Prediction of Students’ Performance in E-learning Courses

Sabaneh, Kefaya; Jayousi, Rashid

doi:10.1109/icpet53277.2021.00016

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…A filter-based approach called IG, which employs statistical tests to find the most important characteristics was used by [40], [36], [47], [48]. A feature selection technique called mutual information (MI) was applied in [40], to attain the ideal feature set where it is a filter technique that estimates entropy reduction by comparing the information gain of each independent feature to the information gain of the dependent feature and choosing the feature with the highest information gain.…”

Section: Information Gain/mutual Informationmentioning

confidence: 99%

Feature selection techniques and classification algorithms for student performance classification: a review

Alias,

Hambali,

Abdul Aziz

et al. 2024

IJECE

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The process of categorizing students’ performance based on input data, encompassing demographic information and final exam results, is recognized as student performance classification. Educational data mining has gained traction in assessing students’ performance. However, this study entails the need to analyze the diverse attributes of students’ information within an educational institution by using data mining techniques. This study thoroughly examines both previous and current methodologies presented by researchers, addressing two main aspects: data preprocessing and classification algorithms applied in student performance classification. Data preprocessing specifically delves into the exploration of feature selection techniques, encompassing three types of feature selection and search methods. These techniques aim to identify the most significant features, eliminate unnecessary ones, and reduce data dimensionality. In addition, classification algorithms play a crucial role in categorizing or predicting student performance. Models such as k-nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANN), and linear models (LR) were scrutinized based on their performance in prior research. Ultimately, this study highlights the potential for further exploration of feature selection techniques like information gain, Chi-square, and sequential selection, particularly when applied to new datasets such as students’ online learning activities, utilizing a variety of classification algorithms.

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