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 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.

scholarly journals A Study of the Applications of Data Mining Techniques in Higher Education

2015 ◽  
pp. 1-4
Author(s):  
SUSHIL VERMA ◽  
R. S. THAKUR ◽  
SHAILESH JALORI
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Business Processes ◽  
Large Data ◽  
Educational Institutions ◽  
Data Set ◽  
Data Mining Techniques ◽  
Higher Education System ◽  
Student’S Performance ◽  
Quality In Higher Education

Data mining is used to extract meaningful information and to develop significant relationships among variables stored in large data set. Few years ago, the information flow in education field was relatively simple and the application of technology was limited. However, as we progress into a more integrated world where technology has become an integral part of the business processes, the process of transfer of information has become more complicated. Today, one of the biggest challenges that educational institutions face is the explosive growth of educational data and to use this data to improve the quality of managerial decisions and student’s performance. The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of Unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students’ performance. The paper aims to purpose the use of Data mining techniques to improve the efficiency of higher educational institutions. If data mining techniques such as clustering, dicision tree and association can be applied to higher education processes, it can help improve student’s performance.

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 DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW

2020 ◽  
Vol 5 (2) ◽  
pp. 166-177 ◽  
Author(s):  
Pragati Sharma ◽  
Dr. Sanjiv Sharma
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Educational Institutions ◽  
Data Set ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
New Knowledge ◽  
Hidden Patterns ◽  
Educational Field

Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining can be used for decision making in higher educational institutions. This paper is based on literature review of different data mining techniques along with certain algorithms like classification, clustering etc. This paper represents the effectiveness of mining techniques with educational data set for higher education institutions.

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 Classifiers ensemble and synthetic minority oversampling techniques for academic performance prediction

2019 ◽  
Vol 8 (3) ◽  
pp. 122
Author(s):  
Abdulazeez Yusuf ◽  
Ayuba John
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Research Work ◽  
Educational Data Mining ◽  
Sampling Technique ◽  
Ensemble Model ◽  
Data Set ◽  
Grade Point ◽  
Matriculation Examination ◽  
The Individual

The increasing need for data driven decision making recently has resulted in the application of data mining in various fields including the educational sector which is referred to as educational data mining. The need for improving the performance of data mining models has also been identified as a gap for future researcher. In Nigeria, higher educational institutions collect various students’ data, but these data are rarely used in any decision or policy making to improve the academic performance of students. This research work, attempts to improve the performance of data mining models for predicting students’ academic performance using stacking classifiers ensemble and synthetic minority over-sampling techniques. The research was conducted by adopting and evaluating the performance of J48, IBK and SMO classifiers. The individual classifiers models, standard stacking classifier ensemble model and stacking classifiers ensemble model were trained and tested on 206 students’ data set from the faculty of science federal university Dutse. Students’ specific previous academic performance records at Unified Tertiary Matriculation Examination, Senior Secondary Certificate Examination and first year Cumulative Grade Point Average of students are used as data inputs in WEKA 3.9.1 data mining tool to predict students’ graduation classes of degrees at undergraduate level. The result shows that application of synthetic minority over-sampling technique for class balancing improves all the various models performance with the proposed modified stacking classifiers ensemble model outperforming the various classifiers models in both performance accuracy and RSME values making it the best model.<strong></strong>

Exploring students digital activities and performances through their activities logged in learning management system using educational data mining approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adel Bessadok ◽  
Ehab Abouzinadah ◽  
Osama Rabie
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Management System ◽  
Learning Management System ◽  
Statistical Significance ◽  
Educational Data Mining ◽  
Data Set ◽  
Content Type ◽  
Learning Management ◽  
The Relationship

Purpose This paper aims to investigate the relationship between the students’ digital activities and their academic performance through two stages. In the first stage, students’ digital activities were studied and clustered based on the attributes of their activity log of learning management system (LMS) data set. In the second stage, the significance of the relationship between these profiles and the associated academic performance was tested statistically. Design/methodology/approach The LMS delivers E-learning courses and keeps track of the students’ activities. Investigating these students’ digital activities became a real challenge. The diversity of students’ involvement in the learning process was proven through the LMS which characterize students’ specific profiles. The Educational Data Mining (EDM) approach was used to discover students’ learning profiles and associated academic performances, where the activity log file exemplified their activities hosted in the LMS. The sample study data is from an undergraduate e-course hosted on the platform of Blackboard LMS offered at a Saudi University during the first semester of the 2019–2020 academic year. The chosen undergraduate course had 25 sections, and the students attending came from science, technology, engineering and math background. Findings Results show three clusters based on the digital activities of the students. The correlation test shows the statistical significance and proves the effect of the student’s profile on his academic performance. The data analysis shows that students with different profiles can still get similar academic performance using LMS. Originality/value This empirical study emphasizes the importance of the EDM approach using clustering techniques which can help the instructor understand how students use the provided LMS content to learn and then can deliver them the best educational experience.

scholarly journals Prediction of Student Performance using Hybrid Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 6566-6570
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Research Work ◽  
Educational Data Mining ◽  
Classification Algorithm ◽  
Classification Algorithms ◽  
Data Types ◽  
Data Set ◽  
Hybrid Classification

Data mining technologies allow collection, storage and processing huge amounts of data and carrying a large variety of data types and samples. Predicting academic performance of student is the most successive research in this era. Previous research work researchers are used different classification algorithm to predict the student performance. There is lot of research work to be taken in the field of educational data mining and big data in education to increase the accuracy of the classification algorithm and predict the academic performance of student. In this research work we used hybrid classification algorithm for predicting the performance of students. Two Popular classification algorithms ID3 and J48 were applied on the data set. To make hybrid classification voting technique is applied using weka machine learning tool. In this work we tested how the hybrid algorithm accurately predicts the student data set. To check the predicted result classification accuracy was computed. This hybrid classification algorithm gives accuracy with 62.67%.

scholarly journals Technical and Vocational Education Analytics Using Punjab TEVTA Students’ Data

2018 ◽  
pp. 37-48
Keyword(s):  
Data Mining ◽  
Vocational Education ◽  
Teaching And Learning ◽  
Teaching Method ◽  
Technical Education ◽  
Educational Data Mining ◽  
Vocational Education And Training ◽  
Classification Problems ◽  
Data Set ◽  
Data Mining Techniques

The development of informative workforce that is skilled in a specific profession is considered as the most recommended and desirable feature of any advanced state. Technical Education & Vocational Trainings provide golden opportunity of growth regarding the output of individuals and prosperity of employers. Subsequently it is the dire need of developing countries to invest in public vocational education and training sector (VET) for the progression of skillful societies. Process of manual predictions and analysis on the basis of students’ data to make decisions that will improve the overall teaching and learning is very difficult and tiring. Data mining is exceptionally helpful when we are talking about education data analysis and prediction. Data mining techniques are being used successfully in different areas especially in student educational and learning analytics called as Educational Data Mining (EDM). In this work, TEVTA students’ data is shaped as a ready-to-mine data set and then various data mining techniques are applied to derive interesting patterns that can potentially derive important decisions for improvement of learning process, enhancement of teaching method and overall development of whole system of technical education and vocational trainings. Besides presenting interesting analytics of TEVTA data, we develop classification problems to predict status of students after completing TEVTA courses. This classification can also help in evaluating success of TEVTA programs. This work can help in analyzing and predicting the aspects affecting students’ as well as institutes’ performance from different dimensions.

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