Min Zhang1This email address is being protected from spambots. You need JavaScript enabled to view it. and Lina Yin2

1Department of Mechanical and Electronic Engineering, Shandong Management University, Ji’nan 250357, Shandong, China

2Department of Logistics, Shandong Management University, Ji’nan 250357, Shandong, China


 

Received: May 31, 2025
Accepted: July 20, 2025
Publication Date: August 16, 2025

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202603_29(4).0020  


This study examines the influence of experiential learning on the academic performance of students in regional applied undergraduate schools. A dataset of 986 students from five universities in Chongqing University City was examined by ordinary least squares regression to investigate the correlation between participation in practical learning activities and academic achievement. The investigation concentrated on four components of experiential learning input: cognitive orientation, practical skills acquisition, student-teacher interaction, and advanced skills development. Findings reveal that all categories have a favorable impact on academic achievements, with the acquisition of practical skills and the development of advanced skills exhibiting the most significant effects. Moreover, disparities in participation levels were noted according to gender, academic year, and topic of study. The findings underscore the need to cultivate experiential learning environments that enhance students’ professional competencies and innovative capacities. This study enhances the existing information on applied education by providing practical insights for policymakers and educators aiming to improve academic success through experiential learning methodologies. Enhancing institutional initiatives in academic competitions, social practice, and teacher-student collaboration can yield more effective training of high-caliber applied talents capable of fulfilling the requirements of contemporary industries.


Keywords: practical learning input; academic achievement; regression analysis; applied undergraduate education


  1. [1] P. S. Aithal and A. K. Maiya, (2023) “Innovations in higher education industry–Shaping the future" International Journal of Case Studies in Business, IT, and Education (IJCSBE) 7(4): 283–311.
  2. [2] A. Pandita and R. Kiran, (2023) “Tapping the potential of academic leadership, experiential learning, and employ ability of students to enhance higher educational institute performance" Sage Open 13(3): 21582440231183932. DOI: 10.1177/21582440231183932.
  3. [3] J. P. Haupt and A. C. Ogden. “Education abroad as a high-impact practice: Linking research and practice to the educational continuum”. In: Education Abroad and the Undergraduate Experience. Routledge, 2023, 43–57.
  4. [4] J. P. Haupt and A. C. Ogden. “Education abroad as a high-impact practice: Linking research and practice to the educational continuum”. In: Education Abroad and the Undergraduate Experience. Routledge, 2023, 43–57.
  5. [5] C. Wei, S. Jusoh, and R. S. A. R. A. Rahman, (2024) “ECONOMICDEVELOPMENTANDEDUCATIONAL INVESTMENT: SHAPINGTALENTTRAININGAND STUDENTINNOVATIONINSHANGHAI’SUNIVER SITIES" Tec Empresarial 6(2): 72–90.
  6. [6] M. Kim, D. Han, and J.-K. K. Rhee, (2021) “Multiview variational deep learning with application to practical indoor localization" IEEE Internet of Things Journal 8(15): 12375–12383. DOI: 10.1109/JIOT.2021.3063512.
  7. [7] E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, (2020) “A survey of autonomous driving: Common practices and emerging technologies" IEEE access 8: 58443 58469. DOI: 10.1109/ACCESS.2020.2983149.
  8. [8] A. Odena, C. Olsson, D. Andersen, and I. Goodfel low. “Tensorfuzz: Debugging neural networks with coverage-guided fuzzing”. In: International conference on machine learning. PMLR. 2019, 4901–4911.
  9. [9] C. Liu, T. Arnon, C. Lazarus, C. Strong, C. Barrett, M. J. Kochenderfer, et al., (2021) “Algorithms for verifying deep neural networks" Foundations and Trends® in Optimization 4(3-4): 244–404. DOI: 10. 1561/2400000035.
  10. [10] J.Wang,G.Dong,J.Sun,X.Wang,andP.Zhang.“Adversarial sample detection for deep neural network throughmodelmutationtesting”.In:2019IEEE/ACM 41st International Conference on Software Engineering (ICSE). IEEE. 2019, 1245–1256. DOI: 10.1109/ICSE.2019.00126.
  11. [11] R. Ashmore, R. Calinescu, and C. Paterson, (2021) “Assuring the machinelearning lifecycle: Desiderata, methods, and challenges" ACM computing surveys(CSUR) 54(5): 1–39. DOI: 10.1145/3453444.
  12. [12] W. Duan, J. Gu, M. Wen, G. Zhang, Y. Ji, and S. Mum taz, (2020) “Emerging technologies for 5G-IoV networks: applications, trends and opportunities" IEEE Network 34(5): 283–289. DOI: 10.1109/MNET.001.1900659.
  13. [13] D. Jiang, F. Wang, Z. Lv, S. Mumtaz, S. Al-Rubaye, A. Tsourdos, and O. Dobre, (2021) “QoE-aware effi cient content distribution scheme for satellite-terrestrial networks" IEEE Transactions on Mobile Computing 22(1): 443–458. DOI: 10.1109/TMC.2021.3074917.
  14. [14] G. Cai, Y. Fang, J. Wen, S. Mumtaz, Y. Song, and V. Frascolla, (2019) “Multi-carrier M-ary DCSK system with code index modulation: an efficient solution for chaotic communications" IEEE Journal of Selected Topics in Signal Processing 13(6): 1375–1386. DOI: 10.1109/JSTSP.2019.2913944.
  15. [15] S. Hussain, S. Gaftandzhieva, M. Maniruzzaman, R. Doneva, and Z. F. Muhsin, (2021) “Regression analysis of student academic performance using deep learning" Education and Information Technologies 26(1): 783 798. DOI: 10.1007/s10639-020-10241-0.
  16. [16] A. Alshanqiti and A. Namoun, (2020) “Predicting student performance and its influential factors using hybrid regression and multi-label classification" Ieee Access 8: 203827–203844. DOI: 10.1109/ACCESS.2020.3036572.
  17. [17] H. T. T. Le, H. T. T. Nguyen, T. P. La, T. T. T. Le, N. T. Nguyen, T. P. T. Nguyen, and T. Tran, (2020) “Factors Affecting Academic Performance of First-Year University Students: A Case of a Vietnamese University." International Journal of Education and Practice 8(2): 221–232.
  18. [18] J. Bravo-Agapito, S. J. Romero, and S. Pamplona, (2021) “Early prediction of undergraduate Student’s aca demic performance in completely online learning: A five year study" Computers in Human Behavior 115: 106595. DOI: 10.1016/j.chb.2020.106595.
  19. [19] M.Ya˘gcı, (2022) “Educational data mining: prediction of students’ academic performance using machine learning algorithms" Smart Learning Environments 9(1): 11. DOI: 10.1186/s40561-022-00192-z.
  20. [20] M. Kokoç and A. Altun, (2021) “Effects of learner interaction with learning dashboards on academic performance in an e-learning environment" Behaviour & In formation Technology 40(2): 161–175. DOI: 10.1080/0144929X.2019.1680731.
  21. [21] S. Lee, J.-H. Lee, and Y. Jeong, (2023) “The effects of digital textbooks on students’ academic performance, academic interest, and learning skills" Journal of Marketing Research 60(4): 792–811. DOI: 10.1177/00222437221130712.
  22. [22] F.-C. O. Yang, H.-M. Lai, and Y. -W. Wang, (2023) “Effect of augmented reality-based virtual educational robotics on programming students’ enjoyment of learning, computational thinking skills, and academic achievement" Computers & Education 195: 104721. DOI: 10.1016/j.compedu.2022.104721.
  23. [23] D. T. T. Loan, N. D. Tho, N. H. Nghia, V. D. Chien, and T. A. Tuan, (2024) “Analyzing students’ performance using fuzzy logic and hierarchical linear regression" International Journal of Modern Education and Computer Science 16(1): 1–10. DOI: 10.5815/ijmecs.2024.01.01.
  24. [24] Y. Xu, Y. Shang, and P. Lu, (2024) “The Relationship Between Family Educational Inputs and Middle School Students’ Academic Achievement: A Moderated Mediated Effects Analysis" Journal of Advanced Research in Education 3(3): 66–77.
  25. [25] A. Srinivasulu and V. Palanisamy, (2024) “Data Driven Revolution in Academic Support for Mathematics Underachievers through Random Forest Individual and Hybrid Model" Journal of Artificial Intelligence and System Modelling 2(03): 1–21. DOI: 10.22034/jaism. 2024.469529.1048.
  26. [26] J. Dong, (2024) “Optimizing Curriculum for Students: A Machine Learning Approach to Time Management Analysis" Journal of Artificial Intelligence and System Modelling 2(02): 36–51. DOI: 10.22034/jaism.2024. 453326.1032.
  27. [27] Z. Zhang, D. Wu, and C. Zhang, (2021) “Study of cellular traffic prediction based on multi-channel sparse LSTM "Computer Science 48(6): 296–300.
  28. [28] H.S. Gill, O. I. Khalaf, Y. Alotaibi, S. Alghamdi, and F. Alassery, (2022) “Multi-Model CNN-RNN-LSTM Based Fruit Recognition and Classification." Intelligent Automation &Soft Computing33(1): DOI: 10.32604/ iasc.2022.022589.
  29. [29] S. Rajendran, O. I. Khalaf, Y. Alotaibi, and S. Al ghamdi,(2021) “MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network" Scientific Reports 11(1): 24138. DOI: 10.1038/s41598-021-03019-y.
  30. [30] O. I. Khalaf and G. M. Abdulsahib, (2021) “Design and Performance Analysis of Wireless IPv6 for Data Ex change." Journal of Information Science & Engineering 37(6): DOI: 10.6688/JISE.202111_37(6).0008.
  31. [31] Y. Yuan and W.Banzhaf, (2018) “Arja: Automated re pair of java programs via multi-objective genetic program ming" IEEE Transactions on software engineering 46(10): 1040–1067. DOI: 10.1109/TSE.2018.2874648.
  32. [32] Y. Chen, H. Chang, J. Meng, and D. Zhang, (2019) “Ensemble Neural Networks (ENN): A gradient-free stochastic method" Neural Networks 110: 170–185. DOI: 10.1016/j.neunet.2018.11.009.
  33. [33] C.Yang, A. Kortylewski, C. Xie, Y. Cao, and A. Yuille. “Patchattack: A black-box texture-based attack with reinforcement learning”. In: European Conference on Computer Vision. Springer. 2020, 681–698. DOI: 10. 1007/978-3-030-58574-7_41.