Mining Educational Data to Predict Students’ Academic Performance

Alsaleem, Mona N.; Al-Kathiry, Norah; Al-Osimi, Sara; Badr, Ghada

doi:10.1007/978-3-319-21024-7_28

Cited by 21 publications

(12 citation statements)

References 7 publications

(5 reference statements)

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“…Similarly, while working on the data of university students, Roy and Garg (2017) concluded that the J48 algorithm (73.92%) was more successful, they concluded that the students' health status, education of their families, alcohol use and friend relationship were among the factors affecting their success. Al-Saleem, Al-Kathiry, Al-Osimi, and Badr (2015) used the J48 and ID3 algorithms to create a model for predicting students' achievements and tried to determine their success in various elective courses with these algorithms. As a result, they concluded that the J48 (83.75%) algorithm gives more successful results than ID3 (69.27%).…”

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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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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“…Several classification algorithms have been used in previous works to predict students' academic performance [22]. In this research, five classification algorithms are used include Decision Tree (J48) [23], Random Forest (RF) [24,2,12], Sequential Minimal Optimization (SMO) [13], Multilayer Perceptron (MLP) [25] and Logistic Regression (Logistic) [26] for predicting students' academic performance. These algorithms are used depending on their effectiveness in previous works for predicting students' performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Predict Students’ Academic Performance based on their Assessment Grades and Online Activity Data

Alhassan¹,

Zafar²,

Mueen³

2020

IJACSA

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The ability to predict students' academic performance is critical for any educational institution that aims to improve their students' learning process and achievement. Although students' performance prediction problem is studied widely, it still represents a challenge and complex issue for educational institutions due to the different features that affect students learning process and achievement in courses. Moreover, the utilization of web-based learning systems in education provides opportunities to study how students learning and what learning behavior leading them to success. The main objective of this research was to investigate the impact of assessment grades and online activity data in the Learning Management System (LMS) on students' academic performance. Based on one of the commonly used data mining techniques for prediction, called classification. Five classification algorithms were applied that decision tree, random forest, sequential minimal optimization, multilayer perceptron, and logistic regression. Experimental results revealed that assessment grades are the most important features affecting students' academic performance. Moreover, prediction models that included assessment grades alone or in combination with activity data perform better than models based on activity data alone. Also, random forest algorithm performs well for predicting student a cademic performance, followed by decision tree.

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“…Tang et al: (2015), Klusener and Fortenbacher (2015), Brinton and Chiang (2015), Shrivas and T iwari b. Articles: Al Shehri et al: (2017), Amaya et al: (2015), Widyaningsih et al: (2019) c. Articles: Okubo et al: (2017), Singh and Kaur (2018), Santoso and Yulia (2019), Sumitha et al: (2016), Al Barrak and Al Razgan d. Articles: Amrieh et al: (2015), Ruby and David (2015), Almasri et al: (2019), Sivakumar et al: (2016), Kumar et al: (2019), Chanlekha and Niramitranon (2018) e. Articles: Livieris et al: (2018), Angiani et al: (2019), Jishan et al: (2015), Livieris et al: (2019c), Al Saleem et al: (2015, f. Articles: Kostopoulos et al: (2015), Athani et al: (2017), Sara et al: (2015), Kasthuriarachchi and Liyanage (2019), Namomsa andSharma (2018) g. Articles: Bergin et al: (2015), Navamani and Kannammal (2015), Ketui et al (2019), Bhegade and Shinde (2016), Pristyanto et al, (2018) h. Articles: Lopez Guarin et al: (2015, Pradeep et al: (2015), Yehuala (2015), Kaur and Singh (2016), Mahboob et al: (2017), Pereira et al, (2018)…”

Section: Attributes Usedmentioning

confidence: 99%

A Critical Review of Data Mining for Education: What has been done, what has been learnt and what remains to be seen

Papadogiannis

Poulopoulos

Wallace

2020

International Journal of Educational Research Review

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This article provides a thorough review of educational data mining (EDM) in the period 2015-2019. Going beyond earlier review works, in this article we examine previous research from a variety of aspects, including the examined data, the algorithms used, the type of conclusions drawn, the educational level/setting of application and the actual exploitation of the results in the educational setting. Our findings indicate that tertiary education dominates the EDM domain, while minimal focus has been given to secondary education and almost none to primary education. Our finding, and suggestion, is that by focusing EDM on earlier education level the field can have a more profound impact on education and on society as a whole.

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Mining Educational Data to Predict Students’ Academic Performance

Cited by 21 publications

References 7 publications

Predicting Academic Achievement with Machine Learning Algorithms

Predicting Academic Achievement with Machine Learning Algorithms

Predict Students’ Academic Performance based on their Assessment Grades and Online Activity Data

A Critical Review of Data Mining for Education: What has been done, what has been learnt and what remains to be seen

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