Student Performance Prediction Using Machine Learning

Student Performance Prediction Using Machine Learning Thesis Ideas

Thesis topics based on students’ performance prediction will be suggested from the high impact journal on that current year. The best work of phdservices.org are listed below, go through our work to know more. We also recommend thesis writing services as we have a team of professional writer’s experts get touch in your research work to gain high rank in your academics.

1. Machine Learning Algorithms based Student Performance Prediction based on Previous Records

Keywords:

Student grade prediction, Performance prediction, Classification, Machine learning algorithm, Bayesian classification, Probability

            The aim of our paper is to increase the student inefficient performance then only they can increase their academic performance. Our biggest is that we have to determine which method can use numerous classification method and can be applicable based on that dataset. We used supervised ML method namely Bayesian classification that utilizes classification technique for ML.  

2. Machine Learning Models for Student Performance Prediction

Keywords:

Student performance system, Logistic Regression, K Nearest Neighbors, Support Vector Machine, Data Visualization

            The Indian education system follows traditional way of learning and that can lack in communicative session. So it is tough to frequently watch student’s performance. To analyse student performance at early stage is difficult. Our paper analyse the student performance affect and to predict students performance by utilizing different ML methods like KNN, SVM and linear regression. The SVM with kernel linear kernel gives the better outcome.    

3. Academic Performance Prediction of At-Risk Students using Machine Learning Techniques

Keywords:

Academic Performance Prediction, SMOTE

            We have to increase the value of education to predict the performance of students and it would help the organization to timely give support to low performance students and increase their performance. Educational Data Mining (EDM) utilizes ML methods and that can permit them to process and calculate the data gathered from various sources. Our paper uses SVM to predict performance of students on unfair dataset.

4. A Systematic Study on Student Performance Prediction from the Perspective of Machine Learning and Data Mining Approaches

Keywords:

Student Performance Evaluation, Decision Tree (DT)

            To improve training and learning the prediction of student’s performance can be effective and we can predict the performance of students by using ML methods like SVM, DT, Ensemble and KNN with the metrics precision, accuracy, recall or F1 score. Support Vector Machine (SVM) has the better performance when compared to KNN, DT and Ensembles.

5. Prediction of Student’s Performance with Learning Coefficients Using Regression Based Machine Learning Models

Keywords:

Adaptive assessment, learning coefficients, regression-based prediction

            We have to predict the student performance by using Advanced Machine learning. Our paper proposes ‘Learning coefficients’ estimated through trajectory based computerized adaptive assessments. To increase their performance learning coefficients, offer computed metrics to students to increase their performance. We used regression-based ML methods Decision tree, Random Forest, Support vector regression, linear regression and ANN to analyse the performance.           

6. Students’ Performance Prediction Using Machine Learning Based on Generative Adversarial Network

Keywords:

Student’s performance, GAN

            We used ML methods to increase accuracy and consistency of student performance. Our paper proposes a student performance prediction by utilizing five ML methods that data analysis, preprocessing techniques and data augmentation using GAN. We can calculate the proposed approach that utilizes real world dataset of student records and contrast them without data augmentation. Our Random Forest classifier gives the better accuracy rate.

7. Predicting Academic Performance of Students Using Machine Learning Models

Keywords:

Random Forest, Education

            Our paper efforts to authorize Higher education institutes (HEI) predict student performance using ML methods based on six factors like Family size, Study time, Time-spent on extra-curricular activities, Absenteeism, Time spent on Internet, and Health. We used three ML methods like KNN, decision tree and random forest can be implemented and contrast with the metrics efficiency and accuracy.

8. Predicting Students’ Performance Using Machine Learning

Keywords:

Data mining, Predictive models

            To increase the student academic performance our paper uses different ML methods like Decision Tree, Artificial Neural Network, Naive Bayes and Random Forest to predict student performance on real dataset. We give out the most effective attribute for prediction that is the course marks, followed by high school average, number of semesters spent in the university. RF and DT gives best outcome compare to NB and ANN.     

9. Classification and prediction of student performance data using various machine learning algorithms

Keywords:

Educational Data Mining, Prediction

            Our study predicts the student performance in a course. To uncover hidden outlines on large amount of current data and these patterns can be valued for analysis and prediction. The collection of data mining application in the field of education is the education data mining. We used various ML methods such as Naïve Bayes, ID3, C4.5 and SVM can be examined. UCI machinery dataset can be applied in our paper.  

10. Institutional Data Analysis and Machine Learning Prediction of Student Performance

Keywords:

Regression, student analytics, educational data

            Our paper examines the predictive influence of characters on student data measured in term of Cumulative grade point average (CGPA). We used four machine learning methods like linear regression, Support vector regression, decision tree and random forest this method will show third year CPGA is a best predictor of final year CPGA and our decision tree is the least performance model.