Data-driven intervention-level prediction modeling for academic performance (original) (raw)

Every year, when the Kenya Certificate of Primary Education (KCPE) examination results are released, the same story of mass failure in rural schools is repeated. Academic performance prediction modelling could provide an opportunity for learners' outcomes to be known early, before they sit for final examinations. This would be particularly useful for education stakeholders to initiate intervention measures to help students who require high intervention to pass final examinations. This study proposed that an academic performance prediction model could be built using Logistic Regression to classify students into two categories: those that will pass and those that will need intervention to pass. A six-step Cross-Industry Standard Process for Data Mining (CRISP-DM) theoretical framework was used to support the modelling process. Modelling was conducted using two datasets collected in Kwale County and Mombasa County. The first dataset had 2426 records having 22 features, collected fr...

ucational Data Mining (EDM) as a new technology has become a field of research as a result of continuous improvement in numerous approaches in statistics, exploring hidden data in educational environment. An application associated with EDM is a predictive system that can be deployed in early prediction of student academic performance. The importance is to identify poor performers and provide necessary remediation to avoid school drop outs and also encourage high performers. This pa- per explores certain features of a population of 103 first year students majoring in Computer Science at University of Nigeria, Nsukka. Due to the high number of predicting variables determining student’s performance, it is necessary to apply feature selection mechanism using rapid miner to filter these variables. Decision tree, a Machine Learning Algorithm (MLA) was used in training and testing. It was observed that the accuracy is dependent on the datasets on which the model is trained. Two dis- similar datasets achieve different accuracy on the same algorithm. This leads us to conclude that the greatest factor in achieving higher accuracy is the type of datasets not actually the type of classification algorithm.

Following the deployment of the Learning Management System (LMS) platform in higher educational institutions in Ethiopia, a massive amount of potentially helpful but as-yet untapped educational data has been generated. Despite the fact that the data is powerful enough to contribute to reducing student dropout rates through the application of modern educational data mining techniques such as machine learning, it has not been successfully employed to tackle student academic performance problems in higher education institutions(HEIs). As a result, a machine learning model was proposed based on data from three semesters of undergraduate students at Bule Hora University. To predict students' academic achievement, five machine learning methods (SVM, Random Forest, KNN, Gradient Boosting, and Decision Tree) were used. The Decision Tree model outperformed other models with a promising result of 97.3% on test accuracy and was selected as a proposed model. Moreover, our findings suggest t...

Predicting classifiers can be used to analyze data in K-12 education. Creating a classification model to accurately identify factors affecting student performance can be challenging. Much research has been conducted to predict student performance in higher education, but there is limited research in using data science to predict student performance in K-12 education. Predictive models are developed and examined in this review to analyze a K-12 education dataset. Three classifiers are used to develop these predictive models, including linear regression, decision tree, and Naive Bayes techniques. The Naive Bayes techniques showed the highest accuracy when predicting SAT Math scores for high school students. The results from this review of current research and the models presented in this paper can be used by stakeholders of K-12 education to make predictions of student performance and be able to implement intervention strategies for students in a timely manner.

Providing quality education to students is the main objective of higher education institutions. The need of identifying students with weak performances has been a rising problem and most teachers have relied on calculating the average of exam grades. The main objective of our project is to predict and identify the students who might fail in semester examinations. This would prove helpful for teachers in providing additional assistance to such students. This review is conducted to find out how different researchers have approached this problem, what the outcomes of their study are and how it can help us in improving the performance of students. This review shows that machine learning, clustering proves useful in predictions, but there is a lot more work to be done using this technology.

This study presents a machine learning-based approach to enhance the classification and optimization of students' academic performance within Nigeria's polytechnic education system. The polytechnic system is pivotal in providing technical and vocational education, but challenges persist in nurturing students' academic achievement. This article explores the complexities influencing academic performance and proposes strategies for improvement using machine learning algorithms. The research utilizes linear and support vector regression models to predict students' cumulative grade point averages (CGPA). A dataset from Akwa Ibom State Polytechnic, Ikot Osurua, comprising total courses, credit units, department, and previous grade point average (GPA), is employed for model development and evaluation. Both models achieve similar predictive performance, but linear regression slightly outperforms support vector regression. The results highlight the significant role of variables like total courses, the type of academic department, and previous GPA in predicting CGPA. This study offers a valuable tool for assessing and improving students' academic performance in Nigeria's polytechnic education system, with potential for broader applications in higher education. Further research involves expanding the dataset and considering additional factors beyond result records to enhance the model's robustness and applicability.

The advancement in Information Technology makes it easier and cheaper to collect large amounts of data, but if this data is not further analyzed, it remains only huge amounts of data. These large amounts of data set have motivated research and development in various fields to extract meaningful information with a view of analyzing it to solve complex problem. With new methods and techniques, data can be analyze and be of great advantage. Data mining and machine learning are two computing disciplines that enable analysis of large data sets using different techniques. This paper gave an overview of several applications using these disciplines in education, with focus on student’s academic performance prediction. Early prediction of students’ performance is useful in taking early action of improving learning outcome. The perfect methods for this are machine learning and data mining. This paper also discusses special use of data mining in education, called educational data mining. Educa...

Educational data mining is an emerging research field concerned with developing methods for exploring the unique types of data that come from educational context. These data allow the educational stakeholders to discover new, interesting and valuable knowledge about students. In this paper, we present a new user-friendly decision support tool for predicting students’ performance concerning the final examinations of a school year. Our proposed tool is based on a hybrid predicting system incorporating a number of possible machine learning methods and achieves better performance than any examined single learning algorithm. Furthermore, significant advantages of the presented tool are that it has a simple interface and it can be deployed in any platform under any operating system. Our objective is that this work may be used to support student admission procedures and strengthen the service system in educational institutions.

Although the educational level of the Portuguese population has improved in the last decades, the statistics keep Portugal at Europe's tail end due to its high student failure rates. In particular, lack of success in the core classes of Mathematics and the Portuguese language is extremely serious. On the other hand, the fields of Business Intelligence (BI)/Data Mining (DM), which aim at extracting high-level knowledge from raw data, offer interesting automated tools that can aid the education domain.

Background - Many factors affect student performance such as the individual’s background, habits, absenteeism and social activities. Using these factors, corrective actions can be determined to improve their performance. This study looks into the effects of these factors in predicting student performance from a data mining approach. This study presents a data mining approach in identify significant factors and predict student performance, based on two datasets collected from two secondary schools in Portugal. Methods – In this study, two datasets collected from two secondary schools in Portugal. First, the data used in the study is augmented to increase the sample size by merging the two datasets. Following that, data pre-processing is performed and the features are normalized with linear scaling to avoid bias on heavy weighted attributes. The selected features are then assigned into four groups comprising of student background, lifestyle, history of grades and all features. Next, ...