Deep learning in higher education students. Systematic review
Keywords:
Character; citizenship; collaboration; communication; critical thinkingAbstract
This study arises as a need to recognize deep learning in higher education students and their integration to become professionals who respond to current challenges. Objective: The research aimed to analyze and recognize deep learning in higher education students. Method: The research is qualitative, with a systematic design, following the methodology proposed by the PRISMA Declaration. Inclusion and exclusion criteria were applied to process the analyzed articles. A study was carried out based on 25 articles published in high-impact scientific journals between the years 2021 and 2022. Results: The results show that deep learning skills in higher education students have to be worked on in an integrated manner: character, collaboration, communication, citizenship, critical thinking and creativity that will result in improved academic performance. Conclusions: There are few studies that address the development of deep learning skills, character, collaboration, communication, citizenship, critical thinking and creativity.
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Alhammadi, S. (2021). The effect of the COVID-19 pandemic on learning quality and practices in higher education—using deep and surface approaches. Education Sciences, 11(9), 1–13. https://doi.org/10.3390/EDUCSCI11090462
Alsayed, S., Alshammari, F., Pasay-an, E., & Dator, W. (2021). Investigating the learning approaches of students in nursing education. Journal of Taibah University Medical Sciences, 16(1), 43–49. https://doi.org/10.1016/J.JTUMED.2020.10.008
Castañeda, M., Recines, A., Baldeón, P., Méndez, J., & Flores, E. (2022). Internal control and its impact on labor productivity in public educational institutions. Systematic review. International Journal of Mechanical Engineering, 7(1), 5791–5800. https://kalaharijournals.com/resources/IJME_Vol7.1_578.pdf
Chaerul, S., & Bagus, W. (2022). Deep Learning Model for Sentiment Analysis on Short Informal Texts. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 10(1). https://doi.org/10.52549/IJEEI.V10I1.3181
Fullan, M., Quinn, J., & Mceachen, J. (2018). Praise for Deep Learning: Engage the World Change the World. Corwin. Ontario Principals Council. (C. nOntario P. Council. (ed.); Corwin. On). Corwin. Ontario Principals Council. https://doi.org/https://lccn.loc.gov/2017038392
Han, L., Yao, X., & Yu, J. (2022). Application of Deep Learning in Medical English Teaching Evaluation. Wireless Communications and Mobile Computing, 2022, 1–9. https://doi.org/10.1155/2022/8671806
He, X., Chen, P., Wu, J., & Dong, Z. (2021). Deep Learning-Based Teaching Strategies of Ideological and Political Courses Under the Background of Educational Psychology. Frontiers in Psychology, 12, 1–12. https://doi.org/10.3389/FPSYG.2021.731166
Hérnandez, R., Fernández, C., & Baptista, P. (2014). Metodología de la investigación (McGRAW-HILL (ed.); 6a ed.).
Hsu, Y.-C. (2021). An action research in critical thinking concept designed curriculum based on collaborative learning for engineering ethics course. Sustainability (Switzerland), 13(5), 1–20. https://doi.org/10.3390/SU13052621
İnce, M. (2022). Automatic and intelligent content visualization system based on deep learning and genetic algorithm. Neural Computing and Applications, 34(3), 2473–2493. https://doi.org/10.1007/S00521-022-06887-1
James, M., Teixeira, A., Barnabas, D., Sadza, A., Smith, S., Usmani, O., & John, C. (2022). Collaborative case-based learning with programmatic team-based assessment: a novel methodology for developing advanced skills in early-years medical students. BMC Medical Education, 22(1). https://doi.org/10.1186/S12909-022-03111-5
Jamil, M. G., & Bhuiyan, Z. (2021). Deep learning elements in maritime simulation programmes: a pedagogical exploration of learner experiences. International Journal of Educational Technology in Higher Education, 18(1), 1–22. https://doi.org/10.1186/S41239-021-00255-0
Jian-Wei, T., Chia-An, L., Nen-Fu, H., Hao-Hsuan, H., & Chin-Feng, L. (2022). MOOC Evaluation System Based on Deep Learning. International Review of Research in Open and Distance Learning, 23(1), 21–40. https://doi.org/10.19173/IRRODL.V22I4.5417
Kurniawati, F., Yhahnazustikarini, A., & Safitri, S. (2022). Effect of intellectual humility on creativity fostering teacher beahavior: Teacher wellbeing as mediator. Jurnal Cakrawala Pendidikan, 41(1). https://doi.org/10.21831/CP.V41I1.40055
Li, H., Deng, H., & Zhang, Y. (2022). Application of the PBL Model Based on Deep Learning in Physical Education Classroom Integrating Production and Education. Computational Intelligence and Neuroscience, 2022, 1–12. https://doi.org/10.1155/2022/4806763
Liu, E., Zhao, J., & Sofeia, N. (2022). Students’ Entire Deep Learning Personality Model and Perceived Teachers’ Emotional Support. Frontiers in Psychology, 12. https://doi.org/10.3389/FPSYG.2021.793548
López-López, E., Tobón, S., & Juárez-Hernández, L. G. (2019). Escala para Evaluar Artículos Científicos en Ciencias Sociales y Humanas- EACSH. REICE. Revista Iberoamericana Sobre Calidad, Eficacia y Cambio En Educación, 17(4), 111–125. https://doi.org/10.15366/reice2019.17.4.006
Ming-Ni, C., & Nagatomo, D. (2022). Study of STEM for sustainability in design education: Framework for student learning and outcomes with design for a disaster project. Sustainability, 14(1), 2–15. https://doi.org/10.3390/SU14010312
Mou, C., Tian, Y., Zhang, F., & Zhu, C. (2022). Current Situation and Strategy Formulation of College Sports Psychology Teaching Following Adaptive Learning and Deep Learning Under Information Education. Frontiers in Psychology, 12, 1–10. https://doi.org/10.3389/FPSYG.2021.766621
Muñoz-García, A., & Villena-Martínez, M. D. (2021). Influences of learning approaches, student engagement, and satisfaction with learning on measures of sustainable behavior in a social sciences student sample. Sustainability (Switzerland), 13(2), 1–14. https://doi.org/10.3390/SU13020541
Page, M. J., McKenziea, J. E., Bossuytb, P. M., Boutronc, I., Hoffmannd, T. C., Mulrowe, C. D., Shamseerf, L., Tetzlaffg, J., Akl, E., Brennana, S. E., Choui, R., Glanvillej, J., Grimshawk, J., Bjartssonl, A., Lalum, M., Lin, T., Lodero, E., Mayo-Wilsonp, E., McDonalda, S., … Mother, D. (2021). Declaración PRISMA2020: una una guía actualizada para la publicación de revisiones sistemáticas. Revista Especial Cardiol, 74(9), 790–799. https://doi.org/https://doi.org/10.1016/j.recesp.2021.06.016
Papier, J. (2021). 21st Century competencies in Technical and Vocational Education and Training: Rhetoric and reality in the wake of a pandemic. Journal of Education (South Africa), 84, 67–84. https://doi.org/10.17159/2520-9868/I84A04
Prianto, A., Qomariyah, U. N., & Firman. (2022). Does Student Involvement in Practical Learning Strengthen Deeper Learning Competencies? International Journal of Learning, Teaching and Educational Research, 21(2), 211–231. https://doi.org/10.26803/IJLTER.21.2.12
Qu, G., Hu, W., Jiao, W., & Jin, J. (2021). Application of Deep Learning-Based Integrated Trial-Error + Science, Technology, Reading/Writing, Engineer, Arts, Mathematics Teaching Mode in College Entrepreneurship Education. Frontiers in Psychology, 12, 12. https://doi.org/10.3389/FPSYG.2021.739362
Rong, Q., Lian, Q., & Tang, T. (2022). Research on the Influence of AI and VR Technology for Students’ Concentration and Creativity. Frontiers in Psychology, 13. https://doi.org/10.3389/FPSYG.2022.767689
Sen, G., Adeboye, A., & Alagbe, O. (2021). The influence of architecture students’ learning approaches on their academic performance in two Nigeria universities. International Journal of Learning, Teaching and Educational Research, 20(2), 137–151. https://doi.org/10.26803/IJLTER.20.2.8
Sun, L., & En, C. (2021). Effects of the Application of Information Technology to E-Book Learning on Learning Motivation and Effectiveness. Frontiers in Psychology, 12, 1–5. https://doi.org/10.3389/FPSYG.2021.752303
Wu, C. C. (2022). Investigating the Effect of the State, Stability, and Change in Deep Approaches to Learning From Kindergarten to Third Grade: A Multilevel Structural Equation Modeling Indicator-Specific Growth Model Approach. Frontiers in Psychology, 13. https://doi.org/10.3389/FPSYG.2022.852508
Zárate-Santana, Z., Patino-Alonso, M., Sánchez-García, A., & Galindo-Villardón, P. (2021). Learning approaches and coping with academic stress for sustainability teaching: Connections through canonical correspondence analysis. Sustainability (Switzerland), 13(2), 1–17. https://doi.org/10.3390/SU13020852
Zhao, X., & Jin, X. (2022). Standardized Evaluation Method of Pronunciation Teaching Based on Deep Learning. Security and Communication Networks, 2022, 1–11. https://doi.org/10.1155/2022/8961836
Zhu, Q., & Zhang, H. (2022). Teaching Strategies and Psychological Effects of Entrepreneurship Education for College Students Majoring in Social Security Law Based on Deep Learning and Artificial Intelligence. Frontiers in Psychology, 13. https://doi.org/10.3389/FPSYG.2022.779669
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