Impact of 3D Simulation Game as a Method to Learn Medication Administration Process: Intervention Research for Nursing Students

Saastamoinen, Tiia; Härkänen, Marja; Vehviläinen‐Julkunen, Katri; Näslindh-Ylispangar, Anita

doi:10.1016/j.ecns.2022.02.005

Cited by 7 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, business simulation games today acquire their role in different human life fields such as information technology, learning, teaching, strategic and operations management (business decision-making), accounting, medicine, administration, engineering, etc. [17,22,24,[31][32][33]. Disciplines that are, at first sight, outside of the scope of business are also often the topic of the simulation's research, which can be the basis for the simulation games' development, such as health care [34], sustainable urban development [35], business process management [36], enterprise resource planning [37], investments [38], and knowledge management [39].…”

Section: Business Simulation Gamesmentioning

confidence: 99%

Quo Vadis Business Simulation Games in the 21st Century?

et al. 2023

View full text Add to dashboard Cite

Business simulation games have become popular in higher education and business environments. The paper aims to identify the primary research trends and topics of business simulation games research using a systematic and automated literature review with the motivation of research (learning driven and domain driven). Based on these findings, the future development of business simulation games research projected papers that research business simulation games were extracted from Scopus. Second, the research timeline, main publication venues and citation trends have been analysed. Third, the most frequent words, phrases, and topics were extracted using text mining. Results indicate that the research on business simulation games has stagnated, with the most cited papers published in the 2000s. There is a balance between learning-driven and domain driven-research, while technology-driven research is scarce, indicating that the technology used for business simulation games is mature. We project that the research on business simulation games needs to be directed in the area of new technologies that could improve communication with and among the users (virtual reality, augmented reality, simulation games) and technologies that could improve the reasoning and decision-making complexity in business simulation games (artificial intelligence).

show abstract

Section: Business Simulation Gamesmentioning

confidence: 99%

Quo Vadis Business Simulation Games in the 21st Century?

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In nursing education, discerning students' learning styles is of paramount importance to developing effective educational programs and promoting student success. Learning styles encompass the preferred methods and approaches that individuals employ to grasp, process, and retain information [1], [2], [3], [4], [5], [6]. By recognizing and accommodating diverse learning styles, educators can foster a more inclusive and engaging learning atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Predicting Optimal Learning Approaches for Nursing Students in Morocco

Fadili,

Ertel,

Mengad

et al. 2024

IJACSA

View full text Add to dashboard Cite

In nursing education, recognizing and accommodating diverse learning styles is imperative for the development of effective educational programs and the success of nursing students. This article addresses the crucial challenge of classifying the learning styles of nursing students in Morocco, where contextual studies are limited. To address this research gap, a contextual approach is proposed, aiming to develop a predictive model of the most appropriate learning approach (observational, experiential, reflective and active) for each nursing student in Morocco. This model incorporates a comprehensive set of variables such as age, gender, education, work experience, preferred learning strategies, engagement in social activities, attitudes toward failure, and self-assessment preferences. We used four multivariate machine learning algorithms, namely SVM, Tree, Neural Network, and Naive Bayes, to determine the most reliable and effective classifiers. The results show that neural network and decision tree classifiers are particularly powerful in predicting the most suitable learning approach for each nursing student. This research endeavors to enhance the success of nursing students and raise the overall quality of healthcare delivery in the country by tailoring educational programs to match individual learning styles.

show abstract