scholarly journals Educational Data Mining, Student Academic Performance Prediction, Prediction Methods, Algorithms and Tools: An Overview of Reviews

2021 ◽  
Author(s):  
Chaka Chaka
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Overview Of Reviews ◽  
Student Demographics ◽  
Student Academic Performance ◽  
Online Databases ◽  
Subject Areas ◽  
Input Variables

This overview study set out to compare and synthesise the findings of review studies conducted on predicting student academic performance (SAP) in higher education using educational data mining (EDM) methods, EDM algorithms and EDM tools from 2013 to June 2020. It conducted multiple searches for suitable and relevant peer-reviewed articles on two online search engines, on nine online databases, and on two online academic social networks. It, then, selected 26 eligible articles from 2,050 articles. Some of the findings of this overview study are worth mentioning. First, only 2 studies explicitly stated their precise sample sizes with maths and science as the two most mentioned subject areas. Second, 16 review studies had purposes related to either EDM techniques, EDM methods, EDM models, or EDM algorithms employed to predict SAP and student success in the higher education sector. Third, there are six commonly used typologies of input variables reported by 26 review studies, of which student demographics was the most commonly utilised variable for predicting SAP. Fourth and last, seven common EDM algorithms employed for predicting SAP were identified, of which Decision Tree emerged both as the most used algorithm and as the algorithm with the highest prediction accuracy rate for predicting SAP.

scholarly journals An Analysis on Student Academic Performance by Using Decision Tree Models

2018 ◽  
pp. 615-620
Author(s):  
Jastini Mohd. Jamil ◽  
Nurul Farahin Mohd Pauzi ◽  
Izwan Nizal Mohd. Shahara Nee
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Decision Tree ◽  
Large Volume ◽  
Educational Data Mining ◽  
Data Mining Techniques ◽  
Student Academic Performance ◽  
Tree Models ◽  
Student’S Performance ◽  
Using Data

Large volume of educational data has led to more challenging in predicting student’s performance. In Malaysia currently, study about the performance of students in Malaysia institutions is very little being addressed. The previous studies are still insufficient to identify what factors contribute to student’s achievements and lack of investigations on exploring pattern of student’s behaviour that affecting their academic performance within Malaysia context. Therefore, predicting student’s academic performance by using decision trees is proposed to improve student’s achievements more effectively. The main objective of this paper is to provide an overview on predicting student’s academic performance using by using data mining techniques. This paper also focuses on identifying the pattern of student’s behaviour and the most important attributes that impact to the student’s achievement. By using educational data mining techniques, the students, lecturers and academic institution are able to have a better understanding on the student’s achievement.

scholarly journals The uses of educational data mining in academic performance analysis at higher education institutions (case study at UNJANI)

2021 ◽  
Vol 11 (1) ◽  
pp. 26-35
Author(s):  
Yulison Herry Chrisnanto ◽  
◽  
Gunawan Abdullah ◽  
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Higher Education Institutions ◽  
Educational Data Mining ◽  
Strategic Decision ◽  
Data Set ◽  
Study Results ◽  
Mining Model

Education is an important thing in a person's life, because by having adequate education, one's life will be better. Education can be obtained formally through formal institutions that constructively provide a person's abilities academically. This study aims to determine student performance in terms of academic and non-academic domains at a certain time during their education using techniques in data mining (DM) which are directed towards academic data analysis. Academic performance is delivered through the Educational Data Mining (EDM) integrated data mining model, in which the techniques used include classification (ID3, SVM), clustering (k-Means, k-Medoids), association rules (Apriori) and anomaly detection (DBSCAN). The data set used is academic data in the form of study results over a certain period of time. The results of EDM can be used for analysis related to academic performance which can be used for strategic decision making in aca-demic management at higher education institutions. The results of this study indicate that the use of several techniques in data mining together can maximize the ability to analyze academic performance with the same data source and produce different analysis patterns.

Using educational data mining techniques to increase the prediction accuracy of student academic performance

2019 ◽  
Vol 120 (7/8) ◽  
pp. 451-467 ◽  
Author(s):  
Gomathy Ramaswami ◽  
Teo Susnjak ◽  
Anuradha Mathrani ◽  
James Lim ◽  
Pablo Garcia
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Random Forest ◽  
Predictive Accuracy ◽  
Process Mining ◽  
Educational Data Mining ◽  
Content Type ◽  
Student Activities ◽  
Data Mining Techniques ◽  
Student Academic Performance

Purpose This paper aims to evaluate educational data mining methods to increase the predictive accuracy of student academic performance for a university course setting. Student engagement data collected in real time and over self-paced activities assisted this investigation. Design/methodology/approach Classification data mining techniques have been adapted to predict students’ academic performance. Four algorithms, Naïve Bayes, Logistic Regression, k-Nearest Neighbour and Random Forest, were used to generate predictive models. Process mining features have also been integrated to determine their effectiveness in improving the accuracy of predictions. Findings The results show that when general features derived from student activities are combined with process mining features, there is some improvement in the accuracy of the predictions. Of the four algorithms, the study finds Random Forest to be more accurate than the other three algorithms in a statistically significant way. The validation of the best-known classifier model is then tested by predicting students’ final-year academic performance for the subsequent year. Research limitations/implications The present study was limited to datasets gathered over one semester and for one course. The outcomes would be more promising if the dataset comprised more courses. Moreover, the addition of demographic information could have provided further representations of students’ performance. Future work will address some of these limitations. Originality/value The model developed from this research can provide value to institutions in making process- and data-driven predictions on students’ academic performances.

Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data

2021 ◽  
pp. 073563312110487
Author(s):  
Ruangsak Trakunphutthirak ◽  
Vincent C. S. Lee
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Internet Usage ◽  
Machine Learning Techniques ◽  
Log Files ◽  
Student Academic Performance ◽  
Learning Techniques ◽  
Usage Data

Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting student academic performance. The key aims of this study are fourfold. First, is to fill the educational literature gap by applying machine learning techniques in educational data mining, making use of the Internet usage behaviour log files and LMS data. Second, LMS data and Internet usage log files were analysed with machine learning techniques for predicting at-risk-of-failure students, with greater explanation added by combining student demographic data. Third, the demographic features help to explain the prediction in understandable terms for educators. Fourth, the study used a range of Internet usage data, which were categorized according to type of usage data and type of web browsing data to increase prediction accuracy.

scholarly journals DATA MINING IN EDUCATION FOR STUDENTS ACADEMIC PERFORMANCE: A SYSTEMATIC REVIEW

2014 ◽  
Vol 13 (9) ◽  
pp. 5020-5028
Author(s):  
Anurag Jindal ◽  
Er. Williamjeet Singh
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Performance ◽  
Nearest Neighbor ◽  
Educational Data Mining ◽  
Educational Systems ◽  
Good Prediction ◽  
Data Set ◽  
Data Mining Techniques ◽  
Higher Education System

Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. Higher education, throughout the world is delivered through universities, colleges affiliated to various universities and some other recognized academic institutes. The main objective of higher education institutes is to provide quality education to its students. Indian education sector has a lot of data that can produce valuable information which can be used to increase the quality of education. Good prediction of student’s success in higher learning institution is one way to reach the higher level of quality in higher education system. In this paper we analyzed the potential use of data mining in education section and survey the most relevant work in this area. Data Mining can be used for dropout students, student’s academic performance, teacher’s performance and student’s complaints. As we know large amount of data is stored in educational database, so in order to get required data and to find the hidden relationship, different data mining techniques are developed & used. Various algorithms and data mining techniques like Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees (CART and CHIAD), Genetic algorithms, Nearest Neighbor method etc. are used for knowledge discovery from databases and helps in prediction of students academic performance. In future work we can apply different data mining techniques on an expanded data set with more distinct attributes to get more accurate results.

scholarly journals An Effectual Ga Based Association Rule Generation and Fuzzy Svm Classification Algorithm for Predicting Students Performance

2019 ◽  
Vol 8 (6) ◽  
pp. 2915-2920
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Association Rule ◽  
Educational Data Mining ◽  
Rule Generation ◽  
Academic Status ◽  
Student Records ◽  
Performance Simulation ◽  
Svm Classification ◽  
Student’S Performance

This investigation provides outcome of utilizing educational data mining [EDM] to design academic performance of students from real time and online dataset collected from colleges. Data mining is determined to examine non-academic and academic data; this model utilizes a classification approach termed as Fuzzy SVM classification with Genetic algorithm to attain effectual understanding of association rule in enrolment and to evaluate data quality for classification, which is identified as prediction task of performance and academic status based on low academic performance. This model attempts to predict student’s performance in grading system. Academic and student records attained from process were considered to train models estimated using cross-validation and formerly records from complete academic performance. Simulation was performed in MATLAB environment and show that academic status prediction is enhanced while hybrid dataset are added. The accuracy was compared with the existing models and shows better trade off than those methods.

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