Educational Data Analytics using Association Rule Mining and Classification

Rojanavasu, Pornthep

doi:10.1109/ecti-ncon.2019.8692274

Cited by 23 publications

(13 citation statements)

References 2 publications

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“…The Apriori algorithm has been used in various classification problems, such as classification of patient care needs by [19], classification in risk prediction to define disaster rules based on models developed using the Neural Networks method [20], admission planning classification and job prediction in education midwives. This combines the Apriori algorithm with the Decision Tree Algorithm [21]. Besides being used without being combined with other algorithms in finding solutions, the Apriori algorithm is usually used to map and analyze data which is then used as a constraint for other algorithms in making decisions, or to define rules based on data obtained from training results using other algorithms.…”

Section: Related Workmentioning

confidence: 99%

Melody Difficulty Classification using Frequent Pattern and Inter-Notes Distance Analysis

Andono¹,

Noersasongko²,

Shidik³

et al. 2022

IJACSA

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This research proposes a novel method for melody difficulty classification performed using frequent pattern and inter-notes distance analysis. The Apriori algorithm was used to measure the frequency of the notes in the note sequence, in which the melody length is also included in the calculation. In addition, the inter-notes distance analysis was also used to measure the difficulty level of composition based on the distance between successive notes. The classification was performed for traditional Javanese compositions known as Gamelan music. Symbolic representation, in which the Gamelan compositions music sheets were collected as the dataset, was chosen by asking experts to divide the compositions based on their difficulty level into basic, intermediate and advanced classes. Then, the proposed method was implemented to measure the difficulty value of each composition. The difference in the interpretation of the difficulty level between the experts and the difficulty value of the composition is solved by calculating the mean value to obtain the range of difficulty values in each class. Evaluation was performed using confusion matrix to measure the accuracy, precision and recall value, and the results reaching 82%, 82.1% and 82%, respectively.

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Section: Related Workmentioning

confidence: 99%

Melody Difficulty Classification using Frequent Pattern and Inter-Notes Distance Analysis

Andono¹,

Noersasongko²,

Shidik³

et al. 2022

IJACSA

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“…By doing so, different inherent relationships become apparent, which provide valuable information that assists managers in decision making [13]. Alternatively, association mining involves discovering relationships between two items or concepts by first identifying the frequent itemset and then generating rules for them [14]. Moreover, classification in data mining techniques consists of two important phases namely, training and testing [11].…”

Section: Related Workmentioning

confidence: 99%

Knowledge Extraction and Data Visualization: A Proposed Framework for Secure Decision Making using Data Mining

Alshareef¹,

Majrashi²,

Helal³

et al. 2021

IJACSA

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The decision-making process, promptly on time, is a crucial success factor in large organizations. Generally, the data warehouses of these organizations grow rapidly with the data generated from various business activities. This huge volume of data needs to be analyzed and decisions must be made quickly to meet the market challenges. Accurate knowledge extraction and its visualization from big data can guide decisionmakers to conduct key analysis and make correct predictions. This paper proposes a decision-making framework that not only takes into account knowledge extraction and visualization but also considers the security of the data. The proposed framework uses data mining techniques to extract useful patterns, then, visualizes those patterns for further analysis and decision making. The significance of the proposed framework lies in the mechanism through which it protects the data from intruders. The data is first processed and then stored in an encrypted format on the cloud. When the data is needed for analysis and decision making, a temporary copy of the data is first decrypted, and then important patterns are visualized. The proposed framework will assist managers and other decision-makers to analyze and visualize the data in real-time with an enhanced security mechanism.

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“…Thus, many comparative results are also interested in and studied by researchers. For example, [9] compared two methods between Decision Tree and Bayesian Network algorithms for predicting the academic performance of students at two academic institutes.In [10], the intelligent course recommendation system uses association rules that can recommend courses to the student by common rule; however, this system is not personalized for each student. Moreover, Huu-Quang Nguyen et al [11] have used the sequential rules algorithm applied to the problem of predicting student performance to give suggestions for students to choose elective courses.…”

Section: Related Workmentioning

confidence: 99%

Integrating courses' relationship into predicting student performance

Huynh-Ly¹

2020

IJATCSE

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In Intelligent Tutoring System (ITS) as well as the E-learning system at the university, predicting student learning performance to suggest courses is an essential task of an academic advisor. Many kinds of research address to solve this problem with diverse approaches such as classification, regression, association rules, and recommender systems. Recently, it was a measurable success in using collaborative filtering in the recommender system, especially the matrix factorization technique, to build the courses' recommendation system. There are many advances to improve the accuracy of the prediction, such as using student profiles, course properties, or course relationships; however, they have not been mined. This study proposes an approach which integrates the course relationships into the courses' recommendation system to improve the prediction accuracy. Experimental results of the proposed approach are positive when we validate the published educational datasets.

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Educational Data Analytics using Association Rule Mining and Classification

Cited by 23 publications

References 2 publications

Melody Difficulty Classification using Frequent Pattern and Inter-Notes Distance Analysis

Melody Difficulty Classification using Frequent Pattern and Inter-Notes Distance Analysis

Knowledge Extraction and Data Visualization: A Proposed Framework for Secure Decision Making using Data Mining

Integrating courses' relationship into predicting student performance

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