Educational Data Mining: A Systematic Review of the Published Literature 2006-2013

Al-Razgan, Muna; Al-Khalifa, Atheer S.; Al‐Khalifa, Hend S.

doi:10.1007/978-981-4585-18-7_80

Cited by 22 publications

(14 citation statements)

References 4 publications

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“…The entire researchers on educational data mining domain are working toward the development of educational games, mobile application learning and ITS for learners. The study found that everyone is focusing on working on the educational games, intelligent tutoring and mobile development application [4].…”

Section: Work Done On Educational Setting By Researcher Since 2009-2016mentioning

confidence: 99%

Literature Survey on Educational Dropout Prediction

Kumar¹,

Singh²,

Handa³

2017

IJEME

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Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.

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Section: Work Done On Educational Setting By Researcher Since 2009-2016mentioning

confidence: 99%

Literature Survey on Educational Dropout Prediction

Kumar¹,

Singh²,

Handa³

2017

IJEME

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“…This is consistent with the metrics used in previous studies (such as Ineson and Kempa, 1997;Young, 1989). A large and growing body of literature has modelled the relationship between prior academic achievement and academic performance (see Al-Razgan et al, 2014;Dakduk et al, 2016). Two important issues emerge from the studies focused on modelling of the relationship between prior academic achievement and academic performance.…”

Section: Introductionmentioning

confidence: 99%

Towards reliable prediction of academic performance of architecture students using data mining techniques

Aluko

Daniel

Oshodi

et al. 2018

JEDT

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PurposeIn recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities.Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. Design/methodology/approachThe input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. FindingsThe results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Research limitationsAlthough the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. Originality/valueThe developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities.

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“…In the field of education, numerous scholars have used big data techniques to analyse education data (Al‐Razgan, Al‐Khalifa, & Al‐Khalifa, ). Education data are formed by or related to human learning activities in educational backgrounds.…”

Section: Introductionmentioning

confidence: 99%

Discovering the associations between test items in an examination

et al. 2018

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An examination is .an important approach to comprehensively evaluate students' abilities. An examination paper is composed of test items. Most studies assume that test items are independent, but there may, in fact, be associations among test items. How to select test items with low correlations into a test paper is our concern. Meanwhile, for diagnostic learning, students must perform highly relevant exercises to help them efficiently understand the concepts included in the test items. People usually find the associations among test items based on properties of individual test items. This paper proposes a test item association model to represent the correlations of test item pairs by mining the response data of examinees. Then, a hypothesis test and an empirical analysis were used to verify and analyse the relationship between the test item correlation and similarity. Next, the effects of test item parameters on the correlation are investigated by applying the model to the simulated response data which are produced by Item Response Theory (IRT) simulation data generation software. Finally, three applications of this test item association model are listed. After verification, the proposed model can be used for precise teaching.

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Educational Data Mining: A Systematic Review of the Published Literature 2006-2013

Cited by 22 publications

References 4 publications

Literature Survey on Educational Dropout Prediction

Literature Survey on Educational Dropout Prediction

Towards reliable prediction of academic performance of architecture students using data mining techniques

Discovering the associations between test items in an examination

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