CLUSTERING STUDENT PERFORMANCE DATA USING k-MEANS ALGORITHMS

Alalawi, Sultan Juma Sultan; Shaharanee, Izwan Nizal Mohd; Jamil, Jastini Mohd

doi:10.32890/jcia2023.2.1.3

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…Description Findings (1) (2023) Clustering student performance data using k-means algorithms Students were grouped into four clusters based on their characteristics and performance in school Found that gender and age of the students play an important role in identifying student performance. Clusters help educators to identify students with the highest risk of failing and underperforming.…”

Section: Table 1 Comparative Study Of Related Work Related Workmentioning

confidence: 99%

“…It aims at revealing implicit structures that were hidden, interesting patterns and relations from datasets, and then adapting the extracted information for the analysis task to ease comparison, interpretation https://www.indjst.org/ and relationship assessment. (1) There are three major techniques in the clustering approach: partitioning, hierarchical, and density-based.…”

Section: Data Interpretations and Recommendationsmentioning

confidence: 99%

“…In recent years, Educational Data Mining (EDM) has emerged as one of the main terminologies in discovering useful and meaningful information that can be used by decision-makers to make active and knowledgeable data-driven decisions. (1) Educational Data Mining (EDM) focuses on using data mining techniques to extract massive data from the educational context and transform it into knowledge that can improve educational systems and decisions. (2) Student performance modelling is one of the challenging and popular research topics in Educational Data Mining (EDM).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Table 1 Comparative Study Of Related Work Related Workmentioning

confidence: 99%

Section: Data Interpretations and Recommendationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Clustering research with LMS log data objects was conducted (Nurdiani et al, 2019), the results of the study resulted in 3 types of clusters with categories of students with a lot of activity and getting high scores, students who do moderate activities and get high scores, and categories of students with a small amount of activity and with low scores. Other research was conducted (Alawi & Shaharanee, Izwan Nizal Mohd Jamil, 2023), (Ademi & Loshkovska, 2020)..Ademi's research (Ademi & Loshkovska, 2020), presented a cluster analysis of Moodle data in terms of students' preferences for various assessment methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of K-Means Algorithm in Clustering Model for Learning Management System Usage Evaluation

Sholeh

Suraya

Andayati

2023

JABT

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The use of a learning management system (LMS) is one of the media that can be used to disseminate lecturer materials to students. Materials that can be uploaded on the LMS can be in the form of lecture materials in the form of files, videos, or questions. The effectiveness of LMS can be evaluated by looking at activities in using LMS. The effectiveness of using LMS can be seen from the log. Log results from LMS can be evaluated in various ways and one way is to use data mining clustering models. The clustering model can be used to create student groupings and the clustering results can be labeled in the form of categories, such as very good, good, and bad categories. This labeling depends on the clustering results that will be processed in the modeling. The research method uses CRISP DM which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The beginning of the research process is carried out by taking log data in the Moodle LMS. The clustering model in this research will use the K-Means algorithm and the evaluation of clustering results will be evaluated for performance using the Davies-Bouldin method. Implementation of data mining processing using Rapid Miner application. The datasheet used is a datasheet taken from the LMS log of the Computer Programming course in the Mechanical Engineering study program - AKPRIND Institute of Science & Technology Yogyakarta odd semester of the 2021/2022 and 2022/2023 academic years. The results of the study resulted in the best clustering based on the Davies Bouldin method of 2. The clustering results, cluster 0 consists of 28 data named the category of frequent access to LMS and cluster 1 consists of 54 with the category of not frequent access to LMS.

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“…In addition to academic performance, clustering methods have been applied to diverse datasets, such as those from the Oman Education Portal (OEP), to identify distinct performance profiles among students; the dataset consisted of 49,588 student records with 11 attributes of age, gender, religion, nationality, final marks, and so on, divided into four performance groups: "Excellent," "Good," "Average," and "Failure." Cluster analysis revealed distinct profiles for each group, with gender and age playing a significant role in performance, and concluded that female students have higher performance than male students [12].…”

Section: Related Workmentioning

confidence: 99%

Clusters of Success: Unpacking Academic Trends with K-Means Clustering in Education

Agha,

Meghji,

Bhatti

2023

VFAST trans. softw. eng.

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Integrating Educational Data Mining (EDM) into the sector of education has heralded a new era, profoundly impacting learning outcomes by analyzing student performance and preventing academic disengagement. Using the K-Means clustering approach, this study carefully examines the academic accomplishments of students at Mehran University of Engineering and Technology and offers a sophisticated view of student performance patterns through the rigorous analysis of their learning data. A dataset comprising of the academic data of three student batches of the Department of Software Engineering was broken down into the subject categories of Computer Core, General, and Mathematics. The approach of clustering was then applied to find distinct performance patterns across the three subject categories. The findings of the research reveal that students have the highest performance in the computer core category, followed by mathematics, while the weakest overall performance across all three batches was exhibited in the general subject category. The study highlights the disparities in academic performance across distinct clusters and adds to our understanding of academic success while also illuminating the complex interactions between student characteristics and educational outcomes, providing useful information for educators and policymakers.

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CLUSTERING STUDENT PERFORMANCE DATA USING k-MEANS ALGORITHMS

Cited by 4 publications

References 6 publications

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

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

Application of K-Means Algorithm in Clustering Model for Learning Management System Usage Evaluation

Clusters of Success: Unpacking Academic Trends with K-Means Clustering in Education

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