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Knowledge Management in Learning and Education
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Knowledge Management in Learning and Education

A special issue of Knowledge (ISSN 2673-9585).

Deadline for manuscript submissions: 5 February 2026 | Viewed by 6388

Special Issue Editors

Dr. Dan-Alexandru Szabo

E-Mail Website
Guest Editor
Department of Human Movement Sciences, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540136 Targu Mures, Romania
Interests: management in education; educational policies; strategies for learning; psychomotor activity; instructional design; medical technology; medical rehabilitation; motor behaviour; physical activity; motor skills; human movement
Special Issues, Collections and Topics in MDPI journals
Dr. Carmen Pârvu

E-Mail Website
Guest Editor
Department of Physical Education and Sports, Dunărea de Jos University, 63–65 Gării Street, Galați, Romania
Interests: strategies for learning; physical activities; physical education; educational policies; sports technology; management in education; motor behaviour; human movement
Dr. George Dănuţ Mocanu

E-Mail Website
Guest Editor
Department of Physical Education and Sports, Dunărea de Jos University, 63–65 Gării Street, Galați, Romania
Interests: strategies for learning; physical education; educational policies; sports technology; management in education; motor behaviour; physical activity; human movement

Special Issue Information

Dear Colleagues,

A paradigm shift in teaching and learning has been experienced at all educational levels due to the global COVID-19 pandemic. The widespread use of online learning facilitated by educational technology, such as social media, open online courses, collaborative virtual environments, virtual classrooms, and artificial intelligence, is one of the most significant changes. Social distancing policies first required these modifications to facilitate flexible learning in these extraordinary times. Nevertheless, as we move into the present day, these technologies—particularly the latest developments in artificial intelligence—are now employed to enhance, enrich, and maintain flexible learning in the years after the pandemic.

Although the intrinsic qualities of these technologies—such as intelligence, connectivity, and interactivity—promote innovative forms of flexible learning, a thorough investigation of their ability to satisfy the changing demands and expectations of learning in this "new normal" era remains vital. In order to guarantee improvements in flexible learning, academics and practitioners in the area must conceptualize, create, and assess a variety of technology-mediated metrics, techniques, and practices, given the possibility that the current circumstances will continue.

This Special Issue aims to gather novel research regarding how technology-mediated policies, knowledge-based education strategies, and practices may support and enhance flexible education. This Special Issue of Knowledge welcomes the submission of original research papers, systematic reviews, and meta-analyses that address the following subjects:

  • Learning management systems;
  • Adaptive learning;
  • Application of AI in teaching and learning;
  • Computer-supported collaborative learning;
  • Content development for blended learning;
  • Improved flexibility in learning processes;
  • Intelligent assessment tools;
  • Intelligent student advising;
  • Intelligent tutoring systems;
  • Interactive learning systems;
  • Learning analytics and education big data;
  • Pedagogical and psychological issues;
  • Personalised learning with AI;
  • Practices in education;
  • Strategies for learning;
  • Technology-enabled teaching and learning strategies;
  • Other topics related to knowledge-based education and practice in education.

Dr. Dan-Alexandru Szabo
Dr. Carmen Pârvu
Dr. George Dănuţ Mocanu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Knowledge is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • learning management
  • management in education
  • educational policies
  • online learning
  • technology-mediated learning
  • open education

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

25 pages, 5773 KiB  
Article
FEA-Assisted Test Bench to Enhance the Comprehension of Vibration Monitoring in Electrical Machines—A Practical Experiential Learning Case Study
by Jose E. Ruiz-Sarrio, Carlos Madariaga-Cifuentes and Jose A. Antonino-Daviu
Knowledge 2025, 5(3), 16; https://doi.org/10.3390/knowledge5030016 - 12 Aug 2025
Viewed by 185
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Knowledge Management in Learning and Education)
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17 pages, 1707 KiB  
Article
A Structural Causal Model Ontology Approach for Knowledge Discovery in Educational Admission Databases
by Bern Igoche Igoche, Olumuyiwa Matthew and Daniel Olabanji
Knowledge 2025, 5(3), 15; https://doi.org/10.3390/knowledge5030015 - 4 Aug 2025
Viewed by 335
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Knowledge Management in Learning and Education)
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16 pages, 358 KiB  
Article
Artificial Intelligence in Curriculum Design: A Data-Driven Approach to Higher Education Innovation
by Thai Son Chu and Mahfuz Ashraf
Knowledge 2025, 5(3), 14; https://doi.org/10.3390/knowledge5030014 - 29 Jul 2025
Viewed by 650
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Knowledge Management in Learning and Education)
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22 pages, 450 KiB  
Article
Ayatutu as a Framework for Mathematics Education: Integrating Indigenous Philosophy with Cooperative Learning Approaches
by Terungwa James Age
Knowledge 2025, 5(2), 11; https://doi.org/10.3390/knowledge5020011 - 9 Jun 2025
Viewed by 1301
Abstract
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Knowledge Management in Learning and Education)
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15 pages, 1182 KiB  
Article
Interpretable Ensemble Learning Approach for Predicting Student Adaptability in Online Education Environments
by Shakib Sadat Shanto and Akinul Islam Jony
Knowledge 2025, 5(2), 10; https://doi.org/10.3390/knowledge5020010 - 3 Jun 2025
Viewed by 827
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Knowledge Management in Learning and Education)
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19 pages, 2058 KiB  
Article
CORE: Cultivation of Collaboration Skills via Educational Robotics
by Emmanouil A. Demetroulis, Ilias Papadogiannis, Manolis Wallace, Vassilis Poulopoulos and Angeliki Antoniou
Knowledge 2025, 5(2), 9; https://doi.org/10.3390/knowledge5020009 - 6 May 2025
Viewed by 2024
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Knowledge Management in Learning and Education)
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