A Review on Prediction of Academic Performance of Students at-Risk Using Data Mining Techniques

Educational data mining is the procedure of converting raw data collected from educational databases into some useful information. It can be helpful in designing and answering research questions like performance prediction of students in academics, factors that affect the students’ performance, help the teachers in understanding the problems faced by the students to understand the course content and complexity of the subject taken so that the teachers can take timely action to control the dropout rate. This also includes improving the teaching learning process so that the interventions can be taken at the right time to improve the performance of the student. This paper is the review of the research work done in the field of educational data mining for the prediction of students’ performance. The factors that influence the performance of the students i.e. the type of classrooms they attend such as traditional or on-line, socio-economic, educational background of the family, attitude toward studies and challenges faced by the students during course progress. These factors leads to the categorization of the students into three groups “Low-Risk”: who have High probability of succeeding, “Medium-Risk”: who may succeed in their examination, “High-Risk”: who have High probability of failing or drop-out. It elaborates the different ways to improve the teaching learning process by providing the students personal assistance, notes, class-assignments and special class tests. The most efficient techniques that are used in educational data mining are also reviewed such as; classification, regression, clustering and and prediction

Methods and Data Sources for Measuring Socio-Economic Factors: A Literature Review

The compiling of the population data, to establish its socioeconomic factors, is a high-cost task for governments and regulatory organizations due to the need for financial and human resources. This limitation makes it almost impossible to count on immediate updated socioeconomic population information. This article compiles a series of alternative data sources and methods that can be applied to reduce the costs and the time required to update such information. The review focus on how these sources and methods have been used in developing countries during time, highlighting the solutions for satisfying the need of updated socioeconomic factors of the population

Ecomining as a pattern of integrated approach towards sustainable mining

This paper briefly describes the Educational Project “EcoMining: Development of Integrated PhD Program for Sustainable Mining & Environmental Activities” (2019–2022), which is being implemented between Dnipro University of Technology (DUT, Ukraine) and Technical University Bergakademie Freiberg (TU BAF, Germany) under support of German Academic Exchange Service (DAAD)

An overview: the impact of data mining applications on various sectors

In recent years, it has become difficult to reach to the reliable information with increasing complicated, non-significant, unclear, large and raw data. The need for accurate analysis of reliable information from large data has also increased in direct proportion to the rate of data growth. The Data Mining Method, which is a statistical application, is used in any desired area to be accessed to the reliable and meaningful information. In this study, the areas where data mining methods are used were explained, a literature review about banking and finance, education, telecommunication, health, public, construction, engineering and science sectors was made, and the impact of the data mining was discussed. This study is aimed to provide a contribution to the literature eliminating the gap in the mentioned area and to bring an innovation to the applications and work in these areas

Exploring the Impact of Students Demographic Attributes on Performance Prediction through Binary Classification in the KDP Model

During the course of this research, binary classification and the Knowledge Discovery Process (KDP) were used. The experimental and analytical capabilities of Rapid Miner's 9.10.010 instructional environment are supported by five different classifiers. Included in the analysis were 2334 entries, 17 characteristics, and one class variable containing the students' average score for the semester. There were twenty experiments carried out. During the studies, 10-fold cross-validation and ratio split validation, together with bootstrap sampling, were used. It was determined whether or not to use the Random Forest (RF), Rule Induction (RI), Naive Bayes (NB), Logistic Regression (LR), or Deep Learning (DL) methods. RF outperformed the other four methods in all six selection measures, with an accuracy of 93.96%. According to the RF classifier model, the level of education that a child's parents have is a major factor in that child's academic performance before entering higher education

Household Characteristics of Higher Education Participants

The aim of this paper is to analyse the characteristics of Irish households that have a member participating in higher education, using surveys of Irish households collected in 1994-95 and 1999-2000. The results do not show a significant effect of income; this is notable, especially alongside the strong result that longer-term factors such as household wealth and cultural capital have a significant effect. This lends support to the argument proposed by Heckman (2000) that family income is only important over the entire educational investment cycle of a child. However, the importance of grant eligibility is a notable result, which suggests that short-term financial constraints cannot be dismissed. A combination of suitably beneficial short-term and long-term factors may be important for encouraging participation in higher education.higher education, human capital, credit constraints

An evaluation of the utilization of remote sensing in resource and environmental management of the Chesapeake Bay region

A nine-month study was conducted to assess the effectiveness of the NASA Wallops Chesapeake Bay Ecological Program in remote sensing. The study consisted of a follow-up investigation and information analysis of actual cases in which remote sensing was utilized by management and research personnel in the Chesapeake Bay region. The study concludes that the NASA Wallops Chesapeake Bay Ecological Program is effective, both in terms of costs and performance

Which noncognitive features provide more information about reading performance? A data-mining approach to big educational data

The purpose of this study is to discover which noncognitive variables provide more information about reading performance. To answer this question, data mining based on information gain, decision tree and random forest methods were utilized in the study. The participants of the study consisted of 606,627 15-year-old students (49.8% female) in a total of 78 countries or economies, 37 of which are OECD members. Reading performance and plausible values of reading, the Student, ICT Familiarity, Financial Literacy, Educational Career, Well-Being and Parent Questionnaire data in PISA 2018 were analyzed to answer the research questions. When 108 features were analyzed as independent variables, it was found that SES (home possessions, cultural possessions, and ICT resources at home), metacognitive skills (assessing credibility and summarizing), and liking/enjoying reading were major variables predicting reading performance. The path analysis revealed that these variables explain 53.3% of the variability in reading performance. It is also remarkable that the decision tree model has a 74.61% accuracy value in estimating the reading performance