Evolution and prediction of radical multi-dimensional e-learning system with cluster based data mining techniques

Rao, N. V. S.; Mangathayaru, Nimmala; Rao, M. Sreenivasa

doi:10.1109/icoei.2017.8300793

Cited by 2 publications

(8 citation statements)

References 28 publications

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“…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%

“…This makes the teachers and researchers pay more attention to students' performance. Classifying and predicting the students' performance becomes a big duty and it is hard for them to do that manually (Rao et al, 2017). Furthermore, the issue of students' failure in courses becomes a very popular area nowadays for teachers and researchers (Costa et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms were applied on datasets which contain features, such as: Academic features, socioeconomic and demographic features. Academic features include Marks, Quizzes, assignments, homework and so on (Mahboob et al, 2016;Kadambande et al, 2017;Rao et al, 2017). Socio-economic and demographic features include gender, marital status, dormitory, family size and so on (Kostopoulos and Lipitakis, 2017;Rao et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Academic features include Marks, Quizzes, assignments, homework and so on (Mahboob et al, 2016;Kadambande et al, 2017;Rao et al, 2017). 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".…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Perguruan tinggi harus mempunyai target dalam mengurangi mahasiswa yang mengikuti her. Untuk mencapai target tersebut perguruan tinggi dapat mencari sebuah keputusan agar mahasiswa tidak mengalami kegagalan dalam pelajaran dengan kumpulan data besar yang dapat menghabiskan waktu dan akurasi yang dihasilkan kurang akurat (Rao, Mangathayaru, & Rao, 2017). Dengan data yang besar dan pemilihan variabel yang sedikit itu dapat membuang banyak informasi yang tersedia, maka dibutuhkan Support Vector Machine (SVM) yang dapat sepenuhnya mengekstraksi sebuah informasi yang berguna terhadap indikator yang diberikan, meminimalkan kesalahan model dan memaksimalkan akurasi model.…”

Section: Pendahuluanunclassified

Klasifikasi Mahasiswa HER Berbasis Algoritma SVM dan Decision Tree

Purnama

Nawawi

Rosyida

et al. 2020

JTIIK

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Mahasiswa di setiap perguruan tinggi dituntut untuk memperoleh pengetahuan dan keterampilan yang memenuhi syarat dengan prestasi akademik. Hasil dari pembelajaran mahasiswa didapat dari ujian teori dan praktek, setiap mahasiswa wajib menuntaskan nilai sesuai kriteria kelulusan minimum dari masing-masing dosen pengajar, jika dibawah batas minimum maka mahasiswa mengikuti her. Her adalah salah satu cara untuk menuntaskan kriteria kelulusan minimum. Mahasiswa yang mengikuti her setiap semesternya hampir mencapai angka yang relatif tinggi dari jumlah seluruh mahasiswa. Untuk mengurangi jumlah mahasiswa yang mengikuti her maka dibutuhkan sebuah metode yang dapat mengurangi hal tersebut, dengan metode Support Vector Machine (SVM) dan Decision Tree (DT). SVM dan DT adalah salah satu metode klasifikasi supervised learning. Oleh karena itu, dalam penelitian ini menggunakan SVM dan DT. SVM dapat menghilangkan hambatan pada data, memprediksi, mengklasifikasikan dengan sampling kecil dan dapat meningkatkan akurasi dan mengurangi kesalahan. Klasifikasi data siswa yang melakukan her/peningkatan dengan mengimprovisasi model kernel untuk visualisasi termasuk bar, histogram, dan sebaran begitu juga Decision Tree mempunyai kelebihan tersendiri. Dari hasil penelitian ini telah didapatkan akruasi dan presisi model DT lebih besar dibandingkan dengan SVM, akan tetapi untuk recall DT lebih kecil dibandingkan SVM. Abstract Students in each tertiary institution are required to obtain knowledge and skills that meet the requirements with academic achievement. The results of student learning are obtained from the theory and practice exams, each student is required to complete grades according to the minimum graduation criteria of each teaching lecturer, if below the minimum limit then students take remedial. Remedial is one way to complete the minimum passing criteria. Students who take remedial every semester almost reach a relatively high number of the total number of students. To reduce the number of students who take remedial, a method that can reduce this is needed, with the Support Vector Machine (SVM) and Decision Tree (DT) methods. SVM and DT are one of the supervised learning classification methods. Therefore, in this study using SVM and DT. SVM can eliminate barriers to data, predict, classify with small sampling and can improve accuracy and reduce errors. Data classification of students who do remedial/improvements by improving the kernel model for visualization including bars, histograms, and distributions as well as the Decision Tree has its own advantages. From the results of this study it has been obtained that the accuracy and precision of DT models is greater than that of SVM, but for recall DT is smaller than SVM.

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Evolution and prediction of radical multi-dimensional e-learning system with cluster based data mining techniques

Cited by 2 publications

References 28 publications

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

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

Klasifikasi Mahasiswa HER Berbasis Algoritma SVM dan Decision Tree

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