Score Prediction Model of MOOCs Learners Based on Neural Network

Zhang, Yuan; Jiang, Wenbo

doi:10.3991/ijet.v13i10.9461

Cited by 9 publications

(8 citation statements)

References 9 publications

(9 reference statements)

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“…The main components of the brain are cells, as are other parts of the body. Brain cells have the ability to remember, think and apply the experiences they have experienced (Zhang, and Jiang, 2018). An ANN generally consists of three layers, namely the input layer, hidden layer, and output layer.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

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

Anwar

2021

IJRICE

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“…However, the maximum consumption in a single month is relatively high, and the number of consumptions is also relatively small. Such students eat irregularly in the cafeteria and usually like to eat out of school or order takeaways (5) The average monthly consumption of type 5 students is relatively low, the number of times is relatively small, and the maximum consumption is not high. Such students do not consume frequently in the cafeteria and are more likely to eat outside of school and consume more outside of school.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Reference [4] uses an improved recurrent neural network to simulate the student's answering process according to the student's answer records and the content of each exercise to predict the student's future performance. Based on MOOCs learner behavior data, reference [5] established a prediction model based on clustering algorithm and neural network to mine the learning rules in the learning process. The predicted results can provide personalized guidance for each learner.…”

Section: Introductionmentioning

confidence: 99%

A College Student Behavior Analysis and Management Method Based on Machine Learning Technology

Shen

Yuan

2021

Wireless Communications and Mobile Computing

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A digital campus will generate a large amount of student-related data. How to analyze and apply these data has become the key to improving the management level of students. The analysis of student behavior data can not only assist schools in early warning of dangerous events and strengthen school safety but also can use real data to describe student behavior, thereby providing quantitative data support for scholarship and grant evaluation. This paper takes a university student as the research object, collects various data in the digital campus platform, and uses an adaptive K -means algorithm in the machine learning algorithm to cluster the data. Analyze the behavior of college students from the clustering results, so as to provide a basis for the education management and learning ability improvement of college students. Specifically, the student’s study, life, and consumption data are selected as the data to describe the student’s behavior at school. This data is input into the adaptive K -means algorithm to obtain different types of student consumption habits, living habits, and learning habits. Through the analysis results, it can be found that the problem of the group of students with low financial ability, the problem of too long online time for students, and the number of books borrowed are too low. According to the characteristics of these problems, teachers and schools are provided with targeted management suggestions. The analysis of student behavior based on machine learning technology provides a reference for the formulation of students’ school management policies and provides teachers with information on students’ personality characteristics, which is conducive to improving teachers’ teaching effects. In short, the management of the results of student behavior analysis can provide a basis for the school to formulate reasonable management policies, thereby promoting precision management and scientific decision-making.

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“…Arsad et al used neural networks to predict the CGPA at the 8 th semester of undergraduate students based on their grade points in fundamental courses [23]. Most recently, the study presented high accuracy results of performance prediction using neural networks in massive online course learner's data [24]. The study, which reviewed research works on performance prediction reported that neural networks presented the highest prediction accuracy compared with other data-mining techniques [7].…”

Section: Related Workmentioning

confidence: 99%

An Artificial Neural Network Based Early Prediction of Failure-Prone Students in Blended Learning Course

Sukhbaatar

Usagawa

Choimaa

2019

Int. J. Emerg. Technol. Learn.

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One of the objectives of the performance measurement of grade-based higher education is to reduce the failure rate of students. To identify and reduce the number of failing students, the learning activities and behaviors of students in the classroom must be continuously monitored; however, monitoring a large number of students is an extremely difficult task. A penetration of web-based learning systems in academic institutions revealed the possibility of evaluating student activities via these systems. In this paper, we propose an early prediction scheme to identify students at risk of failing in a blended learning course. We employ a neural network on the set of prediction variables extracted from the online learning activities of students in a learning management system. The experiments were based on data from 1110 student who attended a compulsory, sophomore-level course. The results indicate that a neural-network-based approach can achieve early identification of students that are likely to fail; 25% of the failing students were correctly identified after the first quiz submission. After the mid-term examination, 65% of the failing students were correctly predicted.

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Score Prediction Model of MOOCs Learners Based on Neural Network

Cited by 9 publications

References 9 publications

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

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

A College Student Behavior Analysis and Management Method Based on Machine Learning Technology

An Artificial Neural Network Based Early Prediction of Failure-Prone Students in Blended Learning Course

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