The Latent of Student Learning Analytic with K-mean Clustering for Student Behaviour Classification

Mansur, Andi Besse Firdausiah; Yusof, Norazah

doi:10.20473/jisebi.4.2.156-161

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…To evaluate the performance of FSNT in feature selection for classification, three real datasets were chosen to assess the accuracy of classification, while another three real datasets were selected to examine the regression performance. The six real datasets used in the study are as follows: Student-por, Student-mat, Online News Popularity, Student Archive, Superconductivity, and TCGA Info with Grade; Student-por and Student-mat encompass scores of secondary school students from two Portuguese schools, collected through questionnaires and provided by [27]. Student-por is employed for regression to predict students' final scores, which are continuous values, whereas Student-mat is utilized for classification to predict students' scores divided into five categories.…”

Section: Methodsmentioning

confidence: 99%

A Data Feature Extraction Method Based on the NOTEARS Causal Inference Algorithm

Wang

Zhu

2023

Applied Sciences

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Extracting effective features from high-dimensional datasets is crucial for determining the accuracy of regression and classification models. Model predictions based on causality are known for their robustness. Thus, this paper introduces causality into feature selection and utilizes Feature Selection based on NOTEARS causal discovery (FSNT) for effective feature extraction. This method transforms the structural learning algorithm into a numerical optimization problem, enabling the rapid identification of the globally optimal causality diagram between features and the target variable. To assess the effectiveness of the FSNT algorithm, this paper evaluates its performance by employing 10 regression algorithms and 8 classification algorithms for regression and classification predictions on six real datasets from diverse fields. These results are then compared with three mainstream feature selection algorithms. The results indicate a significant average decline of 54.02% in regression prediction achieved by the FSNT algorithm. Furthermore, the algorithm exhibits exceptional performance in classification prediction, leading to an enhancement in the precision value. These findings highlight the effectiveness of FSNT in eliminating redundant features and significantly improving the accuracy of model predictions.

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Section: Methodsmentioning

confidence: 99%

A Data Feature Extraction Method Based on the NOTEARS Causal Inference Algorithm

Wang

Zhu

2023

Applied Sciences

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“…Therefore, this type of communication is referred to as dialogical communication. With this type of communication, teaching and learning behaviours occur that are interrelated with one another to achieve instructional goals (Mansur & Yusof, 2018).…”

Section: Introductionmentioning

confidence: 99%

Contextual Learning Strategy in the Digital Era at Aisyiyah Fullday Elementary School, Klaten

Rif’ati¹

2020

Al-Hayat J. Islamic. E.

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The purpose of this study is to identify contextual learning problems in schools and their application in the digital era, where children today tend to return to a perspective that children will learn better if the environment in which they study is a natural environment. This paper is the result of research that uses a logical phenomenon approach, the suggestions and recommendations of this study are expected to increase learning knowledge for both parents, teachers and students to succeed in improving learning outcomes. The method used is qualitative in the form of qualitative descriptive analysis, with semi-structured interviews, focus groups, and participant observation. The findings in this study are that contextual learning at Aisyiyah Fullday Pandes Wedi Elementary School, Klaten, Central Java is accompanied by textual understanding so that there is a student activation process (acquiring knowledge), so students will understand the material optimally.

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“…The set of attributes used in their study about the students' performance, such as grades, tests scores, final examination grades and the semester grades [9]. Mansur and Yusof [10] used data mining method to gain student-learning behavior and used K-Mean clustering to classify the student learning behavior. In this big data era, a numerous approach has already been implemented to analyze educational data, but the validity of these…”

Section: Introductionmentioning

confidence: 99%

Academic Decision Support System for Choosing Information Systems Sub Majors Programs using Decision Tree Algorithm

Fiarni

Sipayung

Tumundo

2019

JISEBI

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Background: Educational data mining is an emerging trend, especially in today Big Data Era. Numerous method and technique already been implemented in order to improve its process to gain better understanding of the educational process and to extract knowledge from various related data, but the implementation of these methods into Decision support system (DSS) application still limited, especially regarding help to choose university sub majors .Objective: To design an academic decision support system (DSS) by adopting Theory of Reasoned Action (TRA) concept and using Data Mining as a factor analytic apporach to extract rules for its knowledge model.Methods: We implemented factor analysis method and decision tree method of C.45 to produce rules of the impact course of the sub- majors and the job interest as the basic rules of the DSS.Results: The proposed academic decision support system able to give sub majors recommendations in accordance with student interest and competence, with 79.03% of precision and 61.11% of recall. Moreover, the system also has a dashboard feature that shows the information about the statistic of students in each sub majors.Conclusion: C.45 algorithm and factor analysis are suitable to build a knowledge model for Academic Decision Support System for Choosing Information System Sub Majors Bachelor Programs. This system could also help the academic adviser on monitoring and make decision accordance with that academic information

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The Latent of Student Learning Analytic with K-mean Clustering for Student Behaviour Classification

Cited by 3 publications

References 22 publications

A Data Feature Extraction Method Based on the NOTEARS Causal Inference Algorithm

A Data Feature Extraction Method Based on the NOTEARS Causal Inference Algorithm

Contextual Learning Strategy in the Digital Era at Aisyiyah Fullday Elementary School, Klaten

Academic Decision Support System for Choosing Information Systems Sub Majors Programs using Decision Tree Algorithm

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