Association Rule Generation for Student Performance Analysis using Apriori Algorithm

Angeline, D. Magdalene Delighta

doi:10.9756/sijcsea/v1i1/01010252

Cited by 51 publications

(32 citation statements)

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“…The rules discovered are applied to show a list of updates in a specific quiz and course. Angeline () used the Apriori algorithm to predict and improve student performance based on student participation in homework and evaluation tests.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Discovering the associations between test items in an examination

et al. 2018

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“…This literature review is used to explore the important factors and different data mining algorithms that have been used in predicting students' performance. In Angeline, et al [4], student's assignment marks, class tests, attendance, lab-work, previous semester grade and their participation in extra-curricular tasks are the basis for internal assessment. In addition, an external assessment of a student is on the bases of marks scored on the final exam.…”

Section: Literature Reviewmentioning

confidence: 99%

Survey on data mining approach for analysis and prediction of student performance

Simon¹,

Bugusa²

2018

IJET

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The quality of education is measured by the academic performance of students and the results they produce. Since the student academic performance is the made up of the environmental, psychological, socio-economic and other factors, it is challenging to measure the academic performance of students. Such difficulties can be reduced by investigation of various factors that influence the student perfor-mance. Many researchers have been used different approaches to identifying the variables that help to predict students' performance. This survey paper examines various data mining methodologies that have been used to analyze and predict students' performance.

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“…It is called educational data mining. Educational data mining is a process used to extract useful information and patterns from a huge educational database [6]. Therefore, a systematic review is proposed to support the objectives of this study, which are:…”

Section: Introductionmentioning

confidence: 99%

A Review of Students’ Graduation Classification: A Comparison of Naive Bayes Classifier and K-Nearest Neighbour

Fauziastuti¹,

Rakhman²

2020

Proceedings of the 1st International Multidisciplinary Conference on Education, Technology, and Engineering (IMCETE 2019)

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Students are the most crucial aspects in determining the successful implementation of every program offered within educational institutions. Monitor the progress and students' achievement, enhance the ability of students, consider the number of students who have graduated, the ratio of the total number of students, the competence of graduates, are several major factors that require attention and serious consideration from the higher educational institutions. This study is mainly based on the data mining technique by implementing two common algorithms namely Naive Bayes Classifier and K-Nearest Neighbour due to classifying students' graduation (on time and overtime). The main objectives of this paper are to compare the achievement of both algorithms (NBC and KNN) towards students' graduation classification. This paper also focuses to identify the most important variables to predict students' performance. Leading to two categories of dependent variables namely graduate on time or graduate overtime. The considerations of these variables are based on the importance of each towards classifying the target. A Crossvalidation technique is applied to evaluate both algorithms. This study is beneficial for the center of graduate studies, educators, policymakers, and others in order to identify the main factors that impact the students' graduation status in higher educational institutions.

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Association Rule Generation for Student Performance Analysis using Apriori Algorithm

Cited by 51 publications

References 0 publications

Discovering the associations between test items in an examination

Discovering the associations between test items in an examination

Survey on data mining approach for analysis and prediction of student performance

A Review of Students’ Graduation Classification: A Comparison of Naive Bayes Classifier and K-Nearest Neighbour

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