Educational data mining to predict students' academic performance: A survey study

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research.  To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naive Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

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Research Methods in Machine Learning: A Content Analysis

Graduate students are unaware of their final qualification for a course. Even though there were many models available, few works with feature selection and prediction with no control over the number of features to be used. As a result of the lack of an improved performance forecasting system, students are only qualified on the second or third attempt. A warning system in place could help the students reduce their arrear count. All students undertaking higher education should obtain the qualification at their desired level of education without delay to transit to their careers on time. Therefore, there should be a predictive system for students to warn during the course work period and guide them to qualify in a first attempt itself. Although so many factors were present that affected the qualifying score, here proposed a feature selection technique that selects a minimal number of well-playing features. Also proposed a model Supervised Learning Approach to unfold Student’s Academic Future Progression through Supervised Learning Approach for Student’s Academic Future Progression (SLASAFP) algorithm that recommends the best fitting machine learning algorithm based on the features dynamically. It has proven with comparable predictive accuracy.

https://www.tandfonline.com/doi/pdf/10.1080/00051144.2022.2060652?needAccess=true

Predicting the academic progression in student’s standpoint using machine learning

Breast tumour detection using machine learning: review of selected methods from 2015 to 2021

This paper focuses on finding the best classification algorithm model in the case study of student performance prediction and comparing the algorithm performance before and after using the normalization method. To achieve this goal, this study uses data mining classification techniques to analyse student performance at Vocational High School in 2020-2021. The steps of the research carried out include: data collection, data pre-processing, build algorithm models without using normalization and with using normalization, and final step are comparing algorithm performance before and after using normalization. The algorithms that will be used include: Random Forest, Decision Tree, Logistic Regression, SVM, Naive Bayes, and KNN. While the normalization methods used are Standard Scaler, Min-Max Scaler, and Robust Scaler. The result of this research is that the normalization method is able to significantly increase the accuracy of the model. Based on the tests and evaluations carried out, the normalization method using the Min-Max Scaler has the biggest impact in improving the overall model performance and the algorithm with the best performance is Random Forest. This paper reviews the effect of the normalization method to improve algorithm performance in predicting student performance, where based on previous research no one has used the normalization method to gain accuracy of the model which actually has a considerable impact on gaining accuracy of the model. 

Which algorithm is better? An implementation of normalization to predict student performance

Educational Data Mining (EDM) is a growing research field that is applied to analyze and predict student's academic performance and makes intervention approaches to elevate that performance. It is a field of study, which is related to various attributes for analysis student's details such as name, attendance, class test, lab test, spot test, assignment and result in the educational institution. In this study, we mainly focus on calculating the academic performance of undergraduate students with a predictive data mining model by using feature selection techniques with classification algorithms. Feature selection techniques are introduced on the data preprocessing process to find the most inherent and important attributes so that we analyze and evaluate the student's better performance by using classifiers with those selected attributes. For this purpose, we collected 800 student's records of the final year, studying at the undergraduate level of the department of Computer Science and Engineering from North Western University, Khulna. Here, we used and evaluated the performance of four feature selection methods: genetic algorithms, gain ratio, relief, and information gain and five classification algorithms: K-Nearest Neighbor, Naive Bayes, Bagging, Random forest, and J48 Decision Tree. The experimental results depict that Genetic algorithms method provides the best accuracy 91.37% with KNN classifier.

A Comprehensive Analysis on Undergraduate Student Academic Performance using Feature Selection Techniques on Classification Algorithms

Day by day, biological data is increasing and researchers fetch various problems to extract informative data from a large scale of dataset. According to available statistical estimates 8.8 million people died every year due to cancer worldwide. Breast cancer is the second most leading cancer occurring in women. Feature selection, classification, neural network, ensemble methods spreads the research area and makes easy to find out more relevant information in the bioinformatics analysis. In our proposed model, we build a machine learning model using some efficient feature selection algorithm and then analyses it through ANN and high performance classification algorithms. We also implement ensemble methods for building our model more accurate. For verifying our model we implement cytological data of breast cancer. Chi Squared Test give the minimum relevant features where we get 99% accuracy from ANN. We use 10 fold cross validation to test the dataset.

A Comprehensive Study of Machine Learning Approach on Cytological Data for Early Breast Cancer Detection

Day by day, biological data is increasing and researchers face various problems to extract informative data from a large scale of dataset. Cancer has been identified as most common and unexpected disease worldwide. In our proposed model, we build a combined Machine Learning model using some efficient feature selection algorithm and then analyses it through ANN and high performance classification algorithms. We also implement ensemble methods for building our model more accurate. For verifying our model we implement breast cancer miRNA data. Chi Squared Test gives the minimum relevant features where we get 99.23% accuracy from SVM. We use 10 fold cross validation to test the dataset. The result is promising and encouraging. With the processing of the data, our system model able to identify most relevant features responsible for breast cancer.

Identification of Most Relevant Breast Cancer miRNA using Machine Learning Algorithms

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A Comprehensive Analysis on Undergraduate Student Academic Performance using Feature Selection Techniques on Classification Algorithms

A Comprehensive Study of Machine Learning Approach on Cytological Data for Early Breast Cancer Detection

Identification of Most Relevant Breast Cancer miRNA using Machine Learning Algorithms