Design of Personalization Exam Classification Model Based on Imbalanced Class

  • Sri Anita Pertiwi University
  • Arif Rachmat Datokarama State Islamic University
  • Sunu Aditya Mahadany PT Integra Solusi Teknotama
Keywords: personalization learning, personalization exam, imbalanced-class, multiple inteligence

Abstract

Currently, personalized learning has become a necessity in the learning process. Research and implementation of personalization in the planning phases and implementation phases of learning have been extensively studied. However, that research has not yet reached the application stage in the learning assessment phase. Providing homogeneous examination material to each student has not considered the characteristics of learners. Even though the achievements from the assessment phase will provide a measure of the quality of the learning process as a whole. This research has analyzed the individual characteristics model, which is derived as a benchmark for identification of the information and characteristics of the test material, which is then formulated into a classification model based on supervised learning. This study identified text dataset questions and labeled unbalanced multi-classes. This presents a challenge to carry out experiments to find the most optimal data training strategy, the results provide optimal strategy combination results Logic: ENS, Verbal: ENS, Visual: CW+RES+ENS, Natural: CW+RES+ENS. Accuracy measurement results Logic (SVM): 0.85, Verbal (LR) 0.87, Visual (LR) : 0.93, Natural (NN) 0.93.

Downloads

Download data is not yet available.

References

Z. Lu, “Instruction and Evaluation of University Physics Experiment under the Theory of Multiple Intelligences,” Proc. - 2015 Int. Symp. Educ. Technol. ISET 2015, pp. 79–83, Mar. 2016, doi: 10.1109/ISET.2015.24.

E. Kusniati, “STRATEGI PEMBELAJARAN BERBASIS MULTIPLE INTELLIGENCES”.

M. Pocinho and C. Mendes, “Primary School Children’s Multiples Intelligences Assessment,” Psicol. Teor. e Pesqui., vol. 37, pp. 1–9, 2021, doi: 10.1590/0102.3772E37304.

X. Liu, J. Gu, and L. Zhao, “Promoting primary school students’ creativity via reverse engineering pedagogy in robotics education,” Think. Ski. Creat., vol. 49, p. 101339, Sep. 2023, doi: 10.1016/J.TSC.2023.101339.

V. Prain et al., “Personalising Learning,” Adapt. to Teach. Learn. Open-Plan Sch., pp. 43–58, 2014, doi: 10.1007/978-94-6209-824-4_3.

B. Said, L. Cheniti-Belcadhi, and G. El Khayat, “An ontology for personalization in serious games for assessment,” Proc. - IEEE 2nd Int. Conf. Artif. Intell. Knowl. Eng. AIKE 2019, pp. 148–154, Jun. 2019, doi: 10.1109/AIKE.2019.00035.

M. Ren, Z. Wang, and X. Yu, “Personalized federated learning: A Clustered Distributed Co-Meta-Learning approach,” Inf. Sci. (Ny)., vol. 647, p. 119499, Nov. 2023, doi: 10.1016/J.INS.2023.119499.

B. Bray and K. Mcclaskey, “Mid-Pacific Institute 1:1 iPad Program Personalization vs Differentiation vs Individualization,” pp. 1–13, 2014, [Online]. Available: http://www.ed.gov/technology/draft-netp-2010/individualized-personalized-differentiated-instruction

J. S. Akbar et al., Model dan Metode Pembelajaran Inovatif (Teori dan Panduan Praktis), vol. 1. 2023.

K. B. Zafman, M. L. Riegel, L. D. Levine, and R. F. Hamm, “An interactive childbirth education platform to improve pregnancy-related anxiety: a randomized trial,” Am. J. Obstet. Gynecol., vol. 229, no. 1, pp. 67.e1-67.e9, Jul. 2023, doi: 10.1016/J.AJOG.2023.04.007.

S. M. Larson, T. Cox, T. Carelock, and E. S. DeMeyer, “Interactive Education: One Way to Measure Success,” Transplant. Cell. Ther., vol. 30, no. 2, pp. S73–S74, Feb. 2024, doi: 10.1016/J.JTCT.2023.12.119.

D. Li, “An interactive teaching evaluation system for preschool education in universities based on machine learning algorithm,” Comput. Human Behav., vol. 157, p. 108211, Aug. 2024, doi: 10.1016/J.CHB.2024.108211.

C. Saul and H. D. Wuttke, “E-assessment meets personalization,” IEEE Glob. Eng. Educ. Conf. EDUCON, pp. 200–206, 2013, doi: 10.1109/EDUCON.2013.6530106.

K. A. McCusker, J. Harkin, S. Wilson, and M. Callaghan, “Intelligent assessment and content personalisation in adaptive educational systems,” 2013 12th Int. Conf. Inf. Technol. Based High. Educ. Training, ITHET 2013, 2013, doi: 10.1109/ITHET.2013.6671025.

D. E. Benchoff, M. P. Gonzalez, and C. R. Huapaya, “Personalization of Tests for Formative Self-Assessment,” Rev. Iberoam. Tecnol. del Aprendiz., vol. 13, no. 2, pp. 70–74, May 2018, doi: 10.1109/RITA.2018.2831759.

L. T. M. Blessing and A. Chakrabarti, “DRM, a design research methodology,” DRM, a Des. Res. Methodol., pp. 1–397, 2009, doi: 10.1007/978-1-84882-587-1/COVER.

“DRM, a Design Research Methodology | SpringerLink.” https://link.springer.com/book/10.1007/978-1-84882-587-1 (accessed Mar. 16, 2024).

G. Zhao, L. Yang, J. Li, J. Chu, and Y. Qi, “Design and Implementation of a Teaching Verbal Behavior Analysis Aid in Instructional Videos,” Proc. - 2022 4th Int. Work. Artif. Intell. Educ. WAIE 2022, pp. 1–5, 2022, doi: 10.1109/WAIE57417.2022.00008.

H. Karso, “Pernyataan dan Kata Hubung Pernyataan Majemuk”.

C. G. Interiano, D. P. T. Tkacik, J. L. Dahlberg, and D. J. H. Lim, “Authentic Knowledge, Learning Outcomes, and Professional Identity: A Mixed-Methods Study of a Successful Engineering Course,” Proc. - Front. Educ. Conf. FIE, vol. 2018-October, Jul. 2018, doi: 10.1109/FIE.2018.8659152.

C. Liu and Z. Chao, “Supervised learning and unsupervised learning on music data with different genres,” Proc. - 2021 IEEE 7th Int. Conf. Big Data Intell. Comput. DataCom 2021, pp. 7–12, 2021, doi: 10.1109/DATACOM53700.2021.00008.

A. R. Mishra, V. K. Panchal, and P. Kumar, “Extractive Text Summarization-An effective approach to extract information from Text,” Proc. 4th Int. Conf. Contemp. Comput. Informatics, IC3I 2019, pp. 252–255, Dec. 2019, doi: 10.1109/IC3I46837.2019.9055636.

C. C. Aggarwal and C. X. Zhai, “A survey of text classification algorithms,” Min. Text Data, vol. 9781461432234, pp. 163–222, Aug. 2012, doi: 10.1007/978-1-4614-3223-4_6/COVER.

F. Gurcan, “Multi-Class Classification of Turkish Texts with Machine Learning Algotirthms,” ISMSIT 2018 - 2nd Int. Symp. Multidiscip. Stud. Innov. Technol. Proc., Dec. 2018, doi: 10.1109/ISMSIT.2018.8567307.

R. Saravanan and P. Sujatha, “A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification,” Proc. 2nd Int. Conf. Intell. Comput. Control Syst. ICICCS 2018, pp. 945–949, Jul. 2018, doi: 10.1109/ICCONS.2018.8663155.

L. Li, S. Ma, and Y. Zhang, “Optimization algorithm based on genetic support vector machine model,” Proc. - 2014 7th Int. Symp. Comput. Intell. Des. Isc. 2014, vol. 1, pp. 307–310, Mar. 2015, doi: 10.1109/ISCID.2014.99.

X. Zhang and B. G. Hu, “A new strategy of cost-free learning in the class imbalance problem,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 12, pp. 2872–2885, Dec. 2014, doi: 10.1109/TKDE.2014.2312336.

Z. Lai, G. Liang, J. Zhou, H. Kong, and Y. Lu, “A joint learning framework for optimal feature extraction and multi-class SVM,” Inf. Sci. (Ny)., vol. 671, p. 120656, Jun. 2024, doi: 10.1016/J.INS.2024.120656.

Z. Tong et al., “Coal structure identification based on geophysical logging data: Insights from Wavelet Transform (WT) and Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithms,” Int. J. Coal Geol., vol. 282, p. 104435, Feb. 2024, doi: 10.1016/J.COAL.2023.104435.

C. Ding, Y. Xia, Z. Yuan, H. Yang, J. Fu, and Z. Chen, “Performance prediction for a fuel cell air compressor based on the combination of backpropagation neural network optimized by genetic algorithm (GA-BP) and support vector machine (SVM) algorithms,” Therm. Sci. Eng. Prog., vol. 44, p. 102070, Sep. 2023, doi: 10.1016/J.TSEP.2023.102070.

Y. Yu et al., “Quantitative analysis of multiple components based on support vector machine (SVM),” Optik (Stuttg)., vol. 237, p. 166759, Jul. 2021, doi: 10.1016/J.IJLEO.2021.166759.

Published
2024-04-29
How to Cite
Sri Anita, Rachmat, A., & Mahadany, S. A. (2024). Design of Personalization Exam Classification Model Based on Imbalanced Class. Jurnal Sistem Cerdas, 7(1), 45 - 54. https://doi.org/10.37396/jsc.v7i1.386
Section
Articles