Abstract
The experiences and feelings in a first phase of transition from undergraduate to graduate courses may lead to some kind of anxiety, depression, malaise or loneliness that are not easily overwhelmed, no doubt the educational character of each one comes into play, since the involvement of each student in academic practice depends on his/her openness to the world. In this study it will be analyzed and evaluated the relationships between academic experiences and the correspondent anxiety levels. Indeed, it is important not only a diagnose and evaluation of the students’ needs for pedagogical and educational reorientation, but also an identification of what knowledge and attitudes subsist at different stages of their academic experience. The system envisaged stands for a Hybrid Artificial Intelligence Agency that integrates the phases of data gathering, processing and results’ analysis. It intends to uncover the students’ states of Adaptation, Anxiety and Anxiety Trait in terms of an evaluation of their entropic states, according to the 2nd Law of Thermodynamics, i.e., that energy cannot be created or destroyed; the total quantity of energy in the universe stays the same. The logic procedures are based on a Logic Programming approach to Knowledge Representation and Reasoning complemented with an Artificial Neural Network approach to computing.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Admi, H.: Nursing student’s stress during the initial clinical experience. J. Nurs. Educ. 36, 323–327 (1997)
Barros, M.: The relevance and quality of academic experience: An active training path. In: Pouzada, A.S., Almeida, L.S., Vasconcelos, R.M. (eds.) Contexts and Dynamics of Academic Life, pp. 99–106. University of Minho, Guimarães (2002)
Evans, N.J., Forney, D.S., Guido, F.M., Patton, L.D., Renn, K.A.: Student Development in College: Theory, Research and Practice. Jossey–Bass, San Francisco (2010)
Neves, J., et al.: Entropy and organizational performance. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 206–217. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_18
Fernandes, B., Vicente, H., Ribeiro J., Capita, A., Analide, C., Neves, J.: Fully informed vulnerable road users – simpler, maybe better. In: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services (iiWAS2019), pp. 600–604. Association for Computing Machinery, New York (2020)
Fernández-Delgado, M., Cernadas, E., Barro, S., Ribeiro, J., Neves, J.: Direct Kernel Perceptron (DKP): ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculation. J. Neural Netw. 50, 60–71 (2014)
Wenterodt, T., Herwig, H.: The entropic potential concept: a new way to look at energy transfer operations. Entropy 16, 2071–2084 (2014)
Spielberger, C.D., Sarason, I.G. (eds.): Stress and Emotion: Anxiety, Anger, and Curiosity, vol. 16. Taylor & Francis, New York (1996)
Neves, J.: A logic interpreter to handle time and negation in logic databases. In: Muller, R., Pottmyer, J. (eds.) Proceedings of the 1984 Annual Conference of the ACM on the 5th Generation Challenge, pp. 50–54. ACM, New York (1984)
Figueiredo, M., Fernandes, A., Ribeiro, J., Neves, J., Dias, A., Vicente, H.: An assessment of students’ satisfaction in higher education. In: Vittorini, P., Di Mascio, T., Tarantino, L., Temperini, M., Gennari, R., De la Prieta, F. (eds.) MIS4TEL 2020. AISC, vol. 1241, pp. 147–161. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52538-5_16
Fernandes, A., Figueiredo, M., Ribeiro, J., Vicente, D., Neves, J., Vicente, H.: Psychosocial risks management. Proc. Comput. Sci. 176, 743–752 (2020). https://doi.org/10.1016/j.procs.2020.09.069
Acknowledgments
This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Costa, A. et al. (2020). Adaptation and Anxiety Assessment in Undergraduate Nursing Students. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-62362-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62361-6
Online ISBN: 978-3-030-62362-3
eBook Packages: Computer ScienceComputer Science (R0)