Myoung Soo Kim; Jung Ha Park; Kyung-Yeon Park

doi:10.4040/jkan.2012.42.5.689

Articles

Page Path: HOME > J Korean Acad Nurs > Volume 42(5); 2012 > Article

Original Article
Development and Effectiveness of a Drug Dosage Calculation Training Program using Cognitive Loading Theory based on Smartphone Application: Myoung Soo Kim¹, Jung Ha Park¹, Kyung-Yeon Park²; Journal of Korean Academy of Nursing 2012;42(5):689-698.
DOI: https://doi.org/10.4040/jkan.2012.42.5.689
Published online: October 12, 2012

¹Department of Nursing, Pukyong National University, Busan, Korea

²Department of Nursing, Silla University, Busan, Korea

¹Department of Nursing, Pukyong National University, Busan, Korea

²Department of Nursing, Silla University, Busan, Korea

Address reprint requests to : Kim, Myoung Soo Department of Nursing, Pukyong National University, 599-1 Daeyeon 3-dong, Nam-gu, Busan 608-737, Korea Tel: +82-51-629-5782 Fax: +82-51-629-7906 E-mail: kanosa@pknu.ac.kr

• Received: February 1, 2012 • Revised: February 22, 2012 • Accepted: September 23, 2012

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

199 Views
1 Download
23 Crossref

prev next

Full Article

Download PDF

Abstract
REFERENCES

Abstract

Purpose
This study was done to develop and evaluate a drug dosage calculation training program using cognitive loading theory based on a smartphone application. Calculation ability, dosage calculation related self-efficacy and anxiety were measured.
Methods
A nonequivalent control group design was used. Smartphone application and a handout for self-study were developed and administered to the experimental group and only a handout was provided for control group. Intervention period was 4 weeks. Data were analyzed using descriptive analysis, χ²-test, t-test, and ANCOVA with the SPSS 18.0.
Results
The experimental group showed more ‘self-efficacy for drug dosage calculation’ than the control group (t= 3.82, p< .001). Experimental group students had higher ability to perform drug dosage calculations than control group students (t= 3.98, p< .001), with regard to ‘metric conversion’ (t= 2.25, p = .027), ‘table dosage calculation’ (t= 2.20, p = .031) and ‘drop rate calculation’ (t= 4.60, p< .001). There was no difference in improvement in ‘anxiety for drug dosage calculation’. Mean satisfaction score for the program was 86.1.
Conclusion
These results indicate that this drug dosage calculation training program using smart-phone application is effective in improving dosage calculation related self-efficacy and calculation ability. Further study should be done to develop additional interventions for reducing anxiety.
Keywords: Medication; Self-efficacy; Anxiety; Calculation ability; Effectiveness

Figure 1.

Program development procedure.

Figure 2.

Developed smartphone-based drug dosage calculation application.

Table 1.

Principles of the Application Development and Examples of the Questionnaires

Principles of the application development
Teaching strategies for reducing cognitive loading	Construction strategies for smartphone based application development
Goal-free effect	Construct the learning area and the gaming area. Doesn't display the aim of the lesson in the learning area.
Worked example effect	Insert the example tabs on the introduction page in the learning area, which provide solved examples.
Completion problem effect	Provide the tips, but make participants write the answers themselves.
Split attention effect	Arrange all contents in one page which prevents scrolling to see the cut screen.
Variability practice effect	Construct gaming area in order to help participants solve the questionnaires repeatedly.
Modality effect	Provide the screen changes and auditory stimulations at the period of scoring.
Redundancy effect	Construct the standardized form of questionnaire to prevent cognitive loading resulting from the various information sources.
Multi-media effect	Use visual stimuli and auditorial stimuli with graphs, illustrations and audio clips.
Examples of the questionnaires
Metric conversion	Convert 1.17 g to mg.
Tablet calculation	62.5 mcg of digoxin is prescribed daily. On hand you have 250 mcg tablets. How many tablets will you give?
Fluid dosage calculation	0.75 g of lincomycin hydrochloride IV 8-12 hourly is prescribed. On hand you have 300 mg in 2 mL. How many mL will you administer?
Drop rate calculation	1 L of Lactated Ringer's solution is prescribed over 10 hours. The drop factor is 15. What is the drip rate (drops/minute) required?

Table 2.

Homogeneity Test of Study Variables at the Baseline (N=78)

Variables	Categories (Numbers of items)	Exp. (n=37)	Cont. (n=41)	t/χ² (p)
		n (%) or M±SD
Age (year)		20.30±1.51	20.15±1.28	0.48 (.633)
Gender*	Female	36 (97.3)	37 (90.2)	(.362)
	Male	1 (0.7)	4 (9.8)
Self-efficacy for drug dosage calculation	Confidence for mathematics (6)	3.30±0.66	3.09±0.79	1.23 (.211)
	Confidence for drug dosage calculation (7)	2.98±0.72	2.71±0.75	1.64 (.106)
	Total (13)	3.13±0.62	2.89±0.72	1.58 (.119)
Anxiety for drug dosage calculation	Fear of asking help (4)	2.97±0.52	3.01±0.69	−0.24 (.812)
	Self-concept (7) Total (11)	2.11±0.67 2.42 0.50	2.60±0.93 2.75 0.67	−2.66 (.010) 2.41 (.018)
	Total (11)	2.42±0.50	2.75±0.67	−2.41 (.018)
Calculation ability	Metric conversion (3)	2.27±0.87	1.98±0.69	1.67 (.100)
	Tablet dosage calculation (3)	0.95±0.23	1.07±0.41	−1.66 (.101)
	Fluid amount calculation (3)	2.76±0.72	2.63±0.80	0.71 (.481)
	Drop rate calculation (3)	1.43±1.07	1.10±0.92	1.48 (.144)
	Total (12)	7.40±2.18	6.78±1.59	1.43 (.156)

Exp.=Experimental group; Cont.=Control group. *Fisher's exact test.

Table 3.

Group Comparisons of Dependent Variables at the Posttest (N=78)

Variables	Categories	Exp. (n=37)	Cont. (n=41)	t or F (p)
Variables	Categories	M±SD	M±SD	t or F (p)
Self-efficacy for drug dosage	Confidence for mathematics	3.48±0.60	3.15±0.75	2.15 (.035)
calculation	Confidence for drug dosage calculation	3.66±0.64	2.95±0.77	4.45 (<.001)
	Total	3.58±0.57	3.04±0.67	3.82 (<.001)
Anxiety for drug dosage	Fear of asking help	3.02±0.67	2.90±0.65	0.79 (.433)
calculation	Self-concept*	2.14±0.65	2.51±0.69	0.82 (.684)
	Total*	2.46±0.49	2.65±0.43	0.66 (.862)
Calculation ability	Metric conversion	2.19±0.91	1.78±0.69	2.25 (.027)
	Tablet dosage calculation	2.54±0.77	2.12±0.90	2.20 (.031)
	Fluid amount calculation	2.81±0.57	2.63±0.70	1.23 (.223)
	Drop rate calculation	2.41±0.80	1.41±1.09	4.60 (<.001)
	Total	9.95±2.31	7.95±2.12	3.98 (<.001)
Satisfaction for program	Effective way to learn	4.38±0.72	−	−
	Generally appropriate	4.30±0.57	−	−
	Recommendable	4.24±0.55	−	−
Frequency	Visit to learning area (number/week)	5.45±7.10	−	−
	Visit to game area (number/week)	7.68±10.26	−	−
	Connect hours (min/week)	21.93±26.61	−	−

Exp.=Experimental group; Cont.=Control group. *ANCOVAs were used to identify group differences at post-test.

REFERENCES

Aspden P., Wolcott J. A., Bootman J. L., Cronenwett L.R. 2007.Preventing medication errors: Quality chasm series. Washington DC: National Academy of Sciences.
Bandura, A. 1978;Self-efficacy: Toward a unifying theory of behavioral change. Advances in Behaviour Research and Therapy. 1:139–161. http://dx.doi.org/10.1016/0146-6402(78)90002-4Article
Bliss-Holtz, J. 1994;Discriminating types of medication calculation errors in nursing practice. Nursing Research. 43:373–375. http://dx.doi.org/10.1097/00006199-199411000-00010
Brown D.L. 2002;Does 1 + 1 still equal 2? A study of the mathematic competencies of associate degree nursing students. Nurse Education. 27:132–135.
Grandell-Niemi H., Hupli M., Leino-Kilpi H., Puukka, P. 2003;Medication calculation skills of nurses in Finland. Journal of Clinical Nursing. 12:519–528. http://dx.doi.org/10.1046/j.1365-2702.2003.00742.xArticle PubMed
Hanna D., Shevlin M., Dempster, M. 2008;The structure of the statistics anxiety rating scale: A confirmatory factor analysis using UK psychology students. Personality and Individual Differences. 45:68–74. http://dx.doi.org/10.1016/j.paid.2008.02.021Article
Heo C. Y., Kim J.E. 2011;June. Developing technology of remote consultation based on mobile and evaluation of the usefulness for chronic wound (pressure ulcer). Paper presented at the meeting of the Korean Society of Medical Informatics, Seoul.
Hicks R. W., Becker S. C., Krenzischeck D., Beyea S.C. 2004;Medication errors in the PACU: A secondary analysis of MEDMARX findings. Journal of Perianesthesia Nursing. 19:18–28. http://dx.doi.org/10.1016/j.jopan.2003.11.007Article
Hollender N., Hofmann C., Deneke M., Schmitz, B. 2010;Integrating cognitive load theory and concepts of human-computer interaction. Computers in Human Behavior. 26:1278–1288. http://dx.doi.org/10.1016/j.chb.2010.05.031Article
Jung H.J. 2010.A study on the development of teaching materials based on the cognitive load theory: Focused on the creative engineering design subject. Unpublished master’s thesis. Dankook University; Seoul.
Keller J.M. 1983.Motivational design of instruction. In: Reigeluth C.M., editor. Instructional-design theories and models: An overview of their current status. Hillsdale, NJ: Lawrence Erlbaum Associates.
Kim C. H., Kim, M. 2009;Defining reported errors on web-based reporting system using ICPS from nine units in a Korean university hospital. Asian Nursing Research. 3:167–176.Article
Kim Y. M., Kim S. Y., Kim M. Y., Kim J. H., Lee S. K., Jang M.K. 2010;Patient safety program and safety culture. Journal of Korean Academy of Nursing Administration. 16:455–465.Article
Lee B.Y. 2010;Development and evaluation of nursing infobuttons on medication to prevent medication administration errors. Unpublished master’s.
Low D., Clark N., Soar J., Padkin A., Stoneham A., Perkins G. D., et al. 2011;A randomised control trial to determine if use of the iResus_© application on a smart phone improves the performance of an advanced life support provider in a simulated medical emergency. Anaesthesia. 66:255–262. http://dx.doi.org/10.1111/j.1365-2044.2011.06649.xArticle PubMed
Mayo A. M., Duncan, D. 2004;Nurse perception of medication errors: What we need to know for patient safety. Journal of Nurse Care Quality. 19:209–217.
McMullan M., Jones R., Lea, S. 2011;The effect of an interactive e- drug calculations package on nursing students’ drug calculation ability and self-efficacy. International Journal of Medical Informatics. 80:421–430. http://dx.doi.org/10.1016/j.ijmedinf.2010.10.021Article PubMed
Moreno-Ger P., Torrente J., Bustamante J., Fernández-Galaz C., Fernán-dez-ManjÓn B., Comas-Rengifo M.D. 2010;Application of a low-cost web-based simulation to improve students' practical skills in medical education. International Journal of Medical Informatics. 79:459–467. http://dx.doi.org/10.1016/j.ijmedinf.2010.01.017Article PubMed
Nasiri E., Babatabar H. D., Mortazavi, Y. 2009;Nurses’ drug calculation ability in intensive care unit. Iranian Journal of Critical Care Nursing. 2:113–115.
Nguyen T. D., Attkisson C. C., Stegner B.L. 1983;Assessment of patient satisfaction: Development and refinement of a service evaluation questionnaire. Evaluation and Program Planning. 6:299–313. http://dx.doi.org/10.1016/0149-7189(83)90010-1Article PubMed
Onwuegbuzie A. J., Wilson V.A. 2003;Statistics anxiety: Nature, etiology, antecedents, effects and treatments-A comprehensive review of the literature. Teaching in Higher Education. 8:195–209. http://dx.doi.org/10.1080/1356251032000052447Article
Polifroni E. C., Allchin L., McNulty J.J. 2005;Limited math skills: A prescription for change. Journal for Nurses in Staff Development. 21:79–81.PubMed
Pozehl B.J. 1996;Mathematical calculation ability and mathematical anxiety of baccalaureate nursing students. Journal of Nursing Education. 35:37–39.Article PubMed
Rainboth L., DeMasi, C. 2006;Nursing students' mathematic calculation skills. Nurse Education Today. 26:655–661. http://dx.doi.org/10.1016/j.nedt.2006.07.022Article PubMed
Røykenes K., Larsen, T. 2010;The relationship between nursing students' mathematics ability and their performance in a drug calculation test. Nurse Education Today. 30:697–701. http://dx.doi.org/10.1016/j.nedt.2010.01.009Article PubMed
Sweller, J. 1994;Cognitive load theory, learning difficulty, and instruction design. Learning and Instruction. 4:295–312. http://dx.doi.org/10.1016/0959-4752(94)90003-5Article
Tilden V. P., Nelson C. A., May B.A. 1990;The IPR inventory: Development and psychometric characteristics. Nursing Research. 39:337–343.PubMed
Tsai S. L., Chai S.K. 2005;Developing and validating a nursing website evaluation questionnaire. Journal of Advanced Nursing. 49:406–413. http://dx.doi.org/10.1111/j.1365-2648.2004.03304.xArticle PubMed
Walsh K.A. 2008;The relationship among mathematics anxiety, beliefs about mathematics, mathematics self-efficacy, and mathematics performance in associate degree nursing students. Nursing Education Perspectives. 29:226–229.PubMed
Wright, K. 2008;Can effective teaching and learning strategies help student nurses to retain drug calculation skills? Nurse Education Today. 28:856–864. http://dx.doi.org/10.1016/j.nedt.2008.01.002Article PubMed

Figure & Data

REFERENCES

Citations

Citations to this article as recorded by

Development and effectiveness of online teaching on practical skills among nursing students: A systematic review and meta-analysis
Afang Li, Norhasmah Mohd Zain, Azlina Yusuf, Haiyan Deng, Qi He
Nurse Education in Practice.2024; 78: 103988. CrossRef
Development and Effectiveness of a Clinical Decision Support System for Pressure Ulcer Prevention Care Using Machine Learning
Myoung Soo Kim, Jung Mi Ryu, Byung Kwan Choi
CIN: Computers, Informatics, Nursing.2023; 41(4): 236. CrossRef
Effectiveness of a virtual reality application‐based education programme on patient safety management for nursing students: A pre‐test–post‐test study
Jae Woo Oh, Ji Eun Kim
Nursing Open.2023; 10(12): 7622. CrossRef
The Effect of Game-Based Clinical Nursing Skills Mobile Application on Nursing Students
Donghee Suh, Hyekyung Kim, Eunyoung E. Suh, Hyunsun Kim
CIN: Computers, Informatics, Nursing.2022; 40(11): 769. CrossRef
The effect of case-based learning based on flipped learning for nursing students
Min Hee Lee, Myung Sook Park
The Journal of Korean Academic Society of Nursing Education.2021; 27(2): 107. CrossRef
Smartphone distraction during nursing care: Systematic literature review
Massimo Fiorinelli, Sofia Di Mario, Antonella Surace, Micol Mattei, Carla Russo, Giulia Villa, Sara Dionisi, Emanuele Di Simone, Noemi Giannetta, Marco Di Muzio
Applied Nursing Research.2021; 58: 151405. CrossRef
A systematic review into the assessment of medical apps: motivations, challenges, recommendations and methodological aspect
A. H. Alamoodi, Salem Garfan, B. B. Zaidan, A. A. Zaidan, Moceheb Lazam Shuwandy, Mussab Alaa, M. A. Alsalem, Ali Mohammed, A. M. Aleesa, O. S. Albahri, Ward Ahmed Al-Hussein, O. R. Alobaidi
Health and Technology.2020; 10(5): 1045. CrossRef
Development and Utilization of a Clinical Decision Support System Contents for Pressure Ulcer Prevention Care
Myoung Soo Kim, Jung Mi Ryu
Journal of Health Informatics and Statistics.2020; 45(4): 365. CrossRef
The Effect of Cell Phones on Attention and Learning in Nursing Students
Lorena Gutiérrez-Puertas, Verónica V. Márquez-Hernández, Vanesa Gutiérrez-Puertas, Genoveva Granados-Gámez, Gabriel Aguilera-Manrique
CIN: Computers, Informatics, Nursing.2020; 38(8): 408. CrossRef
Using Video Feedback Through Smartphone Instant Messaging in Fundamental Nursing Skills Teaching: Observational Study
Xiaoxian Yang, Ri-Hua Xie, Si Chen, Wei Yu, Yan Liao, Daniel Krewski, Shi Wu Wen
JMIR mHealth and uHealth.2019; 7(9): e15386. CrossRef
Effects of Smartphone-Based Mobile Learning in Nursing Education: A Systematic Review and Meta-analysis
Ju Hee Kim, Hanjong Park
Asian Nursing Research.2019; 13(1): 20. CrossRef
Development and Evaluation of “Chronic Illness Care Smartphone Apps” on Nursing Students’ Knowledge, Self-efficacy, and Learning Experience
Jiyoung Kang, Eunyoung E. Suh
CIN: Computers, Informatics, Nursing.2018; 36(11): 550. CrossRef
Mobile Technology in Undergraduate Nursing Education: A Systematic Review
Hyejung Lee, Haeyoung Min, Su-mi Oh, Kaka Shim
Healthcare Informatics Research.2018; 24(2): 97. CrossRef
The Relationship Between Acceptance Intention Toward a Smartphone Healthcare Application and Health-Promoting Behaviors Among Nursing Students
Eun-Jin Choi, Se-Won Kang
CIN: Computers, Informatics, Nursing.2018; 36(10): 494. CrossRef
The Effects of an Interactive Nursing Skills Mobile Application on Nursing Students' Knowledge, Self-efficacy, and Skills Performance: A Randomized Controlled Trial
Hyunsun Kim, Eunyoung E. Suh
Asian Nursing Research.2018; 12(1): 17. CrossRef
Outcomes of a Drug Dosage Calculation Training Smartphone App on Learning Achievement, Metacognition, and Flow State According to Prior Knowledge
Kyung Yeon Park, Myoung Soo Kim
EURASIA Journal of Mathematics, Science and Technology Education.2018;[Epub] CrossRef
Development and Cross-cultural Validation of the Korean Version of SMArtphone’s uSability Heuristics (SMASH)
Yeo Won Jeong, Jung A Kim
Healthcare Informatics Research.2017; 23(4): 328. CrossRef
The Effects of Smartphone Application to Educate Patient on Patient Safety in Hospitalized Surgical Patients
Hyo Jin Choi, Eunjoo Lee
Korean Journal of Adult Nursing.2017; 29(2): 154. CrossRef
Use of mobile devices and medication errors in acute care
Nicole Harder, Jannell Plouffe, Diane Cepanec, Kari Mann, Mê-Linh Lê, Patricia Gregory, Patrick Griffith, Kathy Doerksen
JBI Database of Systematic Reviews and Implementation Reports.2016; 14(9): 47. CrossRef
Development of a Smartphone Application for Clinical Decision Making of Medication Administration
Myoung-Soo Kim, Jung-Ha Park, Sungmin Kim
Journal of the Korea Academia-Industrial cooperation Society.2014; 15(3): 1650. CrossRef
Predictors of Drug Dosage Calculation Error Risk in Newly Graduated Nurses
Myoung Soo Kim, Jung Soon Kim, Won Choon Ha
Journal of Korean Biological Nursing Science.2014; 16(2): 113. CrossRef
Development and Effectiveness of Smartphone Application for the Medication Confirmation of High-alert Medications
Myoung Soo Kim
Korean Journal of Adult Nursing.2014; 26(3): 253. CrossRef
The Mediating Effect of Drug Calculation Confidence in the Relationship between Interest in Medication and Drug Calculation Competency
Hyoung Sook Park, Gyoo Yeong Cho, Dong-Hee Kim, Sang Hee Kim, Myoung Soo Kim
Journal of Korean Biological Nursing Science.2013; 15(4): 155. CrossRef

Cite

CITE: export Copy Download Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

Development and Effectiveness of a Drug Dosage Calculation Training Program using Cognitive Loading Theory based on Smartphone Application

J Korean Acad Nurs. 2012;42(5):689-698. Published online October 12, 2012

DOI: https://doi.org/10.4040/jkan.2012.42.5.689

XML Download

Figure

We recommend

Related articles

Development and Effectiveness of Progressive Simulation Education Program on Medication Safety for Nursing Students

Development and Effectiveness of a Drug Dosage Calculation Training Program using Cognitive Loading Theory based on Smartphone Application

Figure 1. Program development procedure.

Figure 2. Developed smartphone-based drug dosage calculation application.

Figure 1.

Figure 2.

Development and Effectiveness of a Drug Dosage Calculation Training Program using Cognitive Loading Theory based on Smartphone Application

Principles of the application development
Teaching strategies for reducing cognitive loading	Construction strategies for smartphone based application development
Goal-free effect	Construct the learning area and the gaming area. Doesn't display the aim of the lesson in the learning area.
Worked example effect	Insert the example tabs on the introduction page in the learning area, which provide solved examples.
Completion problem effect	Provide the tips, but make participants write the answers themselves.
Split attention effect	Arrange all contents in one page which prevents scrolling to see the cut screen.
Variability practice effect	Construct gaming area in order to help participants solve the questionnaires repeatedly.
Modality effect	Provide the screen changes and auditory stimulations at the period of scoring.
Redundancy effect	Construct the standardized form of questionnaire to prevent cognitive loading resulting from the various information sources.
Multi-media effect	Use visual stimuli and auditorial stimuli with graphs, illustrations and audio clips.
Examples of the questionnaires
Metric conversion	Convert 1.17 g to mg.
Tablet calculation	62.5 mcg of digoxin is prescribed daily. On hand you have 250 mcg tablets. How many tablets will you give?
Fluid dosage calculation	0.75 g of lincomycin hydrochloride IV 8-12 hourly is prescribed. On hand you have 300 mg in 2 mL. How many mL will you administer?
Drop rate calculation	1 L of Lactated Ringer's solution is prescribed over 10 hours. The drop factor is 15. What is the drip rate (drops/minute) required?

Variables	Categories (Numbers of items)	Exp. (n=37)	Cont. (n=41)	t/χ² (p)
		n (%) or M±SD
Age (year)		20.30±1.51	20.15±1.28	0.48 (.633)
Gender*	Female	36 (97.3)	37 (90.2)	(.362)
	Male	1 (0.7)	4 (9.8)
Self-efficacy for drug dosage calculation	Confidence for mathematics (6)	3.30±0.66	3.09±0.79	1.23 (.211)
	Confidence for drug dosage calculation (7)	2.98±0.72	2.71±0.75	1.64 (.106)
	Total (13)	3.13±0.62	2.89±0.72	1.58 (.119)
Anxiety for drug dosage calculation	Fear of asking help (4)	2.97±0.52	3.01±0.69	−0.24 (.812)
	Self-concept (7) Total (11)	2.11±0.67 2.42 0.50	2.60±0.93 2.75 0.67	−2.66 (.010) 2.41 (.018)
	Total (11)	2.42±0.50	2.75±0.67	−2.41 (.018)
Calculation ability	Metric conversion (3)	2.27±0.87	1.98±0.69	1.67 (.100)
	Tablet dosage calculation (3)	0.95±0.23	1.07±0.41	−1.66 (.101)
	Fluid amount calculation (3)	2.76±0.72	2.63±0.80	0.71 (.481)
	Drop rate calculation (3)	1.43±1.07	1.10±0.92	1.48 (.144)
	Total (12)	7.40±2.18	6.78±1.59	1.43 (.156)

Variables	Categories	Exp. (n=37)	Cont. (n=41)	t or F (p)
Variables	Categories	M±SD	M±SD	t or F (p)
Self-efficacy for drug dosage	Confidence for mathematics	3.48±0.60	3.15±0.75	2.15 (.035)
calculation	Confidence for drug dosage calculation	3.66±0.64	2.95±0.77	4.45 (<.001)
	Total	3.58±0.57	3.04±0.67	3.82 (<.001)
Anxiety for drug dosage	Fear of asking help	3.02±0.67	2.90±0.65	0.79 (.433)
calculation	Self-concept*	2.14±0.65	2.51±0.69	0.82 (.684)
	Total*	2.46±0.49	2.65±0.43	0.66 (.862)
Calculation ability	Metric conversion	2.19±0.91	1.78±0.69	2.25 (.027)
	Tablet dosage calculation	2.54±0.77	2.12±0.90	2.20 (.031)
	Fluid amount calculation	2.81±0.57	2.63±0.70	1.23 (.223)
	Drop rate calculation	2.41±0.80	1.41±1.09	4.60 (<.001)
	Total	9.95±2.31	7.95±2.12	3.98 (<.001)
Satisfaction for program	Effective way to learn	4.38±0.72	−	−
	Generally appropriate	4.30±0.57	−	−
	Recommendable	4.24±0.55	−	−
Frequency	Visit to learning area (number/week)	5.45±7.10	−	−
	Visit to game area (number/week)	7.68±10.26	−	−
	Connect hours (min/week)	21.93±26.61	−	−

Table 1. Principles of the Application Development and Examples of the Questionnaires

Table 2. Homogeneity Test of Study Variables at the Baseline (N=78)

Exp.=Experimental group; Cont.=Control group. *Fisher's exact test.

Table 3. Group Comparisons of Dependent Variables at the Posttest (N=78)

Exp.=Experimental group; Cont.=Control group. *ANCOVAs were used to identify group differences at post-test.