论文信息 - A Study on Applying Relational Association Rule Mining Based Classification for Predicting the Academic Performance of Students

A Study on Applying Relational Association Rule Mining Based Classification for Predicting the Academic Performance of Students

Predicting the students’ academic achievements is of great interest within the Educational Data Mining (EDM) field, having the main goal of extracting patterns relevant in deciding the students’ performance at a certain academic course. This paper analyses a classification model SPRAR (Students Performance prediction using Relational Association Rules) for predicting the academic results of students using relational association rules. Relational association rules represent an extension of classical association rules able to capture binary relationships between the attribute values. Three new classification scores are introduced in this paper and used in the classification stage of SPRAR. Their performance is analyzed using three real academic data sets from Babes-Bolyai University, Romania and compared to similar existing results from the literature.

[1] Xuan Hieu Phan,et al. Performance Prediction for Students: A Multi-Strategy Approach , 2017 .

[2] L. M. Crivei. Incremental Relational Association Rule Mining of Educational Data Sets , 2018 .

[3] Jamalul-lail Ab Manan,et al. A neural network students' performance prediction model (NNSPPM) , 2013, 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA).

[4] François Chollet,et al. Keras: The Python Deep Learning library , 2018 .

[5] Robert T. Clemen,et al. From the Editors...Remembering Ward Edwards , 2005, Decis. Anal..

[6] Marzieh Ahmadzadeh,et al. Analysis of factors that affect the students academic performance - Data Mining Approach , 2014, ArXiv.

[7] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[8] L. Brown,et al. Interval Estimation for a Binomial Proportion , 2001 .

[9] Wahidah Husain,et al. A Review on Predicting Student's Performance Using Data Mining Techniques , 2015 .

[10] Rashedur M. Rahman,et al. Improving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique , 2015, Decis. Anal..

[11] Richard A. Huebner. A Survey of Educational Data-Mining Research. , 2013 .

[12] Gabriela Serban Czibula,et al. S PRAR: A novel relational association rule mining classification model applied for academic performance prediction , 2019, KES.