Finite predicate-driven logic networks method for enhanced education data analysis

Zoia Dudar, Andrii Kozyriev

Abstract


The subject matter of the study is intelligent data analysis in the field of academic information. The goal of the study is to create a mathematical model for analyzing students' academic information using the predicate-driven logic networks method, which allows taking into account both logical dependencies and probabilistic transitions between states. To achieve this goal, the following tasks were defined: analysis of the theoretical foundations of the logic networks method and predicate logic, integration of these approaches into a single mathematical model, development of approaches for its application in academic data analysis problems. The research used the methods of mathematical modeling, complex logical analysis, and method for constructing logic networks. The following results were obtained: a theoretical model was developed that integrates the principles of logic networks and predicate logic for analyzing student academic performance; the model accounts for both probabilistic transitions between states and logical dependencies among student parameters; the mathematical model also incorporates logical rules to enhance the accuracy of logical analysis within the academic context. The model was tested on a dataset of student performance, demonstrating its effectiveness in accurately predicting academic outcomes and confirming the validity of the integrated approach. Conclusions. The scientific novelty of the results obtained is as follows: 1) a theoretical model for analyzing student academic data was developed by integrating logic networks and predicate logic, allowing for the simultaneous consideration of probabilistic transitions and logical dependencies among student parameters; 2) the approach enhances the analysis process by incorporating logical rules into the probabilistic framework, providing a more nuanced and accurate tool for analyzing academic data; 3) this combined model offers a novel method for addressing complex logical analysis tasks in educational settings, paving the way for further research and practical applications in intelligent data analysis. The successful testing of the model on actual student data further underscores its potential as a powerful tool in educational data analysis.

Keywords


Intelligent data analysis; academic data; logic networks; algebra of finite predicates; predicate logic

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References


West, S. W., Clubb, J., Blake, T. A., Fern, J., Bowles, H. and Dalen-Lorentsen, T. Big data. Big potential. Big problems? BMJ Open Sport & Exercise Medicine, 2024, vol. 10, iss. 2, article no. e001994. DOI: 10.1136/bmjsem-2024-001994. (accessed 1 August 2024)

Jadhav, S. S. Big Data Analytics. International Journal for Research in Applied Science and Engineering Technology, 2024, vol. 12, iss. 4, pp. 843-847. DOI: 10.22214/ijraset.2024.59806.

Ravi, V. & Cherukuri, A. K. Handbook of Big Data Analytics: Volume 1: Methodologies, 2021. 391 p. DOI: 10.1049/PBPC037F.

Rayala, V. & Kalli, S. R. Big data clustering using Improvised Fuzzy C-Means clustering. Revue d'Intelligence Artificielle, 2021, vol. 34, iss. 6, pp. 701-708. DOI: 10.18280/RIA.340604.

Koenig, M. E. D. What is KM? Knowledge management explained. KMWorld 2024 Is Nov. 18-21 in Washington, DC. Available at: https://www.kmworld.com/About/What_is_Knowledge_Management. (accessed 15 February 2024).

Junwu, W., Yipeng, L., & Jingtao, F. Integrating Bayesian networks and ontology to improve safety knowledge management in construction behavior: A conceptual framework. Ain Shams Engineering Journal, 2024, vol. 15, iss. 9, article no. 102906. DOI: 10.1016/j.asej.2024.102906.

Moshood, D. T., Rotimi, E. F., & Rotimi, O. B. J. An Integrated Paradigm for Managing Efficient Knowledge Transfer: Towards a More Comprehensive Philosophy of Transferring Knowledge in the Construction Industry. Construction Economics and Building, 2022, vol. 22, iss. 3, article no. e8050. DOI: 10.5130/ajceb.v22i3.8050.

Chen, Z., Wang, Y., Zhao, B., Cheng, J., Zhao, X., & Duan, Z. Knowledge Graph Completion: A Review, IEEE Access, 2020, vol. 8, pp. 192435-192456. DOI: 10.1109/access.2020.3030076.

Pérez, D. S., Marbán, U. N., & Robaina, D. A. Methodology to evaluate project management and knowledge in a University. Visión de Futuro, 2020, vol. 24, iss. 1, pp. 114-130. DOI: 10.36995/j.visiondefuturo.2020.24.01.005.en.

Nielbo, K. L., Karsdorp, F., Wevers, M., Lassche, A., Baglini, R. B., Kestemont, M., & Tahmasebi, N. Quantitative text analysis. Nature Reviews Methods Primers, 2024, vol. 4, iss. 1, article no. e25. DOI: 10.1038/s43586-024-00302-w.

Zheng, H. Vocabulary Knowledge and Acquisition. Advances in Humanities Research, 2024, vol. 5, iss. 1, pp. 32-36. DOI: 10.54254/2753-7080/5/2024046.

Gao, J., Xie, T., Li, R., Wang, K., & Zhao, L. APM: Adaptive parameter multiplexing for class incremental learning. Expert Systems with Applications, 2024, vol. 258, article no. 125135. DOI: 10.1016/j.eswa.2024.125135.

Altuhaifa, F., & Al Tuhaifa, D. Developing an Ontology Representing Fall Risk Management Domain Knowledge. Journal of Medical Systems, 2024, vol. 48, iss. 1, article no. e47. DOI: 10.1007/s10916-024-02062-2.

Luo, Q. Research on Performance Optimization Technique of Dynamic Translation System Based on Apparatus Knowledge Algorithm. International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS), 29–31 July 2022, Bristol, United Kingdom, IEEE, 2022, pp. 242-245 DOI: 10.1109/aiars57204.2022.00061.

Kundu, A., & Nguyen, U. T. Automated Fact Checking Using a Knowledge Graph-based Model. International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 19–22 February 2024, Osaka, Japan, IEEE, 2024, pp. 709-716. DOI: 10.1109/icaiic60209.2024.10463196.

Omran, P. G., Wang, K. & Wang, Z. An Embedding-based Approach to Rule Learning in Knowledge Graphs. IEEE Transactions on Knowledge and Data Engineering, 2019, vol. 33, iss. 4, pp. 1348-1359. DOI: 10.1109/tkde.2019.2941685.

Tanon, P. T., Weikum, G. & Suchanek, F. YAGO 4: A Reason-able Knowledge Base. The Semantic Web (ESWC 2020), Lecture Notes in Computer Science, Springer, Cham, 2020, vol. 12123, pp. 583-596. DOI: 10.1007/978-3-030-49461-2_34.

Omran, P. G., Taylor, K., Méndez, R. S. & Armin, H. Learning SHACL shapes from knowledge graphs. Semantic Web, 2023, vol. 14, iss. 1, pp. 101-121. DOI: 10.3233/sw-223063.

Chen, G. An interpretable composite CNN and GRU for fine-grained martial arts motion modeling using big data analytics and machine learning. Soft Computing, 2024, vol. 28, pp. 2223-2243. DOI: 10.1007/s00500-023-09565-z.

Svatoš, M., Schockaert, S., Davis, J., & Kuaelka, O. Strike: Rule-driven relational learning using stratified k-entailment. 24th European Conference on Artificial Intelligence, 29 August–8 September 2020, Santiago de Compostela, Spain – Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020), 2020, vol. 325, pp. 1515–1522. DOI: 10.3233/FAIA200259.

Oparin, G. A., Bogdanova, V. G., & Pashinin, A. A. Classification in Binary Feature Space Using Logical Dynamic Models. 44th International Convention on Information, Communication and Electronic Technology (MIPRO), 27 September–1 October 2021, Opatija, Croatia, IEEE, 2021, pp. 1020-1025. DOI: 10.23919/mipro52101.2021.9596697.

Shubin, I. Yu., Chetverykov, H. H., Snisar, S. M., & Litvin, S. H. Katehornyy analiz lohichnykh merezh dlya radiolokatsiynykh system rozpiznavannya [Categorical analysis of logical networks for radar recognition systems]. Bionika intelektu – Bionics of Intelligence, 2020, vol. 1, iss. 94, pp. 51-56. DOI: 10.30837/bi.2020.1(94).08. (in Ukrainian).

Shivappriya, S. N., Priyadarsini, M. J. P., Stateczny, A., Puttamadappa, C., & Parameshachari, B. D. Cascade Object Detection and Remote Sensing Object Detection Method Based on Trainable Activation Function. Remote Sensing, 2021, vol. 13, iss. 2, article no. 200. DOI: 10.3390/rs13020200.

Shubin I., & Kozyriev A. Method for Solving Quantifier Linear Equations for Formation of Optimal Queries to Databases. COLINS-2023: 7th International Conference on Computational Linguistics and Intelligent Systems, April 20–21, 2023, Kharkiv, Ukraine, 2023, vol. 3396, pp. 449–459. Available at: https://ceurspt.wikidata.dbis.rwth-aachen.de/Vol-3396/paper36.html. (accessed 18 July 2024).

Nicholls, K., Kirk, P. D. W., & Wallace, C. Bayesian clustering with uncertain data. PLOS Computational Biology, 2024, vol. 20, iss. 9, article no. e1012301. DOI: 10.1371/journal.pcbi.1012301.

Jung, P., Marra, G., & Kuželka, O. Quantified Neural Markov Logic Networks. International Journal of Approximate Reasoning, 2024, vol. 171, article no. 109172. DOI: 10.1016/j.ijar.2024.109172.

Malhotra, S., & Serafini, L. On Projectivity in Markov Logic Networks. Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022), Lecture Notes in Computer Science, vol 13717, Cham, Springer Nature Switzerland, 2023, pp. 223-238. DOI: 10.1007/978-3-031-26419-1_14.




DOI: https://doi.org/10.32620/reks.2024.3.14

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