An educational model based on Knowledge Discovery in Databases (KDD) to predict learner's behavior using classification techniques

Comendador, Benilda Eleonor V.; Rabago, Lorena W.; Tanguilig, Bartolome T.

doi:10.1109/icspcc.2016.7753623

Cited by 24 publications

(19 citation statements)

References 4 publications

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“…Khobragade and Mahadik [9] have implemented NB rules in DT algorithm to classify the students into different classes such as high or low performance. Comendador, Rabago and Tanguilig [36] are applying DT in their research because it is simple and powerful way of knowledge representation to predict final grade. Besides, Kumar and Radhika [37] used DT using C4.5 and ID3 algorithm where C4.5 is the best algorithm for prediction with accuracy of 83.66%.…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

A Review on Predictive Modeling Technique for Student Academic Performance Monitoring

et al. 2019

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Despite of providing high quality of education, demand on predicting student academic performance become more critical to improve the quality and assisting students to achieve a great performance in their studies. The lack of existing an efficiency and accurate prediction model is one of the major issues. Predictive analytics can provide institution with intuitive and better decision making. The objective of this paper is to review current research activities related to academic analytics focusing on predicting student academic performance. Various methods have been proposed by previous researchers to develop the best performance model using variety of students data, techniques, algorithms and tools. Predictive modeling used in predicting student performance are related to several learning tasks such as classification, regression and clustering. To achieve best prediction model, a lot of variables have been chosen and tested to find most influential attributes to perform prediction. Accurate performance prediction will be helpful in order to provide guidance in learning process that will benefit to students in avoiding poor scores. The predictive model furthermore can help instructor to forecast course completion including student final grade which are directly correlated to student performance success. To harvest an effective predictive model, it requires a good input data and variables, suitable predictive method as well as powerful and robust prediction model.

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Section: Decision Tree (Dt)mentioning

confidence: 99%

A Review on Predictive Modeling Technique for Student Academic Performance Monitoring

et al. 2019

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“…The discretization technique was applied during preprocessing tasks and tested the REPTree, CART and J48 algorithms on the data sets selected using 10-fold cross validation in WEKA. In this test, using J48 gain the highest accuracy rate of 97.17% [10]. …”

Section: A the Predictive Student Performance Modelmentioning

confidence: 99%

“…The authors used the top five attributes generated by the feature selection techniques to construct the predictive decision tree model. It consists of students' obtained score in the online activity, examination rating and its condition, year of admission, their frequency of log into the portal which served as valuable indicators to the predictive decision tree model [10]. To further aid in the prediction and support service of the system, J48 prediction model was used.…”

Section: B Functionalities Of Ecladssmentioning

confidence: 99%

“…The algorithm works in heuristic based reasoning where the candidate cuts off a smallest number of instances on the numeric attributes. The J48 model used in eCLADSS was derived from our previous work that has an accuracy of 97.17% [10].…”

Section: ) Application Of J48 Decision Tree Classifier For Categoricmentioning

confidence: 99%

“…This means an attribute getting a zero value has 1) Application of feature selection techniques no influence at all to the student performance model. As such, the top five attributes based on their average rank to generate the classifier model were used [10].…”

Section: A the Predictive Student Performance Modelmentioning

confidence: 99%

See 2 more Smart Citations

Adoption of Feature Selection and Classification Techniques in a Decision Support System

Comendador¹,

Sison²,

Medina³

2017

IJIET

Self Cite

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Abstract-This paper presents a decision support system prototype called eCourse Learning Analytics Decision Support System (eCLADSS) using J48 tree classifier and multiple linear regression models. The system identifies students who are falling behind in a course, notifies those at risk of not completing it, then informs the users the predicted grade a student is likely to obtain without intervention. The developed eCLADSS predicts the performance of the Learning Management System (LMS) users which may help the Distance Education (DE) students succeed in the blended learning approach being provided by the DE educators.It is a model-driven decision support system which provides a good platform for prediction model generation.

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Integration of Artificial Intelligence as a Tool for an Online Education Model

Villegas-Ch

Palacios-Pacheco

2020

Innovation and Research

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An educational model based on Knowledge Discovery in Databases (KDD) to predict learner's behavior using classification techniques

Cited by 24 publications

References 4 publications

A Review on Predictive Modeling Technique for Student Academic Performance Monitoring

A Review on Predictive Modeling Technique for Student Academic Performance Monitoring

Adoption of Feature Selection and Classification Techniques in a Decision Support System

Integration of Artificial Intelligence as a Tool for an Online Education Model

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