Author: |
王詩文 Wang, Shih-Wen |
---|---|
Thesis Title: |
3D VR沉浸式藥物濫用防制模組對於高中生預防藥物濫用之學習動機、評量與成效之研究 The study of Three-dimension Virtual Reality Immersive Drug Abuse Preventive Modules on Learning Motivation, Assessment and Effectiveness among Senior High School Students |
Advisor: | 郭鐘隆 |
Degree: |
碩士 Master |
Department: |
健康促進與衛生教育學系 Department of Health Promotion and Health Education |
Thesis Publication Year: | 2020 |
Academic Year: | 108 |
Language: | 中文 |
Number of pages: | 75 |
Keywords (in Chinese): | 虛擬實境 、藥物濫用防制 、學習動機 、學習成效 、高中生 |
Keywords (in English): | Virtual reality, Drug abuse prevention, Learning motivation, Learning effectiveness, High school students |
DOI URL: | http://doi.org/10.6345/NTNU202001418 |
Thesis Type: | Academic thesis/ dissertation |
Reference times: | Clicks: 309 Downloads: 0 |
Share: |
School Collection Retrieve National Library Collection Retrieve Error Report |
本研究以臺北市高中在學學生為研究對象,以預防非法藥物濫用為主要核心,融入科技學習與資訊教育,運用3D VR沉浸藥物濫用防制模組,增加學生學習動機及學習評量,進而達到藥物濫用防制預防成效。
本研究招募50名高中生,有效樣本共49人,體驗時間40分鐘,於活動開始前進行前測問卷填答,於活動後立即填寫後測問卷。使用SPSS for Windows version 23.0 及偏最小平方法進行分析,採用描述性統計、配對t檢定、單因子獨立變異數分析等分析活動前後之變化差異。
研究結果顯示,3D VR沉浸式藥物濫用防制模組之體驗不會因為性別、年級、家庭因素及是否體驗過VR的經驗,而影響其使用VR來學習藥物濫用防制教育之學習動機,而3D VR沉浸式藥物濫用防制模組之體驗教學,能顯著提升高中生對於預防藥物濫用之學習評量結果;藉由偏最小平方法結構方程模式(PLS-SEM)檢測可得知,臨場感顯著影響學習動機,學習動機顯著影響學習成效,而臨場感透過學習動機來影響學習成效也有顯著相關。
本研究進行3D VR沉浸式藥物濫用防制模組體驗教學後,高中生在學習評量有顯著進步,且可增加學生學習動機,強化學習藥物濫用知識,提升學習成效。未來能將此模式運用於其他成癮物質之預防教育(吸菸、飲酒、嚼食檳榔),或是發展以虛擬實境為基礎之青少年健康行為介入計畫,另也可提供給進行藥物濫用防制介入教育之縣市及學校單位,作為推廣的教材,運用創新科技融入教學,以增加學生學習動機,進而達到預防之成效。
This study takes high school students in Taipei City as the research object, takes the prevention of illegal drug abuse as the main core, integrates technology learning and information education, and uses 3D VR immersive drug abuse preventive modules to increase students' learning motivation and learning assessment, so as to achieve the effect of drug abuse prevention and control.
50 senior high school students were recruited in this study. A total of 49 valid samples were selected. The intervention time was 40 minutes. The pre questionnaire was completed before the activity and the post questionnaire was completed immediately after the activity. SPSS for Windows version 23.0 and partial least square method were used for analysis. Descriptive statistics, paired t-test and one-way independent ANOVA were used to analyze the differences before and after the activity.
The results show that the experience of 3D VR immersive drug abuse preventive modules does not affect the learning motivation of using VR to learn drug abuse prevention by one’s gender, grade, family factors and whether individuals have VR experience. The experience teaching of 3D VR immersion drug abuse preventive modules can significantly improve the learning assessment results of high school students on drug abuse prevention. By PLS-SEM, it can be seen that presence significantly affects learning motivation, and learning motivation significantly affects learning effectiveness, while presence influences learning effectiveness through learning motivation.
After experiencing 3D VR immersion drug abuse preventive modules experience teaching, high school students have made significant progress in learning assessment, and this can also increase students' learning motivation, strengthen students’ study of drug abuse knowledge, and improve their learning effectiveness. In the future, this model can also be applied to the prevention education of other addictive substances (smoking, drinking, chewing betel nut), or be developed for the intervention program of adolescent health behavior based on virtual reality. In addition, it can also be provided to counties, cities and local schools for drug abuse prevention and control intervention education. It can be used as teaching materials for promotion, and the innovative technology can be integrated into teaching to increase students' learning motivation and to achieve the effect of prevention.
中文部分
王彥傑(2009)。設計可在混合實境互動的機器人來提昇學習環境中的臨場感與學習動機。國立中央大學資訊工程研究所碩士學位論文。
行政院衛生福利部(106)。藥物濫用案件計檢驗統計資料分析。台北市。
教育部學生事務與特殊教育司(106)。學生藥物濫用各學制統計表。台北市。
國家食品藥品監督管理總局。國家藥物濫用監測年度報告(2016年)。中國。
源自:http://samr.cfda.gov.cn/WS01/CL0844/175994.html
中國禁毒網。2016中國毒品形勢報告。中國。
源自:http://www.nncc626.com/2017-03/27/c_129519255.htm
楊士隆,戴伸峰、曾淑萍(2016)。青少年非法藥物使用盛行率之調查研究-以新北市、台中市、高雄市為例。科技部專題研究計畫。編號103-2410-H-194-097-SS3。
陳志哲、廖容瑜、張萩琴、黃久美、郭鐘隆(2016)。應用理論建構之3D虛擬實境介入對高中職濫用愷他命學生之成效。數位學習科技期刊,第八卷第三期,p.51-69。
陳佳宏 (2010)。資訊多媒體融入運動教育模式對國中生排球學習動機與學習成效之影響。臺灣師範大學體育學系在職進修碩士班學位論文,1-74。
李信良(2004)。家庭環境因素對於青少年藥物濫用之影響-一個後設分析研究。衛生教育學報,第21期,p.19-50。
李光明(2011)。探討苗栗縣國小教師運用e 化專科教室進行創新教學行為意願之研究,國立聯合大學資訊與社會系碩士論文。
英文部分
Allen, C. (2007). Crime, drugs and social theory: a phenomenological approach: Ashgate Publishing Company.
Alrabai, F. (2016). The effects of teachers’ in-class motivational intervention on learners’ EFL achievement. Applied linguistics, 37(3), 307-333.
Ajjan, H.,Hartshorne, R. (2008). Investigating faculty decisions to adopt Web 2.0 technologies:Theoryand empirical tests.The Internet and Higher Education,11(2),71-80.
Barber, J. G. (1993). An application of microcomputer technology to the drug education of prisoners. Journal of Alcohol and Drug Education, 38, 14−22.
Bordnick, P. S., Graap, K. M., Copp, H. L., Brooks, J., & Ferrer, M. (2005). Virtual reality cue reactivity assessment in cigarette smokers. CyberPsychology and Behavior, 8(5), 487-492.
Bordnick PS, Traylor A, Copp HL, Graap KM, Carter B, Ferrer M, Walton AP.(2008) Assessing reactivity to virtual reality alcohol based cues. Addictive Behaviors, 33(6), 743–56.
Bordnick PS, Copp HL, Traylor A, Graap KM, Carter BL, Walton A, Ferrer M.(2009) Reactivity to cannabis cues in virtual reality environments. Psychoactive Drugs, 41(2), 105–12.
Carroll, K. M., Ball, S. A., Martino, S., Nich, C., Babuscio, T. A., Nuro, K. F., et al. (2008).Computer-assisted delivery of cognitive-behavioral therapy for addiction: Arandomized trial of CBT4CBT. American Journal of Psychiatry, 165, 881−888.
Carroll, K. M., Ball, S. A., Martino, S., Nich, C., Babuscio, T. A., & Rounsaville, B. J. (2009).Enduring effects of a computer-assisted training program for cognitive behavioral therapy: A 6-month follow-up of CBT4CBT. Drug and Alcohol Dependence, 100,178−181.
Chandler, R. K., Fletcher, B. W., & Volkow, N. D. (2009). Treating drug abuse and addiction in the criminal justice system. The Journal of the American Medical Association, 301(2), 183-190.
Cheng, C.-H. and C.-H. Su (2012). "A Game-based learning system for improving student's learning effectiveness in system analysis course." Procedia-Social and Behavioral Sciences 31: 669-675.
Chou, L. C., Ho, C. Y., Chen, C. Y., & Chen, W. J. (2006). Truancy and illicit drug use among adolescents surveyed via street outreach. Addictive Behaviors, 31(1), 149-154.
Cheon, J.,Lee, S., Crooks, S. M., Song, J. (2012).An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education ,59(3).
Corkin, D. M., Horn, C., & Pattison, D. (2017). The effects of an active learning intervention in biology on college students’ classroom motivational climate perceptions, motivation, and achievement. Educational Psychology, 37(9), 1106-1124.
Dodge, K. A., Malone, P. S., Lansford, J. E., Miller, S., Pettit, G. S., & Bates, J. E. (2009). A dynamic cascade model of the development of substance-use onset. Monographs of the Society for Research in Child Development, 74(3), vii-119.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Fowler, J. S., Volkow, N. D., Kassed, C. A., & Chang, L. (2007). Imaging the addicted human brain. Science and Practice Perspectives, 3(2), 4.
Froiland, J. M., & Worrell, F. C. (2016). Intrinsic motivation, learning goals, engagement, and achievement in a diverse high school. Psychology in the Schools, 53(3), 321-336.
García-Rodríguez, O., Weidberg, S., Gutiérrez-Maldonado, J., & Secades-Villa, R. (2013). Smoking a virtual cigarette increases craving among smokers. Addictive behaviors, 38(10), 2551-2554.
Guilamo-Ramos, V., Litardo, H. A. & Jaccard, J. (2005). Prevention programs for reducing adolescent problem behaviors: Implications of the co-occurrence of problem behaviors in adolescence. Journal of Adolescent Health, 36(1), 82-86.
Guo, J. L., Huang, C. M. & Lee, T. C. (2012). National Web-Based Survey of Illegal Drug Use Among Adolescents. Taipei, Taiwan.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.
Hair, J. F., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM) European Business Review, 26(2), 106-121.
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566-584.
Hauze, S. W., et al. (2019). Enhancing nursing education through affordable and realistic holographic mixed reality: the virtual standardized patient for clinical simulation. Biomedical Visualisation, Springer: 1-13.
Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: implications for substance abuse prevention. Psychological Bulletin, 112(1), 64.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management Data Systems, 116(1), 2-20.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
Huang, C. M., Lin, L. F., Lee, T. C., & Guo, J. L. (2013). Proximal to distal correlates of the patterns of illicit drug use among night school students in Taiwan. Addictive Behaviors,38(1), 1481-1484.
Hwang, S. Y. and M. J. Kim (2006). "A comparison of problem-based learning and lecture-based learning in an adult health nursing course." Nurse education today 26(4): 315-321.
Ishiguro, K., Majima, Y., & Sakata, N. (2016). Deployment of ARCS Model and Utilization of Communication Robot in Patient Education. HEALTHINF.(pp.371-376).
Johnston, L. D., Miech, R. A., O'Malley, P. M., Bachman, J. G., Schulenberg, J. E., & Patrick, M. E. (2018). Monitoring the Future national survey results on drug use, 1975-2017: Overview, Key Findings on Adolescent Drug Use. Institute for Social Research.
Kandel, D. B., Kessler, R. C., & Margulies, R. Z. (1978). Antecedents of adolescent initiation into stages of drug use: A developmental analysis. Journal of Youth and Adolescence, 7(1), 13-40.
Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development 10(3): 2.
Khalsa, J. H., Genser, S., Francis, H., & Martin, B. (2002). Clinical consequences of marijuana. The Journal of Clinical Pharmacology, 42(11 suppl), 7S-10S.
Lennon, J. L. and D. W. Coombs (2007). The utility of a board game for dengue haemorrhagic fever health education. Health Education.
Marcoulides, G. A., & Saunders, C. (2006). Editor's comments: PLS: a silver bullet? Management Information Systems Quarterly, iii-ix.
Means, T. B., et al. (1997). Enhancing relevance: Embedded ARCS strategies vs. purpose. Educational Technology Research and Development 45(1): 5-17.
Mikropoulos, T. A. (2006). Presence: a unique characteristic in educational virtual environments. Virtual Reality, 10(3-4), 197-206.
Nguyen, K., et al. (2001). Feasibility of using an alcohol-screening and health education system with older primary care patients. The Journal of the American Board of Family Practice 14(1): 7-15.
Nicklas, E., & Mackenzie, M. J. (2013). Intimate partner violence and risk for child neglect during early childhood in a community sample of fragile families. Journal of Family Violence, 28(1), 17-29.
Nora, D.V., N., Wang, G. J.,Fowler, J. S.,Molina, P. E., Logan, J., Gatley, S. J., Gifford, A., Ding, Y. S., Wong, C., Pappas, N. R., Zhu, W., Swanson, J. M. (2003). Cardiovascular effects of methylphenidate in humans are associated with increases of dopamine in brain and of epinephrine in plasma. Psychopharmacology,166(3).
Ondersma, S. J., Chase, S. K., Svikis, D. S., & Schuster, C. R. (2005). Computer-based brief motivational intervention for perinatal drug use. Journal of Substance Abuse Treatment, 28, 305−312.
Ondersma, S. J., Svikis, D. S., & Schuster, C. R. (2007). Computer-based brief intervention: A randomized trial with postpartum women. American Journal of Preventive Medicine, 32, 231−238.
Persky, S., & Eccleston, C. P. (2011). Medical student bias and care recommendations for an obese versus non-obese virtual patient. International Journal of Obesity, 35(5), 728.
Ramie, A. and I. Rajiani (2019). ARCS Module (Attention, Relevance, Confidence, Satisfaction) to Increase Classroom Motivation for Pregnant Women at Public Health Center. Indian Journal of Public Health Research and Development 10(1): 401-405.
Rothbaum, B. O., Hodges, L., Smith, S., Lee, J. H. & Price, L. (2000). A controlled study of virtual reality exposure therapy for the fear of flying. Journal of Consulting and Clinical Psychology, 68(6), 1020.
Saladin, M. E., Brady, K. T., Graap, K., & Rothbaum, B. O. (2006). A preliminary report on the use of virtual reality technology to elicit craving and cue reactivity in cocaine dependent individuals. Addictive Behaviors, 31(10), 1881-1894.
Santelli, J.S., Robin, L.,Brener, N. D., Lowry, R. (2001).Timing of Alcohol and Other Drug Use and Sexual Risk Behaviors among Unmarried Adolescents and Young Adults.Family Planning Perspectives, 33(5).
Schepens, S. L., et al. (2011). Randomized controlled trial comparing tailoring methods of multimedia-based fall prevention education for community-dwelling older adults. American journal of occupational therapy, 65(6): 702-709.
Schwartz, S. J., Mason, C. A., Pantin, H., Wang, W., Brown, C. H., Campo, A. E., et al. (2009). Relationships of social context and identity to problem behavior among high-risk Hispanic adolescents. Youth and Society, 40(4), 541-570.
Shih, Y.-Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study Internet banking in Taiwan. Internet Research, 14(3), 213-223.
Su, C. (2016). The effects of students' learning anxiety and motivation on the learning achievement in the activity theory based gamified learning environment. Eurasia Journal of Mathematics, Science and Technology Education ,13(5): 1229-1258.
Taylor, S.,Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2).
Trucco, E. M., Colder, C. R., Bowker, J. C., & Wieczorek, W. F. (2011). Interpersonal goals and susceptibility to peer influence: Risk factors for intentions to initiate substance use during early adolescence. The Journal of Early Adolescence, 31(4), 526-547.
UNODC. (2020). World Drug Report 20208. Retrieved from https://wdr.unodc.org/wdr2020/
Wei, X., et al. (2015). Teaching based on augmented reality for a technical creative design course. Computers and Education 81: 221-234.
Wongwiwatthananukit, S. and N. G. Popovich (2000). Applying the ARCS model of motivational design to pharmaceutical education. American Journal of Pharmaceutical Education, 64(2): 188-196.