Student performance can be predicted by using machine learning numerous research work are carried out now a day. Get a custom research paper on students’ performance prediction we give a unique paper thus we guarantee no plagiarism paper. Well researched and written term paper will be developed that meet scholar expectation. This system helps educators for detecting the students, who are at risk of failing or not pass, it allows us for appropriate mediation. We take up to a new perspective for your topics further making our work unique for the readers. We provide you the steps that involves in prediction of student performance using machine learning are listed below,
The main aim of this system is, we build a model based on machine learning which predict the performance of student depending on different input characteristics.
We detect the most common variable which impacts on performance of student. The possible features involves like:
The models are classified into two, they are,
The prediction of student performance model using machine learning that act as an influential tool in the education sector and guiding us for customized education and the performance of best student is our obtained result. At the same time, it is important to deal the data with care and aim on developing the experience ion educational field. Stay relaxed we got you we know that scholars face difficult time for writing research paper we are a team of professionals to guide doctoral students in any part of your work. We have necessary components in our concern to write a good paper.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.
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Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link
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