Portrait-Based Academic Performance Evaluation of College Students from the Perspective of Big Data

Cao, Yusong

doi:10.3991/ijet.v16i04.20475

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…However, in the process of data processing, the traditional analysis and early warning of abnormal student behaviors focus more on the analysis and mining of historical data, which lacks dynamics and timeliness [47]. Cao et al [48] established a scientific student portrait evaluation index system, collected various data on college students' academic performance, normalized the collected data, determined the weight of each evaluation index through the analytic hierarchy process (AHP), and then A fuzzy evaluation model based on big data is used to evaluate various dimensions of college student's academic performance. Zhao et al [49] integrated various data of college students' movement trajectory, consumption, social behavior, etc., used machine learning (ML) classifier support vector machine (SVM) to predict English, and analyzed the correlation between students' performance and social relations, then it was used to predict the English grades of college students.…”

Section: Research On Abnormal Behaviors Of Students Deng Et Almentioning

confidence: 99%

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

Wang

Wen

Zhou

et al. 2022

Wireless Communications and Mobile Computing

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With the wide application of information technologies such as big data, the Internet of Things, and cloud computing, college students have accumulated a large amount of personal information and daily behavior data in their daily studies and life. How to dynamically integrate multidimensional information of students to build accurate student portraits, using multi-indicator data of student behavior and comment texts, and finding out students with abnormal behavior from among many students has become an important problem to be solved. This paper proposes an abnormal behavior prediction method integrating multiple indicators of student behavior and text information (ABPM-IMISBTI) for the problem of abnormal behavior prediction of college students in the big data environment. First, given the problems of multidimensionality, timeliness, and dynamics of student behavior information fusion in the construction of student behavior portraits, by integrating students’ objective tags and subjective tags, an optimized K-means algorithm based on a cloud platform environment is proposed. Second, aiming at the problem of insufficient text information analysis in the analysis of students’ abnormal behavior, the ABPM-IMISBTI method is proposed to solve the prediction of students’ abnormal behavior through long and short-term memory networks (LSTM) combined with student behavior multi-index data and text information. Finally, this paper takes student achievement prediction as an example for verification. The experimental results show that, compared with other prediction methods, the ABPM-IMISBTI method proposed in this paper can improve the accuracy of student behavior prediction, and then quickly determine the abnormal behavior of students, to improve the level of education management in universities and promote the development of safe campuses, smart campuses, and smart education.

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Section: Research On Abnormal Behaviors Of Students Deng Et Almentioning

confidence: 99%

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

Wang

Wen

Zhou

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…With the advent of the big data era, significant changes have occurred in every aspect of higher education (Cao, 2021). In recent years, with the rapid development of data mining in higher education, the combination of data mining methods to analyze student behavior data has become a popular trend (Nyoman Sukajaya, Ketut Eddy Purnama, and Purnomo, 2015).…”

Section: Introductionmentioning

confidence: 99%

Prediction of the graduation rate of engineering education students using Artificial Neural Network Algorithms

Anwar

2021

IJRICE

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The graduation rate of engineering education students on time dramatically affects the quality of learning. The purpose of this study is to predict the graduation rate of engineering education students. The method uses an artificial neural network algorithm combined with particle swarm optimization and forward selection, with 234 samples. The test results with Artificial Neural Network obtained 82.61% accuracy with predictions on time 149 and not on time 62. Artificial Neural Network with Particle Swarm Optimization obtained 91.30% accuracy with predictions on time 165, not on time 69. Furthermore, Artificial Neural Network with Particle Swarm Optimization and reduced by forwarding selection obtained 95.65% accuracy with predictions of the number of graduations on time 165 and not on time 69. Thus, the combination of the three algorithms can predict the graduation rate of engineering education students with high accuracy.

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Challenge or hindrance? How and when organizational artificial intelligence adoption influences employee job crafting

Cheng

Lin

Kong

2023

Journal of Business Research

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Portrait-Based Academic Performance Evaluation of College Students from the Perspective of Big Data

Cited by 6 publications

References 31 publications

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

Prediction of the graduation rate of engineering education students using Artificial Neural Network Algorithms

Challenge or hindrance? How and when organizational artificial intelligence adoption influences employee job crafting

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