Abstract
The main challenge for biological engineering educators is that BE is a relatively new and broad discipline that integrates a diverse array of knowledge from the basic sciences and engineering sciences towards application in the biological and medicinal fields. Due to variety of possible career outcomes and limited department resources (money, laboratory space, etc.), it is not possible to create a single undergraduate curriculum that will cover all of the technical skills spanning the entire biological engineering field in a hands-on setting. As laboratory sections have been shown to provide a variety of educational benefits, it is imperative that university engineering departments seek alternative methods to deliver real-life application of classroom concepts.
As such, interest in the development and usage of simulated lab sections has risen. While these lab experiences offer economic benefits to educational institutions and are more convenient for students to access, the exact educational outcomes of simulated labs, especially when compared to traditional hands-on labs, is still unclear. Due to the previously stated challenges, the biological engineering education community could benefit greatly by the implementation of simulated labs; however, there is a limited amount of literature on simulated labs in the context of biological engineering.
Therefore, the purpose of this study is to provide a case study on how the implementation of a commercially available simulated lab alters the motivation of students in a BE course. Data collection will help us answer three research questions: 1) How well does the simulated lab intervention work? 2) How do BE students experience disciplinary-specific simulated labs? 3) How do those experiences inform us on the student motivation? By utilizing the MUSICĀ® Model of Academic Motivation, we aim to pin down the social realities surrounding simulated BE labs, clarify the unique motivations of BE students, and provide vital information for the national discourse of proper BE curricula development.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Geisinger, B.N., Raman, D.R.: Why they leave: understanding student attrition from engineering majors. Int. J. Eng. Educ. 29(4), 914 (2013)
Benson, L., Kirn, A., Faber, C.J.: CAREER: student motivation and learning in engineering. In: 2014 ASEE Annual Conference & Exposition, pp. 24.261. 1ā24.261.9 (2014)
Keshwani, J.R., Curtis, E.: Motivating undergraduate engineering students through real-world applications of biological materials. Trans. ASABE 60(5), 1421ā1427 (2017)
Alpay, E., Ahearn, A.L., Graham, R.H., Bull, A.M.J.: Student enthusiasm for engineering: charting changes in student aspirations and motivation. Eur. J. Eng. Educ. 33(5ā6), 573ā585 (2008)
Hofstein, A., Lunetta, V.N.: The laboratory in science education: Foundations for the twenty-first century. Sci. Educ. 88(1), 28ā54 (2004)
White, R.T.: The link between the laboratory and learning. Int. J. Sci. Educ. 18(7), 761ā774 (1996)
Huang, I., Hwang, G.-J., Yang, I.-J.: Optimization of a cooperative programming learning system by using a Constructivist approach. In: Proceedings of the 18th International Conference on Computers in Education (ICCE-2010). Asia-Pacific Society for Computers in Education, Putrajaya (2010)
Gagnon, G.W., Collay, M.: Constructivist Learning Design: Key Questions for Teaching to Standards. Corwin Press, Thousand Oaks (2005)
Johnson, D.W., Johnson, R.T.: An educational psychology success story: social interdependence theory and cooperative learning. Educ. Res. 38(5), 365ā379 (2009)
Harris, T.R.: Seeking improvement in bioengineering education: academic and organizational concerns. In: Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, Engineering in Medicine and Biology, vol. 3, pp. 2648ā2649 (2002)
Perreault, E.J., Litt, M., Saterbak, A.: Educational methods and best practices in BME Laboratories1. Ann. Biomed. Eng. 34(2), 209ā216 (2006)
Potkonjak, V., Gardner, M., Callaghan, V., Mattila, P., Guetl, C., PetroviÄ, V.M., JovanoviÄ, K.: Virtual laboratories for education in science, technology, and engineering: a review. Comput. Educ. 95, 309ā327 (2016)
Scanlon, E., Colwell, C., Cooper, M., Di Paolo, T.: Remote experiments, re-versioning and re-thinking science learning. Comput. Educ. 43(1ā2), 153ā163 (2004)
Ma, J., Nickerson, J.V.: Hands-on, simulated, and remote laboratories: a comparative literature review. ACM Comput. Surv. 38(3), 7 (2006)
Corter, J.E., Esche, S.K., Chassapis, C., Ma, J., Nickerson, J.V.: Process and learning outcomes from remotely-operated, simulated, and hands-on student laboratories. Comput. Educ. 57(3), 2054ā2067 (2011)
Sauter, M., Uttal, D.H., Rapp, D.N., Downing, M., Jona, K.: Getting real: the authenticity of remote labs and simulations for science learning. Distance Educ. 34(1), 37ā47 (2013)
Chou, S.-W., Min, H.-T.: The impact of media on collaborative learning in virtual settings: the perspective of social construction. Comput. Educ. 52(2), 417ā431 (2009)
Looi, C.-K., Chen, W., Ng, F.-K.: Collaborative activities enabled by GroupScribbles (GS): an exploratory study of learning effectiveness. Comput. Educ. 54(1), 14ā26 (2010)
Reyes, J., Lysaght, M.K.: The growth of tissue engineering. Tissue Eng. 7(5), 485ā493 (2001)
Brown, P.R., McCord, R.E., Matusovich, H.M., Kajfez, R.L.: The use of motivation theory in engineering education research: a systematic review of literature. Eur. J. Eng. Educ. 40(2), 186ā205 (2015)
Jones, B.D.: Motivating students to engage in learning: the MUSIC model of academic motivation. Int. J. Teach. Learn. High. Educ. 21(2), 272ā285 (2009)
Jones, B.: User guide for assessing the components of the MUSICĀ® Model of Motivation (2017)
Seawright, J., Gerring, J.: Case selection techniques in case study research: a menu of qualitative and quantitative options. Polit. Res. Q. 61(2), 294ā308 (2008)
Rogan, A.I., de Kock, D.M.: Chronicles from the classroom: making sense of the methodology and methods of narrative analysis. Qual. Inq. 11(4), 628ā649 (2005)
Smith, C.P.: Content analysis and narrative analysis. In: Handbook of Research Methods in Social and Personality Psychology, pp. 313ā335. Cambridge University Press, New York (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Devine, R., May, D. (2021). Work in Progress: Pilot Study for the Effect of a Simulated Laboratories on the Motivation of Biological Engineering Students. In: Auer, M., May, D. (eds) Cross Reality and Data Science in Engineering. REV 2020. Advances in Intelligent Systems and Computing, vol 1231. Springer, Cham. https://doi.org/10.1007/978-3-030-52575-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-52575-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-52574-3
Online ISBN: 978-3-030-52575-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)