Modeling Student Performance in Higher Education Using Data Mining

Gürüler, Hüseyin; İstanbullu, Ayhan

doi:10.1007/978-3-319-02738-8_4

Cited by 14 publications

(9 citation statements)

References 18 publications

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“…In the context of DS, the Cross-Industry Standard Process for Data Mining (CRISP-DM) model is the most widely used methodology for knowledge discovery (Guruler and Istanbullu 2014;Linan and Perez 2015;Shearer 2000). It has also been incorporated into commercial knowledge discovery systems, such as SPSS Modeler.…”

Section: Methodology Review For Data Sciencementioning

confidence: 99%

The use of data science for education: The case of social-emotional learning

Liu

Huang

2017

Smart Learn. Environ.

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The broad availability of educational data has led to an interest in analyzing useful knowledge to inform policy and practice with regard to education. A data science research methodology is becoming even more important in an educational context. More specifically, this field urgently requires more studies, especially related to outcome measurement and prediction and linking these to specific interventions. Consequently, the purpose of this paper is first to incorporate an appropriate data-analytic thinking framework for pursuing such goals. The well-defined model presented in this work can help ensure the quality of results, contribute to a better understanding of the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Second, a case study of social-emotional learning is presented. We hope the issues we have highlighted in this paper help stimulate further research and practice in the use of data science for education.

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Section: Methodology Review For Data Sciencementioning

confidence: 99%

The use of data science for education: The case of social-emotional learning

Liu

Huang

2017

Smart Learn. Environ.

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“…Predicting students' academic performance is one of the main topics of educational data mining [1,2]. With the advancement of technology, technological investments in the field of education have increased.…”

Section: Related Workmentioning

confidence: 99%

Data Mining for Student Performance Prediction in Education

Ünal¹

2021

Data Mining - Methods, Applications and Systems

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The ability to predict the performance tendency of students is very important to improve their teaching skills. It has become a valuable knowledge that can be used for different purposes; for example, a strategic plan can be applied for the development of a quality education. This paper proposes the application of data mining techniques to predict the final grades of students based on their historical data. In the experimental studies, three well-known data mining techniques (decision tree, random forest, and naive Bayes) were employed on two educational datasets related to mathematics lesson and Portuguese language lesson. The results showed the effectiveness of data mining learning techniques when predicting the performances of students.

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“…At present, a relatively complete study are carried out by the foreign scholars. For example, to find out the students with similar learning characteristics, and judge whether the students have made progress according to the classification results , Divna et al [3] analyze the learning data of the Moodle platform; to predict the factors affecting students' academic performance, decision tree algorithm are used by Huseyin et al [4], and constructed a educational data mining system using multi-model views.…”

Section: Research Basismentioning

confidence: 99%

Research on Model Design about Learning Result Prediction and Intervention Based on Dynamic Data Mining

Zhao¹,

Li²

2019

Proceedings of the 2019 3rd International Seminar on Education, Management and Social Sciences (ISEMSS 2019)

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Predicting the academic performance of online learners and promptly intervening and guiding them is an effective way to improve the effectiveness of online learning. How to predict the academic performance and behavior of online learners, implement academic early warning based on prediction results, and provide evidence for teaching decision-making is one of the problems that network education needs to solve, and also an important research issue in educational big data research. This study uses the decision tree method in data mining technology to predict the learning behavior and performance of online learners, and constructs adaptive learning outcome prediction and intervention models. The application scenarios of the model are analyzed from the perspective of curriculum developers, teachers and students.

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Modeling Student Performance in Higher Education Using Data Mining

Cited by 14 publications

References 18 publications

The use of data science for education: The case of social-emotional learning

The use of data science for education: The case of social-emotional learning

Data Mining for Student Performance Prediction in Education

Research on Model Design about Learning Result Prediction and Intervention Based on Dynamic Data Mining

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