Classification, Clustering and Association Rule Mining in Educational Datasets Using Data Mining Tools: A Case Study

Hussain, S. Perwez; Atallah, Rasha R.; Kamsin, Amirrudin; Hazarika, Jiten

doi:10.1007/978-3-319-91192-2_21

Cited by 44 publications

(26 citation statements)

References 11 publications

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“…[18] stated that in the university context, most of the present system only maintain the information of students by only enabling university management to store and retrieve that information. Technically, with the utilization of the data mining process, it makes the present system becomes smarter [19]. This statement seems to be realistic as [20] stated that Data mining is a perfect tool for gleaning and extracting useful information from the meaningless data.…”

Section: Data Miningmentioning

confidence: 99%

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

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Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students' achievement, university's performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students' academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students' graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naï ve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students' graduation time.

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Section: Data Miningmentioning

confidence: 99%

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Suhaimi¹,

Abdul-Rahman²,

Mutalib³

et al. 2019

IJMECS

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“…The MM is based on unsupervised learning, using an Artificial Neural Network (ANN) [63]. ANNs can be used to solve this translation problem by mapping service-specific SLOs to resource attributes directly.…”

Section: Mapping Mechanismmentioning

confidence: 99%

Managing and Optimizing Quality of Service in 5G Environments Across the Complete SLA Lifecycle

Kapassa¹,

Touloupou²,

Stavrianos³

et al. 2019

Adv. sci. technol. eng. syst. j.

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The 5G is the fifth generation of mobile broadband, cellular technologies, and networks that promises a major change in mobility by evolving connected business realities. In such an emerging environment, reliable Service Level Agreements (SLA) and anticipation of breaches of Service Level Objectives (SLO) become compulsory. Thus, guaranteeing the required service quality, while also ensuring efficient recourse allocation becomes a challenge. In addition, 5G networks are expected to provide diverse Quality of Service (QoS) guarantees for a wide range of services, applications and users with a variety of requirements. However, there is an increased difficulty in translating user-friendly business terms into resource-specific monitoring attributes that can be used to manage resources in the 5G core network. To address these gaps, an SLA management framework, enabling QoS provisioning is introduced. The aforementioned framework will be supported by an adaptive monitoring algorithm, which removes the static time interval used in the monitoring system, in order to provide highly accurate information in real time, without the produce of unnecessary traffic to the network. The proposed architecture also incorporates a recommendation mechanism to determine the significance of various QoS parameters in order to ensure that relevant QoS metrics are included in the SLAs, using enriched metadata information from a Network Function Virtualization (NFV) Catalogue.

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“…Data Mining Education is a sub-area of Data Mining Domain. This new area has great potential to mine various aspects for improving the student's quality as well as in decision making by educational institution authorities [2]. "Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to understand the students better, and the settings which they learn in."…”

Section: Introductionmentioning

confidence: 99%

“…Educational Data Mining has a variety of purposes. Some studies use EDM to predict academic patterns by reviewing the accuracy of the study period of students in their education such as research conducted by [4], [5], [2], [6], [7], [8], [9]. In addition there are studies with the aim of predicting academic patterns by reviewing student performance such as research conducted by [10], [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Graduation Prediction of S1 Industrial Engineering Students IST AKPRIND by Using Data Mining Method

Wibowo

2020

Logic

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There is data of students who experience Drop Out which raises the curiosity in IST AKPRIND's industrial engineering study program on students' graduation patterns. It is necessary to have research on how to classify the data held by industrial engineering study programs in order to obtain students' graduation patterns as evaluation material in the administration of study programs. This study also produced a design to set the goals of Educational Data Mining, this case as a student modeling that would be achieved by predicting using the Decision Tree method. The final results showed a mismatch between the general information data passed and the drop out of the rule obtained using the decision tree algorithm in the Rapidminer software which is shown by an accuracy of 95.83%. This value indicates that there is a match between the prediction of student identity data with the rule obtained using the decision tree algorithm.

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Classification, Clustering and Association Rule Mining in Educational Datasets Using Data Mining Tools: A Case Study

Cited by 44 publications

References 11 publications

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Managing and Optimizing Quality of Service in 5G Environments Across the Complete SLA Lifecycle

Graduation Prediction of S1 Industrial Engineering Students IST AKPRIND by Using Data Mining Method

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