Web Programming Clustering using a Hybrid Algorithm: Assessing Cognitive Ability Datasets
Keywords:
clustering, Cognitive assessment, K-means, K-medoids, ClaraAbstract
Cognitive assessment in the context of web programming learning is a critical aspect for evaluating students' abilities. In this study, three clustering algorithms, namely K-Means, K-Medoids, and CLARA, were applied to cluster students' cognitive assessment data using the elbow and silhouette validation methods to determine the optimal number of clusters. The results were the same, with 2 clusters and 3 clusters. Subsequently, the clustering algorithms were evaluated using the Davies-Bouldin Index (DBI) to compare the quality of clustering produced by these three algorithms. The evaluation results showed that the CLARA algorithm with 3 clusters had the lowest DBI value, indicating better clustering quality. This clustering provides valuable insights for educational institutions in evaluating students' achievements in web programming courses. The clustering results allow for recognition of high-achieving groups while also motivating groups with moderate and low achievements to improve their performance. Thus, this research contributes to understanding the patterns of students' cognitive assessments and provides a useful tool for educational institutions to enhance the quality of web programming learning.
References
[1] K. Thongkoo, P. Panjaburee, and K. Daungcharone, “Integrating inquiry learning and knowledge management into a flipped classroom to improve students’ web programming performance in higher education,” Knowl. Manag. E-Learning, vol. 11, no. 3, pp. 304–324, 2019, doi: 10.34105/j.kmel.2019.11.016.
[2] J. C. López-Pimentel, A. Medina-Santiago, M. Alcaraz-Rivera, and C. Del-Valle-soto, “Sustainable project-based learning methodology adaptable to technological advances for web programming,” Sustain., vol. 13, no. 15, pp. 1–25, 2021, doi: 10.3390/su13158482.
[3] E. Boeren and T. Íñiguez-Berrozpe, “Unpacking PIAAC’s cognitive skills measurements through engagement with Bloom’s taxonomy,” Stud. Educ. Eval., vol. 73, no. January 2021, 2022, doi: 10.1016/j.stueduc.2022.101151.
[4] S. Shaikh, S. M. Daudpotta, and A. S. Imran, “Bloom’s Learning Outcomes’ Automatic Classification Using LSTM and Pretrained Word Embeddings,” IEEE Access, vol. 9, pp. 117887–117909, 2021, doi: 10.1109/ACCESS.2021.3106443.
[5] K. Chrysafiadi, C. Troussas, and M. Virvou, “Combination of fuzzy and cognitive theories for adaptive e-assessment,” Expert Syst. Appl., vol. 161, p. 113614, 2020, doi: 10.1016/j.eswa.2020.113614.
[6] B. Ewing, “An exploration of assessment approaches in a vocational and education training courses in Australia,” Empir. Res. Vocat. Educ. Train., vol. 9, no. 1, 2017, doi: 10.1186/s40461-017-0058-z.
[7] L. G. B. Ruiz, M. C. Pegalajar, R. Arcucci, and M. Molina-Solana, “A time-series clustering methodology for knowledge extraction in energy consumption data,” Expert Syst. Appl., vol. 160, p. 113731, 2020, doi: 10.1016/j.eswa.2020.113731.
[8] W. Xiaochun and W. Xia Li, “ A Fast K -medoids Clustering Algorithm for Image Segmentation based Object Recognition ,” J. Robot. Autom., vol. 4, no. 1, pp. 202–211, 2020, doi: 10.36959/673/371.
[9] I. Enesi, L. Liço, A. Biberaj, and D. Shahu, “Analysing Clustering Algorithms Performance in CRM Systems,” Int. Conf. Enterp. Inf. Syst. ICEIS - Proc., vol. 1, no. 1, pp. 803–809, 2021, doi: 10.5220/0010511008030809.
[10] A. E. Ezugwu, A. K. Shukla, M. B. Agbaje, O. N. Oyelade, A. José-García, and J. O. Agushaka, Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature, vol. 33, no. 11. 2021. doi: 10.1007/s00521-020-05395-4.
[11] L. Yao, Q. Zhang, Q. Chi, and L. Wang, “Teaching Reform of Java Web Programming Course Based on the Concept of OBE,” Proc. - 2020 5th Int. Conf. Inf. Sci. Comput. Technol. Transp. ISCTT 2020, pp. 456–459, 2020, doi: 10.1109/ISCTT51595.2020.00087.
[12] N. A. M. Noor, N. M. Saim, R. Alias, and S. H. Rosli, “Students’ performance on cognitive, psychomotor and affective domain in the course outcome for embedded course,” Univers. J. Educ. Res., vol. 8, no. 8, pp. 3469–3474, 2020, doi: 10.13189/ujer.2020.080821.
[13] G. Kasilingam, M. Ramalingam, and E. Chinnavan, “Assessment of learning domains to improve student’s learning in higher education,” J. Young Pharm., vol. 6, no. 1, pp. 27–33, 2014, doi: 10.5530/jyp.2014.1.5.
[14] Ö. Gökhan Ulum, “OPUS Journal of Society Research Is the Revised Bloom’s Taxonomy Revisited in the EFL/ESL Reading Textbooks?,” OPUS-Journal Soc. Res., vol. 19, no. 45, pp. 170–177, 2022.
[15] E. L. Cahapin, B. A. Malabag, C. S. Santiago, J. L. Reyes, G. S. Legaspi, and K. L. Adrales, “Clustering of students admission data using k-means, hierarchical, and DBSCAN algorithms,” Bull. Electr. Eng. Informatics, vol. 12, no. 6, pp. 3647–3656, 2023, doi: 10.11591/eei.v12i6.4849.
[16] U. N. Wisesty and T. R. Mengko, “Comparison of dimensionality reduction and clustering methods for sars-cov-2 genome,” Bull. Electr. Eng. Informatics, vol. 10, no. 4, pp. 2170–2180, 2021, doi: 10.11591/EEI.V10I4.2803.
[17] D. K. Hashim and L. A. N. Muhammed, “Performance of K-means algorithm based an ensemble learning,” Bull. Electr. Eng. Informatics, vol. 11, no. 1, pp. 575–580, 2022, doi: 10.11591/eei.v11i1.3550.
[18] F. A. A. J. Almahri, D. Bell, and M. Arzoky, “Personas design for conversational systems in education,” Informatics, vol. 6, no. 4, 2019, doi: 10.3390/informatics6040046.
[19] Z. Zhang, T. Wu, X. Sun, and J. Yu, “MPDP k-medoids: Multiple partition differential privacy preserving k-medoids clustering for data publishing in the Internet of Medical Things,” Int. J. Distrib. Sens. Networks, vol. 17, no. 10, 2021, doi: 10.1177/15501477211042543.
[20] R. K. Dinata, S. Retno, and N. Hasdyna, “Minimization of the Number of Iterations in K-Medoids Clustering with Purity Algorithm,” Rev. d’Intelligence Artif., vol. 35, no. 3, pp. 193–199, 2021, doi: 10.18280/ria.350302.
[21] Q. Shang, Y. Yu, and T. Xie, “A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering,” Sustain., vol. 14, no. 17, 2022, doi: 10.3390/su141711068.
[22] A. V. Ushakov and I. Vasilyev, “Near-optimal large-scale k-medoids clustering,” Inf. Sci. (Ny)., vol. 545, pp. 344–362, 2021, doi: 10.1016/j.ins.2020.08.121.
[23] W. Cao, S. Wang, and M. Xu, “Optimal Scheduling of Virtual Power Plant Based on Latin Hypercube Sampling and Improved CLARA Clustering Algorithm,” Processes, vol. 10, no. 11, 2022, doi: 10.3390/pr10112414.
[24] M. Vukčevic, V. Popovic-Bugarin, and E. Dervic, “DBSCAN and CLARA Clustering Algorithms and their usage for the Soil Data Clustering,” 2019 8th Mediterr. Conf. Embed. Comput. MECO 2019 - Proc., no. June, pp. 10–14, 2019, doi: 10.1109/MECO.2019.8760140.
[25] H. Ocagli et al., “Profiling patients by intensity of nursing care: An operative approach using machine learning,” J. Pers. Med., vol. 10, no. 4, pp. 1–13, 2020, doi: 10.3390/jpm10040279.
[26] E. Schubert and P. J. Rousseeuw, “Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms,” Inf. Syst., vol. 101, p. 101804, 2021, doi: 10.1016/j.is.2021.101804.
[27] L. Q. Huatangari, M. A. C. Ríos, and R. T. H. Camones, “Segmenting the eating behaviour of university students using the K-means algorithm,” Bull. Electr. Eng. Informatics, vol. 12, no. 4, pp. 2363–2371, 2023, doi: 10.11591/eei.v12i4.4543.
[28] T. Gupta and S. P. Panda, “Clustering Validation of CLARA and K-Means Using Silhouette DUNN Measures on Iris Dataset,” Proc. Int. Conf. Mach. Learn. Big Data, Cloud Parallel Comput. Trends, Prespectives Prospect. Com. 2019, pp. 10–13, 2019, doi: 10.1109/COMITCon.2019.8862199.
