Motivation Strategies and Exiting Class by Students in Inquiry-Oriented Biology Labs
Experimental inquiry-oriented science labs can be designed to have students regulate their own learning and decide when they leave class or to have the teacher regulate student learning and determine when they leave class. In this study, grades were examined relative to student exit times in a student-regulated class design. Preliminary interviews revealed four motivation strategies likely to differentially influence exit times and grades: proficiency, grade-target-A, grade-target-C, and time-limited. Students were categorized into the four groups of motivation strategies with a survey. Twenty teaching assistants teaching three lab sections each taught the stand-alone lab class. Students recorded the time they left class each week. Grades were determined as the overall percentage of points a student received in class. Results of the survey showed that the four motivation strategies were well represented in the student population, and two additional strategies were also frequently seen: a hybrid-1 between proficiency and grade-target-A, and a hybrid-2 between time-limited and grade-target-C. Grades were significantly higher for grade-target-A and hybrid-1 students, followed by time-limited, proficiency, grade-target-C, and hybrid 2. Time spent in class was not significantly different among categories. Students who chose to stay in class longer had significantly higher grades. If a grade is the goal, these results support the idea of a teacher-controlled exit time for the students in these inquiry-oriented labs. Implications are discussed.
Advisory Committee to the Directorate for Education and Human Resources. 1998. Perspectives on Undergraduate Education in Science, Mathematics, Engineering, and Technology. Contributions to the Review of Undergraduate Education. Vol. 2 of Shaping the Future, 277–311. Arlington: National Science Foundation. https://www.nsf.gov/pubs/1998/nsf98128/nsf98128.pdf.
Arulampalam, Wiji, Robin A. Naylor, and Jeremy Smith. 2011. “Am I Missing Something? The Effects of Absence from Class on Student Performance.” Economics of Education Review 31, no. 4: 363–75. https://doi.org/10.1016/j.econedurev.2011.12.002.
Bratti, Massimiliano, and Stefano Staffolani. 2013. “Student Time Allocation and Educational Production Functions.” Annals of Economics and Statistics, no. 111/112: 103–40. https://doi.org/10.2307/23646328.
Brewer, Carol A., and Diane Smith, eds. 2011. Vision and Change in Undergraduate Biology Education: A Call to Action, 4–6. Washington, DC: American Association for the Advancement of Science. https://live-visionandchange.pantheonsite.io/wp-content/uploads/2013/11/aaas-VISchange-web1113.pdf.
Burnham, Kenneth P., and David R. Anderson. 2002. Model Selection and Inference: A Practical information-Theoretic Approach, 2nd ed., 352-436. New York: Springer-Verlag
Dobkin, Carlos, Ricard Gil, and Justin Marion. 2010. “Skipping Class in College and Exam Performance: Evidence from a Regression Discontinuity Classroom Experiment.” Economic Education Review 29, no. 4: 566–75. https://doi.org/10.1016/j.econedurev.2009.09.004.
Freeman, Scott, David Haak, and Mary P. Wenderoth. 2011. “Increased Course Structure Improves Performance in Introductory Biology.” CBE—Life Sciences Education 10, no. 2: 175–86. https://doi.org/10.1187/cbe.10-08-0105.
Grave, Barbara S. 2011. “The Effect of Student Time Allocation on Academic Achievement.” Education Economics 19, no. 3: 291–310. https://doi.org/10.1080/09645292.2011.585794.
Kassarnig, Valentin, Enys Mones, Andreas Bjerre-Nielsen, Piotr Sapiezynski, David D. Lassen, and Sune Lehmann. 2018. “Academic Performance and Behavioral Patterns.” EPJ Data Science 7, Article 10. https://doi.org/10.1140/epjds/s13688-018-0138-8.
Kwak, Do W., Carl Sherwood, and Kam K. Tang. 2018. “Class Attendance and Learning Outcome.” Empirical Economics 57, no. 1: 177–203. https://doi.org/10.1007/s00181-018-1434-7.
Lukkarinen, Anna, Paula Koivukangas, and Tomi Seppälä. 2016. “Relationship between Class Attendance and Student Performance.” Procedia—Social and Behavioral Sciences, no. 228: 341–47. https://doi.org/10.1016/j.sbspro.2016.07.051.
Murphy, Steve, Amy MacDonald, Cen A. Wang, and Lena Danaia. 2019. “Towards an Understanding of STEM Engagement: A Review of the Literature on Motivation and Academic Emotions.” Canadian Journal of Science, Mathematics and Technology Education 19, no. 3: 304–20. https://doi.org/10.1007/s42330-019-00054-w.
National Research Council. 2003. BIO2010: Transforming Undergraduate Education for Future Research Biologists, 10–27. Washington, DC: The National Academies Press.
Stanca, Luca. 2006. “The Effects of Attendance on Academic Performance: Panel Data Evidence for Introductory Microeconomics.” Journal of Economic Education 37, no. 3: 251–66. https://doi.org/10.3200/JECE.37.3.251-266.
Undorf, Monika, and Rakefet Ackerman. 2017. “The Puzzle of Study Time Allocation for the Most Challenging Items.” Psychonomic Bulletin & Review 24, no. 6: 2003–2011. http://dx.doi.org/10.3758/s13423-017-1261-4.
Zhu, Liugen, Edgar Huang, Joseph Defazio, and Sara A. Hook. 2019. “Impact of the Stringency of Attendance Policies on Class Attendance/Participation and Course Grades.” Journal of the Scholarship of Teaching and Learning 19, no. 2: 130–40. http://doi.org/10.14434/josotl.v19i1.23717.
Copyright (c) 2020 John M. Basey, Clinton D. Francis, Maxwell B. Joseph
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.