Educational Data Mining: A Case Study Perspectives from Primary to University Education in Australia

Alom, B.M. Monjurul; Courtney, Matthew

doi:10.5815/ijitcs.2018.02.01

Cited by 27 publications

(19 citation statements)

References 2 publications

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“…The field of data mining has become standard practice in various disciplines such as business, finance, and marketing allowing to inadvertently impact social sciences and humanities in general [8]. The range of its application has also reached other sectors such as education [9], [10] and healthcare [11], [12]. DM is promising for researches applied in engineering, biomedical sciences, medical systems, web, sports, and shared market because of the accessibility to various vast datasets [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Variable Reduction-based Prediction through Modified Genetic Algorithm

Delima¹,

Sison²,

Medina³

2019

IJACSA

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Due to the massive influence in the use of prediction models in different sectors of society, many researchers have employed hybrid algorithms to increase the accuracy level of the prediction model. The literature suggests that the use of Genetic Algorithms (GAs) can sufficiently improve the performance of other prediction models; thus, this study. This paper introduced a new avenue of prediction integrating GA with the novel Inversed Bi-segmented Average Crossover (IBAX) operator paired with rank-based selection function to the KNN algorithm. The 70% of data from 597 records of student-respondents in the evaluation of the faculty instructional performance from the four State Universities and Colleges (SUC) in Caraga Region, Philippines were used as training set while the 30% was used for testing. The simulation result showed that the use of the proposed prediction model with the integration of the modified GA outperformed the KNN prediction model where GA with average crossover and roulette wheel selection function was used. The KNN where k value is three (3) was identified to be the optimal model for prediction with the 95.53% prediction accuracy compared to KNN with 1, 5, and 7 k values.

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Section: Introductionmentioning

confidence: 99%

Variable Reduction-based Prediction through Modified Genetic Algorithm

Delima¹,

Sison²,

Medina³

2019

IJACSA

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“…Research on data mining especially in educational fields has been proposed by many researchers. Educational data mining (EDM) is one of the popular data mining techniques used for discovering the hidden knowledge from the educational systems [6]. It was the alternative for traditional measures of student attrition that ignored the students who are postponing their study and return in next semester or students who continue their studies but transferring to another institution [5].…”

Section: Introductionmentioning

confidence: 99%

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

Tarmizi¹,

Mutalib²,

Hamid³

et al. 2019

IJMECS

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Student attrition among undergraduate students is among the most concerned issues in higher educational institutions in Malaysia and abroad. This problem arises when these students unable to complete their studies within the stipulated period when there are majoring in the Science, Technology, Engineering, and Mathematics (STEM) fields. Research findings highlight numerous factors contribute to the student attrition. These findings also suggest that the factors differ from one case to another case. Effects of student attrition not only for the student itself but also to the institutions and community. It is challenging to classify the factors based on general assumptions. Moreover, increasing students' information makes the problem more complicated. This student information can provide a useful database for analytical analysis. Methods such as big data analytics and data mining techniques can be deployed to gain insights and pattern that related to student attrition problem. The objective of this paper (i) review the student attrition in higher education (HE) and the contributing factors; and (ii) review the existing computational model to analyze and predict student attrition in HE.

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“…Algorithms used for classification purposes allow significant improvements in many areas such as early diagnosis of disease in medical science (Rokny et al, 2017;Kumar & 203 Sahoo, 2011; Kurt & Ensari, 2017), analysis of student achievement in education (Alom & Courtney, 2018;Fernandes et al, 2019), classification of plants or animals in biology (Salvador et al, 2018;Celik et al, 2017;Koc, Eyduran, & Omer, 2017), detection of spam in information (Abdulhamid et al, 2018), document classification (Rajvanshi & Chowdhary, 2017).…”

Section: Introductionmentioning

confidence: 99%

Data Mining with R: An Applied Study

Durmuş

Güneri

2019

IJCSR

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Purpose -The aim of this study is to analyze different classification algorithms with R programming and to determine the accuracy rates. It also encourages the use of the R program by giving readers the opportunity to experiment.Method -For the purposes mentioned above, different data sets were obtained from the UC Irvine Machine Learning Repository (2019), which was suitable for classification. After preparing data set and R program for data mining, performance evaluation was made with classification algorithms (J48, Random Forest, Naïve Bayes). The 'accuracy' criterion was taken into consideration when interpreting the results.Results -At the end of the study, the accuracy rates were determined for three data sets. Looking at the "wine" data, the performance of all three algorithms is quite successful. The results of the other two data sets (lenses and liver) are parallel. Only the 'liver' dataset gave a slightly lower accuracy than expected with the Naïve Bayes algorithm (0.55). 202Conclusion -In this study, performance comparison of algorithms has been made within the scope of data mining with R program. The accuracy rate was taken as a criterion. All codes are given with their outputs in order to be an example especially for young researchers or students. It is thought that this study can be a source for other researchers, will encourage the use of R and the researchers or students will try new papers by trying the codes.Recommendations -In subsequent studies, a similar study can be done by developing the given codes. Or how to make classification analysis in R with different algorithms can be examined.

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Educational Data Mining: A Case Study Perspectives from Primary to University Education in Australia

Cited by 27 publications

References 2 publications

Variable Reduction-based Prediction through Modified Genetic Algorithm

Variable Reduction-based Prediction through Modified Genetic Algorithm

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

Data Mining with R: An Applied Study

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