Expert System in Enhancing Efficiency in Basic Educational Management using Data Mining Techniques

Inusah, Fuseini; Missah, Yaw Marfo; Ussiph, Najim; Twum, Frimpong

doi:10.14569/ijacsa.2021.0121148

Cited by 6 publications

(12 citation statements)

References 5 publications

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“…Vital aspects of education ranging from access to education through quality to sustainable management of resources can best be achieved by the use of DM techniques. Accurate planning and provision of educational resources can be done through careful predictions (Inusah et al, 2021). Current trends in EDM focus on the performance of learners at higher levels.…”

Section: Dm In Educationmentioning

confidence: 99%

“…In some countries, the age range is from 4-14 years. Other countries expand basic education from 3 to 17 years of elementary education (Inusah et al, 2021). This age cohort needs numeracy and literacy skills to prepare them for future learning (L. Jiang, 2022).…”

Section: Basic Educationmentioning

confidence: 99%

“…Conscious efforts are made by researchers such as(Inusah et al, 2021) in tracing problems of education in the eld of EDM. However, the relegation of basic education raises a concern as presented in Fig.2.…”

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confidence: 99%

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Systematic Review of Data Mining in Education on the Levels and Aspects of Education.

Missah

Inusah

Najim

et al. 2023

Preprint

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The application of Data Mining (DM) in education is helping educational leadership to make informed decisions. This review seeks to identify the pattern of DM research by looking at the levels and aspects of education. As the core of schooling is Basic Education (BE), the research seeks to find out the degree of application of DM at this level to identify the challenges and prepare learners for higher education. The databases of nine (9) top-ranked publishers namely; Elsevier, Springer, Hindawi, Sage, SAI, IEEE, MDPI, Emerald and Wiley are used to identify the most recent and relevant papers in Educational DM (EDM) published from the year 2017 to 2022 specifically applied to a specific level of education. After careful filtering, only ninety-four (94) articles and conference papers were fit for the specification. The investigations revealed that only 7.45% of the published research works in EDM for basic education, 11.70% for pre-tertiary education and an overwhelming 80.85% for the tertiary level. Lower levels of education are marginalized. Also, the available literature on educational DM concentrates on student performance using attributes such as demographic factors, family socio-economic life, school environment, learner behaviour and psychological factors among others neglecting the availability of resources to facilitate quality tuition. The use of pedagogical tools is necessary for learning to improve quality. The research has revealed both a population gag and a knowledge gap.

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Section: Dm In Educationmentioning

confidence: 99%

Section: Basic Educationmentioning

confidence: 99%

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Systematic Review of Data Mining in Education on the Levels and Aspects of Education.

Missah

Inusah

Najim

et al. 2023

Preprint

Self Cite

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“…This negatively affects inclusivity and equity in education. In an initial exploration of educational data in Ghana, a detailed analysis of already existing data from the Educational Management Information System (EMIS) clearly shows the inaccuracies in the data and the repercussions on the quality of education in the country (Inusah et al, 2021). A subsequent classification of data and the use of technical tools to trace the challenges of basic education revealed the challenges of basic education as inadequate resources as a result of improper planning and projections for the growing population (Inusah et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Agile Neural Educational System for Effective Resource Management

Marfo Missah,

Inusah,

Najim

et al. 2023

SAGE Open

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The major challenge of most basic schools is inadequate educational resources despite a conscious effort to constantly provide. This is a result of inaccurate data management leading to inappropriate predictions for effective planning. The actual efficiency of a system is determined by its ability to predict real-life data with speed and accuracy. In this work, the neural educational expert system (ES) is evaluated using mathematical models for predicting the availability of resources for the growing school-aged population using a criteria-based formative evaluation to know resource life and its effect on availability. This will help in the decision to add more resources by knowing when and how the resources should be added. Technical mathematical model generation through differential equations is used to fuse the factors affecting the availability of educational resources. The real-life data is used in prediction regarding the actual enrollment of learners and the availability of resources. The model is evaluated and critically analyzed to know the degree of accuracy and the steady state. The findings revealed that the resources decay and attrite at an exponential rate in the long run and the constant number of resources provided cannot cater for the rate of decay, resulting in inadequacy. A proposed algorithm for managing the resources is presented.

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“…It carefully identi es the challenges of education to determine appropriate solutions which address the challenges. The use of Expert System with DM to manage basic education in the country as a result of challenges can be seen in (Inusah, Missah, Najim, & Twum, 2023b) and (Inusah et al, 2021). Educational Data Mining has been used to track the academic welfare of students and the general administrative procedures of educational institutions worldwide(Inusah, Missah, Najim, & Twum, 2023a) and (Drachsler & Greller, 2016).…”

Section: Introductionmentioning

confidence: 99%

Determining Student Demographic Attributes Influencing Performance Using Binary Classification in KDP Model

Issah

Appiahene

Appiah

et al. 2023

Preprint

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Machine learning (ML) is one way that can help decipher the intricate relationship between students' data and their performance. When implemented correctly in learning environments, machine learning will improve knowledge of fundamental processes by simplifying the identification, extraction, and evaluation of underlying factors that affect student learning and levels of achievement. This study employed the experimental research approach using binary classification techniques based on the six-step Knowledge Discovery Process (KDP) model. Five classifiers were used within the Rapid Miner's 9.10.010 educational environment as both experimental and analytical tool. The dataset comprised of 2334 records, 17 attributes with one class variable (students’ semester average score) inclusive. Twenty different tests were conducted. The experiments' results were evaluated using 10-fold cross-validation and ratio split validation with bootstrap sampling. The Random Forest algorithm (RF), Rule Induction methods (RI), Naive Bayes (NB), Logistic Regression (LR) and Deep Learning (DL) algorithms were used in the experiment. The experimental results demonstrated that the RF method outperforms the other four techniques in all six-evaluation metrics that were employed for the selection process with the accuracy being 93.96%. According to the RF classifier model, the mother's and father's education levels of students are two recognized demographic factors per this study that significantly influence pre-tertiary students’ academic achievement. This study has significantly reduced the gap in practical knowledge observed in the literature by introducing an intervention scheme for respective student's requiring intensive or minimal academic interventions in its prediction procedure.

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Expert System in Enhancing Efficiency in Basic Educational Management using Data Mining Techniques

Cited by 6 publications

References 5 publications

Systematic Review of Data Mining in Education on the Levels and Aspects of Education.

Systematic Review of Data Mining in Education on the Levels and Aspects of Education.

Evaluating Agile Neural Educational System for Effective Resource Management

Determining Student Demographic Attributes Influencing Performance Using Binary Classification in KDP Model

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