Educational Data Mining: Analyzing Teacher Behavior based Student's Performance

2021 ◽  
Author(s):  
Lamia AbedNoor Muhammed
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Teacher Behavior ◽  
Student’S Performance ◽  
Behavior Based

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.

scholarly journals Prediction and Analysis of Student Performance in Secondary Education Based on Data Mining and Machine Learning Techniques

2020 ◽  
pp. 294-301
Author(s):  
Meenal Joshi ◽  
Shiv Kumar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Secondary School ◽  
Student Performance ◽  
Learning Algorithms ◽  
Research Work ◽  
Educational Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Student’S Performance

<p>According to modern era education is the key to achieve success in the future; it develops a human personality, thoughts, and social skills. The purpose of this research work is to focus on educational data mining (EDM) through machine learning algorithms. EDM means to discover hidden knowledge and pattern about student's performance. Machine learning can be useful to predict the learning outcomes of students. From last few years, several tools have been used to judge the student's performance from different points of view like the student's level, objectives, techniques, algorithms, and different methods. In this paper, predicting and analyzing student performance in secondary school is conducted using data mining techniques and machine learning algorithms such as Naive Bayes, Decision Tree algorithm J48, and Logistic Regression. For this the collection of dataset from "Secondary School" and then filtration is applying on desired values using WEKA, tool.</p>

scholarly journals EDM – survey of performance factors and algorithms applied

2018 ◽  
Vol 7 (2.6) ◽  
pp. 93 ◽  
Author(s):  
Deepali R Vora ◽  
Kamatchi Iyer
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Educational Data Mining ◽  
Knowledge Discovery In Databases ◽  
Useful Knowledge ◽  
Performance Factors ◽  
Student’S Performance

Educational Data Mining (EDM) is a new field of research in the data mining and Knowledge Discovery in Databases (KDD) field. It mainly focuses in mining useful patterns and discovering useful knowledge from the educational information systems from schools, to colleges and universities. Analysing students’ data and information to perform various tasks like classification of students, or to create decision trees or association rules, so as to make better decisions or to enhance student’s performance is an interesting field of research. The paper presents a survey of various tasks performed in EDM and algorithms (methods) used for the same. The paper identifies the lacuna and challenges in Algorithms applied, Performance Factors considered and data used in EDM.

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 Enhancing the Quality Education using Predictive and Descriptive Data Mining Model

2019 ◽  
Vol 8 (3) ◽  
pp. 6843-6847
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Educational Data Mining ◽  
Strong Relationship ◽  
Data Mining Techniques ◽  
Huge Data ◽  
Student’S Performance ◽  
Predictive Data Mining ◽  
Mining Model ◽  
The Given

Data mining is the trending field used to get relevant knowledge from the database given. This technique consists of subfield called educational data mining is the emerging area used to extract the hidden patterns from the huge data with the help of tools techniques developed by the researchers of the educational data mining. The purpose of extracting patterns from the educational database is to improve the quality of education can be provided to the students for their better feature. The patterns are extracted by using the existing data mining techniques to enhance student performance. Educational data mining techniques such as classification, regression, clustering are available in the field. Classification is defined as the technique used to categorize the data based on the given label and constraints. In this paper, the algorithms like naves Bayes, Random Forest and J48 algorithms used to classify the data instances under the given labels using the constraints given., the classification algorithms like naves Bayes shows best performance accuracy with the given student dataset. Clustering and apriori rule have a strong relationship in student performance. In this paper, predictive data mining used to predict the student's performance to enhance the study level of the students in the organization.

scholarly journals Educational Data Mining in Predicting Student Final Grades

2021 ◽  
Vol 10 (1) ◽  
pp. 366-371
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Student Performance ◽  
Parameter Optimization ◽  
Nearest Neighbor ◽  
Educational Data Mining ◽  
Real Data ◽  
K Nearest Neighbor ◽  
Student’S Performance ◽  
Student Grades

Educational data mining is a field of science that extracts knowledge from educational data. One of its implementations is to predict student performance, it helps teachers to identify students that need more support. This can potentially increase learning effectiveness and elevate overall student’s grades. There are various algorithms and optimization solutions to predict student’s performance. In this paper, we use real data from one of Indonesia’s public junior high schools to compare naive bayes, decision tree, and k-nearest neighbor algorithms and implement feature selection and parameter optimization to identify which combination of algorithm and optimization can achieve the highest accuracy in predicting student grades, i.e. 7-grade classification.The results show that k-NN achieves the highest accuracy with 77.36%, where both feature selection and parameter optimization are applied

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