The Influence of Machine Learning on the Student Learning Psychology in Industry 5.0

Iin Mutmainnah; Ja’far Shodiq; Nur Faizah

doi:10.61987/edsojou.v1i2.635

Authors

Iin Mutmainnah Sekolah Tinggi Agama Islam Al-Muntahy
Ja’far Shodiq Sekolah Tinggi Agama Islam Al-Muntahy
Nur Faizah Sekolah Tinggi Agama Islam Al-Muntahy

DOI:

https://doi.org/10.61987/edsojou.v1i2.635

Keywords:

Machine Learning, Learning Psychology, Academic Anxiety Reduction, Self-Confidence, Positive Reinforcement

Abstract

This study aims to examine how the application of a machine learning-based learning system to students' learning psychology. This study focuses on improving the quality of learning that not only pays attention to cognitive aspects, but also to students' emotional aspects that often affect their academic achievement. This study uses a qualitative approach with a descriptive research type to explore students' experiences in using a machine learning-based system in the learning process. Data collection techniques are carried out through interviews, observations, and documentation, with data analysis using the Miles and Huberman model. The results of the study indicate that the application of this system is effective in reducing academic anxiety (Academic Anxiety Reduction), increasing students' self-confidence through positive reinforcement (Increased self-confidence through positive reinforcement), and developing students' emotional resilience (Positive Resilience to Emotional Learning). The contribution of this study is to provide new insights into the role of technology, especially machine learning, in supporting the development of students' emotional intelligence which can ultimately improve their academic well-being.

References

Agarwal, J., & Sharma, S. (2024). Artificial Intelligence Enabled Cognitive Computer-Centered Digital Analysis Model for Examination of the Children's Mental Health. Evolutionary Intelligence, 17(5), 3675-3685. https://doi.org/10.1007/s12065-024-00951-6

Albreiki, B., Habuza, T., & Zaki, N. (2022). Framework for Automatically Suggesting Remedial Actions to Help Students at Risk Based on Explainable ML and Rule-Based Models. International Journal of Educational Technology in Higher Education, 19(1), 49. https://doi.org/10.1186/s41239-022-00354-6

Ataman, A., Baharun, H., & Safitri, S. D. (2024). Exploring Complementary Leadership Styles in Madrasahs by Aiming at Their Impact on Integrity and Character Development. Business and Applied Management Journal, 1(2), 118-133. https://doi.org/10.61987/bamj.v1i2.487

Dasi, N. N. J. P. D., Dharmayanti, P. A., & WMP, D. A. (2023). Behavioral Counseling Guidebook with Discussion Techniques and Positive Reinforcement to Increase High School Students' Self-Confidence. Bisma The Journal of Counseling, 7(1), 114-121. https://doi.org/10.23887/bisma.v7i1.58905

Demartini, C. G., Sciascia, L., Bosso, A., & Manuri, F. (2024). Artificial Intelligence Bringing Improvements to Adaptive Learning in Education: A Case Study. Sustainability, 16(3), 1347. https://doi.org/10.3390/su16031347

Fadli Hidayat, M. N., Baharun, H., Aisyah, E. N., Zaini, A. W., & Hasanah, R. (2024). Bridging the Digital Divide: The Role of Public Relations in Enhancing Digital Inclusivity. Proceedings - International Conference on Education and Technology, ICET, 59-66. https://doi.org/10.1109/ICET64717.2024.10778472

Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in E-Learning: A Literature Review. Education Sciences, 13(12), 1216. https://doi.org/10.3390/educsci13121216

Hehir, E., Zeller, M., Luckhurst, J., & Chandler, T. (2021). Developing Student Connectedness Under Remote Learning Using Digital Resources: A Systematic Review. Education and Information Technologies, 26(5), 6531-6548. https://doi.org/10.1007/s10639-021-10577-1

Hermiyati, N., Yurniwati, Y., & Yarmi, G. (2024). The Impact of Problem-Posing Learning Method on Critical Thinking Skills in Terms of Self-Confidence. Jurnal Elementaria Edukasia, 7(2), 2570-2582. https://doi.org/10.31949/jee.v7i2.9336

Jafari, D., & Yazdi, Z. S. (2024). Transforming Education with AI: The Development of a Personalized Learning Algorithm for Individual Learning Styles. Journal of Algorithms and Computation, 56(2), 135-150.

Kasinathan, P., Pugazhendhi, R., Elavarasan, R. M., Ramachandaramurthy, V. K., Ramanathan, V., Subramanian, S., Kumar, S., Nandhagopal, K., Raghavan, R. R. V., & Rangasamy, S. (2022). Realization of Sustainable Development Goals with Disruptive Technologies by Integrating Industry 5.0, Society 5.0, Smart Cities and Villages. Sustainability, 14(22), 15258. https://doi.org/10.3390/su142215258

Khaira, M. K., Raja Gopal, R. L., Mohamed Saini, S., & Md Isa, Z. (2023). Interventional Strategies to Reduce Test Anxiety Among Nursing Students: A Systematic Review. International Journal of Environmental Research and Public Health, 20(2), 1233. https://doi.org/10.3390/ijerph20021233

Kikalishvili, S. (2024). Unlocking the Potential of GPT-3 in Education: Opportunities, Limitations, and Recommendations for Effective Integration. Interactive Learning Environments, 32(9), 5587-5599. https://doi.org/10.1080/10494820.2023.2220401

Ko, G. Y., Shin, D., Auh, S., Lee, Y., & Han, S. P. (2023). Learning Outside the Classroom During a Pandemic: Evidence from an Artificial Intelligence-Based Education App. Management Science, 69(6), 3616-3649. https://doi.org/10.1287/mnsc.2022.4531

Leko, M. M., Cook, B. G., & Cook, L. (2021). Qualitative Methods in Special Education Research. Learning Disabilities Research and Practice, 36(4), 278-286. https://doi.org/10.1111/ldrp.12268

Li, H., Zhang, R., Lee, Y.-C., Kraut, R. E., & Mohr, D. C. (2023). Systematic Review and Meta-Analysis of AI-Based Conversational Agents for Promoting Mental Health and Well-Being. NPJ Digital Medicine, 6(1), 236. https://doi.org/10.1038/s41746-023-00979-5

Li, J., & Xue, E. (2023). Dynamic Interaction Between Student Learning Behaviour and Learning Environment: Meta-Analysis of Student Engagement and Its Influencing Factors. Behavioral Sciences, 13(1), 59. https://doi.org/10.3390/bs13010059

Liu, M., & Yu, D. (2023). Towards Intelligent E-Learning Systems. Education and Information Technologies, 28(7), 7845-7876. https://doi.org/10.1007/s10639-022-11479-6

Lu, G., Xie, K., & Liu, Q. (2022). What Influences Student Situational Engagement in Smart Classrooms: Perception of the Learning Environment and Students' Motivation. British Journal of Educational Technology, 53(6), 1665-1687. https://doi.org/10.1111/bjet.13204

Luan, H., & Tsai, C.-C. (2021). A Review of Using Machine Learning Approaches for Precision Education. Educational Technology & Society, 24(1), 250-266.

Maisuroh, S., & Aisyah, N. (2024). Connecting Tradition with Innovation: The Impact of WordWall on Learning Outcomes in Fiqh Studies. Fondatia, 8(3), 715-728. https://doi.org/10.36088/fondatia.v8i3.5294

Matos, J. F., Piedade, J., Freitas, A., Pedro, N., Dorotea, N., Pedro, A., & Galego, C. (2023). Teaching and Learning Research Methodologies in Education: A Systematic Literature Review. Education Sciences, 13(2), 173. https://doi.org/10.3390/educsci13020173

Mijangos, L. P. J., Rodríguez-Arce, J., Martínez-Méndez, R., & Reyes-Lagos, J. J. (2023). Advances and Challenges in the Detection of Academic Stress and Anxiety in the Classroom: A Literature Review and Recommendations. Education and Information Technologies, 28(4), 3637-3666. https://doi.org/10.1007/s10639-022-11324-w

Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022). A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0. Energies, 15(17), 6276. https://doi.org/10.3390/en15176276

Mulisa, F. (2022). When Does a Researcher Choose a Quantitative, Qualitative, or Mixed Research Approach? Interchange, 53(1), 113-131. https://doi.org/10.1007/s10780-021-09447-z

Munir, H., Vogel, B., & Jacobsson, A. (2022). Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision. Information, 13(4), 203. https://doi.org/10.3390/info13040203

Ohadomere, O., & Ogamba, I. K. (2021). Management-Led Interventions for Workplace Stress and Mental Health of Academic Staff in Higher Education: A Systematic Review. The Journal of Mental Health Training, Education and Practice, 16(1), 67-82. https://doi.org/10.1108/JMHTEP-07-2020-0048

Raja Santhi, A., & Muthuswamy, P. (2023). Industry 5.0 or Industry 4.0 S? Introduction to Industry 4.0 and a Peek into the Prospective Industry 5.0 Technologies. International Journal on Interactive Design and Manufacturing (IJIDeM), 17(2), 947-979. https://doi.org/10.1007/s12008-023-01217-8

Roos, A.-L., Goetz, T., Krannich, M., Jarrell, A., Donker, M., & Mainhard, T. (2021). Test Anxiety Components: An Intra-Individual Approach Testing Their Control Antecedents and Effects on Performance. Anxiety, Stress, & Coping, 34(3), 279-298. https://doi.org/10.1080/10615806.2020.1850700

Saharani, L., & Diana, S. (2024). Optimization of Islamic Values-Based Public Relations Strategy in Increasing New Student Admissions. Managere: Indonesian Journal of Educational Management, 6(2), 194-206.

Sanjani, M. A. F. (2024). The Impact of School Principals on Graduate Quality Through Character Education Initiatives. Journal of Educational Management Research, 3(1), 30-46. https://doi.org/10.61987/jemr.v3i1.347

Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023). A Systematic Review of Teaching and Learning Machine Learning in K-12 Education. Education and Information Technologies, 28(5), 5967-5997. https://doi.org/10.1007/s10639-022-11416-7

Song, C., Shin, S.-Y., & Shin, K.-S. (2024). Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways. Applied Sciences, 14(2), 916. https://doi.org/10.3390/app14020916

Surahman, E., & Wang, T. (2022). Academic Dishonesty and Trustworthy Assessment in Online Learning: A Systematic Literature Review. Journal of Computer Assisted Learning, 38(6), 1535-1553. https://doi.org/10.1111/jcal.12708

Taj, I., & Zaman, N. (2022). Towards Industrial Revolution 5.0 and Explainable Artificial Intelligence: Challenges and Opportunities. International Journal of Computing and Digital Systems, 12(1), 295-320. https://doi.org/10.12785/ijcds/120128

Yabanova, U., & Demirkan, O. (2021). The Effects of a Mobile Pre-Learning System with Surface Learning Approach on Academic Achievement and Mobile Learning Attitude. Educational Process: International Journal, 10(2), 42-58. https://doi.org/10.22521/edupij.2021.102.3

Yang, Y., Tavares, J., & Oliveira, T. (2024). A New Research Model for Artificial Intelligence-Based Well-Being Chatbot Engagement: Survey Study. JMIR Human Factors, 11(1), e59908. https://doi.org/10.2196/59908

Zheng, J., Zhang, Y., & Zhang, S. (2024). Audio-Visual Aesthetic Teaching Methods in College Students' Vocal Music Teaching by Deep Learning. Scientific Reports, 14(1), 29386. https://doi.org/10.1038/s41598-024-80640-7

Zheng, X., Ma, Y., Yue, T., & Yang, X. (2023). Effects of Different Types of Cues and Self-Explanation Prompts in Instructional Videos on Deep Learning: Evidence from Multiple Data Analysis. Educational Technology Research and Development, 71(3), 807-831. https://doi.org/10.1007/s11423-023-10188-2