Knowledge

22 pages, 450 KiB

Open AccessArticle

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

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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, 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

Open AccessArticle

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 243

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 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

Open AccessArticle

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 882

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 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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8 pages, 172 KiB

Open AccessStudy Protocol

The Uncertainty–Certainty Matrix for Licensing Decision Making, Validation, Reliability, and Differential Monitoring Studies

by Richard Fiene

Knowledge 2025, 5(2), 8; https://doi.org/10.3390/knowledge5020008 - 28 Apr 2025

Viewed by 297

Abstract

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. Full article

20 pages, 2209 KiB

Open AccessArticle

Modeling the Knowledge Production Function Based on Bibliometric Information

by Boris M. Dolgonosov

Knowledge 2025, 5(2), 7; https://doi.org/10.3390/knowledge5020007 - 3 Apr 2025

Viewed by 409

Abstract

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. Full article

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Knowledge, Volume 5, Issue 2 (June 2025) – 5 articles

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