Knowledge doi: 10.3390/knowledge6020010

Authors: Qing Zhang Jizhou Tong

Introductory Java programming requires learners to reason about abstract computational concepts such as program state, control flow, and execution order, which often present substantial difficulties for novice programmers. These challenges may be further intensified for collegiate student athletes when programming instruction remains disconnected from the domain knowledge that shapes their prior experiences. This paper proposes a wrestling-inspired, state-based pedagogical framework that leverages the rule system of National Collegiate Athletic Association (NCAA) wrestling as an analogical knowledge domain for introducing foundational Java programming concepts. Within this framework, wrestling match states and scoring actions are systematically mapped to core programming constructs, which include variable assignment, conditional branching, loops, method invocation, and program termination. This paper is positioned as a conceptual and pedagogical framework study rather than an empirical intervention study. It focuses on the theoretical rationale, conceptual alignment, instructional mappings, and classroom implementation possibilities of a wrestling-inspired approach. This paper does not report participant data, learning assessments, or comparative outcome measures. Instead, it illustrates how sport-specific mental models can be transformed into structured instructional representations that may support learners’ reasoning about program execution. By integrating domain-aligned cognitive schemas with programming instruction, the proposed framework offers a structured knowledge scaffolding approach that is designed to support novice understanding of computational processes in introductory programming education.

Knowledge doi: 10.3390/knowledge6020009

Authors: Malefetjane Phineas Phaladi Stevens Phaphadi Mamorobela

This study examined how human resource management (HRM) training and development practices contribute to strengthening knowledge absorptive and protective capacities within South African state-owned enterprises (SOEs). Using an exploratory mixed-methods approach, this research was conducted in two phases. Firstly, 20 HR managers were interviewed, and annual reports from nine SOEs were reviewed. Thematic analysis, supported by Atlas.ti, revealed key insights that informed the design of a survey used in the second phase. In the second phase, the survey was administered to 585 randomly selected employees across three SOEs, achieving a 25% response rate. Data analysis carried out with Statistical Analysis Software (SAS) version 8.4 showed strong reliability, with a Cronbach’s alpha of 0.94. Findings indicate that training and development initiatives play a significant role in building absorptive capacity as they enhance knowledge acquisition and the ability to integrate new skills. These practices also reinforced employees’ tacit knowledge base, particularly through job-specific training and skills development. However, whilst HRM practices were effective in knowledge absorption, they were less successful in safeguarding and protecting critical tacit knowledge against potential loss. This study highlights the dual challenge facing SOEs: advancing employees’ capacity to absorb knowledge whilst also developing stronger mechanisms to protect valuable expertise.

Knowledge doi: 10.3390/knowledge6010008

Authors: Francisco Banha André Rui Graça Beatriz Góis Francisco Miguel Banha

This paper examines the intersection of entrepreneurship, innovation, and sustainability in the tourism sector through the lens of knowledge creation and transfer. It focuses on the Tourism Creative Factory (TCF) ideation programme, developed under Turismo de Portugal’s Fostering Innovation in Tourism 2.0 initiative. Using a case study methodology, the research situates the 2021–2022 “RESTART” edition of TCF within broader theoretical frameworks of knowledge-based development and organisational learning. This study highlights the programme’s role in facilitating knowledge exchange among participants, mentors, and institutional actors, thereby enhancing entrepreneurial readiness and resilience in a post-pandemic context. Emphasis is placed on mentorship, capacity-building, and experiential learning as mechanisms for knowledge management, enabling the 39 selected participants to develop sustainable business models and Minimum Viable Products (MVPs), with the 16 most innovative being selected for a final pitch presentation to a panel of experts representing diverse sectors of the entrepreneurial ecosystem. The findings underscore the transferability of TCF’s methodology to other knowledge-intensive sectors and contribute to advancing theoretical and practical understanding of how structured ideation programmes function as knowledge systems within tourism and beyond.

Knowledge doi: 10.3390/knowledge6010007

Authors: Vishnu Kumar

This study examines the factors driving perceived Study Efficiency and Exam Readiness associated with ChatGPT use among STEM students in higher education. Although prior research on generative artificial intelligence (GenAI) has largely focused on adoption and attitudes using descriptive or linear statistical approaches, limited empirical work has explored how students’ interactions with such tools relate to learning-related outcomes. To address this gap, this study applies an interpretable machine learning (ML) framework to identify key predictors of learning gains from ChatGPT use. Data were obtained from a large-scale global survey of STEM students (n = 10,525) across 109 countries and territories, capturing usage patterns, perceived capabilities, satisfaction, and academic outcomes. Two eXtreme Gradient Boosting (XGBoost)-based ML classification models were developed to predict Study Efficiency and Exam Readiness, and SHapley Additive exPlanations (SHAP) were used to interpret feature-level contributions. The models achieved strong predictive performance for the high-gain class, with an accuracy of 0.93 (F1 = 0.96) for Study Efficiency and 0.86 (F1 = 0.92) for Exam Readiness. Results indicate that motivation, personalized learning support, improved access to knowledge, facilitation of study activities, and exam-focused study assistance are key predictors of learning gains. These findings offer empirical and practical insights for educators and policymakers seeking to design effective and pedagogically sound AI-assisted learning environments in STEM education.

Knowledge doi: 10.3390/knowledge6010006

Authors: Belingtone Eliringia Mariki

The use of Artificial Intelligence (AI) in learning is expanding globally; however, the full potential of AI tools in the Open and Distance Learning (ODL) context, particularly at the Institute of Adult Education (IAE), remains underexplored. This study examined the IAE ODL students’ perspectives on the use of AI tools in learning. Specifically, it investigated ODL students’ familiarity with AI, AI preferences and use in learning, and perspectives on AI tool use in ODL. The study employed a mixed-methods approach, utilising a convergent parallel design to collect data from 93 second- and third-year ODL students at the Dar es Salaam and Morogoro Campuses. The findings revealed that 94.7% of students were familiar with AI, mainly after beginning their studies; 87% used ChatGPT for learning, and 57% used AI to answer their questions. In addition, 98% of students argued that the utilisation of AI in ODL is inevitable, citing its role in enhancing self-learning, improving access to learning materials, and saving time. Based on the findings, the study suggests that enhanced access to and awareness of diverse AI tools may help maximise their potential benefits in learning. The study also calls for academic integrity, ethical use, peer learning, and human-AI interaction among ODL students and institutions for the effective utilisation of AI in ODL.

Knowledge doi: 10.3390/knowledge6010005

Authors: Jacob Striebel Rebecca Myers Tatiana Ringenberg Patrick C. Shih Xiaozhong Liu

Complexity science studies systems in which properties and behaviors emerge at meso- and macroscales that are difficult to predict and model by observing the properties and behaviors exhibited by the system’s components at smaller scales. The set of relationships that exist among post-secondary school curricula and job markets is one example of such a system. Prior work has undertaken the challenge of modeling this system for several purposes, one of which has been to develop retrieval and ranking algorithms in the education–career domain. A particular emergent property that is closely bound up with this prior work, and that is the focus of the present work, is the salience of a course with respect to a specific objective. The specific objective that we are interested in here is career usefulness, which means that our overall task is to rank order courses based on their usefulness in helping a student to obtain and perform a specific job. One aspect of this overall task that remains understudied concerns how it can best be performed in an interpretable manner and whether existing interpretable methods that may be applied to it, such as text-based similarity measures and document-ranking functions, represent workable solutions or whether an approach involving more detailed modeling of the underlying complex system may prove more effective. The purpose of this article is to answer this question, and, in order to do this, most of this article’s content is devoted to the latter kind of approach, because the former kind is described in detail in the existing literature. The specific approach of the latter kind that we investigate is based on, first, developing a heterogeneous knowledge graph model of the overall complex system, and, second, developing a procedure that quantifies salience using the strength of the skill-dependency chains that link a course to a specified job. To evaluate our methods, we perform a human subjects study in which we leverage the domain expertise of fifty participants. The results of the study demonstrate that the latter approach produces career-motivated course recommendations, as well as accompanying explanations, which systematically exceed those produced by the former approach, in terms of both their quality and usability.

Knowledge doi: 10.3390/knowledge6010004

Authors: Modiehi Winnie Rammutloa

The paper sought to examine the role of collaboration in sustaining citizen science activities and projects in academic libraries. The study applied a quantitative approach and a survey design to assess knowledge and understanding of citizen science by academic librarians to advance research relevant to SDGs. A standardised questionnaire was distributed to 185 academic librarians affiliated with the Higher Education and Libraries Interest Group (HELIG). The survey yielded a response rate of 34% since only 63 academic librarians volunteered to participate in the completion of the questionnaire. Data was analysed using SPSS version 29. Findings revealed that citizen science is a new concept in academic libraries in South Africa. To advance the use of citizen science in contributing towards SDGs, academic librarians need to raise awareness, foster collaborations, and initiate advocacy efforts to promote and support citizen science activities. The research further revealed that a work-integrated learning and community engagement department should be established within the library to advocate for citizen science activities. There is a need to visit schools to introduce citizen science at the grassroots level to increase the visibility of the field and to lay a foundation for scientific literacy at an early stage. Although the research setting was in academic libraries, for future research, it will be beneficial to conduct such a study in a public library setting to achieve varying perspectives from the community members where the concept of citizen science emanates.

Knowledge doi: 10.3390/knowledge6010003

Authors: Joseph Jebari Ariel M. Greenberg

The deployment of autonomous systems in human environments demands sophisticated mechanisms for recognizing and preventing harm. This paper proposes an innovative discovery method for identifying harm-relevant features through the systematic analysis of thick harm verbs—semantically and pragmatically rich linguistic concepts like “puncture”, “crush”, or “poison” that encode both the mechanics and normative evaluations of specific harm types. By analyzing thick harm verbs to extract the information they encode, we can systematically identify the objects, properties, mechanisms, and contextual conditions that autonomous systems need to track to recognize and prevent harm. We demonstrate how this discovery method can be implemented with the support of large language models as analytical assistance tools, showing how human analysts can operationalize the framework with current technology. The resulting feature specifications discovered through this method provide foundations for constructing harm ontologies that bridge abstract ethical principles and concrete system requirements, addressing a critical gap in autonomous systems design while maintaining explanatory transparency essential for safe deployment in human environments.

Knowledge doi: 10.3390/knowledge6010002

Authors: Abid Ali Fareedi Muhammad Ismail Shehzad Ahmed Stephane Gagnon Ahmad Ghazawneh Zartashia Arooj Hammad Nazir

This research focuses on ontology-driven conversational agents (CAs) that harness large language models (LLMs) and their mediating role in performing collective tasks and facilitating knowledge-sharing capabilities among multiple healthcare stakeholders. The research addresses how CAs can promote a therapeutic working alliance and foster trustful human–AI collaboration between emergency department (ED) stakeholders, thereby supporting collaborative tasks with healthcare professionals (HPs). The research contributes to developing a service-oriented human–AI collaborative framework (SHAICF) to promote co-creation and collaborative learning among patients, CAs, and HPs, and improve information flow procedures within the ED. The research incorporates agile heavy-weight ontology engineering methodology (OEM) rooted in the design science research method (DSRM) to construct an ontological metadata model (PEDology), which underpins the development of semantic artifacts. A customized OEM is used to address the issues mentioned earlier. The shared ontological model framework helps developers to build AI-based information systems (ISs) integrated with LLMs’ capabilities to comprehend, interpret, and respond to complex healthcare queries by leveraging the structured knowledge embedded within ontologies such as PEDology. As a result, LLMs facilitate on-demand health-related services regarding patients and HPs and assist in improving information provision, quality care, and patient workflows within the ED.

Knowledge doi: 10.3390/knowledge6010001

Authors: Nasser Khalili Mohammad Jahanbakht

This study conducts a systematic bibliometric review of artificial intelligence (AI)-based approaches to tacit knowledge extraction and management. Drawing on data retrieved from Scopus and Web of Science, this study analyzes 126 publications published between 1985 and 2025 using VOSviewer and Biblioshiny to map citation networks, keyword co-occurrence patterns, and thematic evolution. The results identify nine major clusters spanning machine learning, natural language processing, semantic modeling, expert systems, knowledge-based decision support, and emerging hybrid techniques. Collectively, these findings indicate a field-wide shift from manual codification toward scalable, context-aware, and semantically enriched approaches that better support tacit knowing in organizational practice. Building on these insights, the paper introduces the AI–Tacit Knowledge Co-Evolution Model, which situates AI as an epistemic partner—augmenting human interpretive processes rather than merely codifying experience. The framework integrates Polanyi’s concept of tacit knowing, Nonaka’s SECI model, and sociotechnical learning theories to elucidate how human–AI interaction transforms the dynamics of knowledge creation. The review consolidates fragmented research streams and provides a conceptual foundation for guiding future methodological development in AI-enabled tacit knowledge management.

Knowledge doi: 10.3390/knowledge5040028

Authors: John Hulpke Aidan Kelly Cubie Lau Ming Li

Artificial Intelligence, now commonly called AI, is having an increasingly big impact on society. There are fears that may be negatives or downsides, especially when Artificial Intelligence is used unethically. But how are humans guiding these machines to know whether the choice, the decision, is ethical? Since 2007, one way to check the ethicality of any choice has been to apply the JUSTICE model. This framework helps practitioners decide whether a specific action is or is not ethical by looking through one or more of the seven JUSTICE lenses: Justice, Utilitarian, Spiritual Values, TV rule or Transparency, Influence, Core, and Emergency. Now, in this era of increasing prevalence of Artificial Intelligence, with humans making decisions often together with machines, can the JUSTICE framework still be useful? Yes, it can. We look at each of those seven components. Each may give guidance in some situations. Of the seven, it seems that T or the TV test is most likely to give guidance in this new era.

Knowledge doi: 10.3390/knowledge5040027

Authors: Bonginkosi A. Thango Ralebitso K. Letshaba Lerato Matshaka

Small- and medium-sized enterprises (SMEs) are increasingly embracing digital transformation (DT) to remain competitive; however, the enabling role of knowledge management (KM) remains underexplored. This systematic literature review investigates how KM supports DT in SMEs, focusing on strategic processes, tools, barriers, and policy contexts. A structured search was conducted in Google Scholar, Scopus, and Web of Science using the string: (“knowledge management” OR “KM”) AND (“digital transformation” OR “DT”) AND (“small and medium enterprises” OR “SME”). The search yielded 32,547 results, from which 19 studies met the eligibility criteria (English, 2020–2025, KM–DT focus, clear methodology). Results indicate that KM supports DT primarily through change management (31.58%), innovation enablement (21.05%), as well as improved decision-making and agility (15.79%). The most cited tools include KM systems, AI/analytics, and collaborative platforms. Major barriers include limited resources, lack of digital skills, and poor KM culture. Critical success factors identified are leadership commitment (26.32%) and strategic alignment (21.05%). Theoretical foundations are dominated by the Resource-Based View and Dynamic Capabilities Theory. While KM is proven to be a strategic driver of DT in SMEs, more empirical and policy-grounded studies are needed. This review provides a framework to guide future research and inform SME practitioners and policymakers.

Knowledge doi: 10.3390/knowledge5040026

Authors: Suvodeep Mazumdar Fatima Maikore Vitaveska Lanfranchi Harriet Baird Fabio Ciravegna Vyv Huddy Paul Norman Richard Rowe Alexander J. Scott Thomas L. Webb

Almost all societal grand challenges, whether concerning the environment, health, well-being, or the development of sustainable economic models, have at their heart a need to understand people’s behaviour. However, uniting data and insights across disparate fields requires an explicit and shared understanding of concepts, variables, and ideas (e.g., how to characterise and differentiate behaviours). Ontologies provide a mechanism for creating this explicit and shared understanding and are starting to be developed and used in the social and behavioural sciences. This paper proposes an online co-design approach to use and develop ontologies of behaviour to specify the characteristics of behaviour (e.g., habitual, changeable, effortless) and studies that investigate behaviour as part of a project designed to understand how behaviours are related. We report on our experience of collaborative co-development of ontologies using real-time interactive tools and reflect on the benefits and challenges of our approach. We also offer a set of recommendations for researchers interested in applying such methods to co-develop ontologies. The work contributes to efforts to understand the characteristics of behaviour and enable these to be used to understand questions about behaviour (e.g., is poor sleep associated with greater engagement in habitual behaviours?).

Knowledge doi: 10.3390/knowledge5040025

Authors: Jorge Rivera Scarlett Zapata Ricardo Pizarro Brian Keith

This article presents a case study showing the development of a chatbot, named Selene, in a Software-as-a-Service platform for behavioral analysis using Retrieval-Augmented Generation (RAG) integrating domain-specific knowledge and enforcing adherence to organizational rules to improve response quality. Selene is designed to provide deep analyses and practical recommendations that help users optimize organizational behavioral development. To ensure that the RAG pipeline had updated information, we implemented an Extract, Transform, and Load process that updated the knowledge base of the pipeline daily and applied prompt engineering to ensure compliance with organizational rules and directives, using GPT-4 as the underlying language model of the chatbot, which was the state-of-the-art model at the time of deployment. We followed the Generative AI Project Life Cycle Frameworkas the basic methodology to develop this system. To evaluate Selene, we used the DeepEval library, showing that it provides appropriate responses and aligning with organizational rules. Our results show that the system achieves high answer relevancy in 78% of the test cases achieved and a complete absence of bias and toxicity issues. This work provides practical insights for organizations deploying similar knowledge-based chatbot systems.

Knowledge doi: 10.3390/knowledge5040024

Authors: Benedikt Perak Dragana Špica

In recent advancements within natural language processing (NLP), lexical networks play a crucial role in representing semantic relationships between words, enhancing applications from word sense disambiguation to educational tools. Traditional methods for constructing lexical networks, however, are resource-intensive, relying heavily on expert lexicographers. Leveraging GPT-4o, a large language model (LLM), our study presents an automated, scalable approach to creating multi-relational Japanese lexical networks for the general Japanese language. This study builds on previous methods of integrating synonyms but extends to other relations such as hyponymy, hypernymy, meronymy, and holonomy. Using a combination of structured prompts and graph-based data storage, the model extracts detailed lexical relationships, which are then systematically validated and encoded. Results reveal a substantial expansion in network size, with over 155,000 nodes and 700,000 edges, enriching Japanese lexical associations with nuanced hierarchical and associative layers. Comparisons with WordNet show substantial alignment in relation types, particularly with soft matching, underscoring the model’s efficacy in reflecting the multifaceted nature of lexical semantics. This work contributes a versatile framework for constructing expansive lexical resources that hold promises for enhancing NLP tasks and educational applications across various languages and domains.

Knowledge doi: 10.3390/knowledge5040023

Authors: Stefanos Balaskas Vassilios Tsiantos Sevaste Chatzifotiou Dionysia Filiopoulou Kyriakos Komis George Androulakis

Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as affective obstacles, as well as the core predictors of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR). By employing a cross-sectional survey of Greek university students (N = 608) and partial least squares structural equation modeling (PLS-SEM), we tested direct and indirect impacts on behavioral intention (BI) to apply m-learning applications. The results affirm that PU and PE are direct predictors of BI, while PR has no direct impact on BI but acts indirectly through TECH and RTC. Mediation is partial in terms of PE and PU and indirect-only (complete) in terms of PR with respect to the impact of affective states on adoption. Multi-group comparisons found differences in terms of gender, age, confidence, and years of use but not frequency of use, implying that psychological and experiential characteristics have a greater impact on intention than habitual patterns. These results offer theory-driven and segment-specific guidelines for psychologically aware, user-focused m-learning adoption in higher education.

Knowledge doi: 10.3390/knowledge5040022

Authors: Zhiguo Yang Xiang Guo Peng Zhang

The revealed capability of generative AI tools can significantly transform the way knowledge work is conducted. With more tools being built and implemented, generative AI-aided knowledge work starts to emerge as a new normal, where knowledge workers shift a significant portion of their workloads to the tools. This new normal can lead to many concerns and issues including workers’ mental health, employees’ confusion in production, and potential spreading misknowledge. Considering the substantial portion of knowledge work in the US economy, this paper calls for more research to be conducted on this important area. This paper synthesizes relevant economic and behavioral research findings in the AI automation field and opinions of field experts, and presents a comprehensive framework, “generative AI-aided knowledge work”. This framework theoretically addresses concerns such as job replacement and organizational and behavioral factors in using generative AI and provides directions for future research and guidelines for practitioners in incorporating generative AI tools. This is one of the early attempts to provide a comprehensive overview of generative AI’s impacts on knowledge workers and production. It has the potential to seed future research in many areas such as countering misknowledge and employees’ mental health.

Knowledge doi: 10.3390/knowledge5030021

Authors: Xiaojun (Jenny) Yuan Qingyue Guo Yvonne Appiah Dadson Mahsa Goodarzi Jeesoo Jung Yanjun Dong Nisa Albert DeeDee Bennett Gayle Prabin Sharma Oyeronke Toyin Ogunbayo Jahnavi Cherukuru

As artificial intelligence (AI) technologies are increasingly integrated into emergency management, ethical considerations demand greater attention. Essential components of comprehensive emergency management include mitigation, preparedness, response, and recovery, which should serve as the foundation for integrating AI-driven science and technologies to effectively safeguard populations and infrastructure in times of crisis. This paper reviewed the ethical challenges of AI in emergency management in terms of critical issues, best practices, applications, emerging ethical considerations, and strategies addressing ethical challenges. Three core ethical themes are identified: algorithmic bias; privacy, transparency and accountability; and human–AI collaboration. This paper thoroughly analyzed the associated ethical challenges, reviewed the theoretical frameworks and proposed strategies to mitigate ethical challenges by strengthening the audits of algorithms, enhancing transparency in AI decision-making, and incorporating stakeholder engagement. Finally, the importance of creating policies to govern AI ethics was discussed.

Knowledge doi: 10.3390/knowledge5030020

Authors: Dirk H. R. Spennemann

Generative artificial intelligence (AI), in particular large language models such as ChatGPT, have reached public consciousness with a wide-ranging discussion of their capabilities and suitability for use in various professions. Following the printing press and the internet, generative AI language models are the third transformative technological invention, with truly cross-sectoral impact on knowledge transmission and knowledge generation. While the printing press allowed for the transmission of knowledge that is independent of the physical presence of the knowledge holder, with publishers emerging as gatekeepers, the internet added levels of democratization, allowing anyone to publish, along with global immediacy. The development of social media resulted in an increased fragmentation and tribalization in online communities regarding their ways of knowing, resulting in the propagation of alternative truths that resonate in echo chambers. It is against this background that generative AI language models have entered public consciousness. Using the strategic foresight methodology, this paper will examine the proposition that the age of generative AI will emerge as an age of public ignorance.

Knowledge doi: 10.3390/knowledge5030019

Authors: Eleftherios Protopapas

The human brain is one of the most complex parts of the human body. Its function has been studied extensively in biology and medicine. Along this line, applied mathematics plays a crucial role through the formulation and analysis of mathematical models. A student’s ability to learn is an important aspect of these studies. In this paper, a theoretical mathematical model is presented to study the brain’s ability to learn, with parameters such as human intelligence, the expected amount of knowledge a student seeks to acquire, and the tendency to forget. A parametric study of the obtained model is conducted, and by taking into account actual data from the literature, the values of the parameters that fit these data are derived, demonstrating the validity of the model. The findings of this study indicate that the proposed model accurately embodies the core principles of mastery learning and offers a practical framework that educators can employ to improve instructional planning, thereby optimizing students’ readiness for examinations scheduled on fixed dates.

Knowledge doi: 10.3390/knowledge5030018

Authors: Víctor D. Carmona-Galindo Hou Ung Manhao Zeng Christine Broussard Elizaveta Taranenko Yousef Daneshbod David Chappell Todd Lorenz

Generative artificial intelligence (GenAI) is reshaping science, technology, engineering, and mathematics (STEM) education by offering new strategies to address persistent challenges in equity, access, and instructional capacity—particularly within Hispanic-Serving Institutions (HSIs). This review documents a faculty-led, interdisciplinary initiative at the University of La Verne (ULV), an HSI in Southern California, to explore GenAI’s integration across biology, chemistry, mathematics, and physics. Adopting an exploratory qualitative design, this study synthesizes faculty-authored vignettes with peer-reviewed literature to examine how GenAI is being piloted as a scaffold for inclusive pedagogy. Across disciplines, faculty-reported benefits such as simplifying complex content, enhancing multilingual comprehension, and expanding access to early-stage research and technical writing. At the same time, limitations—including factual inaccuracies, algorithmic bias, and student over-reliance—underscore the importance of embedding critical AI literacy and ethical reflection into instruction. The findings highlight equity-driven strategies that position GenAI as a complement, not a substitute, for disciplinary expertise and culturally responsive pedagogy. By documenting diverse, practice-based applications, this review provides a flexible framework for integrating GenAI ethically and inclusively into undergraduate STEM instruction. The insights extend beyond HSIs, offering actionable pathways for other minority-serving and resource-constrained institutions.

Knowledge doi: 10.3390/knowledge5030017

Authors: Ioannis G. Tsoulos Vasileios Charilogis

Artificial neural networks have been used in a multitude of applications in various research areas in recent decades, providing excellent results in both data classification and data fitting. Their success is based on the effective identification (training) of their parameters using optimization techniques, and hence a series of programming methods have been developed for training these models. However, many times these techniques either can identity only some local minima of the error function with poor overall results or present overfitting problems in which the performance of the artificial neural network is significantly reduced when it is applied to different data from the training set. This manuscript introduces a method for the efficient training of artificial neural networks, where a series of genetic algorithms is applied to the network parameters in several stages. In the first stage, an initial identification of the network value interval is performed; in the second stage, the initial estimate of the value interval is improved; and in the third stage, the final adjustment of the network parameters within the previously identified value interval takes place. The new method was tested on some classification and regression problems found in the relevant literature, and the experimental results were compared against the results obtained by the application of other well-known methods used for neural network training.

Knowledge doi: 10.3390/knowledge5030016

Authors: Jose E. Ruiz-Sarrio Carlos Madariaga-Cifuentes Jose A. Antonino-Daviu

Rotating electrical machine maintenance is a core component of engineering education curricula worldwide. Within this context, vibration monitoring represents a widespread methodology for electrical rotating machinery monitoring. However, the multi-physical nature of vibration monitoring presents a complex learning scenario, including concepts from both mechanical and electrical engineering domains. This article proposes a novel knowledge-based educational experience design leveraging an integrated FEA-assisted test bench aimed at comprehensively addressing the electromechanical link between stator current and frame vibration. To this aim, a Finite Element Analysis (FEA) model is utilized to link excitation electrical signals with airgap radial forces acting in the stator. The subsequent correlation of these FEA predictions with measured frame vibrations on a physical test bench provides students with the theoretical concepts and practical tools to adequately comprehend this complex multi-physical phenomenon of wide application in real industrial scenarios. The pedagogical potential of the method also includes the development of critical thinking and problem-solving soft skills, and foundational understanding for digital twin concepts. A Delphi-style expert survey conducted with 25 specialists yielded strong support for the pedagogical robustness and relevance of the method, with mean ratings between 4.32 and 4.64 out of 5 across key dimensions. These results confirm the potential to enhance deep understanding and practical skills in vibration-based electrical machine diagnosis.

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 Castañeda 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 Cañete 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 Lourenço 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 Arandjelović

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: Paweł Turek Ewelina Dudek Mateusz Grzywa Kacper Więcek

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 Dörpinghaus

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 Simões 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 Hörmann 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.