Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

Yang, Stephen J. H.; Lu, Owen H.T.; Huang, Anna Y.Q.; Huang, Jeff; Ogata, Hiroaki; Lin, Albert J. Q.

doi:10.2197/ipsjjip.26.170

Cited by 46 publications

(31 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study used data from multi-sources: institution database, learning management system and survey and applied ensemble of three basic classifiers of decision tree, artificial neural network and support vector machine to predict students' academic performance in an efficient and accurate manner. Yang et al, (2018) also used multiple linear regressions together with principal component analysis to predict SAP. A neuro-fuzzy approach was also applied to the classification of SAP in Do & Chen (2013).…”

Section: Hybrid Methodsmentioning

confidence: 99%

Students’ Academic Performance and Dropout Prediction

Ameen¹,

Alarape²,

Adewole³

2019

MJoC

View full text Add to dashboard Cite

Students’ Academic Performance (SAP) is an important metric in determining the status of students in any academic institution. It allows the instructors and other education managers to get an accurate evaluation of the students in different courses in a particular semester and also serve as an indicator to the students to review their strategies for better performance in the subsequent semesters. Predicting SAP is therefore important to help learners in obtaining the best from their studies. A number of researches in Educational Psychology (EP), Learning Analytics (LA) and Educational Data Mining (EDM) has been carried out to study and predict SAP, most especially in determining failures or dropouts with the goal of preventing the occurrence of the negative final outcome. This paper presents a comprehensive review of related studies that deal with SAP and dropout predictions. To group the studies, this review proposes taxonomy of the methods and features used in the literature for SAP and dropout prediction. The paper identifies some key issues and challenges for SAP and dropout predictions that require substantial research efforts. Limitations of the existing approaches for SAP and dropout prediction are identified. Finally, the paper exposes the current research directions in the area.

show abstract

Section: Hybrid Methodsmentioning

confidence: 99%

Students’ Academic Performance and Dropout Prediction

Ameen¹,

Alarape²,

Adewole³

2019

MJoC

View full text Add to dashboard Cite

show abstract

“…The regression task started with a known value of the target. A regression model is not commonly used for academic purposes; therefore, not many researchers use this model to predict scholastic achievements [14]. However, the linear regression model will be applied in this research to project undergraduate students' academic performance after online teaching and learning activities.…”

Section: Methodology a Regressionmentioning

confidence: 99%

Predictive Analytics on Academic Performance in Higher Education Institution during COVID-19 using Regression Model

Zulfikri

Shaharudin

Rajak

et al. 2021

International Journal of Biology and Biomedical Engineering

View full text Add to dashboard Cite

The coronavirus disease 2019 (COVID-19) outbreak in December 2019 had affected the way of living for people around the world including students in educational institutions. These students had to prepare for the continuity of their study mentally and physically by adapting to the online teaching and learning approach, which can significantly impact their academic performance. Hence, this study examines the students' academic performance on the online teaching and learning approach, thus predicting their academic performance for the upcoming semester. This study enables various actions to be taken in improving and maintaining the student's performance in their study activities. The study was conducted on undergraduate students from the Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris. This paper presents the prediction of the student's academic performance with a linear regression model. Evidently, the result shows that the student's academic performance continually improves while adapting to the online teaching and learning approach. It also shows that there were few respondents affected while adapting to this new norm approach. Hence, the future development of the regression model can be improved by having a more comprehensive range of Malaysian universities' data.

show abstract

“…The method of discriminant analysis was used by Minaei et al and Morris et al to predict course success and student scores [19][20]. Principal component analysis and multiple linear regressions were used by Yang et al to predict student academic performance [21]. These related studies have made progress in identifying at-risk students and early warning of student performance.…”

Section: The Related Methodsmentioning

confidence: 99%

Student Performance Early Warning based on Data Mining

Mi¹

2019

IJPE

View full text Add to dashboard Cite

Student performance in higher education is related to many complicated factors and always has uncertainty, so early warning of it is a very difficult issue. In this study, a systematic review was first carried out on student performance prediction and early warning using data mining techniques, including basic data sources, evaluating factors, predicting methods, application tools, and practices. Then, insufficiencies of the related studies were discussed, including incomprehensive source data, inadaptable and unspecialized calculation methods, and lack of integrated methodology systems in practice. Finally, a solution design was proposed, consisting of learning situation big data, a systematic early warning model, and an integrated information support system. Preliminary experiment results showed that it could identify at-risk students in a timely manner and improve the overall efficiency and effectiveness of early warning education management in practice, so it is of both academic and practical significance in promoting the deep integration of information technology and early warning education.

show abstract

Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

Cited by 46 publications

References 20 publications

Students’ Academic Performance and Dropout Prediction

Students’ Academic Performance and Dropout Prediction

Predictive Analytics on Academic Performance in Higher Education Institution during COVID-19 using Regression Model

Student Performance Early Warning based on Data Mining

Contact Info

Product

Resources

About