Research on the Application of K-Means Clustering Algorithm in Student Achievement

Chi, Dianwei

doi:10.1109/iccece51280.2021.9342164

Cited by 14 publications

(7 citation statements)

References 1 publication

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“…There are two aspects of students' performance: academic achievement and learning progressions and this can be used to predict their success in finishing the study on time [4] or to design intervention to prevent failure [6]. Data mining has three main functions, which are clustering data [7] [8], classifying data [9] [10], and identifying association rules patterns [11]. The current student performance prediction study shows that student performance prediction is challenging due to educational data variants [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Predicting Students Graduate on Time Using C4.5 Algorithm

Yuliansyah

Imaniati

Wirasto

et al. 2021

JISEBI

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Background: Facilitating an effective learning process is the goal of higher education institutions. Despite improvement in curriculum and resources, many students cannot graduate on time. Mostly, the number of students who graduate on time is lower than the number of new students enrolling to universities. This could dilute the chance for students to learn effectively as the ratio between faculty members and students becomes non-ideal.Objective: This study aims to present a prediction model for students’ on-time graduation using the C4.5 algorithm by considering four features, namely the department, GPA, English score, and age.Methods: This research was completed in three stages: data pre-processing, data processing and performance measurement. This predicting scheme make the prediction based on the department of study, age, GPA and English proficiency.Results: The results of this study have successfully predicted students’ graduation. This result is based on the data of students who graduated in 2008-2014. The prediction performance result achieved 90% of accuracy using 300 testing data.Conclusion: The finding is expected to be useful for universities in administering their teaching and learning process.

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Section: Introductionmentioning

confidence: 99%

Predicting Students Graduate on Time Using C4.5 Algorithm

Yuliansyah

Imaniati

Wirasto

et al. 2021

JISEBI

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“…Elective courses were not included for the analysis (6) (2022) Data Analysis of Educational Evaluation Using K-Means Clustering Method K-means technique clusters and analyzes the comprehensive evaluation results. Students are categorized based on their commonality, allowing student managers to provide tailored education management for various types of students Changes in Student achievement concerning time index after the implementation of targeted education management are reported (7) (2021) Research on the Application of K-Means Clustering Algorithm in Student Achievement K-Means algorithm is applied to perform cluster analysis on the final grade data of students majoring in software and information services in a university Finds subjects with higher importance, and provides a reference for teaching management that can adjust teachers and class hours according to the importance. (8) ( 2021) An integrated clustering method for pedagogical performance Captured underlying rules of association among the students' data attributes using clustering techniques in an interdisciplinary context 14 clusters are made available for potential future examinations (9) The fundamental goal of statistical analysis in educational data is to uncover patterns that will improve teaching-learning pedagogies and identify new trends in blended, flipped, and face-to-face learning environments in participatory learning.…”

Section: Table 1 Comparative Study Of Related Work Related Workmentioning

confidence: 99%

“…In terms of student performance evaluation, cluster analysis will grasp the features of each category of students more objectively and impartially, create a more reasonable classification of student groups, and provide teachers with personalized policy assistance. (7) The evaluation obtained by cluster analysis is more scientific and reasonable, without subjective factors, and provides decision-making reference for teachers' group teaching and personalized guidance.…”

Section: Table 1 Comparative Study Of Related Work Related Workmentioning

confidence: 99%

Clustering-based Recommendations for Enhancing Students’ Academic Performance by Recognizing Prevalent Assessment Method using Exploratory Data Analysis

Sofia,

Hema

2024

IJST

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Objectives: To analyse students’ academic performance based on assessment methods and determine the most prevalent one through which students can be categorised for recommending optimal student-centered pedagogies that enhance students’ performance. Methods: Exploratory Data Analysis identifies the implications of the assessment methods based on the marks obtained by students in Continuous Assessments (CA) and the Cumulative Test (CT). Continuous Assessment (CA) and Cumulative Test (CT) marks of three subjects that come under foundation science, elective, and skill-based course of 100 undergraduate students are collected from a reputed Arts and Science Institution using stratified sampling technique, analyzed, and the recommendations are made based on the statistical observations and cluster analysis. Clustering recognises learning patterns of the students’ on the learners’ data. The Elbow method determines the number of clusters where the Silhouette score identifies the best suitable clustering technique for the dataset. K-Means Clustering categorises students based on their performance, that helps to give recommendations to improve. Findings: Based on Univariate and Bivariate analysis on the dataset, this work identifies Continuous Assessment (CA) as a prevalent evaluation strategy that motivates students to get engaged throughout rather than just before the exam. Based on the Silhouette Score (above .5), K-Means clustering is chosen to discover hidden patterns in the assessment marks depending on the three clusters determined by the Elbow method. It helps to identify the underperformers (46%) and suggest personalised recommendations for improving student’s academic performance as per clusters. Novelty: This work integrates Statistical Analysis and Clustering Analysis as per the optimal clusters determined by the Elbow method for identifying patterns hidden in assessment marks based on the prevalent assessment types. As a result, it enables more personalised recommendations for recognising the predominant assessment method and boosting academic achievement. Keywords: Continuous Assessment, Cumulative Test, Statistical Analysis, Exploratory Data Analysis, Univariate, Bivariate, Cluster Analysis, Elbow Method, KMeans Clustering

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“…K-means clustering is an unsupervised machine learning tool and has been used in signal processing, data mining and pattern recognition to classify patterns using a measure of similarity or difference of such pattern [37], [38]. Recently, K-means have been used in electrical energy load balancing [39], students' result grouping so as to focus more attention on students with special needs [40] and in [41] another machine learning technique was used in RFID supply chain to detect false positive readings and filter out same so that it's not registered on a database. The K in the name represents the fact that the algorithm looks for a given number of clusters which are defined in terms of how close data points are to each other.…”

Section: E Overview Of Traditional K-means Clusteringmentioning

confidence: 99%

Efficient Tag Grouping RFID Anti-Collision Algorithm for Internet of Things Applications Based on Improved K-Means Clustering

et al. 2023

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Dynamic Frame Slotted ALOHA (DFSA) is a de facto algorithm in the EPC Global Class-1 Generation-2 protocol for Radio Frequency Identification (RFID) tag collision problem. DFSA fails when the UHF RFID tag deployment becomes dense like in Internet of Things (IoT). Existing works do not provide readers prior tag estimates. Most algorithms assume a collision slot means two tag collision. But in dense IoT applications, much more than two tags can constitute a collision slot. Moreover, research proves collision slot might occur due to other reasons such as error-prone channel. This paper proposes a RFID anti-collision algorithm, kg-DFSA that equips the reader with prior information on accurate tag estimate. In kg-DFSA, tag identification is divided into two stages -initialization and identification. In the initialization stage, the reader uses improved K-means clustering running concurrently with a tag counter algorithm to cluster tags into K groups using tags' RN16 while the counter returns an accurate tag number estimate. In the identification stage, the tags are read only in frame chunks that match their group IDs while a new frame size look up table is developed to boost efficiency. Variants of the proposed kg-DFSA, traditional DFSA and another grouping based DFSA algorithm (FCM-DFSA) were implemented in MATLAB. Extensive Monte Carlo simulation shows the proposed kg-DFSA edges DFSA in terms of success rate 50%, system efficiency 65% and identification time 28%. The proposed model is useful in enhancing the existing MAC protocol to support dense IoT deployment of RFID.INDEX TERMS Dynamic frame slotted aloha, Internet of Things, k-means clustering, RFID anti-collision algorithm, tag grouping, frame size table.

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Research on the Application of K-Means Clustering Algorithm in Student Achievement

Cited by 14 publications

References 1 publication

Predicting Students Graduate on Time Using C4.5 Algorithm

Predicting Students Graduate on Time Using C4.5 Algorithm

Clustering-based Recommendations for Enhancing Students’ Academic Performance by Recognizing Prevalent Assessment Method using Exploratory Data Analysis

Efficient Tag Grouping RFID Anti-Collision Algorithm for Internet of Things Applications Based on Improved K-Means Clustering

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