Comparative Study: Feature Selection Methods in the Blended Learning Environment

Dimić, Gabrijela; Rančić, Dejan; Milentijević, Ivan; Spalević, Petar; Plećić, Katarina

doi:10.22190/fuacr1702095d

Cited by 2 publications

(1 citation statement)

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“…As a result, the DT algorithm generates the highest accuracy of 90.67%. Dimic et al [11] studied the behavior patterns of students in the blended learning environment. Dataset used in the study was created by integrating data from multiple sources into a form applicable for data mining technique application.…”

Section: A Feature Selection Methods and Prediction Models In Edmmentioning

confidence: 99%

Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques

Sokkhey¹,

Okazaki²

2020

IJACSA

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The primary goal of educational systems is to enrich the quality of education by maximizing the best results and minimizing the failure rate of poor-performing students. Early predicting student performance has become a challenging task for the improvement and development of academic performance. Educational data mining is an effective discipline of data mining concerned with information integrated into the education domain. The study is of this work is to propose techniques in educational data mining and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models was conducted. Subsequently, high performing models were developed to get higher performance. The hybrid random forest named Hybrid RF produces the most successful classification. For the context of intervention and improving the learning outcomes, a novel feature selection method named MICHI, which is the combination of mutual information and chi-square algorithms based on the ranked feature scores is introduced to select a dominant set and improve performance of prediction models. By using the proposed techniques of educational data mining, and academic performance prediction system is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental results and evaluation surveys report the effectiveness and usefulness of the developed academic prediction system. The system is used to help educational stakeholders for intervening and improving student performance.

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Section: A Feature Selection Methods and Prediction Models In Edmmentioning

confidence: 99%

Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques

Sokkhey¹,

Okazaki²

2020

IJACSA

View full text Add to dashboard Cite

show abstract

A survey to enhance learning management systems to increase productivity of the students and their performance

Gawande,

Gupta,

Dhakad

2024

14th International Conference on Materials Processing and Characterization 2023

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Comparative Study: Feature Selection Methods in the Blended Learning Environment

Abstract: Abstract. Research presented in this paper deals with the unknown behavior pattern of students in the blended learning environment. In order to improve prediction accuracy it was

Cited by 2 publications

References 25 publications

Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques

Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques

A survey to enhance learning management systems to increase productivity of the students and their performance

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