Analysis of Students Graduation Target Based on Academic Data Record Using C4.5 Algorithm Case Study: Information Systems Students of Telkom University

Putri, Dela Youlina; Andreswari, Rachmadita; Hasibuan, Muhammad Azani

doi:10.1109/citsm.2018.8674366

Cited by 14 publications

(13 citation statements)

References 3 publications

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“…This aspect is the most used in predicting student performance because it has a measurable value for measuring student performance [40]. In higher education, student academic records can also be utilized to estimate their graduation [41,42]. In summary, many prior studies support the finding of this study on this factor.…”

Section: Rq1: What Is the Research Trend Of Prediction-focus Edm Stud...supporting

confidence: 56%

Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015-2021)

Roslan

Chen

2022

Int. J. Emerg. Technol. Learn.

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This systematic literature review aims to identify the recent research trend, most studied factors, and methods used to predict student academic performance from 2015 to 2021. The PRISMA framework guides the study. The study reviews 58 out of 219 research articles from Lens and Scopus databases. The findings indicate that the research focus of current studies revolves around identifying factors influencing student performance, data mining (DM) algorithms performance, and DM related to e-Learning systems. It also reveals that student academic records and demographics are primary aspects that affect student performance. The most used DM approach is classification and the Decision Tree classifier is the most employed DM algorithm.

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Section: Rq1: What Is the Research Trend Of Prediction-focus Edm Stud...supporting

confidence: 56%

Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015-2021)

Roslan

Chen

2022

Int. J. Emerg. Technol. Learn.

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“…The attributes that have been classified in a particular decision form a rule, but the three other attribute values, namely AB, B, and BC, still need to be calculated again because the case is still not classified. Then we need to do the same thing repeatedly to find the next node by calculating the gain ratio value for each remaining attribute until all attributes are used up and all cases are classified according to the class, as was done in previous research [9]. The overall classification results can be seen in Figure 2.…”

Section: B C45 Algorithmmentioning

confidence: 99%

“…At this current study, we conform with our previous works [9,10] in developing a prediction model of graduation-ontime rate were conducted and evaluated by two promising and proven methods, they are FAHP and C4.5 algorithm. The work would be performed by describing the construction process of the models and followed by the comparative analysis of those two models.…”

Section: Introductionmentioning

confidence: 99%

Analysis Comparison of Data Mining Algorithm for Prediction Student Graduation Target

Andreswari

Hasibuan

Putri

et al. 2019

Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018)

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The main objective of a higher education institution is to provide quality education for its students. The most important indicator to measure the quality of higher education performance is the percentage of student graduation on time. However, not all student can successfully have completed their studies during the four years of normal study period where it became problems for academic planners. So, it can affect to the study program accreditation assessment. In this study, C4.5 algorithms and fuzzy AHP are used to predict the number of students graduating on time. An analysis has conducted on how students can graduate on time and plan strategies for groups of students who are likely not to graduate on time. Furthermore, a comparative analysis of the algorithms that have been implemented which will provide more precise and accurate results. Data processing is carried out using the Rapidminer application. The results of the student graduation target analysis were found that the main factor of graduation on time using FAHP was the number of repeating courses (do not pass courses), while in C4.5 it was caused by GPA level 2. Both algorithms had a good level of accuracy, where FAHP and C4.5 were 100% and 82.24% respectively. This research can be used as a reference basis for supporting academic planners in making the right decisions for student groups produced so that all students can graduate on time.

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“…Many efforts have been made in predicting students' graduation development, mainly using traditional machine algorithms and deep learning methods. Traditional machine learning algorithms mainly include the Naive Bayes model [9], C4.5 Algorithm [10], etc. However, with the rise of educational data mining [11] and increased student data processing, traditional algorithms show disadvantages such as weak generalization and insufficient computational abilities.…”

Section: Introductionmentioning

confidence: 99%

“…Benefiting from the merit of deep learning [12][13][14], neural networks such as Back Propagation Neural Network (BP) [15], Generative Adversarial Networks (GAN) [16], Long Short-Term Memory (LSTM) [16], Graph Convolutional Network (GCN) [8] and Attention mechanism [8] have also been applied to the prediction of graduation development. However, the above-mentioned research work only predicts students' graduation development based on their grades [8][9][10]16], credits [8,9] or their regular performances [10,15], ignoring the potential impact of multi-dimensional social relations of college students on graduation development prediction research.…”

Section: Introductionmentioning

confidence: 99%

Social-path embedding-based transformer for graduation development prediction

Yang

Ouyang

et al. 2022

Appl Intell

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As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students' learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students' graduation development choices. To fully explore social relationships among students, we propose a Social-path Embedding-based Transformer Neural Network (SPE-TNN) for the task of graduation development prediction in this paper. Specifically, SPE-TNN is divided into the Social-path selection layer, the Social-path embedding layer, the Transformer layer, and the Multi-layer projection layer. Firstly, the Social-path selection layer is designed to find social relationships that impact graduation development and embed them into the student's performance features through the Social-path embedding layer. Secondly, the Transformer layer is adopted to balance the weights of the students' features. Finally, the Multi-layer projection layer is used to achieve the student graduation development prediction. Experimental results on the real-world datasets show that SPE-TNN outperforms the existing popular approaches.

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Analysis of Students Graduation Target Based on Academic Data Record Using C4.5 Algorithm Case Study: Information Systems Students of Telkom University

Cited by 14 publications

References 3 publications

Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015-2021)

Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015-2021)

Analysis Comparison of Data Mining Algorithm for Prediction Student Graduation Target

Social-path embedding-based transformer for graduation development prediction

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