Mining Student Information System Records to Predict Students’ Academic Performance

Saa, Amjad Abu; Al-Emran, Mostafa; Shaalan, Khaled

doi:10.1007/978-3-030-14118-9_23

Cited by 50 publications

(40 citation statements)

References 20 publications

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“…Many studies have been carried out in which classification is most the commonly used method to make predictions. In order to achieve their research targets, researchers have used the Decision Tree, Random Forest, Naïve Bayes, Support Vector Machines, Linear Regression or Logistic Regression models, and K means approaches [33][34][35][36][37][38]. The studies mainly suggest the prediction of students' academic performance either before the classes, at the middle of the session or at the end of the term.…”

Section: Predictionmentioning

confidence: 99%

Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques

et al. 2020

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Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with flipped teaching can help improve student’s academic performance. This study was carried out with 772 examples of students registered in e-commerce and e-commerce technologies modules at an HEI. The study aimed to predict student’s overall performance at the end of the semester using video learning analytics and data mining techniques. Data from the student information system, learning management system and mobile applications were analyzed using eight different classification algorithms. Furthermore, data transformation and preprocessing techniques were carried out to reduce the features. Moreover, genetic search and principle component analysis were carried out to further reduce the features. Additionally, the CN2 Rule Inducer and multivariate projection can be used to assist faculty in interpreting the rules to gain insights into student interactions. The results showed that Random Forest accurately predicted successful students at the end of the class with an accuracy of 88.3% with an equal width and information gain ratio.

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Section: Predictionmentioning

confidence: 99%

Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques

et al. 2020

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“…They concluded that the Naive Bayes algorithm (86.19%) had the highest success rate. Saa, Al-Emran, and Shaalan (2019) used seven different algorithms to predict the academic success of university students. While the random forest algorithm (75.52%) yielded the most successful results, they revealed that the factors affecting the success are information about the high school, university entrance exam, and the performance of the student in the previous courses.…”

Section: Discussionmentioning

confidence: 99%

Predicting Academic Achievement with Machine Learning Algorithms

Yildiz¹,

Börekci²

2020

Journal of Educational Technology and Online Learning

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Education systems produce a large number of valuable data for all stakeholders. The processing of these educational data and making studies on the future of education based on the data reveal highly meaningful results. In this study, an insight was tried to be developed on the educational data collected from ninth-grade students by using data mining methods. The data contains demographic information about students and their families, studying routines, behaviours of attending learning activities, and their epistemological beliefs about science. Thus, this research aimed to solve a classification problem, two-class (successful or unsuccessful according to the exam result) was tried to be estimated from the collected data. In the study, the prediction accuracy of the supervised classification algorithms were compared and it was defined which variables were effective in the formation of classes. When the prediction accuracy of machine learning algorithms was compared, the findings indicated that the Neural Network algorithm (98.6%) had the highest score. The accuracy rate of the other algorithms are kNN (86.2%), Logistic Regression (78.4%), SVM (90.3%), Decision Tree (91.9%), Random Forest (90.0%), and Naive Bayes (81.7%). The information gain coefficient of the variables was examined to determine the factors affecting the prediction accuracy. It was revealed that demographic variables of the family, scientific epistemological beliefs of the student, study routines and attitudes towards some courses affected the classification. It can be concluded that there was a relationship between these variables and academic success. Studies on these variables will support students' academic success.

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“…Data yang bisa dimanfaatkan untuk proses klasifikasi diambil dari data orang tua mahasiswa, data diri mahasiswa, data demografis, dan riwayat pendidikan [4]. Klasifikasi dapat dilakukan dengan banyak metode namun metode yang umum digunakan pada Educational Data Mining (EDM) adalah Decision Tree [19].…”

Section: Iunclassified

Komparasi Model Prediksi Daftar Ulang Calon Mahasiswa Baru Menggunakan Metode Decision Tree Dan Adaboost

2021

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Every year, all the colleges hold new student enrollment. It is needed to start a new school academic year. Unfortunately, the number of students who resigned is considerably high to reach 837 students and caused 324 empty seats. The college’s stakeholders can minimize the resignation number if the selection phase of new students is done accurately. Making a machine learning-based model can be the answer. The model will help predict which candidates who potentially complete the enrollment process. By knowing it in the first place will help the management in the selection process. This prediction is based on historical data. Data is processed and used to train the model using the Adaboost algorithm. The performance comparison between Adaboost and Decision Tree model is performed to find the best model. To achieve the maximum performance of the model, feature selection is performed using chi-square calculation. The results of this research show that the performance of Decision Tree is lower than the performance of the Adaboost algorithm. The Adaboost model has f-measure score of 90.9%, precision 83.7%, and recall 99.5%. The process of analyzing the data distribution of prospective new students was also conducted. The results were obtained if prospective students who tended to finish the enrollment process had the following characteristics: graduated from an Islamic school, 19-21 years old, parents' income was IDR 1,000,000 to IDR. 5,000,000, and through the SBMPTN program.

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Mining Student Information System Records to Predict Students’ Academic Performance

Cited by 50 publications

References 20 publications

Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques

Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques

Predicting Academic Achievement with Machine Learning Algorithms

Komparasi Model Prediksi Daftar Ulang Calon Mahasiswa Baru Menggunakan Metode Decision Tree Dan Adaboost

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