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Predicting Student Performance Using Educational Data Mining
Abstract
Data mining methods have been employed successfully in several industries, including education, where they are known as educational data mining methods. Educational data mining aims to extract in-depth knowledge from raw data to build automated systems that could be used in the educational sector. With the advancement of data mining technologies, it is now possible to mine educational data to enhance educational practices. This study, therefore, uses educational data mining techniques to predict the final grades of secondary school students. This study has employed several Machine Learning (ML) algorithms, such as classification trees, regression trees, logistic Regression, and Multiple Regression. In addition, the R programming language was used to develop the prediction models. The dataset used in this study was obtained from two secondary schools in Portugal. According to the findings, classification trees and logistic Regression fared better than regression trees and multiple Regression.
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Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of e ducational datasets further catalyse this interestingness, especially in online learning. Although several EDM surveys are available in the literature, we could find only a few specific surveys on student performance analysis and prediction. These specific surveys are limited in nature and primarily focus on studies that try to identify possible predictor or model student performance. However, the previous works do not address the temporal aspect of prediction. Moreover, we could not find any such specific survey which focuses only on classroom-based education. In this paper, we present a systematic review of EDM studies on student performance in classroom learning. It focuses on identifying the predictors, methods used for such identification, time and aim of prediction. It is significantly the first systematic survey of EDM studies that consider only classroom learning and focuses on the temporal aspect as well. This paper presents a review of 140 studies in this area. The meta-analysis indicates that the researchers achieve significant prediction efficiency during the tenure of the course. However, performance prediction before course commencement needs special attention.
The ability to predict the performance tendency of students is very important to improve their teaching skills. It has become a valuable knowledge that can be used for different purposes; for example, a strategic plan can be applied for the development of a quality education. This paper proposes the application of data mining techniques to predict the final grades of students based on their historical data. In the experimental studies, three well-known data mining techniques (decision tree, random forest, and naive Bayes) were employed on two educational datasets related to mathematics lesson and Portuguese language lesson. The results showed the effectiveness of data mining learning techniques when predicting the performances of students.
The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health,
social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms.
In the last decade Data mining (DM) has been applied in the field of education, and is an emerging
interdisciplinary research field also known as Educational Data Mining (EDM). One of the goals of EDM
is to better understand how to predict student academic performance given personal, socio-economic,
psychological and other environmental attributes. Another goal is to identify factors and rules that
influence educational academic outcomes. In this paper, we use multiple classifiers (Decision Trees-J48,
Naïve Bayes and Random Forest) to improve the quality of student data by eliminating noisy instances,
and hence improving predictive accuracy. We also identify association rules that influence student
outcomes using a combination of rule based techniques (Apriori, Filtered Associator and Tertius). We
empirically compare our technique with single model based techniques and show that using ensemble
models not only gives better predictive accuracies on student performance, but also provides better rules
for understanding the factors that influence better student outcomes.
Although the educational level of the Portuguese pop-ulation has improved in the last decades, the statistics keep Portugal at Europe's tail end due to its high stu-dent failure rates. In particular, lack of success in the core classes of Mathematics and the Portuguese lan-guage 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. The present work intends to ap-proach student achievement in secondary education us-ing BI/DM techniques. Recent real-world data (e.g. student grades, demographic, social and school related features) was collected by using school reports and ques-tionnaires. The two core classes (i.e. Mathematics and Portuguese) were modeled under binary/five-level clas-sification and regression tasks. Also, four DM mod-els (i.e. Decision Trees, Random Forest, Neural Net-works and Support Vector Machines) and three input selections (e.g. with and without previous grades) were tested. The results show that a good predictive accuracy can be achieved, provided that the first and/or second school period grades are available. Although student achievement is highly influenced by past evaluations, an explanatory analysis has shown that there are also other relevant features (e.g. number of absences, parent's job and education, alcohol consumption). As a direct out-come of this research, more efficient student prediction tools can be be developed, improving the quality of ed-ucation and enhancing school resource management.
This paper introduces a new technique for feature selection and illustrates it on a real data set. Namely, the proposed approach creates subsets of attributes based on two criteria: (1) individual attributes have high discrimination (classification) power; and (2) the attributes in the subset are complementary - that is, they misclassify different classes. The method uses information from a confusion matrix and evaluates one attribute at a time. Keywords: classification, attribute selection, confusion matrix, k-nearest neighbors;.
In this article, we present a predictive analysis of the academic performance of students in public schools of the Federal District of Brazil during the school terms of 2015 and 2016. Initially, we performed a descriptive statistical analysis to gain insight from data. Subsequently, two datasets were obtained. The first dataset contains variables obtained prior to the start of the school year, and the second included academic variables collected two months after the semester began. Classification models based on the Gradient Boosting Machine (GBM) were created to predict academic outcomes of student performance at the end of the school year for each dataset. Results showed that, though the attributes ‘grades’ and ‘absences’ were the most relevant for predicting the end of the year academic outcomes of student performance, the analysis of demographic attributes reveals that ‘neighborhood’ ‘school’ and ‘age’ are also potential indicators of a student's academic success or failure.
The tremendous growth in electronic data of universities creates the need to have some meaningful information extracted from these large volumes of data. The advancement in the data mining field makes it possible to mine educational data in order to improve the quality of the educational processes. This study, thus, uses data mining methods to study the performance of undergraduate students. Two aspects of students' performance have been focused upon. First, predicting students' academic achievement at the end of a four-year study programme. Second, studying typical progressions and combining them with prediction results. Two important groups of students have been identified: the low and high achieving students. The results indicate that by focusing on a small number of courses that are indicators of particularly good or poor performance, it is possible to provide timely warning and support to low achieving students, and advice and opportunities to high performing students.
paper presents an educational data mining model for predicting student performance in programming courses. Identifying variables that predict student programming performance may help educators. These variables are influenced by various factors. The study engages factors like students' mathematical background, programming aptitude, problem solving skills, gender, prior experience, high school mathematics grade, locality, previous computer programming experience, and e learning usage. The proposed model includes three phases; data preprocessing, attribute selection and rule extraction algorithm.
Educational data mining & students' performance prediction
- A A Saa
Student Alcohol Consumption
- U M Learning
