Technology-mediated method for prediction of global government investment in education toward sustainable development and aid using machine learning and classification | IEEE Conference Publication | IEEE Xplore

Technology-mediated method for prediction of global government investment in education toward sustainable development and aid using machine learning and classification


Abstract:

Predicting and monitoring of global government investment, e.g., using AI-based method, is becoming an emerging topic aimed at applying technological-based solutions to a...Show More

Abstract:

Predicting and monitoring of global government investment, e.g., using AI-based method, is becoming an emerging topic aimed at applying technological-based solutions to address critical issues for benefit of human. Using data from UNESCO's Institute for Statistics (UIS) about government expenditure in education on sustainable development (SDG) between 1970-2020; this study implements a machine learning method for prediction of the values or rate of governments' expenditure as a proportion of gross domestic product (GDP%), done by training and testing key features (year of investment and regions' classification) that are technically considered adequate for prediction of the investment values by region. The proposed method was designed based on the cross industry standard process for data mining (CRISP-DM) methodology, and implemented using supervised machine learning technique such as K-Nearest Neighbor (KNN), and validated using k-fold cross-validation. The results prove that performance of the executed model was efficient for prediction of values of the global government expenditure in education with Precision=0.77, Recall=1.00, Accuracy=0.78, F1-score=0.87, and low Error-rate of 0.22, respectively. Also, the study empirically shed light on both the socio-technical and pedagogical implications of the results and output towards achieving a sustainable educational practice, particularly SDG4 that promotes quality of education, and decision/policy making by the governments, educators, and policy makers.
Date of Conference: 12-15 October 2023
Date Added to IEEE Xplore: 20 December 2023
ISBN Information:

ISSN Information:

Conference Location: Radnor, PA, USA

I. Introduction

The past few decades has seen a gradual but universal increase in share of income that the various government or economies dedicate to education [1]-[3]. This means that the total amount of funding spent on education is increasing in absolute terms, globally [1]. However, when analyzing the correlates, determinants, and consequences of the educational investment; existing data shows that whereas the global government spendings on education does not necessarily explain the cross-national disparities as it concerns the impact on the learning processes amongst the different countries or economies [1], [2]. Perhaps, such educational bankrolling or development conundrum, on the other hand, suggests a complex "education production function" where for each level of expenditure spent by the governments or economies, the output achieved has been shown to depend significantly on the input synthesis or magnitudes [1]. For example, existing research has provided a conceptual framework for measuring the determinants of the educational outcomes to include A = a(s, Q, C, H, I) [1], [4], where A represents the level of skills learned (achievement), s the years spent on schooling, Q the teacher characteristics or vector of the school (quality), C the vector of the learners' characteristics including the innate ability, H the vector of the learners household characteristics, and I the vector of the schools' inputs versus the margin of control from the learners' household (such as daily attendance, homework, purchase of school supplies) [1]. Apparently, such pedagogical framework points to the fact that for each level of educational expenditure, whether globally or nationally, the output achieved inadvertently depends on the input mix [1], [3]. Therefore, didactically implying that to explain the educational outcomes or the socio-technical and economic impact of global spending, the several stakeholders (government, educators, policy makers) must rely on both data or information about the specific inputs in education. And, also results of various empirical studies that considers key factors or features that drive those developmental actions or ventures for common good of humanity and/or society-oriented changes, such as this current study.

References

References is not available for this document.