Educational Decisions Research Articles

Towards Sustainability: Tracking Carbon Footprint Trends at Ezemvelo KZN Wildlife

Carbon footprint assessment is important to combat global warming and promote sustainability. Globally, organizations committed to biodiversity conservation are essential for maintaining ecosystems and the people who inhabit them. Nonetheless, these organizations produce a carbon footprint due to their operating operations. Hence, this study aimed to assess the specific carbon footprint of the Ezemvelo KZN Wildlife (EKZNW) to improve its understanding of its environmental implications and encourage sustainable behaviors within its particular missions. Using the greenhouse gas protocol corporate accounting and reporting standard as a guide, the study methodology examines greenhouse gas emissions from direct (Scope 1) and indirect (Scopes 2 and 3) sources related to Ezemvelo wildlife activities over five years (2014/2015--2018/2019). The results show that 34,016.62 tons of carbon dioxide equivalent (tCO2e) are emitted on average each year. The majority of these emissions are caused by Scope 2 electricity consumption, which accounts for 23,475.82 tCO2e, and Scope 1 emissions, which account for 7,826.20 tCO2e. Furthermore, there was a noticeable difference in emissions between the reserves, with the Imfolozi Game Reserve having the highest emissions. The findings of this study direct EKZNW toward ecologically conscious behaviors by acting as a catalyst for educated decision-making. The insight gained paves the way for proactive steps to lower carbon emissions, coordinating conservation efforts with more general goals of sustainability and climate resilience.

Determinants of Household Choice of Pre-Tertiary Education in Ghana

Purpose: This research examined the factors influencing household decisions between public and private pre-tertiary education in Ghana, focusing on the relationship between socioeconomic household variables and school characteristics. Design/Methodology/Approach: This study employed data from the Ghana Living Standard Survey Round 7 (GLSS7), which comprised a sample of 15,000 households drawn from 1,000 enumeration areas. It employed an explanatory research design and used a binary logistic regression technique for analysis. Findings: The research found that though higher spending on pre-tertiary education decreases the likelihood of choosing private schools, higher school fees, transportation costs, and proximity to schools increase the likelihood of opting for private schools. It also revealed significant disparities between urban and rural households, with rural families less inclined to choose private schools due to lower income levels and limited access to private schools. Financial inclusion, reflected by households' bank accounts, was a key factor enabling private school choices. Research Limitation: Although credible, the use of secondary data from the Ghana Statistical Service (GLSS 7) suffers from the usual problem of not asking all the questions that the research would have asked. Social Implication: This research has the potential to inform the policies of the Ministry of Education and the Ghana Education Service to implement strategies that enhance the accessibility and affordability of quality education nationwide. Practical Implication: This includes expanding transportation infrastructure in rural areas to reduce distance barriers and improve access to public and private schools. Additionally, efforts to expand financial inclusion should be intensified to empower parents to make school choices for their children. Originality/ Value: This research focused on socio-economic and school-related factors affecting school choice in Ghana, analysing how financial inclusion and regional disparities influence household educational decisions.

Application of Machine Learning Algorithms in Predicting Academic Performance of Students in Higher Education Institutes (HEIs): A Systematic Review and Bibliographic Analysis

Purpose: This study aims to identify trends, key research areas, popular predictive features, machine learning algorithms used, and gaps in the existing literature. Design/Methodology/Approach: This study selected 60 articles published between 2018 and August 31, 2023, from Google Scholar and Scopus databases to address the identified knowledge gap. A systematic literature review and bibliographic analysis were conducted using both quantitative and qualitative approaches, with PRISMA chosen as the reporting format. Findings: The study reveals that machine learning models, particularly Decision Trees, followed by Random Forests, Artificial Neural Networks, Support Vector Machines, and Naïve Bayes, have significantly contributed to predicting student academic performance. The datasets predominantly utilised by researchers include students' academic records, demographics, activities, and behaviour, which are crucial for predicting academic performance and evaluating model effectiveness. Research Limitation: These limitations highlight the challenges in conducting comprehensive reviews of machine learning applications in educational contexts while acknowledging the evolving nature of both technology and educational assessment methods. Practical Implication: This research has the potential to inform evidence-based decision-making, promote personalised learning experiences and enable early interventions for at-risk students Social Implication: The study underscores the potential of machine learning algorithms to promote equity and inclusion, provide targeted student support, empower individuals, raise ethical considerations, foster community engagement, and support lifelong learning initiatives. Originality and Value: This research uniquely explores machine learning applications to predict student academic performance, identifying classroom participation and examination scores as key predictors. It offers a pragmatic approach to educational practice, identifies future research opportunities, and advocates for data-driven decision-making in higher education.

The profile of the high school graduate towards the era of education 5.0: A case study

Introduction: The present study aimed to examine characteristics of high school graduates in a context of education 5.0 in Pedernales, Manabí, Ecuador, to promote the integration of technologies in the academic learning process. Methods: The research used a quantitative, descriptive methodology, combining literature review, surveys of high school graduates (n=480) and interviews with education experts.Results: 42% of high school graduates have difficulties with economic resources to access university education, while only 4% consider technology and digital tools a relevant factor in their decision to enter university. These difficulties suggest a critical analysis between the demands of the digital labor market and the perception of graduates on the importance of technological skills.Conclusions: High school graduates face complex decisions ranging from academic program selection to financing options, followed by the low prioritization of technology in educational decisions. Strategies should be implemented to increase awareness of the criticality of Education 5.0 in the context of the global digital economy. The research thus contributes to the literature on preparing the workforce of the future in emerging economies and provides educational policy design adapted to the digital age, highlighting the need to align high school students' perceptions with the technological demands of the labor market.

HOW DOES THE (DE)CENTRALİZATİON OF SECONDARY EDUCATİON AFFECT İNDİVİDUAL İNNOVATİVENESS? EVİDENCE FROM AZERBAİJAN

This paper explores the relationship between centralization of decision-making in secondary education and individual innovativeness in developing countries, specifically within the context of Azerbaijan. The research examines to what level centralized decision-making can hinder or promote individual innovativeness, utilizing secondary data and quantitative analysis. The outcomes of this study, even if a significant difference was not found, indicate the need for a broader lens that considers various factors affecting individual innovativeness. This bears significant implications for politicians, policymakers, and other stakeholders in Azerbaijan and other developing countries to reconsider their current form of decision-making structure in educational systems.

Decision Support System for Recommendation Sharia Banking Investment Products Using Simple Additive Weighting (SAW)

The swift expansion of Islamic banking has resulted in the creation of many investment products, such as mudharabah deposits, sukuk, and Islamic mutual funds, all compliant with sharia rules. Notwithstanding this advancement, the customers' inadequate financial literacy presents considerable obstacles in identifying appropriate investment alternatives that correspond with their objectives and risk appetite. This work seeks to fill this gap by creating a Decision Support System (DSS) that employs the Simple Additive Weighting (SAW) method to aid users in making educated decisions on Islamic banking investments. The SAW technique was selected for its straightforwardness and efficacy in normalizing data and assessing alternatives according to established criteria, including profit potential, risk level, investment duration, and compliance with sharia principles. The research encompassed multiple phases, including the identification of evaluation criteria, the collection and analysis of pertinent data, and the application of the SAW method to rank investment choices. The DSS effectively delivered precise and dependable suggestions, designating Sharia Deposits as the optimal investment choice due to its robust agreement with the established criteria. The results underscore the system's capability to improve financial inclusion and literacy, especially in Indonesia, where comprehension of sharia-compliant financial products is still inadequate. Future improvements to the DSS involve the incorporation of real-time data and the expansion of its application to include more financial industries, such as insurance and microfinance. This study highlights the importance of utilizing technology to address deficiencies in financial literacy and facilitate the sustainable development of the Islamic banking sector.

Application of the teaching games for understanding model to improve decision-making in sport learning: a systematic review and meta-analysis.

Issues related to sport teaching at different educational stages is a subject of wide interest. Teaching Games for Understanding has been established as the most effective way to teach students the elements related to the field of sport. The objectives of this study were (a) to examine the impact of the Teaching Games for Understanding model on decision-making in sports education and (b) to compare the effect of the interventions analysed according to educational stage. A systematic review and meta-analysis of studies published before August 2024 was conducted. A total of 4937 scientific studies were obtained. The quantitative synthesis consisted of 25 scientific articles (n = 1692). The studies were analyzed using three-level random effects models with variance estimation. Results were calculated as raw mean differences and Hedges' g effect sizes. This model is suitable for decision-making in sports education (g = 0.82; CI 95% = [0.55; 1.09]). This pedagogical model was also found to be effective for working on decision-making in primary education (g = 0.6108; CI 95% = [0.3587; 0.8628]), secondary education (g = 0.7523; CI 95% = [0.2348; 1.2706]) and higher education (g = 0.8803 [CI 95% = 0.2851 to 1.4855]). Teaching games for understanding effectively addresses decision-making during sports learning. In addition, this pedagogical model is effective for facilitating decision-making according to the role and the moment of the game. The use of this model enables effective technical-tactical learning to solve various problematic actions in real game situations.

Narrative Review of the Morphofunctional Underpinnings and Comprehensive Rehabilitation Strategies for Smartphone-addicted University Students

The widespread availability of mobile phones has contributed to a high prevalence of digital addiction worldwide. Smartphone addiction or problematic smartphone use (SA/PSU) has become a widespread issue, especially among university students. Due to concerns about SA/PSU and cyber addiction in young people, research has focused on the structural and functional effects on the brain and the behavioral consequences of spending too much time on mobile phones. This review investigates the impact of excessive smartphone use on this demographic group's physical and mental health. An advanced search on PubMed based on primary keywords, clustered in syntactic associations looked for academic work focusing on: ((((smartphone addiction) and (problematic smartphone use)) and (students) and (Physical exercises)) and (Physical Activity). The relationship (((smartphone addiction) and (problematic smartphone use) and (systematic review) identified 22 papers, published between 2016 and 2024, 16 of which were during the Covid era and post-pandemic periods (2020-2024). Smartphone overuse-induced alterations in cerebral gray matter were observed in four trials. The beneficial influence of physical activity on smartphone addiction was emphasized in 11 publications (selected 9) identified by the advanced search with the syntactic relationship. The narrative review offers a comprehensive overview of effective strategies for addressing SA/PSU. This includes utilizing exercise and psychological interventions to combat digital addiction. Furthermore, studies have looked into the potential benefits of physical exercise and specific strategies addressing these issues. Engaging in physical activity may help offset the negative effects of SA/PSU, and targeted strategies are necessary for preventing and treating these problems in young people. Political and educational decision factors must implement targeted strategies and integrated social education policies to prevent smartphone overuse and pejorative consequences in adolescents and young people.

Perancangan Model Deteksi Potensi Siswa Putus Sekolah Menggunakan Metode Logistic Regression Dan Decision Tree

The phenomenon of student dropouts is one of the main challenges in education, influenced by various factors such as absenteeism, economic pressures on families, low academic performance, and lack of motivation. This issue not only affects the personal development of students but also tarnishes the reputation of educational institutions. Therefore, an innovative technology-based approach, such as data mining, is needed to detect students at risk of dropping out early. This study aims to design a model for detecting the potential of school dropout students using Logistic Regression and Decision Tree methods based on student data from SMA N 4 Tegal. The variables used in the analysis include demographic, academic, and social information such as absenteeism, average semester grades, parental income, and transportation type. The dataset is processed using one-hot encoding and label encoding techniques to convert categorical data into numeric values. The results indicate that both methods have their respective advantages. The Decision Tree model achieves high precision, especially in predicting students who continue their education, with a precision of 0.99 for the "Continue School" class. However, recall for the "Dropout" class remains low (0.60), indicating the need for improvements in detecting students at risk of dropping out. On the other hand, the Logistic Regression model shows better balance in detecting both classes, with more balanced accuracy and recall. This study concludes that both models can be used to monitor the potential of school dropouts and provide data-driven recommendations for more accurate educational decision-making.

EXPRESS: Fixing Onlies versus Advancing Multiples: Number of Children and Parents’ Preferences for Educational Products

Due to a continuous decline in fertility rates in recent decades, the number of one-child families has been increasing in both developing and developed countries. Given this significant shift in family structure, it is imperative to investigate the complex dynamics of how the number of children within a family—whether it is a one-child family or a family with multiple children—affects parents’ decision-making in the realm of education. Using a mixed-method approach, seven main studies, including a secondary data analysis (Study 1) and a field study (Study 2), and four supplementary studies, consistently found that, compared with multi-child parents, one-child parents have a stronger relative preference for deficit-based over strength-based educational products. Both mediation and moderation analyses suggest that this effect is driven by one-child parents’ heightened parenting prevention focus. The current research not only enriches our understanding of parenting dynamics, educational decision-making, and parenting regulatory focus, but also has implications for a myriad of disciplines, including marketing, psychology, economics, and sociology. More importantly, it carries substantial implications for marketers, educators, and policymakers.

Digital transformation and its role in FMCG supply chain resilience

The main aim was to determine how the supply chains of the FMCG industry may enhance their resilience through the adoption of digital technologies. Additionally, enhancing visibility and transparency through the augmentation of operational efficiency to elevate overall performance. This analysis utilised the Uncertainty Framework theory, which provides a systematic approach for identifying, categorising, and managing uncertainties within the supply chain. Organisations can formulate strategies to effectively manage and mitigate risks by understanding the many dimensions of uncertainty within the supply chain and their interrelations. The research utilised a qualitative methodology, gathering data via semi-structured interviews with 25 supply chain specialists and senior executives possessing extensive experience in the FMCG sector. The study's findings indicate that digital transformation enhances agility, transparency, and data-driven decision-making, which is crucial for bolstering the resilience of the FMCG supply chain. Six themes emerged from the findings: data-driven decision-making, supply chain visibility and transparency, automation and robots, collaborative platforms and ecosystems, predictive analytics and forecasting, and traceability and sustainability. The research will enhance our understanding of the implementation of digital transformation initiatives throughout FMCG supply chains. This involves analysing the application of digital technologies in supply chain operations. The study may ascertain the impact of these technologies on various resilience criteria, including flexibility, agility, visibility, and responsiveness, by analysing the role of digital transformation in FMCG supply chains. The research findings can assist supply chain managers in making educated decisions on investments and strategic planning for digital transformation initiatives.Research on digital transformation in the FMCG sector is limited, particularly on these unprecedented upheavals. Moreover, there is little study connecting digital transformation with sustainability and resilience in FMCG supply chains, which is essential for comprehending how these projects might collaboratively enhance robust supply systems.

Design and Evaluation of Online Educational Content: AI-Informed Guidelines Based on Machine Learning Analysis of Learners’ Interactions Traces

This study proposes an intelligent educational decision support system that empowers instructional designers to evaluate online educational contents in order to improve their design and effectiveness. The key challenge in developing it lies in automating and objectifying the evaluation process. To address this, the study pursues two main objectives. The first one is to propose a Multicriteria Approach for Learning Experience Analysis (MALEA) on which we have based the evaluation through the learners’ traces. The second objective consists of proposing the Approach for Content Success Prediction (ACSP), which can be used to evaluate educational content before its deployment. ACSP combines logistic regression and MALEA. This combination helps to guard against the possible imprecision of human judgment affecting the decision-making process. A case study is conducted and proves that the system meets the objective sought and thus is retained for online educational content evaluation. Results are promising with high values of precision, accuracy, specificity, and sensitivity. Different perspectives are finally proposed.

The Academic Intensity Use of Chatbot-Based Artificial Intelligence and Its Relation to Academic Well-Being: A Correlational Study at the University of Jordan

In terms of artificial intelligence (AI) applications, chatbot-based AI such as ChatGPT have the potential to improve students’ academic well-being and success. Despite the wide spread of chatbot-based AI development, no studies have explored their correlation with psychological constructs in the realm of education. This study aimed to measure the academic intensity use of chatbot-based AI and academic well-being among undergraduates and investigate the correlation between these constructs. The data was gathered using a self-administered web-based questionnaire, which includes the Academic Well-Being Scale and the developed academic intensity use of chatbot-based AI scale (AIUCA). The study sample consists of 340 undergraduates from the School of Educational Science. The findings revealed a moderate level of usage of chatbot-based AI and a moderate level of academic well-being among undergraduates. It also demonstrated a significant positive correlation (r = 0.68) between the intensity use of chatbot-based AI and academic well-being (p < 0.01). These results recommend decision-makers in higher education encourage students to integrate chatbot-based AI with their learning processes and activities to improve their academic well-being.

The Authentic Learning of Humans Is a Creative Process and Very Different from the Artificially Creative Output of AI

ABSTRACTThis article examines theories which suggest that authentic learning requires creativity. The connection between creativity and learning has been recognized for quite some time. Several of the theories examined herein are decades old. The older theories include Piaget's and Dewey's, and less obviously the humanistic theory of self‐actualization. It is quite useful to take these perspectives, though older, into account. The idea of authentic learning has evolved, however, as is evidenced by newer theories. One of these is the theory of personal creativity. This theory is interpreted in the context of the previous perspectives on learning and creativity. Newer evidence from the neurosciences is also cited because it confirms that learning is more than memory and that the brain uses special operations when original ideas are constructed. Implications of the idea that creativity and learning are related are explored. These include suggestions for educators and the classroom. Several surprising implications for our understanding of AI are also explored. Most important is probably the conclusion that an appreciation of what exactly makes up authentic learning goes a long way towards distinguishing the artificial creativity of AI from the creativity of humans. It is the latter which should guide educational decisions.

The Role of Educators and Caregivers in Supporting Participation of Children with Disabilities in Educational Decision Making

This qualitative study examines the communication and language supports available for children in educational environments. By doing this, it also examines the support provided by adults in educational settings for the participation of children with disabilities in educational decision making and the facilitating factors and barriers in children’s participation. This study was conducted with 11 participants, including six teachers, one parent, and four children. The data was analyzed using thematic analysis and the results of the analysis revealed four themes. The outcomes of this research emphasize the impact of varied multidimensional factors involving the individual and their proximal environment.

Evolving from Legacy Protocols: Transitioning from RIP and EIGRP to OSPF, IS-IS and BGP

With the advent of the internet and growing complexity in the networking environment, old protocols like Routing Information Protocol (RIP) or Enhanced Interior Gateway Routing Protocol (EIGRP) pose significant bottlenecks for modern businesses and service providers. This paper provides an overview on the evolution of RIP and EIGRP, its limitations as Interior Gateway Protocols (IGPs), the benefits of moving to newer and more powerful protocols such as Open Shortest path First (OSPF), Intermediate System to Intermediate System (IS-IS) and Border Gateway Protocol (BGP). The paper further dives deep into migration strategies from legacy protocols to OSPF or ISIS. OSPF and IS-IS are considered the best enterprise routing solutions and service provider routing solutions respectively due to their scalability, performance and flexibility. In addition, it highlights the importance of BGP as an external gateway protocol to control routing across autonomous systems. The findings aims to aid network engineers in making educated decisions on protocol implementation and migration efforts. Keywords— RIP, OSPF, IS-IS, BGP, Migration Strategies, Convergence time, Scalability.

Risk of Reading Difficulties in School-age Children Treated for Non-syndromic Craniosynostosis.

We assessed the prevalence of guardian-reported reading difficulties in children treated for non-syndromic craniosynostosis (NSC) and in relation to the location of the closed suture. Additionally, we determined correlations with guardian-reported neurodevelopmental disorders (NDDs) and associations between reported reading ability, sex, timing of surgical intervention, parental education, and heredity of reading difficulties in guardians. Cross-sectional, prospective, population-based study. Tertiary hospital. The cohort included 127 participants (aged 9-10 years) treated for NSC (76 sagittal, 38 metopic, 9 unicoronal, and 1 each of bicoronal, lambdoid, frontosphenoidal, and multisuture NSC). The prevalence of guardian-reported reading difficulties was defined as a score ≥8 out of 14 points on the Short Dyslexia Scale. We determined associations between reported reading ability and reported NDDs through binomial regression analysis. The cohort demonstrated a 14.2% risk of of guardian-reported reading difficulties, with sagittal, metopic, and unicoronal NSCs exhibiting respective prevalences of 10.5%, 18.4%, and 23.1%. NDDs were reported in 22% of participants and significantly associated with reading difficulties. The results indicated that participants with sagittal NSC showed the lowest risk of reading difficulties. Additionally, we found that NDDs represent significant risk factors for guardian-reported reading difficulties across the patient cohort. These findings underscore the need for targeted screening and support, especially for patients with metopic and unicoronal NSC, to guide clinical and educational decisions.

Pengembangan Media Layanan Berbasis Aplikasi Android Tentang Informasi Studi Lanjut pada Siswa SMP Muhammadiyah 2 Yogyakarta

This study aims to develop an Android application-based service media called INSTULA that provides information related to further study to grade IX students of SMP Muhammadiyah 2 Yogyakarta. Further study information is defined as a collection of data, knowledge, and guidance that helps students understand and choose further education paths according to their potential, interests, talents, and future career prospects. This application contains features such as types of secondary schools (SMA, SMK, MA), interests, simple aptitude tests, and job prospects. The research model used is Research and Development (R&D) with the Borg and Gall approach which is carried out up to the seventh stage, namely product revision. The development process involves validation by material experts, media experts, and service experts. The total results of the expert assessment showed an average score of 90.7 out of 100, with the category "very good." The assessment includes aspects of material, media, and service quality that are declared suitable for use for further study guidance. Based on the results of the Paired Samples t-test conducted to compare the results of the pre-test and post-test, a significant difference was found between the two scores. Descriptively, the average pre-test score was 14.36 with a standard deviation of 2.49, while the average post-test score increased to 18.97 with a standard deviation of 2.02. This average increase indicates a positive effect after the intervention. This increase indicates that the application is effective in supporting students' understanding of further study information. The INSTULA application is declared suitable for use as an innovative media in classical guidance services. This media supports students in exploring further education with an interactive and easily accessible approach, helping them make better educational decisions according to their needs and potential.

Cognitive or non-cognitive? The Effect of Maternal Dominance on Adolescent Human Capital: Evidence from Adolescents' Educational Decisions

Cognitive or non-cognitive? The Effect of Maternal Dominance on Adolescent Human Capital: Evidence from Adolescents' Educational Decisions

A Data Warehousing Framework for Predictive Analytics in Higher Education: A Focus on Student at-Risk Identification

Abstract This paper will examine the development of a data warehouse aimed at improving decision-making in higher education, which focuses on the identification of students at-risk of academic failure through machine learning techniques. This research utilizes South East European University (SEEU) as a case study to show how data warehousing can integrate various student data—including demographics, academic performance, grades, attendance, and engagement—into an integrated framework that enables predictive analytics. The overall approach allows SEEU decision-makers, administrators, and faculty to proactively identify and assist at-risk students, which improves student retention and their academic success. The results underscore the crucial role of data warehousing in enhancing student achievement and facilitating informed decision-making in higher education. The paper concludes with concrete suggestions for leveraging data to enhance decision-making processes in a digital educational institution.