Educational Data Mining: A Predictive Model for Cisco Certification Exam using Classification Algorithms

Abstract Educational data mining (EDM) processes have shifted towards open-ended processes with visualizations and parameter and predictive model adjusting. Data and models in hyperdimensions can be visualized for end-users with popular data mining platforms such as Weka and RapidMiner. Multiple studies have shown how the adjusting and even creating the decision tree classifiers help EDM end-users to better comprehend the dataset and the context where the data has been collected. To harness the power of such open-ended approach in EDM, we introduce a novel Augmented Intelligence method and a cluster analysis algorithm Neural N-Tree. These contributions allow EDM end-users to analyze educational data in an iterative process where the knowledge discovery and the accuracy of the predictive model generated by the algorithm increases over time through the interactions between the models and the end-users. In contrast to other similar approaches, the key in our method is in the model adjusting and not in parameter tuning. We report a study where the potential EDM end-users clustered data from an education setting and interacted with Neural N-Tree models by following Augmented Intelligence method. The findings of the study suggest that the accuracy of the models evolve over time and especially the end-users who have a adequate level of knowledge from data mining benefit from the method. Moreover, the study indicates that the knowledge discovery is possible through AUI.

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Academic Educational Data Mining predictive model for early detection of students at academic risk

Journal of the ACS Advances in Computer Science ◽

10.21608/asc.2018.158379 ◽

2018 ◽

Vol 9 (1) ◽

pp. 21-42

Keyword(s):

Data Mining ◽

Early Detection ◽

Predictive Model ◽

Educational Data Mining ◽

Academic Risk

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Comparison of Some Classification Algorithms for the Analysis of Students Academic Performance in Educational Data Mining Using Orange

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1394 ◽

2021 ◽

pp. 318-324

Author(s):

Vanthana V

Keyword(s):

Data Mining ◽

Academic Performance ◽

Random Forest ◽

Educational Data Mining ◽

Evaluation Process ◽

Support Vector ◽

Classification Algorithms ◽

Academic Improvement ◽

Academic Information ◽

Tools And Techniques

In the modern education system, many higher education institutions prefer data mining tools and techniques to analyze the academic improvement of their students. To support that many data mining techniques and tools are available. This paper uses the classification concept to analyze the student’s academic performance. This paper presents the comparison result of five classification algorithms – Decision Tree, Naïve Bayesian, K-Nearest Neighbour, Support Vector Machine and Random Forest which is applied to the data collected from three colleges of Assam, India. The data consists of socio-economic, demographic as well as academic information of three hundred students with twenty-four attributes. The data mining tool used was ORANGE. The internal assessment attribute in the continuous evaluation process makes the highest impact in the final semester results of the students in the dataset. The results showed that Random Forest out performs the other classifiers based on accuracy.

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Prediction of Student Performance using Hybrid Classification

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrted8241.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 6566-6570

Keyword(s):

Data Mining ◽

Academic Performance ◽

Student Performance ◽

Research Work ◽

Educational Data Mining ◽

Classification Algorithm ◽

Classification Algorithms ◽

Data Types ◽

Data Set ◽

Hybrid Classification

Data mining technologies allow collection, storage and processing huge amounts of data and carrying a large variety of data types and samples. Predicting academic performance of student is the most successive research in this era. Previous research work researchers are used different classification algorithm to predict the student performance. There is lot of research work to be taken in the field of educational data mining and big data in education to increase the accuracy of the classification algorithm and predict the academic performance of student. In this research work we used hybrid classification algorithm for predicting the performance of students. Two Popular classification algorithms ID3 and J48 were applied on the data set. To make hybrid classification voting technique is applied using weka machine learning tool. In this work we tested how the hybrid algorithm accurately predicts the student data set. To check the predicted result classification accuracy was computed. This hybrid classification algorithm gives accuracy with 62.67%.

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