Student Performance Prediction in Sebelas Maret University Based on the Random Forest Algorithm

Gusnina, Maulida; Wiharto, Wiharto; Salamah, Umi

doi:10.18280/isi.270317

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…The research method is based on the Cross-Industry Standard Process for Data Mining (CRISP-DM) [45][46][47]. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a popular and exhaustive framework for directing data mining operations.…”

Section: Methodsmentioning

confidence: 99%

An ID3 Decision Tree Algorithm-Based Model for Predicting Student Performance Using Comprehensive Student Selection Data at Telkom University

Widaningsih,

Muhamad,

Hendriyanto

et al. 2023

ISI

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Telkom University, in its routine admission process, generates a rich dataset consisting of various attributes of prospective students. These attributes extend beyond academic parameters like the grade point average (GPA) from the final high school year, encompassing non-academic data such as parental occupation, income, student's gender, origin province, high school major, and school category. Previous research has predominantly focused on academic and sociodemographic data, such as GPA and family income, respectively, for predicting study performance. However, factors like school major, study program, and school category have often been overlooked. In this study, the objective is to utilize the comprehensive Student Selection Data (SMB) to devise a model for predicting the performance of students in their first semester at Telkom University. The aim is to address the issue of a low rate of on-time graduation by leveraging the untapped potential of SMB data. An Iterative Dichotomiser 3 (ID3) decision tree algorithm forms the backbone of the proposed model, enabling the classification of student performance based on a range of diverse attributes. Information gain-based feature selection revealed the five attributes with the greatest influence on student performance in the first semester: gender, grade point average from the final year of high school, study program, high school major, and school category. These findings underscore the potential of a more inclusive approach to student data analysis in predicting academic success in higher education.

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Section: Methodsmentioning

confidence: 99%

An ID3 Decision Tree Algorithm-Based Model for Predicting Student Performance Using Comprehensive Student Selection Data at Telkom University

Widaningsih,

Muhamad,

Hendriyanto

et al. 2023

ISI

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“…With the rapid development of informationization, the digitalization and informationization in the field of education are constantly promoted and improved, and the use of educational administration application system, access control system, all-in-one card data system and library lending system is gradually popularized [1][2][3][4][5][6][7][8][9]. It's possible to mine a large number of students' behavior data stored in the above systems, and analyze the differences in behavior characteristics among students [10][11][12][13][14][15]. Students' learning enthusiasm is an important indicators to measure their learning effectiveness and teaching quality, which can be characterized by students' time and energy for their studies [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Influence Mechanism of Students' Learning Enthusiasm Based on Educational Big Data

Liu¹,

Bi²

2023

Int. J. Emerg. Technol. Learn.

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Mining a large number of students' behavior data can analyze the differences of students' behavior characteristics. Mining based on students' behavior data can explore the influence mechanism of students' learning enthusiasm, and can provide reference opinions and suggestions for improving students' positive emotions in learning and helping students with poor grades to achieve academic success. The data collected and conclusions obtained by the existing research are not accurate and reliable, and other personality characteristics of students are not fully considered. Therefore, this article studies the influence mechanism of students' learning enthusiasm based on educational big data. It studies the performance of students with good learning enthusiasm in social relations, and mines students’ social intercourse at school based on association rules. It analyzes the regularity of students' behavior and their diligence, and realizes the characterization of students' sustainability and enthusiasm in learning. Combining the differences of behavior characteristics between students with and without learning enthusiasm, this article constructs a learning enthusiasm recognition model and compares it by classification. Experimental results verify the effectiveness of the model.

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“…Internet and big data technologies enable researchers and teachers to mine the data of students' learning behaviors and combine classroom teaching with the data mining results, thereby creating new education modes and adjusting teaching strategies constantly [1][2][3][4][5][6][7][8][9]. When participating in OVE, students can acquire learning resources suitable for themselves, create, edit, and share their learning experience based on the acquired professional knowledge and skills, communicate with teachers and other students to solve their own learning problems or help others solve their learning problems.…”

Section: Introductionmentioning

confidence: 99%

The Learning Behavior Analysis of Online Vocational Education Students and Learning Resource Recommendation Based on Big Data

Jia¹,

Zhao²

2022

Int. J. Emerg. Technol. Learn.

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The Learning Behavior Analysis (LBA) of students is a major function provided by most online education platforms. Based on the results of LBA, students can get customized learning path and recommended learning resources that suit their own situations, which can help them plan their individual training programs of Online Vocational Education (OVE). At first, this paper studied the resource recommendation algorithm based on the modelling of students’ online learning behavior sequences, and solved the problem of conventional learning resource recommendation algorithms of ignoring the dynamic changes in students’ learning preferences. Then, this paper presented the process of LBA applicable for OVE students, and gave a diagram of the flow of using big data to perform LBA. After that, this paper developed a bidirectional encoder based on self-attention model, and described the co-occurrence characteristics and dependencies of students’ online learning behavior sequences. At last, the model pre-training and fine-tuning processes were introduced in detail, and the experimental results verified the effectiveness of the proposed analysis and recommendation methods.

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Student Performance Prediction in Sebelas Maret University Based on the Random Forest Algorithm

Cited by 4 publications

References 18 publications

An ID3 Decision Tree Algorithm-Based Model for Predicting Student Performance Using Comprehensive Student Selection Data at Telkom University

An ID3 Decision Tree Algorithm-Based Model for Predicting Student Performance Using Comprehensive Student Selection Data at Telkom University

Influence Mechanism of Students' Learning Enthusiasm Based on Educational Big Data

The Learning Behavior Analysis of Online Vocational Education Students and Learning Resource Recommendation Based on Big Data

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