Oleksandr Derevyanchuk; Zhengbing Hu; Serhiy Balovsyak; Serhii Holub; Hanna Kravchenko; Iryna Sapsai

Complex of Specialized Methods of Educational Data Mining for the Training of Vocational Education Teachers

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Author(s)

Oleksandr Derevyanchuk ¹ Zhengbing Hu ² Serhiy Balovsyak ¹ Serhii Holub ³ Hanna Kravchenko ⁴ Iryna Sapsai ⁵

1. Yuriy Fedkovych Chernivtsi National University, Chernivtsi, 58012, Ukraine

2. School of Computer Science, Hubei University of Technology, Wuhan, China

3. Cherkasy State Technological University, Cherkasy, 18006, Ukraine

4. High State Educational Establishment «Chernivtsi transport college», Chernivtsi, 58000, Ukraine

5. Institute of Postgraduate Education, Borys Grinchenko Kyiv Metropolitan University, Kyiv, 02152, Ukraine

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2025.01.03

Received: 23 Aug. 2024 / Revised: 13 Oct. 2024 / Accepted: 17 Nov. 2024 / Published: 8 Feb. 2025

Index Terms

Educational Data Mining, Vocational Education Teachers, Clustering, Digital Image Processing, Image Segmentation, Object Detection, Fuzzy Logic

Abstract

In the work, an analysis of modern methods of Educational Data Mining (EDM) was carried out, on the basis of which a set of methods of EDM was developed for the training of vocational education teachers. The basic methods of EDM are considered, namely Prediction, Clustering, Relationship Mining, Distillation of Data for Human Judgment, Discovery with Models. The possibilities of using artificial neural networks, in particular, networks of Long-Short-Term Memory (LSTM), to predict the results of the educational process are described. The main methods of clustering and segmentation of educational data are considered. The basic methods of EDM are complemented by specialized methods of digital image pre-processing and methods of artificial intelligence, taking into account the peculiarities of the training of future specialists in engineering and pedagogical specialties. As specialized methods of digital image pre-processing, methods of filtering, contrast enhancement and contour selection are used. As specialized methods of artificial intelligence, methods of image segmentation, object detection on images, object detection using fuzzy logic were used. Methods of object detection on images using convolutional neural networks and using the Viola-Jones method are described. To process data with a certain degree of uncertainty, it is proposed to apply the methods of EDM and Fuzzy Logic in a integral manner. Ways of integrating Fuzzy Logic with methods of data clustering, image segmentation and object detection on images are considered. The possibilities of applying the developed complex of specialized methods of EDM in the educational process, in particular, when performing STEM (Science, Technology, Engineering and Mathematics) projects, are described.

Cite This Paper

Oleksandr Derevyanchuk, Zhengbing Hu, Serhiy Balovsyak, Serhii Holub, Hanna Kravchenko, Iryna Sapsai, "Complex of Specialized Methods of Educational Data Mining for the Training of Vocational Education Teachers", International Journal of Modern Education and Computer Science(IJMECS), Vol.17, No.1, pp. 28-46, 2025. DOI:10.5815/ijmecs.2025.01.03

Reference

[1]H. Aldowah, H. Al-Samarraie, and W.M. Fauzy, “Educational data mining and learning analytics for 21st century higher education: A review and synthesis,” Telemat. Inform., vol. 37, pp. 13-49, 2019. doi: 10.1016/j.tele.2019.01.007.
[2]Mina Fazlikhani, and Iman Bagheri, “Educational Computer Science Development Based on Data Mining,” In 5th International Conference on Science, Engineering, and role of Technology in new Busines, Copenhagen-Denamrk, pp.1-8, December 16, 2023.
[3]M. Zhang, Y. Cheng, J. Gu, Y. Yang, L. Chen, and B. Cui, “Digital Evaluation of Innovative Logistics Talents Based on Improved SAGA-FCM Algorithm,” In: Hu, Z., Zhang, Q., He, M. (eds), Advances in Artificial Systems for Logistics Engineering, Springer, Cham., vol. 223, pp. 374-383, 2024. doi: 10.1007/978-3-031-72017-8_34.
[4]R. Baker, and K. Yacef, “The State of Educational Data Mining in 2009: A Review and Future Visions,” Journal of Educational Data Mining, vol. 1, no.1, pp. 3–17, 2009. doi: 10.5281/zenodo.3554658.
[5]R. Baker et al., “Data mining for education,” International encyclopedia of education, vol. 7, pp. 112–118, 2010.
[6]Saurabh Bilgaiyan, Kunwar Aditya, Samaresh Mishra, Madhabananda Das, "A Swarm Intelligence based Chaotic Morphological Approach for Software Development Cost Estimation", International Journal of Intelligent Systems and Applications, Vol.10, No.9, pp.13-22, 2018.
[7]Adlen Kerboua, Mohamed Batouche, "3D Skeleton Action Recognition for Security Improvement", International Journal of Intelligent Systems and Applications, Vol.11, No.3, pp.42-52, 2019.
[8]DaeEun Kim, Dosik Hwang, Intelligent Imaging and Analysis. Switzerland, Basel, MDPI, 2020.
[9]Z. Yang, J. Yang et al. “Using convolutional neural network to recognize learning images for early warning of at-risk students,” IEEE Trans Learn Technol, vol. 13, no. 3, pp. 617–630, 2020. doi: 10.1109/TLT.2020.2988253.
[10]Nataliya Mutovkina, Zhongfeng Pan, "Solving the Problem of Pre-Selection of Personnel Based on Fuzzy Sets", International Journal of Information Engineering and Electronic Business, Vol.16, No.5, pp. 85-94, 2024.
[11]Ahmed Fahim, "Finding the Number of Clusters in Data and Better Initial Centers for K-means Algorithm", International Journal of Intelligent Systems and Applications, Vol.12, No.6, pp.1-20, 2020.
[12]Serhiy Balovsyak, Oleksandr Derevyanchuk, Hanna Kravchenko, Yuriy Ushenko, and Zhengbing Hu, “Clustering Students According to their Academic Achievement Using Fuzzy Logic,” International Journal of Modern Education and Computer Science, vol. 15, no. 6, pp. 31-43, 2023.
[13]O.V. Derevyanchuk, and S.V. Balovsyak, Certificate of copyright registration for the work, No. 123369, 31.01.2024. ID CR3206310124. Computer program “Data clustering using fuzzy logic” (“ClusterFuzzy23”). Ukrainian National Office for Intellectual Property and Innovations (IP Office). URL: https://sis.nipo.gov.ua/uk/services/original-document/
[14]F. Castro, A. Vellido, A. Nebot, and F. Mugica, “Applying data mining techniques to e-learning problems,” in Evolution of teaching and learning paradigms in intelligent environment, Springer, pp. 183–221, 2007.
[15]Abdulmohsen Algarni, “Data Mining in Education,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 7, no. 6, pp. 456-461, 2016.
[16]C. Romero, and S. Ventura, “Data mining in education,” Wiley Interdisciplinary Reviews: Data mining and knowledge discovery, vol. 3, no. 1, pp. 12-27, 2013. doi: 10.1002/widm.1075.
[17]Mais Haj Qasem, Raneem Qaddoura, and Bassam Hammo, “Educational Data Mining (EDM): A Review,” In Conf. New Trends in Information Technology (NTIT)-2017, 25-27 April 2017, The University of Jordan, Amman, Jordan, pp. 149-156, 2017.
[18]Q. Zhou, W. Quan et al, “Predicting high-risk students using internet access logs,” Knowl, Inf, Syst., vol. 55(2), pp. 393–413, 2018. doi: 10.1007/s10115-017-1086-5.
[19]S. Batool, J. Rashid, M.W. Nisar, et al., “Educational data mining to predict students' academic performance: A survey study,” Educ. Inf. Technol, vol. 28, pp. 905-971, 2023. doi: 10.1007/s10639-022-11152-y.
[20]Zareen Alamgir, Habiba Akram, Saira Karim, and Aamir Wali, “Enhancing Student Performance Prediction via Educational Data Mining on Academic data,” Informatics in Education, vol. 23, no. 1, pp. 1-24, 2024. doi: 10.15388/infedu.2024.04.
[21]Bassam M. Kanber, Naglaa F. Noaman, Amr M. H. Saeed, Mansoor Malas, "DDoS Attacks Detection in the Application Layer Using Three Level Machine Learning Classification Architecture", International Journal of Computer Network and Information Security, Vol.14, No.3, pp.33-46, 2022.
[22]Nyme Ahmed, Sultanul Arifeen Hamim, Dip Nandi, "A Comprehensive Study to Analyze Student Evaluations of Teaching in Online Education", International Journal of Modern Education and Computer Science, Vol.16, No.5, pp. 105-117, 2024.
[23]Maxwell Kontor Owusu, Bakari Yusuf Dramanu, Mark Owusu Amponsah, " Classroom Management Strategies and Academic Performance of Junior High School Students ", International Journal of Education and Management Engineering (IJEME), Vol.11, No.6, pp. 29-38, 2021.
[24]N, Ahmad, N. Hassan, H. Jaafar, and N.I.M. Enzai, “Students' Performance Prediction using Artificial Neural Network,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 1176, pp.1-8, 2021. doi: 10.1088/1757-899X/1176/1/012020.
[25]Carlos Felipe Rodríguez-Hernández, Mariel Musso, Eva Kyndt, and Eduardo Cascallar, “Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation,” Computers and Education: Artificial Intelligence, vol. 2, pp.1-14, 2021. doi: 10.1016/j.caeai.2021.100018.
[26]S. Sarsa, J. Leinonen, and A. Hellas, “Empirical Evaluation of Deep Learning Models for Knowledge Tracing: Of Hyperparameters and Metrics on Performance and Replicability,” Journal of Educational Data Mining, vol. 14, no. 2, 2022. doi: 10.5281/zenodo.7086179.
[27]P. Nayak, S. Vaheed, S. Gupta, et al., “Predicting students' academic performance by mining the educational data through machine learning-based classification model,” Educ. Inf. Technol, vol. 28, pp. 14611-14637, 2023. doi: 10.1007/s10639-023-11706-8.
[28]M. Delianidi, and K. Diamantaras, “KT-Bi-GRU: Student Performance Prediction with a Bi-Directional Recurrent Knowledge Tracing Neural Network,” Journal of Educational Data Mining, vol. 15, no. 2, pp. 1-21. 2023. doi: 10.5281/zenodo.7808087.
[29]X. Li, Y. Zhang, H. Cheng, et al., “Student achievement prediction using deep neural network from multi-source campus data,” Complex Intell. Syst., vol. 8, pp. 5143–5156, 2022. doi: 10.1007/s40747-022-00731-8.
[30]P. Hai-tao, F. Ming-qu, Z. Hong-bin et al., “Predicting academic performance of students in Chinese-foreign cooperation in running schools with graph convolutional network,” Neural Comput & Applic, vol. 33, pp. 637–645, 2021. doi: 10.1007/s00521-020-05045-9.
[31]Sadi Mohammad, Ibrahim Adnan Chowdhury, Niloy Roy, Md. Nazim Hasan, Dip Nandi, "Investigation of Student Dropout Problem by Using Data Mining Technique", International Journal of Education and Management Engineering, Vol.13, No.5, pp. 43-61, 2023.
[32]R. Ahuja, A. Jha, R. Maurya, and R. Srivastava, “Analysis of Educational Data Mining,” In Harmony Search and Nature Inspired Optimization Algorithms, Springer: Singapore, pp. 897–907, 2019. doi: 10.1007/978-981-13-0761-4_85.
[33]T. Omar, A. Alzahrani, M. Andzohdy, “Clustering approach for analyzing the student’s efficiency and performance based on data,” Journal of Data Analysis and Information Processing, vol. 8, no. 03, pp. 171–182, 2020. doi: 10.4236/jdaip.2020.83010.
[34]Moez Ali, Clustering in Machine Learning: 5 Essential Clustering Algorithms. URL: https://www.datacamp.com/blog/clustering-in-machine-learning-5-essential-clustering-algorithms.
[35]M. Wang, and X. Wei, “Research on Logistics Center Location-Allocation Problem Based on Two-Stage K-Means Algorithms,” Advances in Computer Science for Engineering and Education, Hu, Z., Petoukhov, S., Dychka, I., He, M. (Eds.), Springer International Publishing, vol. 1247, pp. 52-62, 2021. doi: 10.1007/978-3-030-55506-1_5.
[36]X. Li, Y. Zhang, H. Cheng, F. Zhou and B. Yin, “An Unsupervised Ensemble Clustering Approach for the Analysis of Student Behavioral Patterns,” IEEE Access, vol. 9, pp. 7076-7091, 2021, doi: 10.1109/ACCESS.2021.3049157.
[37]C. Maithri, H. Chandramouli, “Parallel DBSCAN Clustering Algorithm Using Hadoop Map-reduce Framework for Spatial Data”, International Journal of Information Technology and Computer Science, vol. 14, no. 6, pp. 1-12, 2022.
[38]Mohamed Nafuri, Ahmad Fikri, Nor Samsiah Sani, Nur Fatin Aqilah Zainudin, Abdul Hadi Abd Rahman, and Mohd Aliff, “Clustering Analysis for Classifying Student Academic Performance in Higher Education,” Applied Sciences, vol. 12, no. 19, pp. 9467, 2022. doi: 10.3390/app12199467.
[39]Denon Arthur Richmond Gono, Bi Tra Goore, Yves Tiecoura, and Kouame Abel Assielou, “Multi-relational Matrix Factorization Approach for Educational Items Clustering,” International Journal of Information and Education Technology, vol. 13, no. 1, pp. 42-47, 2023. doi: 10.18178/ijiet.2023.13.1.1778.
[40]Aydin Sinan, and Ozturk Aylin, “Segmenting Learners in Online Learning Environments,” In The Online, Open and Flexible Higher Education Conference, pp. 29-39, 2015.
[41]D.S. Hooda, and Vivek Raich, Fuzzy Logic Models. An Introduction, U.K., Oxfird: Alpha Science International Ltd, 2017.
[42]A.R. Fayek, “Fuzzy Logic and Fuzzy Hybrid Techniques for Construction Engineering and Management,” Journal of Construction Engineering and Management, vol. 146, no. 7, pp. 1-12, 2020. doi: 10.1061/(ASCE)CO.1943-7862.0001854.
[43]A. Heni, I. Jdey and H. Ltifi, “K-means and fuzzy c-means fusion for object clustering,” In 8th International Conference on Control, Decision and Information Technologies (CoDIT), Istanbul, Turkey, pp. 177-182, 2022. doi: 10.1109/CoDIT55151.2022.9804078.
[44]Student Performance Prediction. URL: https://www.kaggle.com/datasets/souradippal/student-performance-prediction.
[45]Tengku Zatul Hidayah Tengku Petra, and Mohd Juzaiddin Ab Aziz, “Analysing Student Performance In Higher Education Using Fuzzy Logic Evaluation,” I. J. scientific & technology research, vol. 10, no. 01, pp. 322-327, 2021.
[46]Riska Ekawita, Nori Wirahmi, Elfi Yuliza, "Project Based Learning of Free-Falling Objects in Physics: Microcontroller-Based Multi-Sensor Test in Granular Flow Rate Measurement", International Journal of Engineering and Manufacturing, Vol.14, No.5, pp. 1-13, 2024.
[47]S.V. Balovsyak, and Kh. S. Odaiska, “Automatic Determination of the Gaussian Noise Level on Digital Images by High-Pass Filtering for Regions of Interest,” Cybernetics and Systems Analysis, vol. 54, no. 4, pp. 662-670, 2018.
[48]S.V. Balovsyak, O.V. Derevyanchuk, I.M. Fodchuk, O.P. Kroitor, Kh.S. Odaiska, O.O. Pshenychnyi, A. Kotyra, and A. Abisheva, “Adaptive oriented filtration of digital images in the spatial domain,” Proc. SPIE 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments, vol. 11176, pp 111761A-1–111761A-6, 2019. doi: https://doi.org/10.1117/12.2537165.
[49]S.V. Balovsyak, O.V. Derevyanchuk, H.O. Kravchenko, O.P. Kroitor, and V.V. Tomash, “Computer system for increasing the local contrast of railway transport images,” Proc. SPIE, Fifteenth International Conference on Correlation Optics, vol. 12126, pp. 121261E1-7, 2021. doi: 10.1117/12.2615761.
[50]Serhiy Balovsyak, Oleksandr Derevyanchuk, Vasyl Kovalchuk, Hanna Kravchenko, Yuriy Ushenko, and Zhengbing Hu, “STEM Project for Vehicle Image Segmentation Using Fuzzy Logic,” International Journal of Modern Education and Computer Science, vol. 16, no. 2, pp. 45-57, 2024.
[51]Stanford Cars Dataset. URL: https://www.kaggle.com/code/pamhohhgkgm/stanford-cars-dataset-a-quick-look-up.
[52]S.V. Balovsyak, O.V. Derevyanchuk, and I.M. Fodchuk, “Method of calculation of averaged digital image profiles by envelopes as the conic sections,” Advances in Intelligent Systems and Computing, Hu Z., Petoukhov S., Dychka I., He M. (Eds.), Springer International Publishing, vol. 754, pp. 204–212, 2019. doi: 10.1007/978-3-319-91008-6_21.
[53]O.V. Derevyanchuk, H.O. Kravchenko, Y.V. Derevianchuk, and V.V. Tomash, “Recognition images of broken window glass,” Proceedings of SPIE, vol. 12938, pp. 210-213, 2024. doi: 10.1117/12.3012995.
[54]A. K. M. Fahim Rahman, Mostofa Rakib Raihan, and S.M. Mohidul Islam, “Pedestrian Detection in Thermal Images Using Deep Saliency Map and Instance Segmentation,” International Journal of Image, Graphics and Signal Processing, vol. 13, no. 1, pp. 40-49, 2021.
[55]O. Berezsky, M. Zarichnyi, and O. Pitsun, “Development of a metric and the methods for quantitative estimation of the segmentation of biomedical images,” Eastern-European Journal of Enterprise Technologies, vol. 6, no. 4(90), pp. 4–11, 2017. doi: 10.15587/1729-4061.2017.119493.
[56]Ihor Tereikovskyi, Zhengbing Hu, Denys Chernyshev, Liudmyla Tereikovska, Oleksandr Korystin, and Oleh Tereikovskyi, “The Method of Semantic Image Segmentation Using Neural Networks,” International Journal of Image, Graphics and Signal Processing, vol. 14, no. 6, pp. 1-14, 2022.
[57]Kama Ramudu, Gajula Laxmi Bhavani, Manabolu Nishanth, Akula Prakash Raj, and Vamshika Analdas, “Level Set Segmentation of Images using Block Matching Local SVD Operator based Sparsity and TV Regularization,” International Journal of Image, Graphics and Signal Processing, vol. 15, no. 2, pp. 47-58, 2023.
[58]Serhiy Balovsyak, Oleksandr Derevyanchuk, Vasyl Kovalchuk, Hanna Kravchenko, and Maryna Kozhokar, “Face Mask Recognition by the Viola-Jones Method Using Fuzzy Logic,” International Journal of Image, Graphics and Signal Processing, vol. 16, no. 3, pp. 39-51, 2024.
[59]Deepa Indrawal, and Archana Sharma, “Multi-Module Convolutional Neural Network Based Optimal Face Recognition with Minibatch Optimization,” International Journal of Image, Graphics and Signal Processing, vol.14, no.3, pp. 32-46, 2022.
[60]Freepik. URL: https://www.freepik.com.
[61]Caltech Vision Lab. Datasets. URL: http://www.vision.caltech.edu/datasets.
[62]O.V. Derevyanchuk, V.I. Kovalchuk, V.M. Kramar, H.O. Kravchenko, D.V. Kondryuk, А.V. Kovalchuk, and B.V.Onufriichuk, “Implementation of STEM education in the process of training of future specialists of engineering and pedagogical specialties,” Proceedings of SPIE, vol. 12938, pp. 214-217, 2024. doi: 10.1117/12.3012996.
[63]V. Kovalchuk, A. Androsenko, O. Derevyanchuk, N. Volkova, and Y. Piven, “Development of pedagogical skills of students of technology and pedagogical specialties using STEM technologies,” Edelweiss Applied Science and Technology, vol. 8, no. 4, pp. 498-506, 2024. https://doi.org/10.55214/25768484.v8i4.1125

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