Prediction of Students' Academic Performance by K-Means Clustering

Goh, Yann Ling; Goh, Yeh Huann; Yip, Chun-Chieh; Ting, Chen Hunt; Bin, Raymond Ling Leh; Chen, Kah Pin

doi:10.31580/sps.v2i1.1205

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Goh et al‘s school uses test scores to evaluate students in a single way, which causes valuable data resources of wasting. It is recommended to use better models and software to analyse the scores [ 18 ]. Omolewa et al analysed evaluation activities in terms of pedagogical elements and constructed a new evaluation model in terms of objectives, form, and content [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm

Wang

2022

Computational Intelligence and Neuroscience

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Monitoring and guiding instructional management require student performance evaluation. Traditional evaluation and analysis methods based on absolute scores, on the other hand, have certain flaws and are unable to fully reflect the information contained in student performance, thus limiting the impact of student performance evaluation on teaching and learning management. Data mining is regarded as the backbone technology for future information processing, and it introduces a new concept to the way humans use data. Schools must analyse and evaluate the performance of students in the same grade level and secondary school in a timely and staged manner. Clustering is a type of data mining that uses similarity rules to classify sample data into groups with a high degree of similarity. To address the difficulties caused by the wide variation in course difficulty in student performance evaluation, a method based on the K-means clustering algorithm is proposed. The K-means algorithm and the improved K-means algorithm with student information are investigated. The test results showed that the K-means clustering algorithm, the improved algorithm in this paper, and the fast global mean clustering algorithm all cluster the same randomly generated data set with noisy points, but the clustering time of the algorithm in this paper is only 0.04, which has obvious advantages. As a result, the clustering algorithm-based higher education management and student performance evaluation mechanism provides some insights for future research on student learning patterns. It is hoped that instructional administrators will gain a better understanding of students’ learning characteristics so that they can better guide their teaching.

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Section: Related Workmentioning

confidence: 99%

Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm

Wang

2022

Computational Intelligence and Neuroscience

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“…The Web program is responsible for receiving the requests of users and system administrators and can use the server to display the processing results of the requests on the browser interface. The B/S architecture adopts the working mode of browser request and server response (Goh et al, 2020). The working principle of B/S architecture is shown in Figure 4.…”

Section: Analysis Of Related Technologiesmentioning

confidence: 99%

Exploration of micro-video teaching mode of college students using deep learning and human–computer interaction

et al. 2022

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In order to improve the efficiency of teaching and learning in Colleges and Universities (CAUs), this work combines the Browser/Server (B/S) framework with Model View Presenter (MVP) technology to build a college student–oriented micro-video teaching system based on Deep Learning (DL) and Human–Computer Interaction (HCI) technology. Firstly, it makes an in-depth analysis of the problems in the classroom teaching of Chinese CAUs. Three functional modules are designed for the micro-video online teaching platform: video management, user learning, and system management. Then, it uses MVP technology to analyze the use-cases of these three functional modules in detail. Based on this, the micro-video online teaching platform is designed using the B/S framework. The teaching platform interface layer realizes the HCI between the platform and users. The business logic layer responds to the user requests submitted and returns the processing results to the interface layer. Finally, the function test and stress test of each module of the micro-video online teaching platform is carried out. The test results show that the response time of the proposed micro-video teaching platform increases with the number of users. Under the peak concurrent users, the system response time is 6 s, without abnormalities. Meanwhile, the proposed teaching platform has improved students' satisfaction with classroom teaching by nearly 15% and improved the compactness of the college classroom by nearly 12%. When the number of virtual users increases and the number of services increases linearly, the Random Access Memory and Central Processing Unit growth rate is significantly lower than that of the number of services. These outcomes indicate that many system resources are reused, and the system has good scalability, which can meet users' needs for the network video teaching system. The proposed teaching platform provides a new idea for applying DL and HCI technology in researching college students' micro-video teaching mode.

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“…As the scale of the system gradually increases and the structure becomes more complex, the disadvantages of the centralized control method become more obvious. In the development process of predictive control, in view of the shortcomings of centralized control methods, decentralized MPC (Model Predictive Control) was proposed [11][12]. Its core idea is to convert a single complex optimization problem into the solution of multiple subsystems, which has a simple structure and is easy to implement.…”

Section: Distributed Model Predictive Controlmentioning

confidence: 99%

Distributed System Optimization Based on K-means Clustering

2021

DPS

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The regional energy internet is a distributed complex system with deep integration of energy and information. If different energy systems are planned and operated independently and lack of coordination with each other, problems such as low energy utilization rate, weak self-healing ability, and low system security and reliability will be caused. Therefore, scientific and reasonable planning methods and operation strategies are crucial to the overall efficiency and economy of distributed multi-energy systems. The main purpose of this paper is to study the optimization of distributed systems based on K-Means clustering. In this paper, a multi-energy unified clustering model based on the K-means algorithm is established to analyze the data characteristics in the cluster, and at the same time, the energy-side data is clustered to evaluate the available value of various types of energy. Experiments show that according to the 2/5/10 principle, when the system response time is within 2s, the user experience is very good. When the response time is between 2s and 5s, the user experience is better. Because the number of concurrent users of the system exceeds 500, the response time of the system is within the normal response time range acceptable to users and meets the needs of enterprises.

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Prediction of Students' Academic Performance by K-Means Clustering

Cited by 3 publications

References 2 publications

Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm

Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm

Exploration of micro-video teaching mode of college students using deep learning and human–computer interaction

Distributed System Optimization Based on K-means Clustering

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