A Method for Unsupervised Ensemble Clustering to Examine Student Behavioral Patterns

Authors

  • K. Shyam Sunder Reddy Department of CSE, Maturi Venkata Subba Rao (MVSR) Engineering College, Hyderabad.
  • P. Rajya Lakshmi Assistant Professor, Department of CSE, TKR College of Engineering and Technology, Hyderabad
  • D. Maruthi Kumar Associate Professor, Dept of ECE, Srinivasa Ramanujan Institute of Technology, Ananthapuramu
  • P. Naresh Assistant Professor, Dept of IT, Vignan Institute of Technology and Science(A),Hyderabad
  • Y. N. Gholap Assistant Professor, Dept of Information Technology, Army Institute of Technology, Pune
  • K. Gurnadha Gupta Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist., Andhra Pradesh - 522302,India.

Keywords:

Density based clustering, DBSCAN, k-means algorithm, entropy based approach, statistical analysis, student behavior, behavioral patterns

Abstract

Identification of student behavior time to time for projected throughput in terms of performance in academics is  the primary goal of any educational organization. Prediction of unconventional behavioral patterns may useful to the goal. Based which educational institutes build the learning modules and the respective support for the development of student performances. Many existed studies worked on it by means of conducting surveys, taking reports and used questionnaire are not sufficient for the objective. Hence, we proposed a frame work that can be integrated with the advanced algorithms for getting hidden patterns too. The proposed method used unsupervised clustering method and results can be refined with ensemble algorithms. We collected real time data when students are in the campus to getting better behavioral patterns. For we developed two approaches for extracting features of patterns with DBSCAN and K-Means algorithm. And also adapted density based spatial clustering techniques concepts based on statistical and entropy approaches. For the experimental purposed various types of patterns produced by the student behavior is used. With the final experimental results, we concluded that our frame work is better that the accuracy rate of 96.3% in abnormal students’ behavioral patterns. With which the educational organizations improve the academic targets as per their goals. Empirical research shows a significant correlation between these behavioural characteristics and academic achievement. We also examine the relationship between each student's academic performance and that of other students who exhibit behaviors that are similar to his or her own, prompted by the social impact idea. The association is substantial, according to statistical testing.

Downloads

Download data is not yet available.

References

Othman, N.T.A.; Misnon, R.; Abdullah, S.R.S.; Kofli, N.T.; Kamarudin, S.K.; Mohamad, A.B. Assessment of programme outcomes through exit survey of Chemical/Biochemical Engineering students. Procedia-Soc. Behav. Sci. 2011, 18, 39–48.

Ka Yuk Chan, C.; Luo, J. Investigating student preparedness for holistic competency assessment: Insights from the Hong Kong context. Assess. Eval. High. Educ. 2022, 47, 636–651.

Mohamed, H.; Judi, H.M.; Jenal, R. Soft Skills Assessment Based On Undergraduate Student Perception. Asia-Pac. J. Inf. Technol.Multimed. 2019, 8, 27–35.

Sabri, M.Z.M.; Majid, N.A.A.; Hanawi, S.A. Model Development in Predicting Academic Performance of Students Based on Self-Efficacy Using K-Means Clustering. J.Phys. Conf. Ser. 2021, 2129, 012047.

Amin, H.M.; Mustafa, N.H. The Undergraduate Learning Style and Achievement in Programming Language Amongst Undergraduates at FTSM. Asia-Pac. J. Inf. Technol. Multimed. 2014, 3, 1–12.

Ghazali, A.S.M.; Noor, S.F.M.; Mohamed, H. E-Hospitality and Tourism Course Based on Students’ Learning Styles. Asia-Pacific J.Inf. Technol. Multimed. 2021, 10, 100–117.

Naresh, P., & Suguna, R. (2021). Implementation of dynamic and fast mining algorithms on incremental datasets to discover qualitative rules. Applied Computer Science, 17(3), 82-91. https://doi.org/10.23743/acs-2021-23.

Francis, B.K.; Babu, S.S. Predicting academic performance of students using a hybrid data mining approach. J. Med. Syst. 2019,43, 162.

Khan, I.; Ahmad, A.R.; Jabeur, N.; Mahdi, M.N. A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models. Int. J. Interact. Mob. Technol. 2021, 15, 4–19.

Aggarwal, D.; Mittal, S.; Bali, V. Significance of non-academic parameters for predicting student performance using ensemble learning techniques. Int. J. Syst. Dyn. Appl. (IJSDA) 2021, 10, 38–49.

Asif, R.; Merceron, A.; Ali, S.A.; Haider, N.G. Analyzing undergraduate students’ performance using educational data mining. Comput. Educ. 2017, 113, 177–194.

Abdelhafez, H.A.; Elmannai, H. Developing and Comparing Data Mining Algorithms ThatWork Best for Predicting Student Performance. Int. J. Inf. Commun. Technol. Educ. (IJICTE) 2022, 18, 1–14.

M. I. Thariq Hussan, D. Saidulu, P. T. Anitha, A. Manikandan and P. Naresh (2022), Object Detection and Recognition in Real Time Using Deep Learning for Visually Impaired People. IJEER 10(2), 80-86. DOI: 10.37391/IJEER.100205.

Nagesh, C., Chaganti, K.R. , Chaganti, S. , Khaleelullah, S., Naresh, P. and Hussan, M. 2023. Leveraging Machine Learning based Ensemble Time Series Prediction Model for Rainfall Using SVM, KNN and Advanced ARIMA+ E-GARCH. International Journal on Recent and Innovation Trends in Computing and Communication. 11, 7s (Jul. 2023), 353–358. DOI:https://doi.org/10.17762/ijritcc.v11i7s.7010.

G. Kostopoulos, S. Kotsiantis, N. Fazakis, G. Koutsonikos, and C. Pierrakeas, ``A semi-supervised regression algorithm for grade prediction of students in distance learning courses,'' Int. J. Artif. Intell. Tools, vol. 28, no. 4, Jun. 2019, Art. no. 194000.1

D. Hooshyar, M. Pedaste, and Y. Yang, ``Mining educational data to predict Students' performance through procrastination behavior,'' Entropy, vol. 22, no. 1, p. 12, Dec. 2019.

I. HarwatiR Virdyanawaty and A. Mansur, ``Drop out estimation students based on the study period: Comparison between naive Bayes and support vector machines algorithm methods,'' in Proc. ICET4SD, Yogyakarta, IN, USA, 2015.

V. Krishna, Y. D. Solomon Raju, C. V. Raghavendran, P. Naresh and A. Rajesh, "Identification of Nutritional Deficiencies in Crops Using Machine Learning and Image Processing Techniques," 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, 2022, pp. 925-929, doi: 10.1109/ICIEM54221.2022.9853072.

T. Aruna, P. Naresh, A. Rajeshwari, M. I. T. Hussan and K. G. Guptha, "Visualization and Prediction of Rainfall Using Deep Learning and Machine Learning Techniques," 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), Tashkent, Uzbekistan, 2022, pp. 910-914, doi: 10.1109/ICTACS56270.2022.9988553.

R. Kosara, F. Bendix, and H. Hauser, ``Parallel sets: Interactive exploration and visual analysis of categorical data,'' IEEE Trans. Vis. Comput. Graph- ics, vol. 12, no. 4, pp. 558_568, Jul. 2006.

Y. Cao, J. Gao, D. Lian, Z. Rong, J. Shi, Q. Wang, Y. Wu, H. Yao, and T. Zhou, ``Orderliness predicts academic performance: Behavioural analysis on campus lifestyle,'' J. Roy. Soc. Interface, vol. 15, no. 146, Sep. 2018, Art. no. 20180210.

B. Narsimha, Ch V Raghavendran, Pannangi Rajyalakshmi, G Kasi Reddy, M. Bhargavi and P. Naresh (2022), Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application. IJEER 10(2), 87-92. DOI: 10.37391/IJEER.100206..

R.D. Cox, It was just that I was afraid promoting success by addressing students’ fear of failure, Community Coll. Rev. 37 (1) (2009) 52–80.

Crowe, C. Dirks, M.P. Wenderoth, Biology in bloom: implementing Bloom’s taxonomy to enhance student learning in biology, CBE-Life Sci. Educ. 7 (4) (2008) 368–381.

M. Devlin, Taking responsibility for learning isn't everything: a case for developing tertiary students’ conceptions of learning, Teach. Higher Educ. 7 (2) (2002) 125–138.

V.Krishna, Dr.V.P.C.Rao, P.Naresh, P.Rajyalakshmi “ Incorporation of DCT and MSVQ to Enhance Image Compression Ratio of an image” International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 03 | Mar-2016.

E. Fitkov-Norris, A. Yeghiazarian, Measuring study habits in higher education: the way forward? Paper Presented at the Journal of Physics: Conference Series (2013).

Naresh, P., & Suguna, R. (2021). IPOC: An efficient approach for dynamic association rule generation using incremental data with updating supports. Indonesian Journal of Electrical Engineering and Computer Science, 24(2), 1084. https://doi.org/10.11591/ijeecs.v24.i2.pp1084-1090.

B. Fogg, Creating persuasive technologies: an eight-step design process, Paper presented at the Proceedings of the 4th International Conference on Persuasive Technology (2009).

P. Naresh, S. V. N. Pavan, A. R. Mohammed, N. Chanti and M. Tharun, "Comparative Study of Machine Learning Algorithms for Fake Review Detection with Emphasis on SVM," 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, 2023, pp. 170-176, doi: 10.1109/ICSCSS57650.2023.10169190.

J.C. Hilpert, J. Stempien, K.J. van der Hoeven Kraft, J. Husman, Evidence for the latent factor structure of the MSLQ a new conceptualization of an established questionnaire, SAGE Open 3 (4) (2013) (2158244013510305).

Hussan, M.I. & Reddy, G. & Anitha, P. & Kanagaraj, A. & Pannangi, Naresh. (2023). DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization. Cluster Computing. 1-22. 10.1007/s10586-023-04187-4.

C.R. Hynd, Teaching students to think critically using multiple texts in history, J. Adolesc. Adult Lit. (1999) 428–436.

Downloads

Published

23.02.2024

How to Cite

Sunder Reddy, K. S. ., Lakshmi, P. R. ., Kumar, D. M. ., Naresh, P. ., Gholap, Y. N. ., & Gupta, K. G. . (2024). A Method for Unsupervised Ensemble Clustering to Examine Student Behavioral Patterns. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 417–429. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4854

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.

Most read articles by the same author(s)

Similar Articles

You may also start an advanced similarity search for this article.