The effects of computer-based virtual learning environments on nursing students\u2019 mathematical learning in medication processes

Diana P Zwart,Omid Noroozi,Johannes E H Van Luit,Sui Lin Goei

doi:10.1186/s41039-021-00147-x

Abstract

Computer-based virtual learning environments (CBVLEs) are potentially useful teaching tools for training nursing students in professional duties such as the mathematical tasks associated with medication processes. In this study, a CBVLE was designed with well-structured instructional activities such as interleaved practice and feedback. Mathematical medication scenarios and basic arithmetic exercises were integrated into the CBVLE. Four training conditions were used in the CBVLE to facilitate extra support for mathematical medication learning: (1) learning without worked examples, (2) learning with worked examples involving domain-specific knowledge, (3) learning with worked examples involving regular thinking strategies, and (4) learning with combined worked examples. This study was conducted with 118 nursing students enrolled in post-secondary nursing education and Bachelor’s nursing programmes. Students were pre-tested and post-tested on their mathematical medication learning. Training in the CBVLE improved mathematical medication learning for all students from pre-test to the post-test stages, but no differences were found among the four different conditions. Nursing students’ prior knowledge, non-verbal intelligence, and number of correct tasks predicted mathematical medication learning outcomes. When controlling for non-verbal intelligence, students in the condition 1 benefited more than students in condition 3 in terms of their mathematical medication learning outcomes. The same accounted for the support of the low-achieving students in the CBVLE. The support conditions for the high-achieving group appeared to be unimportant for mathematical medication learning. It seems that technology is taken over some of the capacity of working memory, which accounts for the benefits to the low-achieving learners.

Highlights

Nursing students are trained in mathematical medication skills for future high-risk professional care situations in which they will need to check, prepare, distribute, administer, and evaluate medication for patients
The main purpose of this study is to investigate how training in a computer-based virtual learning environment (CBVLE) with or without worked examples can facilitate mathematical medication learning by nursing students
At the end of each practice round, which involves solving six mathematical medication problems and two sets of five short exercises involving the underlying domain-specific principles, nursing students are given feedback at the process level to reflect their learning (Hattie & Timperly, 2007; Nicol & Macfarlane-Dick, 2006). It is currently unclear how learning via a CBVLE, involving a structured learning environment based on the instructional design aspects of interleaved practice and feedback, can facilitate mathematical medication learning by nursing students

Summary

Introduction

Nursing students are trained in mathematical medication skills for future high-risk professional care situations in which they will need to check, prepare, distribute, administer, and evaluate medication for patients. The medication process is complex and involves the application of theoretical knowledge of medicines, administration of medications, communication, hygiene, and calculation of dosages The latter requires a conceptual understanding, since it involves knowledge about the underlying unifying principles (Canobi, 2009). To train for this complexity and the challenges of conceptual understanding, nursing students’ skills in terms of these high-risk professional practices can be trained using a lifelike computer-based virtual learning environment (CBVLE). This is a simulated environment designed to contain a variety of mathematical medication scenarios and basic computational arithmetic exercises that visualise the underlying unifying mathematical principles that students need to apply when solving complex mathematical medication tasks (Wang, Kirschner, Spector, & Ge, 2018)

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