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

Pallathadka, Harikumar; Wenda, Alex; Ramírez-Asís, Edwin; Asís-López, Maximiliano; Flores-Albornoz, Judith; Phasinam, Khongdet

doi:10.1016/j.matpr.2021.07.382

Cited by 85 publications

(33 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This study predicts student performance in a course which is also related to student and teacher data. The c4.5 method can classify and predict the solution of the problem accurately [11].…”

Section: Methodsmentioning

confidence: 99%

Use of the C4.5 Algorithm to Analyze Student Interest in Continuing to College

2022

View full text Add to dashboard Cite

Abstract. Higher education is one of the goals of several options when a student completes education in high school. However, not all students wish to continue their education in college. There are many reasons behind why students do not continue their education in college. Several factors that influence in this research include self-motivation, scholarships provided, parental support, ease of getting a job and academic competition. This study took a sample of 25 student data that will be used to find the most influential factors in continuing education in higher education. This study uses a data mining classification approach using the c4.5 method in making a decision tree. The results that can be concluded, a student has a high desire to continue his education in college because it will make it easier for them to get a job and the factor in getting an education scholarship.

show abstract

Section: Methodsmentioning

confidence: 99%

Use of the C4.5 Algorithm to Analyze Student Interest in Continuing to College

2022

View full text Add to dashboard Cite

show abstract

“…Classification can be used to optimize the prediction results. Research work in [12] used personal and academic data to predict the student performance and then classify students individually based on their talents.…”

Section: Student Performance Predictionmentioning

confidence: 99%

Progressive Prediction of Students’ Future Performance on Coherent Vertical Curriculum

Priyambada

Wafda

Usagawa

et al. 2022

IIAI Letters on Institutional Research

View full text Add to dashboard Cite

Predicting students' future performance is important for academic stakeholders as the students' success is the objective of the higher educational institutes. The prediction based on past performance and alignment with the curriculum is crucial to support decision-making action effectively for the university with a coherent vertical curriculum. The result of the prediction can be used to intervene and ensure that the student can graduate on time, also preventing the student from dropping out. In this paper, we proposed a methodology for predicting progressively the students' future assessment including feature engineering. Using the ensemble learning techniques, we adapt the existing Ensemble-based Progressive Prediction so it can be applied on students' data that used the Coherent Vertical Curriculum. In this paper, the behavioral data is used instead of domain knowledge-based data. The results show that the algorithm's accuracy has been improved on a real-world student dataset.

show abstract

“…The effectiveness of classification techniques such as NB, SVM, KNN, and Neural Network (NN) models was also evaluated [30]. Furthermore, the paper of [31] utilized several algorithms, including Artificial Neural Networks (ANN), NB, Ensemble classifiers, and k-means, and classified tweets as negative, neutral, or positive.The findings indicated that the researchers obtained an SVM accuracy of 75%.…”

Section: Literature Reviewmentioning

confidence: 99%

Flexible Learning Experience Analyzer (FLExA): Sentiment Analysis of College Students through Machine Learning Algorithms with Comparative Analysis using WEKA

Pahuriray¹,

Basanta²,

Arroyo³

et al. 2022

IJETAE

View full text Add to dashboard Cite

The spread of the COVID-19 pandemic broughtsignificant changes in society. Emerging technologies like artificial intelligence and machine learning devices improved several industries, especially in academe and higher education institutions. In this study, a model to analyze and predict college students' sentiments from the Flexible Learning Experience portal was built using several supervised machine-learning techniques. Waikato Environment for Knowledge Analysis (WEKA) application was used to apply the Naive Bayes (NB), C4.5, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. Additionally, a comparative analysis of different machine-learning methods was applied. The experimental results revealed that the C4.5 algorithmobtained the highest accuracy than other algorithms. The effectiveness of each algorithm was evaluated and compared using 10-fold crossvalidation (CV), taking into account the major accuracy metrics, instances that were accurately or inaccurately classified, kappa statistics, mean absolute error, and modeling time. Moreover, results show that the C4.5 algorithm outperformed other algorithms by classifying the model with 98.13% accuracy, 0.0132 mean absolute error, and 0.00 seconds of training time. Furthermore, teachers and college administrations were well accustomed to the sentiments and problems of college students and might act as a decisionsupport mechanism mainly as they deal with the new setting during this time of crisis.

show abstract

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

Cited by 85 publications

References 6 publications

Use of the C4.5 Algorithm to Analyze Student Interest in Continuing to College

Use of the C4.5 Algorithm to Analyze Student Interest in Continuing to College

Progressive Prediction of Students’ Future Performance on Coherent Vertical Curriculum

Flexible Learning Experience Analyzer (FLExA): Sentiment Analysis of College Students through Machine Learning Algorithms with Comparative Analysis using WEKA

Contact Info

Product

Resources

About