A Review on Predicting Student's Performance Using Data Mining Techniques

By Amirah Mohamed Shahiri, W. Husain, N. Rashid. 2015

775 citations recorded by the source corpus.

Abstract Predicting students performance becomes more challenging due to the large volume of data in educational databases. Currently in Malaysia, the lack of existing system to analyze and monitor the student progress and performance is not being addressed. There are two main reasons of why this is happening. First, the study on existing prediction methods is still insufficient to identify the most suitable methods for predicting the performance of students in Malaysian institutions. Second is due to the lack of investigations on the factors affecting students achievements in particular courses within Malaysian context. Therefore, a systematical literature review on predicting student performance by using data mining techniques is proposed to improve students achievements. The main objective of this paper is to provide an overview on the data mining techniques that have been used to predict students performance. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a students data. We could actually improve students achievement and success more effectively in an eff i cient way using educational data mining techniques. It could bring the benefits and impacts to students, educators and academic institutions.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/5c08ca4f97bcbd9a602193089949c59f0ce83c50

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In Artificial Intelligence

In Computer Networks and Communications

In Computer Science Applications

Citations in the corpus

Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020 (2022, background): “, 2021), applications (Alyahyan & Düştegör, 2020; Hooshyar et al., 2016; Liz-Domínguez et al., 2019; Shahiri et al., 2015), theoretical paradigms (Ouyang & Jiao, 2021) and AI roles in education ( Xu & Ouyang, 2021).”
Predicting academic performance of students from VLE big data using deep learning models (2020, background): “Shahiri & Husain (2015) conducted a systematic literature review to observe the characteristics substantially contributing to the prediction of class-room performances.”
Enhancing the prediction of student performance based on the machine learning XGBoost algorithm (2021, background): “…to identify more precisely the student cognitive features (such as his capacity of memorization), to determine student e ﬀ ect (such as student motivation), or to predict student performance during answering a new question ((Asselman et al., 2018), (Pelánek, 2017), and (Shahiri & Husain, 2015)).”
Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review (2020, methodology): “Previous reviews showed that the cumulative GPA and course assessments are the most used predictors of student performance and success [24,25].”