The effects of computer-based virtual learning environments on nursing students’ mathematical learning in medication processes
Tóm tắt
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.
Tài liệu tham khảo
Alloway, T. P., & Alloway, R. G. (2010). Investigating the predictive roles of working memory and IQ in academic attainment. Journal of Experimental Child Psychology, 106, 20–29. https://doi.org/10.1016/j.jecp.2009.11.003.
Anderson, J. R., Fincham, J. M., & Douglas, S. (1997). The role of examples and rules in the acquisition of a cognitive skill. Journal of Experimental Psychology, 23, 932–945.
Ausubel, D. P. (1968). Educational psychology: A cognitive view. New York: Holt, Rinehart and Winston.
Ben-David, A., & Zohar, A. (2009). Contribution of meta-strategic knowledge to scientific inquiry learning. International Journal of Science Education, 31, 1657–1682. https://doi.org/10.1080/09500690802162762.
Boyle, E., Hainey, T., Connolly, M., Gray, G., Earp, J., Ott, M., … Riberio, C. (2016). An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Computers in Education, 94, 178–192. https://doi.org/10.1016/j.compedu.2015.11.003.
Canobi, K. H. (2009). Concept-procedure interactions in children’s addition and subtraction. Journal of Experimental Child Psychology, 102, 131–149. https://doi.org/10.1016/j.jecp.2008.07.008.
Chen, O., Kalyuaga, S., & Sweller, J. (2016). Relations between the worked example and generation effects on immediate and delayed tests. Learning and Instruction, 45, 20–30. https://doi.org/10.1016/j.learninstruc.2016.06.007.
Clarke, T., Ayres, P., & Sweller, J. (2005). The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications. Educational Technology Research and Development, 53, 15–24. https://doi.org/10.1007/BF02504794.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper Collins Publishers.
De Vos, T. (1992). Tempo Toets Rekenen [tempo test mathematics]. Nijmegen: Berkhout.
Dunlosky, J. (2013). Strengthening the student toolbox. Student strategies to boost learning American Educator, Fall 2013. Retrieved from https://www.aft.org/sites/default/files/periodicals/dunlosky.pdf.
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14, 4–58.
Eiriksdottir, E., & Catrambone, R. (2011). Procedural instructions, principles, and examples: How to structure instructions for procedural tasks to enhance performance, learning, and transfer. Human Factors, 53, 749–770. https://doi.org/10.1177/0018720811419154.
Gick, M. L., & Holyoak, K. J. (1987). The cognitive basis of knowledge transfer. In S. M. Cormier, & J. D. Hagman (Eds.), Transfer of learning: Contemporary research and applications, (pp. 9–46). San Diego: Academic Press.
Hattie, J., & Gan, M. J. (2011). Instruction based on feedback. In R. E. Mayer, & P. A. Alexander (Eds.), Handbook of research on learning and instruction, (pp. 249–271). New York: Routledge.
Hattie, J., & Timperly, H. (2007). The power of feedback. Review of Educational Research, 77, 81–112. https://doi.org/10.3102/003465430298487.
Hirsch Jr., E. D. (2016). Why knowledge matters. Rescuing our children from failed educational theories. Cambridge: Harvard Education Press.
Kalyuga, S., Chandler, P., & Sweller, J. (2001a). Learner experience and efficiency of instructional guidance. Educational Psychology, 21, 5–23. https://doi.org/10.1080/01443410124681.
Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001b). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93, 579–588. https://doi.org/10.1037/0022-0663.93.3.579.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41, 75–86. https://doi.org/10.1207/s15326985ep4102_1.
Kirschner, P. A., Sweller, J., Kirschner, F., & Zambrano, R. J. (2018). From cognitive load theory to collaborative load theory. International Journal of Computer-Supported Collaborative Learning, 13, 213–233. https://doi.org/10.1007/s11412-018-9277-y.
Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory Into Practice, 41, 212–218.
Lampe, T., Straetmans, G., & Eggen, T. (2011). De rekenvaardigheid van de Nederlandse verpleegkundige [nurses’ mathematical skills in the Netherlands]. Onderwijs en Gezondheidszorg, 35(3), 3–9. https://doi.org/10.1007/s12477-011-0030-y.
Lee, E. A.-L., & Wong, K. W. (2014). Learning with desktop virtual reality: Low spatial ability learners are more positively affected. Computers in Education, 79, 49–58. https://doi.org/10.1016/j.compedu.2014.07.010.
Lee, E. A.-L., Wong, K. W., & Fung, C. C. (2010). How does desktop virtual reality enhance learning outcomes? A structural equation modeling approach. Computers & Education, 55, 1424–1442. https://doi.org/10.1016/j.compedu.2010.06.006.
Margulieux, L., & Catrambone, R. (2016). Improving problem solving with subgoal labels in expository text and worked examples. Learning and Instruction, 42, 58–71. https://doi.org/10.1016/j.learninstruc.2015.12.002.
Mayer, R. M. (2001). Multimedia learning. New York: Cambridge University Press.
Morrison, G. R., Ross, S. M., Kalman, H. K., & Kemp, J. E. (2011). Designing effective instruction. Hoboken: Wiley.
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31, 199–218. https://doi.org/10.1080/03075070600572090.
Noroozi, O., Biemans, H. J. A., Weinberger, A., Mulder, M., & Chizari, M. (2013). Scripting for construction of a transactive memory system in multidisciplinary CSCL environments. Learning and Instruction, 25, 1–12. https://doi.org/10.1016/j.learninstruc.2012.10.002.
Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In R. A. Calvo, & S. K. D’Mello (Eds.), New perspectives on affect and learning technologies, Exploration in the learning sciences, instructional systems and performance technologies (vol. 3, pp. 23–39). New York: Springer.
Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86. https://doi.org/10.1016/S0959-4752(01)00016-0.
Polya, G. (1973). How to solve it: A new aspect of mathematical method. Princeton: Princeton University Press.
Prins, H., Zwart, D. P., Voogt, J., & Hettinga, M. (2019). Which professional contexts can enrich the training of medication skills of nurses through virtual reality? Retrieved from http://healthbytech.com/wp-content/uploads/Abstract-book-SupportingHealth-5.pdf.
Raven, J. C., Court, J. H., & Raven, J. (1992). Standard progressive matrices (SPM). (1992nd). Oxford: Oxford Psychologists Press.
Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W. (2001). Developing conceptual understanding and procedural skill in mathematics: An iterative process. Journal of Educational Psychology, 93, 346–362. https://doi.org/10.1037/0022-0663.93.2.346.
Roediger, H. L., & Karpicke, J. D. (2006). The power of testing memory: Basic research and implications for educational practice. Perspectives on Psychological Science, 1, 181–210.
Rohrer, D., Dedrick, R. F., & Burgress, K. (2014). The benefit of interleaved mathematics practice is not limited to superficially similar kinds of problems. Psychonomic Bulletin & Review, 21, 1323–1330. https://doi.org/10.3758/s13423-014-0588-3.
Rohrer, D., Dedrick, R. F., & Stershic, S. (2015). Interleaved practice improves mathematics learning. Journal of Educational Psychology, 107, 900–908. https://doi.org/10.1037/edu0000001.
Sadler, D. R. (2010). Beyond feedback: Developing student capability in complex appraisal. Assessment & Evaluation in Higher Education, 35, 535–550. https://doi.org/10.1080/02602930903541015.
Schön, D. A. (1983). The reflective practitioner. London: Basic Books.
Shute, V., Ventura, M., Bauer, M., & Zapata-Rivera, D. (2009). Melding the power of serious games and embedded assessment to monitor and foster learning. In U. Ritterfeld, M. Cody, & P. Vorderer (Eds.), Serious games: Mechanisms and effects, (pp. 295–321). New York: Routledge.
Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22, 123–138. https://doi.org/10.1007/s10648-010-91285.
Tricot, A., & Sweller, J. (2014). Domain-specific knowledge and why teaching generic skills does not work. Educational Psychology Review, 26, 265–283. https://doi.org/10.1007/s10648013-9243-1.
Tynjälä, P. (2013). Toward a 3-P model of workplace learning: A literature review. Vocations and Learning, 6, 11–36. https://doi.org/10.1007/s12186-012-9091-z.
Van Gog, T., Kester, L., & Paas, F. (2011). Effects of worked examples, example-problem, and problem-example pairs on novices’ learning. Contemporary Educational Psychology, 36, 212–218. https://doi.org/10.1016/j.cedpsych.2010.10.004.
Van Merriënboer, J. J., & Sweller, J. (2010). Cognitive load theory in health professional education: Design principles and strategies. Medical Education, 44, 85–93. https://doi.org/10.1111/j.1365-2923.2009.03498.x.
Wang, M., Kirschner, P. A., Spector, J. M., & Ge, X. (2018). Computer-based learning environments for deeper learning in problem-solving contexts. Computers in Human Behavior, 87, 403–405. https://doi.org/10.1016/j.chb.2018.06.026.
Warner, D. O., Nolan, M., Garcia-Marcinkiewicz, A., Schultz, C., Warner, M. A., Schroeder, R., & Cook, D. A. (2020). Adaptive instruction and learner interactivity in online learning: A randomized trial. Advances in Health Sciences Education, 25, 95–109. https://doi.org/10.1007/s10459-019-09907-3.
Weeks, K. W., Hutton, B. M., Young, S., Coben, D., Clochesy, J. M., & Pontin, D. (2013). Competency modelling and diagnostic error assessment in medication dosage calculation problem-solving. Nurse Education in Practice, 13, 23–32. https://doi.org/10.1016/j.nepr.2012.10.013.
Xinhao, X., & Fengfeng, K. (2016). Designing a virtual reality based, gamelike math learning environment. American Journal of Distance Education, 30, 27–38. https://doi.org/10.1080/08923647.2016.1119621.
Zohar, A., & David, A. B. (2008). Explicit teaching of meta-strategic knowledge in authentic classroom situations. Metacognition and Learning, 3, 59–82. https://doi.org/10.1007/s11409-007-9019-4.