Skip to main content
SpringerLink
Log in

A Robot-assisted real case-handling approach to improving students’ learning performances in vocational training

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

In vocational education, cultivating students’ ability to deal with real cases is a crucial training objective. The BSFE (i.e., Brainstorming, Screening, Formation, Examination) model is a commonly adopted training procedure. Each stage is designed for guiding students to analyze and find solutions to handle real cases. However, as one teacher is generally responsible for several dozen students, it becomes challenging for the teacher to adequately address each student’s questions and individual needs. Therefore, this study proposed the robot teaching assistant-supported learning (RTAL) mode following the BSFE model to cope with this problem. This investigation assessed its efficacy through an experiment within an Acute Asthma Attack curriculum. The research involved 103 nursing students in their third year from two distinct classes at a vocational university. Fifty-three students from a class constituted the experimental group that implemented the RTAL approach, whereas the other class, comprising 50 students, was the control group utilizing the standard technology-supported learning (CTL) approach. Findings indicated that the experimental group surpassed the control group in various aspects, including learning outcomes, learning attitudes, problem-solving tedencies, critical thinking awareness, acceptance of technology, and satisfaction with the learning experience. The interview findings also revealed that the RTAL mode could cater to individualized learning needs, facilitate interaction, and serve as an auxiliary instructional tool.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The data and materials are available upon request to the corresponding author.

Code availability

Not applicable.

References

  • Aralihond, A., Shanta, Z., Pullattayil, A., & Powell, C. V. E. (2020). Treating acute severe asthma attacks in children: Using aminophylline. Breathe, 16(4). https://doi.org/10.1183/20734735.0081-2020.

  • Banaeian, H., & Gilanlioglu, I. (2021). Influence of the NAO robot as a teaching assistant on university students’ vocabulary learning and attitudes. Australasian Journal of Educational Technology, 37(3), 71–87. https://doi.org/10.14742/ajet.6130.

    Article  Google Scholar 

  • Bell, D. (1996). The formation of concepts and the structure of thoughts. Philosophy and Phenomenological Research, 56(3), 583–596.

    Article  Google Scholar 

  • Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., & Tanaka, F. (2018). Social robots for education: A review. Science Robotics, 3(21), eaat5954. https://doi.org/10.1126/scirobotics.aat5954.

    Article  Google Scholar 

  • Chen, H., Park, H. W., & Breazeal, C. (2020). Teaching and learning with children: Impact of reciprocal peer learning with a social robot on children’s learning and emotive engagement. Computers & Education, 150, 103836. https://doi.org/10.1016/j.compedu.2020.103836.

    Article  Google Scholar 

  • Chen, B., Hwang, G. H., & Wang, S. H. (2021). Gender differences in cognitive load when applying game-based learning with Intelligent Robots. Educational Technology & Society, 24(3), 102–115.

    Google Scholar 

  • Chen, X., Cheng, G., Zou, D., Zhong, B., & Xie, H. (2023). Artificial intelligent robots for precision education. Educational Technology & Society, 26(1), 171–186.

    Google Scholar 

  • Chookaew, S., & Panjaburee, P. (2022). Implementation of a robotic-transformed five-phase inquiry learning to foster students’ computational thinking and engagement: A mobile learning perspective. International Journal of Mobile Learning and Organisation, 16(2), 198–220.

    Article  Google Scholar 

  • Chu, H. C., Hwang, G. J., Tsai, C. C., & Tseng, J. C. R. (2010). A two-tier test approach to developing location-aware mobile learning systems for natural science courses. Computers & Education, 55(4), 1618–1627. https://doi.org/10.1016/j.compedu.2010.07.004.

    Article  Google Scholar 

  • Dubovi, I., Levy, S. T., & Dagan, E. (2017). Now I know how! The learning process of medication administration among nursing students with non-immersive desktop virtual reality simulation. Computers & Education, 113, 16–27. https://doi.org/10.1016/j.compedu.2017.05.009.

    Article  Google Scholar 

  • Engwall, O., & Lopes, J. (2020). Interaction and collaboration in robot-assisted language learning for adults. Computer Assisted Language Learning, 1–37. https://doi.org/10.1080/09588221.2020.1799821.

  • Fildes, E. E., Campell, N. J., & Garcia, R. (2021). International nursing collaboration to establish the Philippine quit line using a conceptual model for partnership and sustainability in global health. Journal of Addictions Nursing, 32(1), 27–31. https://doi.org/10.1097/JAN.0000000000000382.

    Article  Google Scholar 

  • Galvão, E. C. F., & Püschel, V. A. A. (2012). Multimedia application in mobile platform for teaching the measurement of central venous pressure. Revista da Escola de Enfermagem da Usp, 46, 107–115. https://doi.org/10.1590/S0080-62342012000700016.

  • Hong, Z. W., Huang, Y. M., Hsu, M., & Shen, W. W. (2016). Authoring robot-assisted instructional materials for improving learning performance and motivation in EFL classrooms. Educational Technology & Society, 19(1), 337–349.

    Google Scholar 

  • Hsieh, J. C., & Lee, J. S. (2021). Digital storytelling outcomes, emotions, grit, and perceptions among EFL middle school learners: Robot-assisted versus Power Point-assisted presentations. Computer Assisted Language Learning. https://doi.org/10.1080/09588221.2021.1969410.

    Article  Google Scholar 

  • Hwang, G. J., Yang, T. C., Tsai, C. C., & Yang, S. J. H. (2009). A context-aware ubiquitous learning environment for conducting complex science experiments. Computers & Education, 53(2), 402–413. https://doi.org/10.1016/j.compedu.2009.02.016.

    Article  Google Scholar 

  • Hwang, G. J., Yang, L. H., & Wang, S. Y. (2013). A concept map-embedded educational computer game for improving students’ learning performance in natural science courses. Computers & Education, 69, 121–130. https://doi.org/10.1016/j.compedu.2013.07.008.

    Article  Google Scholar 

  • Jahan, A., Ismail, M. Y., Sapuan, S. M., & Mustapha, F. (2010). Material screening and choosing methods–a review. Materials & Design, 31(2), 696–705. https://doi.org/10.1016/j.matdes.2009.08.013.

    Article  Google Scholar 

  • Johnson, D. W., & Johnson, R. T. (2018). Cooperative learning: The foundation for active learning. IntechOpen, 1–12. https://doi.org/10.5772/intechopen.81086.

  • Jones, H., Lawton, A., & Gupta, A. (2022). Asthma attacks in children—challenges and opportunities. Indian Journal of Pediatrics, 89(4), 373–377.

    Article  Google Scholar 

  • Kanero, J., et al. (2018). Social robots for early language learning: Current evidence and future directions. Child Development Perspectives, 12(3), 146–151. https://doi.org/10.1111/cdep.12277.

    Article  Google Scholar 

  • Kennedy, J., Baxter, P., Senft, E., & Belpaeme, T. (2016, March). Social robot tutoring for child second language learning. 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 231–238). IEEE.

  • Kim, R. H., & Mellinger, J. D. (2020). Educational strategies to foster bedside teaching. Surgery, 167(3), 532–534. https://doi.org/10.1016/j.surg.2019.06.007.

    Article  Google Scholar 

  • Konijn, E. A., & Hoorn, J. F. (2020). Robot tutor and pupils’ educational ability: Teaching the times tables. Computers & Education, 157, 103970. https://doi.org/10.1016/j.compedu.2020.103970.

    Article  Google Scholar 

  • Kubilinskiene, S., Zilinskiene, I., Dagiene, V., & Sinkevicius, V. (2017). Applying robotics in school education: A systematic review. Baltic Journal of Modern Computing, 5(1), 50–69. https://doi.org/10.22364/bjmc.2017.5.1.04.

    Article  Google Scholar 

  • Lai, C. L., & Hwang, G. J. (2014). Effects of mobile learning time on students’ conception of collaboration, communication, complex problem-solving, meta-cognitive awareness and creativity. International Journal of Mobile Learning and Organization, 8(3), 276–291. https://doi.org/10.1504/IJMLO.2014.067029.

    Article  MathSciNet  Google Scholar 

  • Lei, X., & Rau, P. L. P. (2021). Effect of Robot Tutor’s feedback Valence and Attributional Style on Learners. International Journal of Social Robotics, 1–19.

  • Li, D., & Chen, X. (2020). Study on the Application and Challenges of Educational Robots in Future Education Paper presented at the 2020 International Conference on Artificial Intelligence and Education (ICAIE). Lin, C. Y., & Wang, T. H. (2017). Implementation of personalized e-assessment for remedial teaching in an e-learning environment. Eurasia Journal of Mathematics, Science and Technology Education, 13(4), 1045–1058. https://doi.org/10.12973/eurasia.2017.00657a.

  • Liao, Y. J., Jao, Y. L., Boltz, M., Adekeye, O. T., Berish, D., Yuan, F., & Zhao, X. (2023). Use of a Humanoid Robot in supporting Dementia Care: A qualitative analysis. SAGE Open Nursing, 9, 23779608231179528.

    Article  Google Scholar 

  • Lin, H. C., Hwang, G. J., & Hsu, Y. D. (2019). Effects of ASQ-based flipped learning on nurse practitioner learners’ nursing skills, learning achievement and learning perceptions. Computers & Education, 139, 207–221. https://doi.org/10.1016/j.compedu.2019.05.014.

    Article  Google Scholar 

  • Mater, E. A. M., Ahmed, E. I., ElSayed, A. A., Shaikh, M. A. E., & Farag, M. K. (2014). The impact of the objective structured clinical examination approach for clinical evaluation skills on the student’s performance in nursing college. World Journal of Medical Sciences, 11(4), 609–613. https://doi.org/10.5829/idosi.wjms.2014.11.4.91139.

    Article  Google Scholar 

  • Mazzoni, E., & Benvenuti, M. (2015). A robot-partner for preschool children learning English using socio-cognitive conflict. Educational Technology & Society, 18(4), 474–485.

    Google Scholar 

  • Obafemi, K. E. (2024). Enhancing pupils’ academic performance in mathematics using brainstorming instructional strategy. ASEAN Journal of Science and Engineering Education, 4(2), 99–106.

    Google Scholar 

  • Papadopoulos, I., Lazzarino, R., Miah, S., & Weaver, T. (2020). A systematic review of the literature regarding socially assistive robots in pre-tertiary education. Computers & Education, 155, 103924. https://doi.org/10.1016/j.compedu.2020.103924.

    Article  Google Scholar 

  • Park, H. W., Grover, I., Spaulding, S., Gomez, L., & Breazeal, C. (2019, July). A model-free affective reinforcement learning approach to personalization of an autonomous social robot companion for early literacy education. In Proceedings of the AAAI Conference on Artificial Intelligence, 33 (l), 687–694. https://doi.org/10.1609/aaai.v33i01.3301687.

  • Pfadenhauer, M., & Dukat, C. (2015). Robot caregiver or robot-supported caregiving? The performative deployment of the social robot PARO in dementia care. International Journal of Social Robotics, 7, 393–406.

    Article  Google Scholar 

  • Pueyo-Garrigues, M., Whitehead, D., Pardavila-Belio, M. I., Canga-Armayor, A., Pueyo-Garrigues, S., & Canga-Armayor, N. (2019). Health education: A rogerian concept analysis. International Journal of Nursing Studies, 94, 131–138. https://doi.org/10.1016/j.ijnurstu.2019.03.005.

    Article  Google Scholar 

  • Randall, N. (2019). A Survey of Robot-assisted Language Learning (RALL).ACM transactions on Human-Robot Interaction, (9) 1.7:1–736. https://doi.org/10.1145/3345506.

  • Rezasoltani, A., Saffari, E., Konjani, S., Ramezanian, H., & Zam, M. (2022). Exploring the viability of robot-supported flipped classes in English for medical purposes reading com-prehension. arXiv Preprint arXiv:2208 07442. https://doi.org/10.48550/arXiv.2208.07442.

    Article  Google Scholar 

  • Richert, A., Schiffmann, M., & Yuan, C. (2020). A nursing robot for social interactions and health assessment. In Advances in Human Factors in Robots and Unmanned Systems: Proceedings of the AHFE 2019 International Conference on Human Factors in Robots and Unmanned Systems, July 24–28, 2019, Washington DC, USA 10 (pp. 83–91). Springer International Publishing.

  • Rowe, B. H., Sevcik, W., & Villa-Roe, C. (2011). Management of severe acute asthma in the emergency department. Current Opinion in Critical Care, 17(4), 335–341. https://doi.org/10.1097/MCC.0b013e328348bf09.

    Article  Google Scholar 

  • Shimada, M., Kanda, T., & Koizumi, S. (2012). How can a social robot facilitate children’s collaboration? In Social Robotics: 4th International Conference, ICSR 2012, Chengdu, China, October 29–31, 2012. Proceedings 4 (pp. 98–107). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3642-34103-810.

  • Su, C. H., He, Y. H., & Peng, Y. H. (2012). Respiratory care for a patient undergoing extracorporeal life support for Status Asthmaticus. Journal of Respiratory Therapy, 11(1), 45–55. https://doi.org/10.6269/JRT.2012.11.1.05.

    Article  Google Scholar 

  • Trollvik, A., Nordbach, R., Silen, C., & Ringsberg, K. C. (2011). Children’s experiences of living with asthma: Fear of exacerbations and being ostracized. Journal of Pediatric Nursing, 26(4), 295–303. https://doi.org/10.1016/j.pedn.2010.05.003.

    Article  Google Scholar 

  • Varney, M. W., Janoudi, A., Aslam, D. M., & Graham, D. (2012). Building young engineers: TASEM for third graders in woodcreek magnet elementary school. IEEE Transactions on Education, 55(1), 78–82. https://doi.org/10.1109/TE.2011.2131143.

    Article  Google Scholar 

  • Yang, K. H., & Lu, B. C. (2021). Towards the successful game-based learning: Detection and feedback to misconceptions is the key. Computers & Education, 160, 104033. https://doi.org/10.1016/j.compedu.2020.104033.

    Article  Google Scholar 

  • Yueka, L. (2019). Current situation, dynamic and problems of AI + teacher education. Journal of Modern Education Technology, 29(11), 114–120. Zhong, S.

    Google Scholar 

Download references

Funding

This study is supported in part by the National Science and Technology Council of Taiwan under contract numbers NSTC 112-2410-H-011-012-MY3 and MOST 111-2410-H-011 -007 -MY3. The study is also supported by the “Empower Vocational Education Research Center” of National Taiwan University of Science and Technology (NTUST) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

Author information

Authors and Affiliations

  1. Department of Nursing, Chang Gung University of Science and Technology, Taoyuan City, Taiwan

    Chun-Chun Chang

  2. Graduate Institute of Educational Information and Measurement, National Taichung University of Education, Taichung City, Taiwan

    Gwo-Jen Hwang

  3. Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei City, Taiwan

    Gwo-Jen Hwang

Authors
  1. Chun-Chun Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gwo-Jen Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed to the conceptualization and design of the study. Material preparation, data collection, analysis, project management and methodology were performed by Chun-Chun Chang. Methodology and supervision were performed Gwo-Jen Hwang.

Corresponding author

Correspondence to Gwo-Jen Hwang.

Ethics declarations

Ethical approval

The study has been evaluated and approved by the research ethics committee of Chang Gung with the IRB number 202300304B0C101.

Consent to participate

The participants all agreed to take part in this study.

Consent for publication

The publication of this study has been approved by all authors.

Conflicts of interest

There is no potential conflict of interest in this study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, CC., Hwang, GJ. A Robot-assisted real case-handling approach to improving students’ learning performances in vocational training. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12778-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10639-024-12778-w

Keywords

Search

Navigation