Clustering Analysis for Classifying Student Academic Performance in Higher Education
Source abstract
Abstract
There are three income categories for Malaysians: the top 20% (T20), the middle 40% (M40), and the bottom 40% (B40). The government has extended B40′s access to higher education to eliminate socioeconomic disparities and improve their lives. The number of students enrolled in bachelor’s degree programmes at universities has risen annually. However, not all students who enrolled graduated. Machine learning approaches have been widely used and improved in education. However, research studies related to unsupervised learning in education are generally lacking. Therefore, this study proposes a clustering-based approach for classifying B40 students based on their performance in higher education institutions (HEIs). This study developed three unsupervised models (k-means, BIRCH, and DBSCAN) based on the data of B40 students. Several data pre-processing tasks and feature selection have been conducted on the raw dataset to ensure the quality of the training data. Each model is optimized using different tuning parameters. The observational results have shown that the optimized k-means on Model B (KMoB) achieved the highest performance among all the models. KMoB produced five clusters of B40 students based on their performance. With KMoB, this study may assist the government in reducing HEI drop-out rates, increasing graduation rates, and eventually boosting students’ socioeconomic status.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics · Educational Technology and Assessment · Education and Vocational Training
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for Artificial Intelligence Technology, Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Quality Engineering Research Cluster, Instrumentation and Control Engineering, Malaysian Institute of Industrial Technology, Universiti Kuala Lumpur, Kuala Lumpur 81700, Malaysia
Funding
Universiti Kebangsaan Malaysia
References (42)
Show source-provided references
1. Ahuja, R., Jha, A., Maurya, R., and Srivastava, R. (2019). Analysis of Educational Data Mining. Harmony Search and Nature Inspired Optimization Algorithms, Springer.10.1007/978-981-13-0761-4_85 ↗
2. Vahdat, M., Oneto, L., Ghio, A., Anguita, D., Funk, M., and Rauterberg, M. (2015, January 22–24). Advances in Learning Analytics and Educational Data Mining. Proceedings of the 23rd ESANN 2015, Bruges, Belgium.
3. Drop-out Prediction in Higher Education among B40 Students
4. Student Clustering Based on Learning Behavior Data in the Intelligent Tutoring System10.4018/IJDET.2020040105 ↗
5. Hooshyassr, D., Pedaste, M., and Yang, Y. (2019). Mining Educational Data to Predict Students’ Performance through Procrastination Behavior. Entropy, 22.10.3390/e22010012 ↗
6. Comparison of Clustering Algorithms for Learning Analytics with Educational Datasets10.9781/ijimai.2018.02.003 ↗
7. Comparative Performance Analysis of Clustering Techniques in Educational Data Mining
8. An Unsupervised Ensemble Clustering Approach for the Analysis of Student Behavioral Patterns10.1109/ACCESS.2021.3049157 ↗
9. Performance Evaluation and Comparison of Clustering Algorithms Used in Educational Data Mining
10. Educational Data Mining Using Cluster Analysis and Decision Tree Technique: A Case Study
11. Nayak, J., Abraham, A., Krishna, B.M., Chandra Sekhar, G.T., and Das, A.K. (2019). Student Performance Analysis with Using Statistical and Cluster Studies. Soft Computing in Data Analytics, Springer.10.1007/978-981-13-0514-6 ↗
12. Analysis of Student Academic Performance Using Clustering Techniques
13. Knowledge Discovery of the Students Academic Performance in Higher Education Using Intuitionistic Fuzzy Based Clustering
14. Students Academic Performance Using Partitioning Clustering Algorithms
15. Educational data mining and learning analytics for 21st century higher education: A review and synthesis10.1016/j.tele.2019.01.007 ↗
16. Hafzan, M.Y.N.N., Safaai, D., Asiah, M., Saberi, M.M., and Syuhaida, S.S. (2019, January 3–6). Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment. Proceedings of the 9th EASN International Conference on “Innovation in Aviation & Space”, MATEC Web Conference, Athens, Greece.
17. A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs10.1109/JSTSP.2017.2692560 ↗
18. Early Drop-out Prediction Using Data Mining: A Case Study with High School Students10.1111/exsy.12135 ↗
19. (2022, May 12). Statistics of Higher Education. Ministry of Higher Education, Available online: https://www.mohe.gov.my/muat-turun/statistik/2020/493-statistik-pendidikan-tinggi-2020-04-bab-2-universiti-awam/file.
20. Palani, K., Stynes, P., and Pathak, P. (2021, January 23–25). Clustering Techniques to Identify Low-Engagement Student Levels. Proceedings of the 13th International Conference on Computer Supported Education, Online.10.5220/0010456802480257 ↗
21. Data Mining Methods for Detecting the Most Significant Factors Affecting Students’ Performance
22. Hu, Y.-C., Tiwari, S., Mishra, K.K., and Trivedi, M.C. (2019). Analyzing Student Performance Using Data Mining. Ambient Communications and Computer Systems, Springer.10.1007/978-981-13-5934-7 ↗
23. Predicting Academic Performance of Students Using a Hybrid Data Mining Approach10.1007/s10916-019-1295-4 ↗
24. Macedo, M., Santana, C., Siqueira, H., Rodrigues, R.L., Ramos, J.L.C., Silva, J.C.S., Maciel, A.M.A., and Bastos-Filho, C.J.A. (2019, January 15–18). Investigation of College Dropout with the Fuzzy C-Means Algorithm. Proceedings of the IEEE 19th International Conference on Advanced Learning Technologies (ICALT), Maceió, Brazil.10.1109/ICALT.2019.00055 ↗
25. Student Engagement Effectiveness in E-Learning System10.21786/bbrc/12.1/24 ↗
26. Student Engagement Predictions in an E-Learning System and Their Impact on Student Course Assessment Scores10.1155/2018/6347186 ↗
27. Holistic Prediction of Student Attrition in Higher Learning Institutions in Malaysia Using Support Vector Machine Model
28. Kemiskinan Keluarga Dan Pengaruhnya Terhadap Tahap Pendidikan Rendah Masyarakat Luar Bandar: Kajian Kes Di Jajahan Bachok, Kelantan
29. Perez, B., Castellanos, C., and Correal, D. (2018, January 16–18). Applying Data Mining Techniques to Predict Student Dropout: A Case Study. Proceedings of the 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence (ColCACI), Medellin, Colombia.10.1109/ColCACI.2018.8484847 ↗
30. School Engagement: Potential of the Concept, State of the Evidence10.3102/00346543074001059 ↗
31. Tingkah Laku, Emosi Dan Kognitif Murid Sebagai Faktor Peramal Pencapaian Akademik
32. Nasif, A., Othman, Z.A., and Sani, N.S. (2021). The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities. Sensors, 21.10.3390/s21124223 ↗
33. Calculation of substructural analysis weights using a genetic algorithm10.1021/ci500540s ↗
34. Ligand-Based Virtual Screening Using a Genetic Algorithm with Data Fusion
35. Ensemble Learning for Multidimensional Poverty Classification10.17576/jsm-2020-4902-24 ↗
36. Machine Learning for Predicting Employee Attrition
37. Household Overspending Model Amongst B40, M40 and T20 Using Classification Algorithm
38. Rahman, A.M., Sani, N.S., Hamdan, R., Ali Othman, Z., and Abu Bakar, A. (2021). A Clustering Approach to Identify Multidimensional Poverty Indicators for the Bottom 40 Percent Group. PLoS ONE, 16.10.1371/journal.pone.0255312 ↗
39. Gaurav, M. (2022, June 05). The Most Comprehensive Guide to Automated Feature Selection Methods in Python. Available online: https://datagraphi.com/blog/post/2019/9/23/feature-selection-with-sklearn-in-python.
40. Using Internal Evaluation Measures to Validate the Quality of Diverse Stream Clustering Algorithms10.1007/s40595-016-0086-9 ↗
41. Shutaywi, M., and Kachouie, N.N. (2021). Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering. Entropy, 23.10.3390/e23060759 ↗
42. A Cluster Analysis of Population Based Cancer Registry in Brunei Darussalam: An Exploratory