Predictive Modeling of Students Performance Through the Enhanced Decision Tree

Sivakumar, Subitha; Rajalakshmi, S.

doi:10.1007/978-981-10-4765-7_3

Cited by 15 publications

(9 citation statements)

References 11 publications

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“…These methods help in understanding how students learn along with understanding their behavior (Monjurul Alom and Courtney, 2018). In educational institutions, the students' performance is the most important issue in any educational process (Sivakumar et al, 2018). Hence, classifying and predicting student's' performance by using educational data mining techniques takes more attention by researchers according to the large amount of data that is generated yearly in educational institutions.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, classifying and predicting student's' performance by using educational data mining techniques takes more attention by researchers according to the large amount of data that is generated yearly in educational institutions. Furthermore, the EDM is used to extract new hypotheses and discoveries about the performance of the students (Sivakumar et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the results of the prediction can be used to help teachers and students make the right decisions (Baker, 2010). The aforementioned techniques are responsible for exploring data from educational institutions which provide quality education regarding the students' performance as a main purpose (Baradwaj and Pal, 2012) Indeed, many researchers used EDM techniques in order to classify and predict the students' performance (Costa et al, 2017;Rao et al, 2017;Hussain et al, 2018;Kiu, 2018;Sivakumar et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have done their research in classifying and predicting the students' performance using classification algorithms. Based on the literature review, the most popular algorithms used by many researchers are Decision Tree C4.5, Random Forest, SVM and Naïve Bayes (Costa et al, 2017;Sivakumar et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Socio-economic and demographic features include gender, marital status, dormitory, family size and so on (Kostopoulos and Lipitakis, 2017;Rao et al, 2017). Moreover, some of these algorithms still need improvement as (Sivakumar et al, 2018) said "we improved the decision tree to get more effective accuracy results from the attribute values". This study tries to improve the Decision Tree C4.5 algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Classifying and Predicting Students’ Performance using Improved Decision Tree C4.5 in Higher Education Institutes

Sadiq

Ahmed

2019

Journal of Computer Science

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Students' information in higher education institutions increases yearly. It is hard for them to extract meaningful information from the huge amount of data manually. Such information can support academic staff to stop students from dropping out at the end of courses. This can be done by evaluating the students' performance for the course and also by predicting their performance in the final exam early by using classification algorithms. Four classification algorithms, which are Decision Tree C4.5, Random Forest, Support Vector Machine (SVM) and Naive Bayes, were used in this research in order to classify and predict the students' performance. Furthermore, this research aimed at improving the Decision Tree C4.5 algorithm by adding a grid search function in order to improve prediction accuracy in classifying and predicting the students' performance. Also, the features of this evaluation have been extracted through the interviews with academic staff of three universities (

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%