory and their competence in electronic instrumentation are strong arguments for their appropriateness. The next section of the paper explores a detailed analysis of academic results based on surveys and student performance indices. Through a thorough examination of these data, this case study demonstrates, within the context of all UPC degrees, that assigning a research group made up of experienced professors and researchers in the field who are accustomed to working as a team produces superior academic results compared to assignments to professors who do not work as a team. Teamwork presents specific skills necessary for operating the infrastructures and equipment associated with an experimental degree.

Knowledge doi: 10.3390/knowledge4040025

Authors: Divas Karimanzira

In this study, we extend our previous work on a two-dimensional convolutional neural network (2DCNN) for spatial prediction of groundwater nitrate, focusing on improving uncertainty quantification. Our enhanced model incorporates a fully probabilistic Bayesian framework and a structure aimed at optimizing both specific value predictions and predictive intervals (PIs). We implemented the Prediction Interval Validation and Estimation Network based on Quality Definition (2DCNN-QD) to refine the accuracy of probabilistic predictions and reduce the width of the prediction intervals. Applied to a model region in Germany, our results demonstrate an 18% improvement in the prediction interval width. While traditional Bayesian CNN models may yield broader prediction intervals to adequately capture uncertainties, the 2DCNN-QD method prioritizes quality-driven interval optimization, resulting in narrower prediction intervals without sacrificing coverage probability. Notably, this approach is nonparametric, allowing it to be effectively utilized across a range of real-world scenarios.

Knowledge doi: 10.3390/knowledge4030024

Authors: Jonas Bambi Kehinde Olobatuyi Yudi Santoso Hanieh Sadri Ken Moselle Abraham Rudnick Gracia Yunruo Dong Ernie Chang Alex Kuo

Individuals from a variety of backgrounds are affected by the opioid crisis. To provide optimal care for individuals at risk of opioid overdose and prevent subsequent overdoses, a more targeted response that goes beyond the traditional taxonomical diagnosis approach to care management needs to be adopted. In previous works, Graph Machine Learning and Natural Language Processing methods were used to model the products for planning and evaluating the treatment of patients with complex issues. This study proposes a methodology of partitioning patients in the opioid overdose cohort into various communities based on their patterns of service utilization (PSUs) across the continuum of care using graph community detection and applying survival analysis to predict time-to-second overdose for each of the communities. The results demonstrated that the overdose cohort is not homogeneous with respect to the determinants of risk. Moreover, the risk for subsequent overdose was quantified: there is a 51% higher chance of experiencing a second overdose for a high-risk community compared to a low-risk community. The proposed method can inform a more efficient treatment heterogeneity approach for a cohort made of diverse individuals, such as the opioid overdose cohort. It can also guide targeted support for patients at risk of subsequent overdoses.

Knowledge doi: 10.3390/knowledge4030023

Authors: Leena Nezamuldeen Mohsin Saleet Jafri

Variations in the genetic code for proteins are considered to confer traits and underlying disease. Identifying the functional consequences of these genetic variants is a challenging endeavor. There are online databases that contain variant information. Many publications also have described variants in detail. Furthermore, there are tools that allow for the prediction of the pathogenicity of variants. However, navigating these disparate sources is time-consuming and sometimes complex. Finally, text mining and large language models offer promising approaches to understanding the textual form of this knowledge. This review discusses these challenges and the online resources and tools available to facilitate this process. Furthermore, a computational framework is suggested to accelerate and facilitate the process of identifying the phenotype caused by a particular genetic variant. This framework demonstrates a way to gather and understand the knowledge about variants more efficiently and effectively.

Knowledge doi: 10.3390/knowledge4030022

Authors: Mohammad Munzir Ahanger Mohd Arif Wani Vasile Palade

This paper introduces a parameter-efficient transformer-based model designed for scientific literature classification. By optimizing the transformer architecture, the proposed model significantly reduces memory usage, training time, inference time, and the carbon footprint associated with large language models. The proposed approach is evaluated against various deep learning models and demonstrates superior performance in classifying scientific literature. Comprehensive experiments conducted on datasets from Web of Science, ArXiv, Nature, Springer, and Wiley reveal that the proposed model’s multi-headed attention mechanism and enhanced embeddings contribute to its high accuracy and efficiency, making it a robust solution for text classification tasks.

Knowledge doi: 10.3390/knowledge4030021

Authors: Carla Terron-Santiago Javier Martinez-Roman Jordi Burriel-Valencia Angel Sapena-Bano

Undergraduate courses in electrical machines often include an introduction to the air gap magnetic field as a basic element in the energy conversion process. The students must learn the main properties of the field produced by basic winding configurations and how they relate to the winding current and frequency. This paper describes a new test equipment design aimed at helping students achieve these learning goals. The test equipment is designed based on four main elements: a modified slip ring induction machine, a winding current driver board, the DAQ boards, and a PC-based virtual instrument. The virtual instrument provides the winding current drivers with suitable current references depending on the user selected machine operational status (single- or three-phase/winding with DC or AC current) and measures and displays the air gap magnetic field for that operational status. Students’ laboratory work is organized into a series of experiments that guide their achievement of these air gap field-related abilities. Student learning, assessed based on pre- and post-lab exams and end-of-semester exams, has increased significantly. The students’ opinions of the relevance, usefulness, and motivational effects of the laboratory were also positive.

Knowledge doi: 10.3390/knowledge4030020

Authors: Karl Jensen Cayme Vince Andrei Retutal Miguel Edwin Salubre Philip Virgil Astillo Luis Gerardo Ca?ete Gaurav Choudhary

In response to the recent formalization of Filipino Sign Language (FSL) and the lack of comprehensive studies, this paper introduces a real-time FSL gesture recognition system. Unlike existing systems, which are often limited to static signs and asynchronous recognition, it offers dynamic gesture capturing and recognition of 10 common expressions and five transactional inquiries. To this end, the system sequentially employs cropping, contrast adjustment, grayscale conversion, resizing, and normalization of input image streams. These steps serve to extract the region of interest, reduce the computational load, ensure uniform input size, and maintain consistent pixel value distribution. Subsequently, a Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) model was employed to recognize nuances of real-time FSL gestures. The results demonstrate the superiority of the proposed technique over existing FSL recognition systems, achieving an impressive average accuracy, recall, and precision rate of 98%, marking an 11.3% improvement in accuracy. Furthermore, this article also explores lightweight conversion methods, including post-quantization and quantization-aware training, to facilitate the deployment of the model on resource-constrained platforms. The lightweight models show a significant reduction in model size and memory utilization with respect to the base model when executed in a Raspberry Pi minicomputer. Lastly, the lightweight model trained with the quantization-aware technique (99%) outperforms the post-quantization approach (97%), showing a notable 2% improvement in accuracy.

Knowledge doi: 10.3390/knowledge4030019

Authors: William Lawless Ira S. Moskowitz

We develop a new theory of knowledge with mathematics and a broad-based series of case studies to seek a better understanding of what constitutes knowledge in the field and its value for autonomous human–machine teams facing uncertainty in the open. Like humans, as teammates, artificial intelligence (AI) machines must be able to determine what constitutes the usable knowledge that contributes to a team’s success when facing uncertainty in the field (e.g., testing “knowledge” in the field with debate; identifying new knowledge; using knowledge to innovate), its failure (e.g., troubleshooting; identifying weaknesses; discovering vulnerabilities; exploitation using deception), and feeding the results back to users and society. It matters not whether a debate is public, private, or unexpressed by an individual human or machine agent acting alone; regardless, in this exploration, we speculate that only a transparent process advances the science of autonomous human–machine teams, assists in interpretable machine learning, and allows a free people and their machines to co-evolve. The complexity of the team is taken into consideration in our search for knowledge, which can also be used as an information metric. We conclude that the structure of “knowledge”, once found, is resistant to alternatives (i.e., it is ordered); that its functional utility is generalizable; and that its useful applications are multifaceted (akin to maximum entropy production). Our novel finding is the existence of Shannon holes that are gaps in knowledge, a surprising “discovery” to only find Shannon there first.

Knowledge doi: 10.3390/knowledge4020018

Authors: Tyron Rakeiora Love C. Michael Hall

Despite increased attention being given to Indigenous rights, decolonization, and reconciliation in a broader business setting, the engagement of business, marketing, and consumer studies with Indigenous cultures and peoples is negligible. Although Indigenous and First Nations peoples have a significant position in the social sciences, there is no specific body of marketing or consumer knowledge that is dedicated to Indigenous knowledge and practices, even though there is a growing interest in more inclusive and transformative marketing. This paper reports on current research on Indigenous worldviews and marketing, with a continuum of Indigenous research being presented which is particularly informed by Māori experiences in Aotearoa New Zealand. Several appropriate research methods for advancing Indigenous knowledge are presented. The paper concludes by noting the potential contributions that Indigenous knowledge may provide and some of the challenges faced.

Knowledge doi: 10.3390/knowledge4020017

Authors: David Finnie Rehan Masood Liam Grant

Commercial construction projects (CCPs) in New Zealand contribute more to the economy than other project types. However, many face cost and time increases due to inadequate planning. Procurement pathways that involve contractors during design development provide more time to plan, collaboratively. Nevertheless, most projects are procured through traditional tender where contractors are only involved after detailed design. Through two-stage early contractor involvement (2S-ECI), contractors can provide design buildability advice for complex projects, contribute value management, carry out exploratory works, and order materials. The role of subcontractors in 2S-ECI can be significant. Six semi-structured interviews were conducted with clients, consultants, main contractors, and a subcontractor involved in large complex commercial construction projects. The findings build on the emerging body of knowledge about 2S-ECI by providing insight into subcontractor early involvement. Project complexity and market conditions were the main reasons for early subcontractor involvement. Common challenges include a lack of information sharing among the parties, non-competitive selection, and a lack of standard contract documentation. Opportunities for improvement include clarifying client expectations, educating stakeholders, and providing more equitable compensation for pre-construction services. Key drivers for subcontractor involvement include project complexity, market conditions, ordering long-lead-time systems, and performance specifications. Specialist early sub-trades include electrical, mechanical, structural steel, and façades. Subcontractors should typically be engaged as early as possible, often concurrently via main contractors to share performance risk. Pre-construction services provided by subcontractors include planning and sequencing; design buildability analysis; risk mitigation; value management; budget advice; systems procurement; design solutions; and document control systems. Advantages include obtaining specialist project knowledge and improving completion certainty. Producing a pre-construction services agreement (PCSA) for subcontractors may address challenges, as has been carried out for main contractors, but there is still a gap in the contractual framework for 2S-ECI for subcontractors.

Knowledge doi: 10.3390/knowledge4020016

Authors: Abílio Afonso Louren?o Maria Olímpia Paiva

The Program for International Student Assessment highlights the persistent lack of commitment and motivation among students worldwide in their school activities, which are resulting in decreased proficiency levels in reading, mathematics, and science. The magnitude of this phenomenon, with its clear social implications, suggests that we are facing a concerning quest for immediate answers and results. This research focuses on the impact of the relationships between self-regulated learning processes and the planning of time management that is dedicated to school activities on student performance, specifically in the subjects of the Mother Tongue and Mathematics. The instruments used for analysis included the Inventory of Self-Regulated Learning Processes, the Inventory of Time Management Planning, a personal data sheet, and a school data sheet. The sample in this study consisted of 688 students from primary schools in northern Portugal. The results reveal that self-regulated learning has a positive influence on how students plan time management, both in the short and long term. Additionally, a positive and statistically significant relationship is observed between short-term and long-term time management planning and students’ academic performance. This study provides an in-depth perspective on the dynamics between these elements, shedding light on the crucial nuances that shape students’ academic journeys.

Knowledge doi: 10.3390/knowledge4020015

Authors: Ognjen Arandjelovi?

Background: In an era of dramatic technological progress, the consequent economic transformations, and an increasing need for an adaptable workforce, the importance of education has risen to the forefront of the social discourse. The concurrent increase in the awareness of issues pertaining to social justice and the debate over what this justice entails and how it ought to be effected, feed into the education policy more than ever before. From the nexus of the aforementioned considerations, concern about the so-called education gap has emerged, with worldwide efforts to close it. Methods: I analyze the premises behind such efforts and demonstrate that they are founded upon fundamentally flawed ideas. Results: I show that in a society in which education is delivered equitably, education gaps emerge naturally as a consequence of differentiation due to talents, the tendency for matched mate selection, and the heritability of intellectual traits. Conclusion: I issue a call for a redirection of efforts away from the ill-founded idea of closing the education gap to the understanding of the magnitude of its unfair contributions, as well as to those social aspects that can modulate it in accordance with what a society deems fair according to its values.

Knowledge doi: 10.3390/knowledge4020014

Authors: Pawe? Turek Ewelina Dudek Mateusz Grzywa Kacper Wi?cek

This paper presents the process of digital data flow in RE/CAD/RP/CAI systems to develop models for planning surgical procedures in the craniofacial area. At the first RE modeling stage, digital data processing, segmentation, and the reconstruction of the geometry of the anatomical structures were performed. During the CAD modeling stage, three different concepts were utilized. The first concept was used to create a tool that could mold the geometry of the cranial vault. The second concept was created to prepare a prototype implant that would complement the anterior part of the mandibular geometry. And finally, the third concept was used to design a customized prototype surgical plate that would match the mandibular geometry accurately. Physical models were made using a rapid prototyping technique. A Bambu Lab X1 3D printer was used for this purpose. The process of geometric accuracy evaluation was carried out on manufactured prototypes of surgical plates made of ABS+, CPE, PLA+, and PETG material. In the geometric accuracy evaluation process, the smallest deviation values were obtained for the ABS plus material, within a tolerance of ±0.1 mm, and the largest were obtained for CPE (±0.2 mm) and PLA plus (±0.18 mm). In terms of the surface roughness evaluation, the highest value of the Sa parameter was obtained for the PLA plus material, which was 4.15 µm, and the lowest was obtained for the CPE material, equal to 3.62 µm. The knowledge of the flow of digital data and the identification of factors determining the accuracy of mapping the geometry of anatomical structures allowed for the development of a procedure that improves the modeling and manufacturing of anatomical structures within the craniofacial region.

Knowledge doi: 10.3390/knowledge4020013

Authors: Jonas Bambi Gracia Yunruo Dong Yudi Santoso Ken Moselle Sophie Dugas Kehinde Olobatuyi Abraham Rudnick Ernie Chang Alex Kuo

Healthcare organizations have a contractual obligation to the public to address population-level inequities to health services access and shed light on them. Various studies have focused on achieving equitable access to healthcare services for vulnerable patients. However, these studies do not provide a nuanced perspective based on the local reality across the full continuum of care. In previous work, graph topology was used to provide visual depictions of the dynamics of patients’ movement across a complex healthcare system. Using patients’ encounters data represented as a graph, this study expands on previous work and proposes a methodology to identify and quantify cohort-specific disparities in accessing healthcare services across the continuum of care. The result has demonstrated that a more nuanced approach to assessing access-to-care disparity is doable using patients’ patterns of service utilization from a longitudinal cross-continuum healthcare dataset. The proposed method can be used as part of a toolkit to support healthcare organizations that wish to structure their services to provide better care to their vulnerable populations based on the local realities. This provides a first step in addressing inequities for vulnerable patients in accessing healthcare services. However, additional steps need to be considered to fully address these inequities.

Knowledge doi: 10.3390/knowledge4020012

Authors: Ho Fai Chan Nikita Ferguson David Stadelmann Benno Torgler

Science has been an incredibly powerful and revolutionary force. However, it is not clear whether science is suited to performance under pressure; generally, science achieves best in its usual comfort zone of patience, caution, and slowness. But, if science is organized knowledge and acts as a guiding force for making informed decisions, it is important to understand how science and scientists perform as a reliable and valuable institution in a global crisis. This paper provides insights and reflections based on the experience of the COVID-19 pandemic and from an analytical perspective. In particular, we analyze aspects such as speed, transparency, trust, data sharing, scientists in the political arena, and the psychology of scientists—all of which are areas inviting more detailed investigations by future studies conducting systematic empirical studies.

Knowledge doi: 10.3390/knowledge4020011

Authors: Mariángela Napoli

Over the last two decades, the knowledge production, research, and reconfiguration of universities have been understood as ways of giving new meanings to the university–society binomial. In this regard, humanities are the subject of multiple debates in the face of ideas about their impact in relation to the “other sciences”. Based on these premises, this article sets out to explore possible meanings attributed by researchers to the concepts of commitment, mobilization, and transfer of research in humanities in view of the debates on the university–society interaction and the third mission of the university. The methodology used will address bibliographical analysis, theoretical background, and statements from different institutions, as well as the analysis of material from four interviews. As a first instance, the preliminary results show that strengthening critical thinking as forms of commitment emerge as central senses, focusing on Hungarian characteristics and productions in order to unravel the ways of understanding and imagining Eastern European reality. In this respect, the discussion of certain aspects of Western knowledge is seen as a task associated with social commitment with public universities as a focus of resistance.

Knowledge doi: 10.3390/knowledge4020010

Authors: Carla Terron-Santiago Jordi Burriel-Valencia Javier Martinez-Roman Angel Sapena-Bano

This contribution describes the change in methodology introduced in the subject of electrical technology within the industrial technologies engineering degree at Escuela Técnica Superior de Ingeniería Industrial, Universitat Politècnica de València. The main purpose of the methodology change was to improve the attainment of student outcomes by the introduction of project-based learning supported by flipped teaching. Moreover, a software tool was developed that generates standard exercise statements for the design of electrical installations. Using this tool, students can practice with different problem exercises, enter their solution, and receive immediate feedback on their results, improving the teaching–learning experience. The level of student outcomes attained was improved, and other positive aspects arose from the experience, such as boosting students’ responsibility in their own learning (learn to learn), their ability to solve problems, and students’ motivation. Furthermore, the instructors’ opinions on the methodology change were highly positive.

Knowledge doi: 10.3390/knowledge4020009

Authors: Isabela Evora Moreira Diego de Castro Fettermann Viviane Vasconcellos Ferreira Grubisic

This study aims to analyze the perceived value of services provided by the Brazilian Company of Research and Industrial Innovation (EMBRAPII) to its contracting ministries and institutional partners. It utilizes the theory of value perception analysis and Constructivist Multi-criteria Decision Analysis to identify critical elements for evaluating EMBRAPII’s contracting organizations. Brainstorming sessions with experts led to the identification of five criteria and 14 sub-criteria. These criteria include a relationship with EMBRAPII, a signed agreement, EMBRAPII’s reputation, technical capacity, and the ability to adapt to changes. Data were entered into the second version of the MyMCDA-C software for value perception analysis. The findings showed a positive perceived value, with the best-performing sub-criteria relating to the organization’s reputation and the agreement signed. The study concludes that EMBRAPII needs to improve in areas such as adapting to change, the adequacy of its proposals for distinct types of partnership, and social media positioning. However, the contracting organizations generally support EMBRAPII’s direction and proposed solutions.

Knowledge doi: 10.3390/knowledge4020008

Authors: Salman Mahmood Raza Hasan Nor Adnan Yahaya Saqib Hussain Muzammil Hussain

This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions.

Knowledge doi: 10.3390/knowledge4020007

Authors: Michael Grieves

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model is a hierarchical model that is often referenced in both academic and practitioner circles. This model will be discussed and shown to be faulty on several levels, including a lack of definitional agreement. A new DIKW framework with systems orientation will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move toward Intelligent Digital Twins (IDTs) with Artificial Intelligence (AI).

Knowledge doi: 10.3390/knowledge4010006

Authors: Sikha S. Bagui Dustin Mink Subhash C. Bagui Sakthivel Subramaniam

One of the major problems in classifying network attack tactics is the imbalanced nature of data. Typical network datasets have an extremely high percentage of normal or benign traffic and machine learners are skewed toward classes with more data; hence, attack data remain incorrectly classified. This paper addresses the class imbalance problem using resampling techniques on a newly created dataset, UWF-ZeekData22. This is the first dataset with tactic labels, labeled as per the MITRE ATT&CK framework. This dataset contains about half benign data and half attack tactic data, but specific tactics have a meager number of occurrences within the attack tactics. Our objective in this paper was to use resampling techniques to classify two rare tactics, privilege escalation and credential access, never before classified. The study also looks at the order of oversampling and undersampling. Varying resampling ratios were used with oversampling techniques such as BSMOTE and SVM-SMOTE and random undersampling without replacement was used. Based on the results, it can be observed that the order of oversampling and undersampling matters and, in many cases, even an oversampling ratio of 10% of the majority data is enough to obtain the best results.

Knowledge doi: 10.3390/knowledge4010005

Authors: Abdu Arslanyilmaz Margaret L. Briley Gregory V. Boerio Katie Petridis Ramlah Ilyas Feng Yu

There has been no study examining the effectiveness of an accessible computing curriculum for students with autism spectrum disorder (ASD) on their learning of computational thinking concepts (CTCs), flow control, data representation, abstraction, user interactivity, synchronization, parallelism, and logic. This study aims to investigate the effects of an accessible computing curriculum for students with ASD on their learning of CTCs as measured by the scores of 312 computing artifacts developed by two groups of students with ASD. Conducted among 21 seventh-grade students with ASD (10 in the experimental group and 11 in the control), this study involved collecting data on the computing projects of these students over 24 instructional sessions. Group classification was considered the independent variable, and computing project scores were set as the dependent variables. The results showed that the original curriculum was statistically significantly more effective for students in learning logic than the accessible one when all seven CTCs were examined as a single construct. Both curriculums were statistically significantly effective in progressively improving students’ learning of data representation, abstraction, synchronization, parallelism, and all CTCs as a single construct when examining the gradual increase in their computing artifact scores over the 24 sessions. Both curriculums were statistically significantly effective in increasing the scores of synchronization and all CTCs as a single construct when the correlations between CTCs and sessions for individual groups were analyzed. The findings underscore that students with ASD can effectively learn computing skills through accessible or standard curriculums, provided that adjustments are made during delivery.

Knowledge doi: 10.3390/knowledge4010004

Authors: John P. Bartkowski Xiaohe Xu Katherine Klee

Medical professionals are rarely trained to treat the unique healthcare needs and health disparities of people with intellectual and developmental disabilities (IDD). The Curriculum in IDD Healthcare (CIDDH) eLearn course aims to redress gaps in the delivery of medical care to people with IDD. An initial comprehensive evaluation of CIDDH in-person training content had previously underscored its knowledge and skill transfer efficacy for Mississippi healthcare providers. Training content has recently become available to medical professionals nationwide through an online self-paced modality to address physicians’ IDD education needs. This study introduces and applies a new evaluation framework called SEAM (Streamlined Evaluation and Analysis Method) that offers a promising avenue for rendering a follow-up appraisal after rigorous evidence of program effectiveness has been previously established. SEAM reduces the data-reporting burden on trainees and maximizes instructor–trainee contact time by relying on an abbreviated post-only questionnaire focused on subjective trainee appraisals. It further reduces methodological and analytical complexity to enhance programmatic self-assessment and facilitate sound data interpretation when an external evaluator is unavailable. Ratings from a small sample of early-cohort trainees provide an important test of effectiveness during CIDDH’s transition to online learning for clinicians nationwide. Using SEAM, CIDDH achieved high ratings from this initial wave of trainees across various evaluative domains. The study concludes by highlighting several promising implications for CIDDH and SEAM.

Knowledge doi: 10.3390/knowledge4010003

Authors: Andreas Fischer Jens D?rpinghaus

The labor market is highly dependent on vocational and academic education, training, retraining, and further education in order to master challenges such as advancing digitalization and sustainability. Further training is a key factor in ensuring a qualified workforce, the employability of all employees, and, thus, national competitiveness and innovation. In the contribution at hand, we explore an innovative way to derive knowledge about learning pathways by connecting the dots from different data sources of the German labor market. In particular, we focus on the web mining of online resources for German labor market research and education, such as online advertisements, information portals, and official government websites. A key question for working with different data sources is how to find the ground truth and common data structures that can be used to make the data interoperable. We discuss how to classify and summarize web data from different platforms and which methods can be used for extracting data, entities and relationships from online resources on the German labor market to build a network of educational pathways. Our proposed solution is based on the classification of occupations (KldB) and related document codes (DKZ), and combines natural language processing and knowledge graph technologies. Our research provides the foundation for further investigation into educational pathways and linked data for labor market research. While our work focuses on German data, it is also useful for other German-speaking countries and could easily be extended to other languages such as English.

Knowledge doi: 10.3390/knowledge4010002

Authors: Paulo R. M. Correia Ivan A. I. Soida Izabela de Souza Manolita C. Lima

The exploration of threshold concepts, which represent a transformed way of understanding, interpreting, or viewing something necessary for a learner’s progress, has significantly influenced teaching and learning in higher education, gaining broad acceptance in academic circles. Despite widespread enthusiasm, the scientific development of the field faces obstacles, especially epistemological and ontological uncertainties, directly implying the reliability of identification techniques and, by extension, raising questions about the validity of previous findings. This comprehensive review delves into 60 articles sourced from the Web of Science database to scrutinize the literature on threshold concept identification. The findings confirm the adaptability of threshold concepts across diverse disciplines. However, the fluid definition inherent in these concepts introduces ontological challenges, influencing biases in the identification process. The review highlights the diverse identification methods influenced by knowledge area specificities, community affinities, and research practice traditions. A diagram depicting the methods employed to identify threshold concepts is offered to highlight five central decisions to be considered. Acknowledging professors as pivotal mediators adept at navigating the epistemological and ontological dimensions of threshold concepts while integrating theoretical and applied knowledge, this study enhances our nuanced understanding of threshold concept identification. Emphasizing methodological validity and reliability, it acknowledges the crucial role of experienced educators in this issue and presents future perspectives for advancing current research, fostering the maturation of the field.

Knowledge doi: 10.3390/knowledge4010001

Authors: Jennifer D’Souza

We introduce the Open Research Knowledge Graph Agriculture Named Entity Recognition (the ORKG Agri-NER) corpus and service for contribution-centric scientific entity extraction and classification. The ORKG Agri-NER corpus is a seminal benchmark for the evaluation of contribution-centric scientific entity extraction and classification in the agricultural domain. It comprises titles of scholarly papers that are available as Open Access articles on a major publishing platform. We describe the creation of this corpus and highlight the obtained findings in terms of the following features: (1) a generic conceptual formalism focused on capturing scientific entities in agriculture that reflect the direct contribution of a work; (2) a performance benchmark for named entity recognition of scientific entities in the agricultural domain by empirically evaluating various state-of-the-art sequence labeling neural architectures and transformer models; and (3) a delineated 3-step automatic entity resolution procedure for the resolution of the scientific entities to an authoritative ontology, specifically AGROVOC that is released in the Linked Open Vocabularies cloud. With this work we aim to provide a strong foundation for future work on the automatic discovery of scientific entities in the scholarly literature of the agricultural domain.

Knowledge doi: 10.3390/knowledge3040042

Authors: Carey Ann Mather Joshua Fraser Bailey Helen Mary Almond

In today’s demanding healthcare landscape, the use of theoretical frameworks is paramount for navigating the complexities of digital health challenges. The Context Optimisation Model for Person-centred Analysis and Systematic Solutions (COMPASS) theoretical framework and implementation model serves as an invaluable direction tool in planning, implementing, and evaluating digital healthcare initiatives. This paper showcases the tangible value of the COMPASS implementation model through a use case scenario involving an accredited exercise physiologist and a healthcare user with Type 2 Diabetes Mellitus who seeks credible information via a mobile digital device. Within this example, the COMPASS model demonstrates the ability to enhance systematic processes, streamline the workflow of health professionals and develop their capabilities to actively contribute to the transformative realm of digital health. Through exploration of the use case and the significance of the systematic processes as a research direction, the empowerment of health professionals to play pivotal roles in ongoing digital health transformation is emphasised. The COMPASS model emerges as a powerful tool, guiding health professionals and organisations towards innovative and sustainable solutions in the dynamic landscape of digital healthcare.

Knowledge doi: 10.3390/knowledge3040041

Authors: Angelo Rosa Alessandro Massaro

Process mining organizatioQn (PMO) is an innovative approach based on artificial intelligence (AI) decision making suitable for designing healthcare processes for human resource (HR) organizations. The proposed work suggests some examples of PMO-based Business Process Modeling and Notation (BPMN) workflows by highlighting the advances in HR management and in risk decrease according to healthcare scenarios. Specifically proposed are different examples of “TO BE” process pipelines related to an upgrade of the organizational healthcare framework, including digital technologies and telemedicine. Important elements are provided to formulate HR management guidelines supporting PMO design. The proposed BPMN workflows are the result of different consulting actions in healthcare institutions based on the preliminary mapping of “AS IS” processes highlighting bottlenecks and needs in HR organization. A pilot experimental dataset is used to show how it is possible to apply AI algorithms providing organization corrective actions. The paper is mainly focused on discussing some validated BPMN models managing HR in the healthcare sector. The methodology is based on the application of the BPMN approach to deploy human resource organizational processes. The results show AI data-driven workflows adopted in healthcare and examples of AI fuzzy c-means outputs addressing organizational actions.

Knowledge doi: 10.3390/knowledge3040040

Authors: Micah Watanabe Danielle S. McNamara

Past research on knowledge has differentiated between dimensions (e.g., amount, accuracy, specificity, coherence) of knowledge. This paper introduces a novel dimension of knowledge, the Motivational Utility of Knowledge (MUK), that is based on fundamental human needs (e.g., physical safety, affiliation, actualization, reproduction). Adults in the United States (N = 190) were recruited from an online survey platform and paid for participation. Participants read a set of four texts arguing different views of houselessness and were administered a comprehension test after each text. Participants were asked about their conceptions of houselessness before and after reading. Finally, they were given the MUK scale, a demographics questionnaire, including questions about their personal experience with houselessness, and were administered a general prior knowledge test and a vocabulary knowledge test. We examined MUK, the factor structure of the scale and the relationship between MUK and other measures of knowledge. The analyses showed that the subscales of MUK loaded onto a single factor—an overall value of houselessness knowledge. In addition, we found that MUK was correlated with conceptions of houselessness and comprehension of texts on houselessness, indicating that the scale was valid. Overall, the findings demonstrate that MUK is an important dimension of knowledge to consider in learning tasks.

Knowledge doi: 10.3390/knowledge3040039

Authors: Gokula Vasantha Jonathan Corney Chandra Kant Upadhyay

Crowdsourcing offers on-demand access to large numbers of human workers to implement new forms of human–computer collaborative functionalities that can be seamlessly integrated into advanced software and algorithms. However, crowdsourcing tasks are primarily undertaken by urban rather than rural workers. To enable the development of skilled rural employment, this research aims to assess rural crowdsourcing workers’ spatial reasoning and creative abilities and their abilities to solve irregular strip packing problems associated with the manufacture of sheet materials. The study conducted experiments and data collection with 140 rural Business Processing Outsourcing (BPO) workers located in six states of India. The statistical analyses of the data collected from seven rural BPO firms (140 rural workers) reveal that rural workers can achieve a 2D packing efficiency that is up to 8% higher than that of commercial algorithm outcomes. The results suggest that rural crowdsourcing can lead to effective job creation, skill development, and, for a modest cost, it can support industries that employ CAD/CAM systems to generate geometric data for common manufacturing processes.

Knowledge doi: 10.3390/knowledge3040038

Authors: Anna Isabel Silva Loureiro Adriano Bressane Victor Fernandez Nascimento José Victor Orlandi Sim?es Rogério Galante Negri

The global surge in urbanization and population growth has led to a significant increase in municipal solid waste generation, posing a considerable challenge in identifying suitable landfill sites. This study proposes a novel framework that enhances landfill site monitoring and assessment by combining GIS-based hierarchical analytical processes with a fuzzy inference system (FIS). The study employs a systematic approach involving phases such as feature selection, spatial analysis, criteria weighting, FIS building, and a case study conducted in São Paulo State, Brazil. The proposed framework effectively assesses landfill suitability and offers practical recommendations for landfill management and future site selection. This framework provides actionable recommendations for landfill monitoring and assessment, supporting landfill management while minimizing environmental and social impacts. It offers a comprehensive approach to landfill assessment, enhancing the sustainability of waste management practices. Further research can improve the proposed framework by refining feature selection and incorporating real-time data for continuous monitoring. Additionally, exploring the integration of emerging technologies, such as remote sensing and artificial intelligence, can further enhance landfill site monitoring and assessment.

Knowledge doi: 10.3390/knowledge3040037

Authors: Georgios Vouzaxakis Vassilios Makrakis Nelly Kostoulas-Makrakis

This paper describes the processes of embedding Sustainability Justice in secondary education curricula for economic courses in Greece applying the DeCoRe plus methodology and participatory action research. These processes resulted in a reconstructed curriculum that was implemented by nine teachers teaching courses in economics. Sustainability justice emphasizes the ethics and praxis of education for sustainability and requires an understanding of the curriculum as a process and praxis and teaching as an ethical and political praxis. The implementation of the diagnostic evaluation of DeCoRe plus showed that economics teachers in Greece select more behavioral than constructive-emancipatory teaching approaches. On the other hand, the implementation of the reconstructed curriculum units in their courses using the DeCoRe plus methodology revealed a shift from instructive to constructivist and emancipatory teaching and learning approaches. Teachers by the great majority declared the political and ethical perspective of teaching and seeing curriculum as a living text that can always be under the process of deconstruction, construction, and reconstruction.

Knowledge doi: 10.3390/knowledge3040036

Authors: Dimitrios Sofronas Dimitrios Margounakis Maria Rigou Efthimios Tambouris Theodore Pachidis

Over the years, various software quality measurement models have been proposed and used in academia and the software industry to assess the quality of produced code and to obtain guidelines for its improvement. In this article, we describe the design and functionality of SQMetrics, a tool for calculating object-oriented quality metrics for projects written in Java. SQMetrics provides the convenience of measuring small code, mainly covering academic or research needs. In this context, the application can be used by students of software engineering courses to make measurements and comparisons in their projects and gradually increase their quality by improving the calculated metrics. Teachers, on the other hand, can use SQMetrics to evaluate students’ Java projects and grade them in proportion to their quality. The contribution of the proposed tool is three-fold, as it has been: (a) tested for its completeness and functionality by comparing it with widely known similar tools, (b) evaluated for its usability and value as a learning aid by students, and (c) statistically tested for its value as a teachers’ aid assisting in the evaluation of student projects. Our findings verify SQMetrics’ effectiveness in helping software engineering students learn critical concepts and improve the quality of their code, as well as in helping teachers assess the quality of students’ Java projects and make more informed grading decisions.

Knowledge doi: 10.3390/knowledge3040035

Authors: Martin Wynn Peter Jones

The term “metaverse” came to the fore in 2021 when Facebook rebranded its corporate identity to Meta and signalled its intention to invest at least USD 10 billion in developing the concepts and related products that year. However, there is still little consensus in defining what constitutes the metaverse, although there is a widespread, though not universal, agreement that it will bring a wide range of benefits across society. More specifically, the advent and continuing evolution of the metaverse has strategic and operational implications for, and impacts on, industry and business at large. Adopting an inductive, interpretivist approach, this exploratory research article presents case examples of the guidance on the responsible development of the metaverse provided by two IT business services companies. This article identifies the major risks and responsibilities associated with the metaverse and assesses how companies might address these responsibilities. Very little research has been published in this area, and this article attempts to make a small contribution to filling this gap in the literature. This article finds that these responsibilities are largely in line with those currently associated with corporate digital responsibility, and concludes that the strategic impact and extent of regulatory change will depend on the nature of the metaverse that materialises in the forthcoming decade.

Knowledge doi: 10.3390/knowledge3040034

Authors: Emmanuel Adamides Nikos Karacapilidis Konstantinos Konstantinopoulos Georgios Kournetas

Cooperation is an important aspect of open innovation (OI) facilitated by information and communication technology (ICT). Cooperation may have two distinct forms, namely dialectic or dialogic, and it has already been argued that dialogic cooperation is more appropriate for knowledge creation and innovation. In this paper, we test the hypothesis that the choice of the form of cooperation by an organisation, and its implementation in an OI-enabling Information System, are contingent to the organisation’s strategic orientation and competitive and innovation strategies, and it is mediated by the past experience of its OI initiative managers. We also examined, for the first time, which are the antecedents of the adoption of dialogic (and indirectly, dialectic) cooperation in OI initiatives. The empirical research carried out in a sample of senior managers of different sectors in Greece suggests that companies that have extrospective strategic orientations and that adopt differentiation/innovation strategies are more likely to implement dialogic cooperation in their OI endeavors, thus increasing their knowledge creation potential. This choice is further supported by managers who have participated in other organisations’ OI initiatives in the past.

Knowledge doi: 10.3390/knowledge3030033

Authors: Ilias Papadogiannis Vassilis Poulopoulos Nikos Platis Costas Vassilakis Georgios Lepouras Manolis Wallace

The GPA is a universally recognised and utilised metric of academic performance that is considered to also measure a student’s potential for academic performance in the future. In this short communication we examine to what extent the GPA of the first grade of high school predicts performance in the later grades of high school, either generally (as classified in an excellent student, strong student, weak student, or very weak student) or more accurately (as indicated by the exact GPA in the next grade). We also put to the test the widely held notion that it might be best if core courses such as language and mathematics contributed more to the calculation of the GPA compared to secondary courses such as physical education or music. Our findings confirm the predictive properties of the GPA but strongly rebut the notion that a weighted GPA might achieve a better reflection of students’ potential. The study is based on the academic records of every student in Greece that progressed from the first to third grade of high school in the 2016–2019 period. This dataset contains records of more than 85,000 students, making it one of the most extensive studies ever conducted on the topic of the properties of the GPA.

Knowledge doi: 10.3390/knowledge3030032

Authors: Dirk H. R. Spennemann

The public release of ChatGPT, a generative artificial intelligence language model, caused wide-spread public interest in its abilities but also concern about the implications of the application on academia, depending on whether it was deemed benevolent (e.g., supporting analysis and simplification of tasks) or malevolent (e.g., assignment writing and academic misconduct). While ChatGPT has been shown to provide answers of sufficient quality to pass some university exams, its capacity to write essays that require an exploration of value concepts is unknown. This paper presents the results of a study where ChatGPT-4 (released May 2023) was tasked with writing a 1500-word essay to discuss the nature of values used in the assessment of cultural heritage significance. Based on an analysis of 36 iterations, ChatGPT wrote essays of limited length with about 50% of the stipulated word count being primarily descriptive and without any depth or complexity. The concepts, which are often flawed and suffer from inverted logic, are presented in an arbitrary sequence with limited coherence and without any defined line of argument. Given that it is a generative language model, ChatGPT often splits concepts and uses one or more words to develop tangential arguments. While ChatGPT provides references as tasked, many are fictitious, albeit with plausible authors and titles. At present, ChatGPT has the ability to critique its own work but seems unable to incorporate that critique in a meaningful way to improve a previous draft. Setting aside conceptual flaws such as inverted logic, several of the essays could possibly pass as a junior high school assignment but fall short of what would be expected in senior school, let alone at a college or university level.

Knowledge doi: 10.3390/knowledge3030031

Authors: Angelos Dalaklis Alexios Dalaklis Dimitrios Dalaklis

“Knowledge transfer” is achieved through sharing or disseminating knowledge, and providing inputs to problem solving; it is commonly associated with attending a series of classroom lectures and maintaining students’ engagement with the taught subject. This paper examines how a specific radio frequency identification (RFID) based information system (IS) solution could be utilized to help monitor and increase engagement of university students during lecturing activities. This IS solution relies on student attendance as the main method to measure their engagement. Initially, the main stakeholders were identified: students, lecturers, administration team and the Student Loans Company (source of funding). A value proposition canvas was subsequently created, and potential system requirements were identified. A design of the proposed RFID based system was created based on these requirements and then compared with a real-life (already existing) system at Henley Business School. By comparing these two systems, the authors determined related benefits/drawbacks of the proposed system in monitoring student engagement. Potential benefits consisted of allowing all parties to easily capture attendance (with very minimal involvement of the university’s staff) and increased efficiency in analyzing student attendance data. Its main limitation was inaccurately capturing the exact time a student leaves a session. Building a working prototype for detailed evaluation and further fine-tuning/improvements must be part of future work.

Knowledge doi: 10.3390/knowledge3030030

Authors: Zhi Yong Lim Joel Weijia Lai Jun Hong Yap Ankit Mishra Intan Azura Mokhtar Darren J. Yeo Kang Hao Cheong

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.

Knowledge doi: 10.3390/knowledge3030029

Authors: Yaseen Ullah Shahid Jan Hamid Ullah

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.

Knowledge doi: 10.3390/knowledge3030028

Authors: Antonio Sarasa-Cabezuelo

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.

Knowledge doi: 10.3390/knowledge3030027

Authors: Michela Balestri Marco Campera Budiadi Budiadi Muhammad Ali Imron K. A. I. Nekaris

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.

Knowledge doi: 10.3390/knowledge3030026

Authors: Hang Vu Malindu Sandanayake Guomin Zhang

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.

Knowledge doi: 10.3390/knowledge3030025

Authors: Christian Spreafico Matteo Spreafico

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.

Knowledge doi: 10.3390/knowledge3030024

Authors: Ilias Papadogiannis Manolis Wallace Vassilis Poulopoulos Costas Vassilakis Georgios Lepouras Nikos Platis

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.

Knowledge doi: 10.3390/knowledge3030023

Authors: Dimitrios Varveris Vassilis Saltas Vassilis Tsiantos

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.

Knowledge doi: 10.3390/knowledge3030022

Authors: Dirk H. R. Spennemann

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.

Knowledge doi: 10.3390/knowledge3030021

Authors: Bryn Richards Nwabueze Emekwuru

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).

Knowledge doi: 10.3390/knowledge3030020

Authors: Jie Zhu Luis Leon Novelo Ashraf Yaseen

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.

Knowledge doi: 10.3390/knowledge3020019

Authors: Ines Zeitlhofer Sandra H?rmann Bettina Mann Katharina Hallinger Joerg Zumbach

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.

Knowledge doi: 10.3390/knowledge3020018

Authors: Alan Randall Jonathan Ogland-Hand

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.

Knowledge doi: 10.3390/knowledge3020017

Authors: Emmanuel D. Adamides

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.

Knowledge doi: 10.3390/knowledge3020016

Authors: Marta Montenegro-Rueda José Fernández-Cerero José María Fernández-Batanero

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.

Knowledge doi: 10.3390/knowledge3020015

Authors: Alexander Robitzsch

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.

Knowledge doi: 10.3390/knowledge3020014

Authors: Constantin Bratianu Ruxandra Bejinaru

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.

Knowledge doi: 10.3390/knowledge3020013

Authors: Kerstin Felser Martin Wynn

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.

Knowledge doi: 10.3390/knowledge3020012

Authors: Hatim Madkhali Salahaldeen Duraib Linh Nguyen Mukesh Prasad Manu Sharma Sudhanshu Joshi

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.

Knowledge doi: 10.3390/knowledge3020011

Authors: Arne Jarrick

An outline of a research program on knowledge progress and strategies for knowledge breakthroughs through time, in two parts: A. An outline of five steps/stages in such processes and studies via a set of cases: 1. the inception of a new finding/discovery; 2. cognitive resistance to the new finding; 3. cognitive acceptance of the new finding but continuing resistance to its practical implementation or behavioural adjustment to it; 4. practical as well as cognitive acceptance; 5. openness (or not) to related further findings. B. Defense of my approach in view of criticism from representatives of new lines of research in what has been called knowledge history.

Knowledge doi: 10.3390/knowledge3020010

Authors: Jinrui Zhang Chuanqi Li Tingting Zhang

Predicting the post-blast re-entry time precisely can improve productivity and reduce accidents significantly. The empirical formulas for the time prediction are practical to implement, but lack accuracy. In this study, a novel method based on the back-propagation neural network (BPNN) was proposed to tackle the drawbacks. A numerical model was constructed and 300 points of sample data were recorded, with consideration to fresh air volume, occupational exposure limit, toxic gas volume per kg of explosives and roadway length. The BPNN model with six neurons in a hidden layer was then developed and prediction performance was discussed in terms of four indicators, namely, the root mean square error (RMSE), the coefficient of determination (R2), the mean absolute error (MAE) and the sum of squares error (SSE). Furthermore, one representative empirical formula was introduced and calibrated for the comparison. The obtained results showed that the BPNN model had a more remarkable performance, with RMSE of 21.45 (R2: 0.99, MAE: 10.78 and SSE: 40934), compared to the empirical formula, with RMSE of 76.89 (R2: 0.90, MAE: 42.06 and SSE: 526147). Hence, the BPNN model is a superior method for predicting the post-blast re-entry time. For better practical application, it was then embedded into the software.

Knowledge doi: 10.3390/knowledge3010009

Authors: Maura A. E. Pilotti Khadija El Alaoui

The present study examines the extent to which models of honesty predict the magnitude of current or future self-serving assessment of performance in Middle Eastern students, a population often neglected in the extant literature. Specifically, the study asks whether Middle Eastern students’ predictions regarding future performance rectify prior self-serving inflated assessment, thereby restoring honesty, or glorify it through enhanced optimism, thereby discounting prior dishonesty. In this study, students believed that their self-assessment of performance would be either anonymous, allowing them to cheat, or identifiable. Before self-assessment, participants were exposed to reminders of honesty or dishonesty (i.e., priming conditions) or neutral reminders (i.e., the control condition). In agreement with the self-concept maintenance model and evidence of earlier studies conducted in the Western world, students inflated their self-assessments very little, and even less when presented with either secular or religious reminders of honesty. However, reminders were ineffective on participants’ predictions of future performance, which were biased in favor of optimism. The study offers concrete evidence on the presumed generality of a theoretical model of ethical conduct while it also adds evidence on its limitations.

Knowledge doi: 10.3390/knowledge3010008

Authors: Thomas Mandl Sylvia Jaki Hannah Mitera Franziska Schmidt

In times of crisis, science communication needs to be accessible and convincing. In order to understand whether these two criteria apply to concrete science communication formats, it is not enough to merely study the communication product. Instead, the recipient’s perspective also needs to be taken into account. What do recipients value in popular science communication formats concerning COVID-19? What do they criticize? What elements in the formats do they pay attention to? These questions can be answered by reception studies, for example, by analyzing the reactions and comments of social media users. This is particularly relevant since scientific information was increasingly disseminated over social media channels during the COVID-19 crisis. This interdisciplinary study, therefore, focuses both on science communication strategies in media formats and the related comments on social media. First, we selected science communication channels on YouTube and performed a qualitative multi-modal analysis. Second, the comments responding to science communication content online were analyzed by identifying Twitter users who are doctors, researchers, science communicators and those who represent research institutes and then, subsequently, performing topic modeling on the textual data. The main goal was to find topics that directly related to science communication strategies. The qualitative video analysis revealed, for example, a range of strategies for accessible communication and maintaining transparency about scientific insecurities. The quantitative Twitter analysis showed that few tweets commented on aspects of the communication strategies. These were mainly positive while the sentiment in the overall collection was less positive. We downloaded and processed replies for 20 months, starting at the beginning of the pandemic, which resulted in a collection of approximately one million tweets from the German science communication market.

Knowledge doi: 10.3390/knowledge3010007

Authors: Mohammad Abul Kashem Mohammad Shamsuddoha Tasnuba Nasir Asma Akter Chowdhury

In response to significant disruption, supply chain optimization became sensitive to increasing consumer expectations, unexpected demand fluctuation, and inventory costs. Proactive movement, understanding, and empowerment have fostered the beneficial results of supply chain optimization, cooperation, and operational resilience. These pioneering activities are critical to achieving a paradigm shift in the supply chain, even agility in response to changing demand. However, sophisticated analytics such as artificial intelligence (AI) and blockchain are supposed to overcome these challenges to make smarter decisions on a daily basis. Due to these facts, this study aimed to model AI’s and blockchain’s role in supply chain optimization by conducting a systematic literature review based on the idealized framework of Rejeb et al. (2022) and the SALSA mechanism. In addition, this paradigm-shifting approach will provide fairer views and options for managing forecasting, planning, monitoring, and reporting across the entire supply chain. The emphasis remains on real-time accuracy, easy access, and optimization of operational indicators such as sales, visibility, and end-to-end supply chain operations at all times and from any location. It will be an eye-opening experience to enable stakeholders and partners to communicate information collaboratively, consistently, and efficiently.

Knowledge doi: 10.3390/knowledge3010006

Authors: Nasrun Rully Charitas Indra Prahmana Irwan Akib

Representation in mathematics is essential as a basis for students to be able to understand and apply mathematical ideas. This study aims to describe how students produce different representations in solving word problems. In solving word problems, students make verbal–written representations, image representations, and symbol representations. This research uses a qualitative descriptive study involving 75 fifth-grade students at one of the private schools in Makassar, Indonesia. Setting and Participants: two subjects were chosen from 75 participants based on the completion of word problems that resulted in different representations, including verbal–written representations, picture representations, and symbol representations. The instruments used were word problems and interview sheets, although some other students only used one or two forms of mathematical representation. The results of this study indicate that, from the different representations produced that include verbal–written representations, image representations, and symbol representations, students carry out the process of translation, integration, solution, and evaluation until finding answers. In addition, other findings were students’ ‘mathematical literacy which immensely helped the students’ representation process in solving word problems. three forms of representation were found to be produced by students: verbal–written, image representation, and symbol representation. Furthermore, the three forms of representation were created through carrying out four representation processes, namely the processes of translation, integration, solution, and evaluation.

Knowledge doi: 10.3390/knowledge3010005

Authors: Knowledge Editorial Office Knowledge Editorial Office

High-quality academic publishing is built on rigorous peer review [...]

Knowledge doi: 10.3390/knowledge3010004

Authors: Ines Zeitlhofer Joerg Zumbach Verena Aigner

We assume that learners generate self-reference to a topic dealt with in class through motivational prompts of a pedagogical agent (PA). This assumption is based on self-determination theory and organismic integration theory. Consequently, learners are more motivated and achieve better learning results. We examined the influence of motivational prompts on learning success and motivation in a digital learning environment. Therefore, we implemented a PA within a web-based learning environment in order to scaffold learners’ autonomous motivation. In an experimental pre-post design (n = 60), learning success and motivation were analyzed comparing learning environments with and without PA/prompting. Results suggest that learners with a PA reach a higher level of knowledge than learners without a PA. There was no significant influence of motivational prompts on motivation itself. The limitations and conclusions of this study are discussed.

Knowledge doi: 10.3390/knowledge3010003

Authors: Manuel Alcázar-Ortega Lina Montuori Javier Rodríguez-García Carlos Vargas-Salgado

The Bologna Plan adopted by European universities ended the hegemony of an evaluation system exclusively based on the performance of traditional examinations. In this area, with a view to revitalizing grading models in university education, a wide range of evaluation mechanisms has been developed in recent years. Using them, teachers may evaluate the learning levels of their students, including both the specific competences of the taught subject and the transversal competences that help students further develop their professional careers. This article presents a methodology based on a multi-criteria procedure through which students could be evaluated from different points of view, based on different types of evaluation mechanisms that are diversely weighted. Therefore, their levels of learning could be assessed more objectively. This article shows a practical case of applying this methodology, which has been used for the last five years in a course on energy markets taught as part of the Degree in Energy Engineering at the UPV.

Knowledge doi: 10.3390/knowledge3010002

Authors: Alexandra Zbuchea Elena Dinu Andra-Nicoleta Iliescu Roxana-Maria St?neiu Bianca-Roxana Salageanu (?oldan)

KIBS are increasingly important organizations for ensuring sustainable development. Their core asset is knowledge, manifested in many ways and managed in a complex manner, sometimes jointly with clients. Like other organizations, KIBS companies have been greatly impacted by the COVID-19 pandemic. At the same time, they could provide support to their customers to better cope with the challenges associated with the pandemic. Therefore, the present paper investigates how Romanian KIBS coped with the pandemic by developing 16 interviews with key persons from four different organizations, covering a range of specializations (technical, professional, and creative). The purpose of the present study is to identify the challenges for knowledge management caused by the COVID-19 pandemic and how the pandemic influenced knowledge management performance within Romanian KIBS. The investigation reveals that the pandemic was an opportunity for organizational development and adopting more formal knowledge management practices, as well as for developing the digital profile of companies.

Knowledge doi: 10.3390/knowledge3010001

Authors: Shoma Mukai Yusuke Yamada

The Suzuki coupling is a transition metal-catalyzed, cross-coupling carbon–carbon (C–C) bond forming reaction between organic boron compounds and organic halides. As an operationally simple and versatilely applicable procedure, the Suzuki coupling reaction has found immense applications in drug discovery and development in the pharmaceutical industry. Recently, the topic of catalyst recycling has undergone intensive investigations with ever-growing interest in eco-friendly and sustainable synthesis. To recapitulate the latest progress in catalyst recycling in the Suzuki coupling reaction, this invited paper reviews key principles, benefits, challenges, and cutting-edge applications of recyclable catalysts for green synthesis of industrially valuable bioactive molecules. This review paper also discusses how artificial intelligence (AI) could further advance green synthesis of pharmaceutical products.

Knowledge doi: 10.3390/knowledge2040042

Authors: Jennifer D’Souza

We are faced with an unprecedented production in scholarly publications worldwide. Stakeholders in the digital libraries posit that the document-based publishing paradigm has reached the limits of adequacy. Instead, structured, machine-interpretable, fine-grained scholarly knowledge publishing as Knowledge Graphs (KG) is strongly advocated. In this work, we develop and analyze a large-scale structured dataset of STEM articles across 10 different disciplines, viz. Agriculture, Astronomy, Biology, Chemistry, Computer Science, Earth Science, Engineering, Material Science, Mathematics, and Medicine. Our analysis is defined over a large-scale corpus comprising 60K abstracts structured as four scientific entities process, method, material, and data. Thus, our study presents, for the first time, an analysis of a large-scale multidisciplinary corpus under the construct of four named entity labels that are specifically defined and selected to be domain-independent as opposed to domain-specific. The work is then inadvertently a feasibility test of characterizing multidisciplinary science with domain-independent concepts. Further, to summarize the distinct facets of scientific knowledge per concept per discipline, a set of word cloud visualizations are offered. The STEM-NER-60k corpus, created in this work, comprises over 1 M extracted entities from 60k STEM articles obtained from a major publishing platform and is publicly released.

Knowledge doi: 10.3390/knowledge2040041

Authors: Abdul-Fatahi Abdulai Lyndon Murphy Andrew Thomas Brychan Thomas

Research into formal and informal technology transfer between universities and industry in economical developed counties is well-documented. However, such studies are limited in number in developing economies. In the context of developing economies, this study analyses technology transfer offices’ role in university technology transfer to Ghanaian firms. We incorporate informal mechanisms as a moderating variable to explore the role of human interaction in the technology transfer value chain. In a cross-sectional survey in Ghana, using structural equation modelling with 245 firms, our research finds a negative moderating effect of informal mechanisms on the effect of technology transfer offices on innovation performance in firms. The findings are of significance to universities and corporate bodies in economically developing nations such as Ghana. Policies to improve the effect of informal mechanisms of university technology transfer offices are proposed in developing economies.

Knowledge doi: 10.3390/knowledge2040040

Authors: Constantin Bratianu Dan Paiuc

The purpose of this paper is to present a bibliometric analysis of the literature, focusing on knowledge dynamics in managerial decision making. The motivation of our research is based on the new theory of knowledge fields and knowledge dynamics and its influence on decision making in business and management. The methodology used is based on a bibliometric analysis performed with the specialized software VOSviewer. The analysis graphically presents a series of semantic clusters which show the co-citation distances between different concepts related to the search expressions used like “knowledge dynamics”, “managerial decision”, and “decision making”. As a database, we used the papers published in journals indexed in Web of Science. The outcomes of our analysis are some graphical representations of semantic clusters for the expressions “knowledge dynamics” and “managerial decision making”, and a series of tables with the content analysis of the clusters and some other data concerning publications and authors. The findings demonstrate that there is a consistent link between knowledge dynamics and the managerial decision making process. The contribution of the paper comes from the fact that it is a first bibliometric analysis of the correlations between knowledge dynamics and managerial decision making as reflected in papers indexed in Web of Science. Also, the analysis includes for the first time the topic of entropic knowledge dynamics as reflected in papers indexed in Web of Science.

Knowledge doi: 10.3390/knowledge2040039

Authors: Alessandro Massaro

The proposed paper introduces an innovative approach based on the implementation of a multi-level Decision Support System (DSS) modelling processes in the industry. Specifically, the work discusses a theoretical Process Mining (PM) DSS model gaining digital knowledge by means of logics that are able to select the best decisions. The PM model is applied to an open dataset simulating a working scenario and defining a possible safety control method based on the risk assessment. The application of the PM model provides automatic alerting conditions based on a threshold of values detected by sensors. Specifically, the PM model is applied to worker security systems characterized by the environment with a risk of emission of smoke and gases. The PM model is improved by Artificial Intelligence (AI) algorithms by strengthening information through prediction results and improving the risk analysis. An Artificial Neural Network (ANN) MultilaLayer Perceptron (MLP) algorithm is adopted for the risk prediction by achieving the good computational performance of Mean Absolute Error (MAE) of 0.001. The PM model is first sketched by the Business Process Modelling and Notation (BPMN) method, and successively executed by means of the Konstanz Information Miner (KNIME) open source tool, implementing the process-controlling risks for different working locations. The goal of the paper is to apply the theoretical PM model by means of open source tools by enhancing how the multi-level approach is useful for defining a security procedure to control indoor worker environments. Furthermore, the article describes the key variables able to control production and worker safety for different industry sectors. The presented DSS PM model also can be applied to industry processes focused on production quality.

Knowledge doi: 10.3390/knowledge2040038

Authors: Simon Bhekumuzi Khoza

Educating is an action of using knowledge to assist students to address their real-world personal needs, generating unique personal skills and values. Digital images, as images that consist of picture elements, have become important resources for sustaining education. A cause of concern is that academics use such images to drive learning for sustainability. The intention is to motivate students thereby, thus enhancing their education. In the process, students must comprehend their unique internal intelligence. As such, this study explores the academics’ usage of digital images to educate curriculum studies students at a university in South Africa. Ten academics were purposively and conveniently sampled for this study. Online reflective activities, focus-group discussions, observations, and semi-structured interviews were used for data collection. A pragmatic paradigm with participatory action research, content analysis, and a natural identity framework were used to frame this study. The findings reveal that academics were able to educate students to understand both large and small pictures of the digital images. The large and small pictures represented performance-based and competence-based curricula. However, it was difficult for the students to apply or link what they experienced by way of the digital images to their curriculum studies and real-world problems until the participatory action research was extended from Phase One to Phase Two. Consequently, this study suggests that the reflection stage of participatory action research is the dominant stage of the education process. Reflection supports students in their quest for understanding their internal intelligence, thus naturally addressing their personal, societal, and professional needs.

Knowledge doi: 10.3390/knowledge2040037

Authors: Aelita Skar?auskien? Daiva Labanauskait? Ana Timonina-Mickevi?ien?

The popularity of water and coastal tourism is steadily increasing. Marinas, an essential part of water tourism activity, are complex organizations with heterogeneous business structures with numerous suppliers of various tourist services. The purpose of this research was to determine the components of marina services that are relevant for visitors of the marina in the context of sustainable water and coastal tourism. The study’s preliminary results show that the orientation towards sustainable tourism significantly influences the behavior of visitors. Consumers of marina services would like the marinas to offer more services: not only boatyard facilities, but also catering, accommodation services, safe infrastructure for children, additional entertainment, and public events.

Knowledge doi: 10.3390/knowledge2040036

Authors: Ho-Pun Lam Mustafa Hashmi

Due to pressure from regulatory authorities, the requirement to remain compliant has tremendously increased over the last decade. To support compliance-related activities, a plethora of compliance management frameworks (CMFs), compliance languages and systems have emerged, which is on one hand advantageous, but may cause confusion when deciding which CMF can be used to best fulfil the organisation’s internal requirements. This is due to the lack of acceptable compliance tools and methodologies in the compliance domain to uncover and compare the multidimensionality of capability between different frameworks and users’ needs, which give raise to the question of how to formally evaluate a CMF. In this paper, we propose methodologies to formally evaluate CMFs, compliance languages and systems, in particular the underlying formal language of a CMF; and present the formal evaluation of two prominent formal language-based CMFs, namely, PENELOPE and PCL, with a business contract using formal analysis approach. Our evaluations formally validate that the proposed methodologies are instrumental in deciding on the suitability of a CMF when is comes to evaluating the underlying formal logic of the framework to represent different types of norms.

Knowledge doi: 10.3390/knowledge2040035

Authors: Joaquín Del Río Fernández Spártacus Gomáriz Castro Joaquim Olivé i Duran Antoni Mànuel Làzaro

Although society, governments and the business sector are increasingly demanding that Higher Academic Centers translate their innovative potential into products and services that affect society, little is known or done about the entrepreneurial skills that both lecturers and students need in the context of electronic engineering academic degrees. Inevitably, the fact that some teachers lack certain skill sets will have a negative impact on some students’ professional careers. This article demonstrates a range of initiatives in knowledge transfer which have been carried out over more than 30 years by the university research team making the proposal. Such initiatives have allowed the integration of, not only creative thinking, but also other skills differing from traditional teaching and which can help students to successfully compete for jobs requiring higher qualifications. Following the methodology of a case study within the framework of the Polytechnic University School of Vilanova i la Geltrú (EPSEVG) of the Polytechnic University of Catalonia (UPC), the novelty of the research lies in its focus on the results obtained. In the 1990s, entrepreneurial initiatives encouraged the creation of reference laboratories and the teaching of innovative subjects in the field of virtual instrumentation. Years later, this teaching activity would be complemented by the launch of lines of research/technology transfer. It should be noted that, in its initial stages, this entrepreneurial trajectory was characterized by limited material and human resources, but thanks to the discovery of little-explored areas of activity (niches), opting for working in cooperation networks (science-technology symbiosis), and the entrepreneurial profile of the members of the research team, it has been possible to gain a space in the research/development of marine technologies in the national and European context, as stated in the text. This empirical research has contributed significantly to shaping a new degree in Marine Sciences and Technologies. Likewise, the results of this study reveal that a significant number of electronic engineering students have acquired entrepreneurial skills by engaging in teaching and research and development activities in different Higher Academic Centers.

Knowledge doi: 10.3390/knowledge2040034

Authors: Jean-Yves Le Corre Thierry Burger-Helmchen

This article presents the lessons learned from an online teaching experience in the field of managerial control. The study aims to identify the determinants which influence collaborative behaviors between students in management studies and more specifically during the construction of performance measures. A business simulation specifically elaborated for this study is used. The study is conducted through a particular research design consisting of an online learning environment built on constructivist learning principles. The learning environment simulates the different steps of a performance dashboard creation (a set of performance indicators) for a fictitious organization. The study adopts an exploratory sequential design to explore and examine social behaviors during the process of knowledge construction related to performance measures. It contributes to a better understanding of the role of cognitive and behavioral skills in the profession of management accounting and how teachers can incorporate such aspects into their courses. It helps to build awareness among educators about the benefits of using digital learning solutions to help students in accounting and managerial control develop their professional skills most effectively.

Knowledge doi: 10.3390/knowledge2040033

Authors: Demetrio Panarello Andrea Gatto

In recent years, tourist destinations around the world have witnessed an exponential growth in the number of Chinese tourists. With a view of understanding the consequences of their shopping activity in terms of behavioral response, this study inspects Chinese college students traveling overseas by analyzing 180 questionnaires. The reasoned action theory is applied to examine the impact of a number of factors influencing shopping behavior. The inquiry at hand makes use of methods, including factor analysis, regression analysis, and moderation analysis, to explore the relationship among perceived service quality, perceived value and shopping behavioral intention of tourists. The results show, inter alia, that service quality is a relevant dimension influencing the likelihood of tourists to share their shopping experience with friends and relatives, encouraging them to shop in the same destination, and to continue to shop in the same destination themselves in the future, regardless of the costs of visiting. Therefore, improving service quality may increase college students’ consumption during travel as well as their word of mouth after returning home. The outcomes of this study may contribute to the existing tourism economics and management scholarship and the tourism industry.

Knowledge doi: 10.3390/knowledge2040032

Authors: Francisco Banha Ad?o Flores Luís Serra Coelho

In this conceptual paper, we bring forward a new theoretical proposition in the form of an Integrated Policy-Making Process Framework. This tool arose from the discussion and hypothesis that the main models used to analyse public policies have practical and theoretical limitations and/or are due for an update, especially when it comes to the study of phenomena with many actors involved, vast legislation, large timeframes, and high degrees of complexity. Our original model encompasses the three fundamental stages of the public policy cycle (agenda; decision making; implementation). Our approach can have a wide spectrum of applications and contribute to the field of knowledge of political sciences. Our proposal of using three frameworks in an integrated way enables researchers and users to gain a holistic vision concerning policy processes, and it offers the possibility to compare and rank categories. The Integrated Policy-Making Process Framework is thus proposed as a new tool to tackle research and studies on decision making in public policies and the policy-making cycle.