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The Relationships Between Peer-to-Peer Interactions, Group Formation, Choice of Research, and Course Performance in an Online Environment

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Abstract

This study explores the relationships between peer-to-peer interactions and (1) group formation among students, (2) choice of research, and (3) course performance in an online asynchronous ecology course at a research-intensive university. Peer-to-peer interactions have been known to enhance learning experience for students in a wide array of contexts, including online courses. However, less is known about how these interactions shape the students’ performance and their choice of research over the course of time. Most previous studies have focused on either large introductory-level courses, where peer-to-peer interactions are usually lower, or analyses across a large number of courses, which introduce additional sources of variance. To explore how online peer-to-peer interactions develop, influence course dynamics, and impact student success, we collected data from a single medium-sized ecology course about peer-to-peer interactions, course performance, and student demographics. The course was repeated over six different semesters with the same instructor, same teaching assistant (TA), and an unchanged course structure to maintain certain homogeneity. Average class size was 20–25 students, and the educational format required intense discussions and peer interactions. Adopting a network science approach to the analyses, we find that peer-to-peer interactions not only affect student performance, but also shape class-wide interactions (e.g., working group formation), and choice of course research topic. Understanding this interplay of peer-to-peer interactions, group formation, and choice of research is important in forging necessary skills in students for a variety of contexts, and through such insights might better shape teamwork and choice of research, which are very important for molding future scientists in the twenty-first century.

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Data Availability

The data used in this study are available from the corresponding author upon request.

References

  • Aggarwal, P., & O’Brien, C. L. (2008). Social loafing on group projects: Structural antecedents and effect on student satisfaction. Journal of Marketing Education, 30(3), 255–264.

    Article  Google Scholar 

  • American Chemical Society Committee on Professional Training. (2015). Undergraduate professional education in chemistry: ACS guidelines and evaluation procedures for bachelor’s degree programs. Retrieved July 27, 2022, from https://www.acs.org/content/dam/acsorg/about/governance/committees/training/2015-acs-guidelines-for-bachelors-degree-programs.pdf

  • Apugliese, A., & Lewis, S. E. (2017). Impact of instructional decisions on the effectiveness of cooperative learning in chemistry through meta-analysis. Chemistry Education Research and Practice, 18(1), 271–278.

    Article  Google Scholar 

  • Azer, S. A., & Azer, D. (2015). Group interaction in problem-based learning tutorials: A systematic review. European Journal of Dental Education, 19(4), 194–208.

    Article  Google Scholar 

  • Bento, R., & Schuster, C. (2003). Participation: The online challenge. In A. Aggarwal (Ed.). Web-based education: Learning from experience (pp. 156–164). Idea Group Publishing.

    Chapter  Google Scholar 

  • Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkes, M. A., & Bethel, E. C. (2009). A meta-analysis of three types of interaction treatments in distance education. Review of Educational Research, 79(3), 1243–1289.

    Article  Google Scholar 

  • Bettinger, E., Liu, J., & Loeb, S. (2016). Connections matter: How interactive peers affect students in online college courses. Journal of Policy Analysis and Management, 35(4), 932–954.

    Article  Google Scholar 

  • Borokhovski, E., Bernard, R. M., Tamim, R. M., Schmid, R. F., & Sokolovskaya, A. (2016). Technology-supported student interaction in post-secondary education: A meta-analysis of designed versus contextual treatments. Computers & Education, 96, 15–28.

    Article  Google Scholar 

  • Cohen, M. (1991). Making class participation a reality. PS: Political Science & Politics, 24(4), 699– 703.

  • Crone, J. A. (1997). Using panel debates to increase student involvement in the introductory sociology class. Teaching Sociology, 25(3), 214–218.

    Article  Google Scholar 

  • Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.

    Google Scholar 

  • Daggett, L. M. (1997). Teaching tools: Quantifying class participation. Nurse Educator, 22(2), 13–14.

    Article  Google Scholar 

  • Demaria, M. C., Hodgson, Y., & Czech, D. P. (2018). Perceptions of transferable skills among biomedical science students in the final-year of their degree: What are the implications for graduate employability?. International Journal of Innovation in Science and Mathematics Education, 26(7).

  • Donovan, D. A., Connell, G. L., & Grunspan, D. Z. (2018). Student learning outcomes and attitudes using three methods of group formation in a nonmajors biology class. CBE—Life Sciences Education, 17(4), ar60.

  • Dori, Y. J., & Belcher, J. (2005). How does technology-enabled active learning affect undergraduate students’ understanding of electromagnetism concepts? The Journal of the Learning Sciences, 14(2), 243–279.

    Article  Google Scholar 

  • Dziuban, C., & Moskal, P. (2001). Emerging research issues in distributed learning. In Online education: Proceedings of the 2001 Sloan-C international conference on asynchronous learning networks. Needham, MA: Sloan-C Press.

  • Finch, D. J., Hamilton, L. K., Baldwin, R., & Zehner, M. (2013). An exploratory study of factors affecting undergraduate employability. Education and Training, 55(7), 681–704.

    Article  Google Scholar 

  • Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415.

    Article  Google Scholar 

  • Freeman, S., Theobald, R., Crowe, A. J., & Wenderoth, M. P. (2017). Likes attract: Students self-sort in a classroom by gender, demography, and academic characteristics. Active Learning in Higher Education, 18(2), 115–126.

    Article  Google Scholar 

  • Gašević, D., Zouaq, A., & Janzen, R. (2013). “Choose your classmates, your GPA is at stake!” The association of cross-class social ties and academic performance. American Behavioral Scientist, 57(10), 1460–1479.

    Article  Google Scholar 

  • Gilmore, J., Vieyra, M., Timmerman, B., Feldon, D., & Maher, M. (2015). The relationship between undergraduate research participation and subsequent research performance of early career STEM graduate students. The Journal of Higher Education, 86(6), 834–863.

  • Gray, F. E., Emerson, L., & MacKay, B. (2005). Meeting the demands of the workplace: Science students and written skills. Journal of Science Education and Technology, 14(4), 425–435.

    Article  Google Scholar 

  • Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). statnet: Software tools for the representation, visualization, analysis and simulation of network data. Journal of Statistical Software, 24(1), 1548.

    Article  Google Scholar 

  • Hart Research Associates. (2015). Falling short? College learning and career success. https://www.aacu.org/sites/default/files/files/LEAP/2015employerstudentsurvey.pdf

  • Hartman, J. L., & Truman-Davis, B. (2001). Factors related to the satisfaction of faculty teaching online courses at the University of Central Florida. In Online education: Proceedings of the 2000 Sloan summer workshop on asynchronous learning networks. Needham, MA: Sloan-C Press.

  • Heard, N. A., & Rubin-Delanchy, P. (2018). Choosing between methods of combining-values. Biometrika, 105(1), 239–246.

    Article  Google Scholar 

  • Heller, P., & Hollabaugh, M. (1992). Teaching problem solving through cooperative grouping. Part 2: Designing problems and structuring groups. American journal of Physics, 60(7), 637–644.

  • Heron, H., et al. (2016). Joint Task Force on Undergraduate Physics Programs. Phys21: Preparing physics student for 21st-century careers. http://www.compadre.org/JTUPP/report.cfm

  • Hora, M. T., Benbow, R. J., & Oleson, A. K. (2016). Beyond the skills gap: Preparing college students for life and work. Harvard Education Press.

    Google Scholar 

  • Jang, H. (2016). Identifying 21st century STEM competencies using workplace data. Journal of Science Education and Technology, 25(2), 284–301.

    Article  Google Scholar 

  • Jensen, J. L., & Lawson, A. (2011). Effects of collaborative group composition and inquiry instruction on reasoning gains and achievement in undergraduate biology. CBE—Life Sciences Education, 10(1), 64–73.

  • Joksimović, S., Manataki, A., Gašević, D., Dawson, S., Kovanović, V., & De Kereki, I. F. (2016). Translating network position into performance: Importance of centrality in different network configurations. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, 314–323.

  • Junn, E. (1994). Pearls of wisdom: Enhancing student class participation with an innovative exercise. Journal of Instructional Psychology, 21(4), 385–387.

    Google Scholar 

  • Krivitsky, P. N., Handcock, M. S., Hunter, D. R., & Krivitsky, M. P. N. (2012). Package ‘ergm. count.’ Journal of Statistics, 6, 1100–1128.

    Google Scholar 

  • Lavi, R., Tal, M., & Dori, Y. J. (2021). Perceptions of STEM alumni and students on developing 21st century skills through methods of teaching and learning. Studies in Educational Evaluation, 70, 101002. https://doi.org/10.1016/j.stueduc.2021.101002

    Article  Google Scholar 

  • Lievens, F., & Sackett, P. R. (2012). The validity of interpersonal skills assessment via situational judgment tests for predicting academic success and job performance. The Journal of Applied Psychology, 97(2), 460–468.

    Article  Google Scholar 

  • Lou, Y., Abrami, P. C., & d’Apollonia, S. (2001). Small group and individual learning with technology: A meta-analysis. Review of Educational Research, 71(3), 449–521.

    Article  Google Scholar 

  • Marbach-Ad, G., Hunt, C., & Thompson, K. V. (2019). Exploring the values undergraduate students attribute to cross-disciplinary skills needed for the workplace: An analysis of five STEM disciplines. Journal of Science Education and Technology, 28(5), 452–469.

  • Marbach-Ad, G., Rietschel, C., & Thompson, K. V. (2016). Validation and application of the survey of teaching beliefs and practices for undergraduates (STEP-U): Identifying factors associated with valuing important workplace skills among biology students. CBE—Life Sciences Education, 15(4), ar59.

  • Martınez, A., Dimitriadis, Y., Rubia, B., Gómez, E., & De la Fuente, P. (2003). Combining qualitative evaluation and social network analysis for the study of classroom social interactions. Computers & Education, 41(4), 353–368.

    Article  Google Scholar 

  • McGunagle, D., & Zizka, L. (2020). Employability skills for 21st-century STEM students: the employers' perspective. Higher education, skills and work-based learning.

  • Oztok, M. (2016). Reconceptualizing the pedagogical value of student facilitation. Interactive Learning Environments, 24(1), 85–95.

    Article  Google Scholar 

  • Ouyang, F., & Scharber, C. (2017). The influences of an experienced instructor’s discussion design and facilitation on an online learning community development: A social network analysis study. The Internet and Higher Education, 35, 34–47.

    Article  Google Scholar 

  • Pokhrel, S., & Chhetri, R. (2021). A literature review on impact of COVID-19 pandemic on teaching and learning. Higher Education for the Future, 8(1), 133–141.

    Article  Google Scholar 

  • Pozón-López, I., Higueras-Castillo, E., Muñoz-Leiva, F., & Liébana-Cabanillas, F. J. (2021). Perceived user satisfaction and intention to use massive open online courses (MOOCs). Journal of Computing in Higher Education, 33(1), 85–120.

    Article  Google Scholar 

  • Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223–231.

    Article  Google Scholar 

  • Rayner, G., & Papakonstantinou, T. (2015). Employer perspectives of the current and future value of STEM graduate skills and attributes: An Australian study. Journal of Teaching and Learning for Graduate Employability, 6(1), 100.

    Article  Google Scholar 

  • Rocca, K. A. (2010). Student participation in the college classroom: An extended multidisciplinary literature review. Communication Education, 59(2), 185–213.

    Article  Google Scholar 

  • Romero, C., López, M. I., Luna, J. M., & Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion forums. Computers & Education, 68, 458–472.

    Article  Google Scholar 

  • Rosser, S. V. (1998). Group work in science, engineering, and mathematics: Consequences of ignoring gender and race. College Teaching, 46(3), 82–88.

    Article  Google Scholar 

  • Saqr, M., Fors, U., & Nouri, J. (2018). Using social network analysis to understand online Problem-Based Learning and predict performance. PLoS ONE, 13(9), e0203590.

    Article  Google Scholar 

  • Shea, P., Fredericksen, E., Pickett, A., Pelz, W., & Swan, K. (2001). Measures of learning effective- ness in the SUNY learning network. In Online education: Proceedings of the 2001 Sloan-C inter- national conference on asynchronous learning networks. Needham, MA: Sloan-C Press.

  • Shu, H., & Gu, X. (2018). Determining the differences between online and face-to-face student–group interactions in a blended learning course. The Internet and Higher Education, 39, 13–21.

    Article  Google Scholar 

  • Takeda, S., & Homberg, F. (2014). The effects of gender on group work process and achievement: An analysis through self-and peer-assessment. British Educational Research Journal, 40(2), 373–396.

    Article  Google Scholar 

  • Thompson, K. V., Nelson, K. C., Marbach-Ad, G., Keller, M., & Fagan, W. F. (2010). Online interactive teaching modules enhance quantitative proficiency of introductory biology students. CBE—Life Sciences Education, 9(3), 277–283.

  • Traxler, A., Gavrin, A., & Lindell, R. (2018). Networks identify productive forum discussions. Physical Review Physics Education Research, 14(2), 020107.

    Article  Google Scholar 

  • Viskupic, K., Egger, A. E., McFadden, R. R., & Schmitz, M. D. (2021). Comparing desired workforce skills and reported teaching practices to model students’ experiences in undergraduate geoscience programs. Journal of Geoscience Education, 69(1), 27–42. https://doi.org/10.1080/10899995.2020.1779568

    Article  Google Scholar 

  • Wanstreet, C. E. (2006). Interaction in online learning environments: A review of the literature. Quarterly Review of Distance Education, 7(4), 399.

    Google Scholar 

  • Watson, W. E., Kumar, K., & Michaelsen, L. K. (1993). Cultural diversity’s impact on interaction process and performance: Comparing homogeneous and diverse task groups. Academy of Management Journal, 36(3), 590–602.

    Google Scholar 

  • Wei, H. C., & Chou, C. (2020). Online learning performance and satisfaction: Do perceptions and readiness matter? Distance Education, 41(1), 48–69.

    Article  Google Scholar 

  • Wilson, K. J., Brickman, P., & Brame, C. J. (2018). Group work. CBE—Life Sciences Education, 17(1), fe1.

  • Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330(6004), 686–688.

    Article  Google Scholar 

  • Xu, Y., Lynch, C. F., & Barnes, T. (2018). How many friends can you make in a week?: Evolving social relationships in MOOCs over time. International Educational Data Mining Society.

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Acknowledgements

We would like to thank the COMBINE program at the University of Maryland (National Science Foundation award DGE-1632976) for providing training (non-financial) to AS and an opportunity for the authors to work together.

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Authors and Affiliations

  1. Department of Biology, University of Maryland, College Park, MD, 20742, USA

    Anshuman Swain & William F. Fagan

  2. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA

    Anshuman Swain

  3. The Museum of Comparative Zoology Labs, 26 Oxford Street, Cambridge, MA, 02138, USA

    Anshuman Swain

  4. Department of Entomology, University of Maryland, College Park, MD, 20742, USA

    Marcia Shofner

  5. College of Computer, Mathematical and Natural Sciences Teaching and Learning Center, University of Maryland, College Park, MD, 20742, USA

    Gili Marbach-Ad

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  1. Anshuman Swain
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  2. Marcia Shofner
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  4. Gili Marbach-Ad
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All four authors conceptualized the project and wrote the manuscript. AS performed the analysis and data collection.

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Correspondence to Anshuman Swain.

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The study was approved by the University of Maryland IRB listed as 1221080–4 (“BSCI361 Principles of Ecology Group Project Survey”).

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Swain, A., Shofner, M., Fagan, W.F. et al. The Relationships Between Peer-to-Peer Interactions, Group Formation, Choice of Research, and Course Performance in an Online Environment. J Sci Educ Technol 31, 707–717 (2022). https://doi.org/10.1007/s10956-022-10000-5

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