An Adaptive Educational Data Mining Technique for Mining Educational Data Models in Elearning Systems

Murugananthan, V.; Shivakumar, B. L.

doi:10.17485/ijst/2016/v9i3/86392

Cited by 17 publications

(11 citation statements)

References 14 publications

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“…One of those procedures is the so-called association rules , one of the most commonly used and best known Data Mining techniques ( Romero et al, 2010a ) in very different research disciplines such as medicine ( Antonie et al, 2001 ), earth sciences ( Tan et al, 2001 ), banking ( Aburrous et al, 2010 ), telecommunications ( Wei and Chiu, 2002 ), and the stock-market ( Hajizadeh et al, 2010 ), and also in the educational field. This methodology has been extensively used to identify e-learning indicators and their influence on student performance ( Paule-Ruiz et al, 2015 ), describe learning behavioral profiles ( Goda et al, 2015 ), point out variables that influence instruction ( Romero et al, 2010a ), to improve a collaborative learning experience ( Mora et al, 2014 ), to test 3D virtual reality environments ( Cherenkova et al, 1996 ), to understand the role of social networks in learning ( Paredes and Chung, 2012 ), and as the basis of adaptive learning systems ( Murugananthan and ShivaKumar, 2016 ). Based on this body of previous research, and as Han already concluded in 2001, it seems as though this methodology could produce enough knowledge to discover patterns from a huge amount of data which would be a useful base for a decision-making process ( Han and Kamber, 2001 ).…”

Section: Introductionmentioning

confidence: 99%

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

et al. 2017

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Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs). Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques.Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples.Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance.Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

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Section: Introductionmentioning

confidence: 99%

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

et al. 2017

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“…Zhang Xizheng [1] proposes a recommendation structure using connection control mining and course of action. Set of connection rules are mined from customers' necessities databases using apriori computation Tanbeer et al [2] have proposed a tree-based data structure, called RPS-tree that gets customer given consistency edge and mines reliable cases in a data streams with the help of fp advancement count and prohibitive illustration based and relating unexpected trees. Wang et al, [3] suggested a direct weighting approach in Classification Association Rule Mining.…”

Section: Literature Surveymentioning

confidence: 99%

“…The dimensions and excessive dimensionality of datasets most of the time available as input to the challenge of sample detection make it a excellent project of fixing multiple nodes in parallel. Reminiscence and CPU speed boundaries are the main motives that faced through a single node [2] [3]. So it can be giant to design an robust parallel algorithm to do the job.…”

Section: Introductionmentioning

confidence: 99%

Regular pattern mining on multidimensional databases

Sreedevi¹,

Harika²,

N³

et al. 2018

IJET

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Extracting general patterns from a multidimensional database is a tricky task. Designing an algorithm to seek the frequency or no. of occurring patterns and really first-class transaction dimension of a mining pattern, general patterns from a multidimensional database is the objective of the task. Analysis prior to mining required patterns from database hence, Apriori algorithm is used. After the acquiring patterns, they have been improved to many further patterns. Nevertheless, to mine the required patterns from a multidimensional database we use FP development algorithm. Here, now we have carried out a pop-growth procedure to mine fashionable patterns from multidimensional database established on their repute values. Utilizing this opportunity, we studied about recognizing patterns which give the reputation of every object or movements inside the entire database. Whereas Apriori and FP-growth algorithm is determined by the aid or frequency measure of an object set. As a result, to acquire required patterns utilizing these programs one has to mine FP-growth tree recursively which involves extra time consumption. We have utilized a mining process, which is meant for multidimensional recognized patterns. It overcomes the limitations of present mining ways by implementing lazy pruning method followed by showing downward closure property.

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“…These days the most persuasive apparatus to snatch designs and connections inside in us information is Data mining [1]. The information mining system can likewise be utilized as a part of conventional instructive system [2]. The technique for information mining is additionally utilized as a part of stock trade in organizations for recovering expansive sum of data [3].…”

Section: Introductionmentioning

confidence: 99%

Regular frequent crime pattern mining on crime datasets

Kumar¹,

Sreedevi²,

Krishna³

et al. 2018

IJET

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The objective of violation information mining is to comprehend different violation designs in criminal conduct in request to foresee viola-tions and expect criminal movement to stay away from the violation not to happen. Foreseeing violation is one of the worldwide difficulties looking by Law authorization office and it requires tireless endeavors with a specific end goal to limit. In this paper we are presenting anoth-er violation design called general incessant violation design which happens frequently at certain time interims utilizing vertical information arrange additionally fulfills descending conclusion property. Violation designs were not characterized by insights and its distinguishing proof is some-thing other than checking and abridging violations that are comparable in attributes and additionally area on a guide. Violation design is a gathering of at least one violations answered to or on the other hand found by the police.

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An Adaptive Educational Data Mining Technique for Mining Educational Data Models in Elearning Systems

Cited by 17 publications

References 14 publications

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

Regular pattern mining on multidimensional databases

Regular frequent crime pattern mining on crime datasets

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