A comparative study of feature selection techniques for classify student performance

Punlumjeak, Wattana; Rachburee, Nachirat

doi:10.1109/iciteed.2015.7408984

Cited by 42 publications

(29 citation statements)

References 12 publications

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“…A very good feature selection method will provide a subset of data to a manageable level without losing its originality that bring sense and meanings for research goals (effective), increased predictive accuracy, lowering computational complexity and its storage, building generalizable models and finally producing it at an acceptable timing. This was concurred by [23,[31][32].…”

Section: S M Muthukrishnan Et Al J Fundam Appl Sci 2017 9(4s) 7supporting

confidence: 78%

Systematic mapping review on student’s performance analysis using big data predictive model

Muthukrishnan

Govindasamy²,

Mustapha

2018

J. Fundam and Appl Sci.

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This paper classify the various existing predicting models that are used for monitoring and improving students' performance at schools and higher learning institutions. It analyses all the areas within the educational data mining methodology. Two databases were chosen for this study and a systematic mapping study was performed. Due to the very infant stage of this research area, only 114 articles published from 2012 till 2016 were identified. Within this, a total of 59 articles were reviewed and classified. There is an increased interest and research in the area of educational data mining, particularly in improving students' performance with various predictive and prescriptive models. Most of the models are devised for pedagogical improvements ultimately. It is a huge scarcity in producing portable predictive models that fits into any educational environment. There is more research needed in the educational big data.

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Section: S M Muthukrishnan Et Al J Fundam Appl Sci 2017 9(4s) 7supporting

confidence: 78%

Systematic mapping review on student’s performance analysis using big data predictive model

Muthukrishnan

Govindasamy²,

Mustapha

2018

J. Fundam and Appl Sci.

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“…The original form of data contains a lot of features and attributes describing students and courses, so we have worked with domain experts in SVU to use some feature selection techniques to minimize redundancy and maximize relevance feature subset while retaining a high accuracy without losing any important information about students. The effective process of feature selection can play as an important role by improving learning performance, lowering computational complexity, and building better generalizable models [16] [24].…”

Section: Proccessing Bit Data and Research Methodologymentioning

confidence: 99%

Studying Academic Indicators within Virtual Learning Environment Using Educational Data Mining

Aldikanji¹,

Ajami²

2016

IJDKP

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“…There are two categories of feature selection methods, namely: wrapper-based and filter-based approaches [27]. Wrapper-based subset selection (WBSE) is built by a classifier to estimate the worth of each feature subset.…”

Section: Feature Selectionmentioning

confidence: 99%

Identifying Dominant Characteristics of Students’ Cognitive Domain on Clustering-based Classification

Yamasari¹,

Nugroho²,

Yoshimoto³

et al. 2020

IJIES

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The rapid growth of information and communications technology-based educational tools generates a large volume of student data with many features (characteristics). However, the mining process in the clustering task of student data is not often done optimally, so the performance of the system decreases. To overcome this problem, we propose a discretization method on logistic regression to determine the most optimal number of clusters. Additionally, we introduce a technique that combines the features selection using a filter-and wrapper-based procedures (HFS) to identify the dominant features of the students' cognitive domains. Furthermore, we evaluate the identification result by three clustering methods, namely: K-means, EM, and Farthest first. Finally, we propose the clustering-based classification so the results can be measured by using the classification metrics. Here, we apply two evaluation techniques, namely: cross-validation and percentage split. The experimental results indicate that our approach describes predominance, in terms of classification metrics over conventional methods. Our approach is around 10,847-11,134 percent higher in terms of accuracy average than the original features on both the assessment techniques. Also, this approach significantly reduces the time taken to create a prototype between 0.0167-0.027 seconds. This gives the impact on a significant reduction in the model created to the number of unsuitable students on classes based on the cognitive domain, namely: 3-12 students.

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A comparative study of feature selection techniques for classify student performance

Cited by 42 publications

References 12 publications

Systematic mapping review on student’s performance analysis using big data predictive model

Systematic mapping review on student’s performance analysis using big data predictive model

Studying Academic Indicators within Virtual Learning Environment Using Educational Data Mining

Identifying Dominant Characteristics of Students’ Cognitive Domain on Clustering-based Classification

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