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,

  1. Objective Definition

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.

  1. Data Collection
  • Institutional Databases: Even school and colleges managing the data that are deploying by us, such records like attendance, grades, and students behaviours.
  • Surveys: Fetching the data’s based on factors such as study habits, family background, and personal interests.
  1. Data Pre-processing
  • Data Cleaning: Through this, we hold the missing values, the anomalies and other errors in data.
  • Encoding: The conversion of categorical variables into numerical format with the help of one-hot encoding or label encoding (eg. Gender, course name).
  • Feature Scaling: Normalize or order our converted numerical attributes for the definite algorithms like SVM (Support Vector Machine) or K-NN (Nearest Neighbour).
  1. Feature Selection

We detect the most common variable which impacts on performance of student. The possible features involves like:

  • Attendance rate
  • Past academic performance
  • Participation in extracurricular activities
  • Study hours per week
  • Parental education and occupation
  • Allowance for learning resources such as private tutoring and books.
  1. Model Selection and Development

The models are classified into two, they are,

  • Regression Models: This model is used when we are supposed to predict the constant output like final grade. Some techniques involves in this area,
  • Linear Regression
  • Decision Trees or Random Forests
  • Gradient Boosting Machines
  • Classification Models: While we are predicting the categorical or absolute outcomes like “Pass/Fail”. The tool are,
  • Logistic Regression
  • SVM (Support Vector Machine )
  • Neural Networks
  1. Training the Model
  • Dataset is separated into the sets of training and testing sets.
  • Our model is trained on the training set and validating or checking the performance.
  1. Model Evaluation
  • The regression task is performing by us with few metrics like Mean Absolute Error (MAE) or Root Mean Square Error (RMSE).
  • Consider accuracy, precision, recall, F1-score, and ROC curves for performing classification tasks.
  • Cross-validation offers us the accurate estimation about powerful performance of a model.
  1. Optimization and Hyper parameter Tuning
  • For attaining the best performance, we must examine various model parameters.
  • Grid search algorithm or random search utilizes for systematic hyper parameter tuning.
  1. Deployment
  • By integrating the prediction model into educational management systems or students portals for contributing educators with predictive observations.
  • We construct a dashboard or article which helps the staff members or faculty for review and response.
  1. Feedback Loop
  • The feedback is must gathered from the educators and students about our model’s performance based on predictions.
  • Retrain our created model usually with fresh data and make sure of its relevancy and accuracy.
  1. Conclusion and Future Enhancements
  • The achievements of project, faced challenges and enhanced areas are must being recorded by us.
  • The future enhancements, such as,
  • It predicts the other forms of student performance like, dropout risk, potential for honours.
  • Involves with real-time reviews from students and as well as teachers.
  • Through (NLP) Natural Language Processing, we observe the textual feedback from essays or some reviews.

Reminders:

  • Ethical Considerations: The student’s information are highly sensitive, so always make sure that our data privacy and get required authorization before obtaining and handling such kind of data .
  • Feature Interpretability: This is necessary for learning about the features that influence prediction; it is beneficial for us in mediations. The models like Random Forests or tools like SHAP supply the significant observations of features.
  • Human Judgment: Machine Learning provides judgement, still human observations from the educators are efficiently important. The predictions are being deploys as an add-on tool, not an indefinite one.

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.

Student Performance Prediction using Machine Learning Ideas

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.  

  1. 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.    

  1. 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.

  1. 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.

  1. 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.           

  1. 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.

  1. 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.

  1. 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.     

  1. 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.  

  1. 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.

Important Research Topics