Abstract
The availability of naturally occurring educational discourse data within educational platforms presents a golden opportunity to make advances in understanding online learner ecologies and enabling new kinds of personalized interventions focused on increasing inclusivity and equity. However, to gain a more substantive view of how peer interaction is influenced by group composition and gender, learning and computational sciences require new automated methodological approaches that will provide a deeper understanding of learners’ communication patterns and interaction dynamics across digitally-meditated group learning platforms. In the current research, we explore learners’ discourse by employing Group Communication Analysis (GCA), a computational linguistics methodology for quantifying and characterizing the discourse sociocognitive processes between learners in online interactions. The aim of this study is to use GCA to investigate the influence of gender and gender pairing on students’ intra- and interpersonal discourse processes in online environments. Students were randomly assigned to one of three groups of varying gender composition: 75% women, 50% women, or 25% women. Our results suggest that the sociocognitive discourse patterns, as captured by the GCA, reveal deeper level patterns in the way individuals interact within online environments along gender and group composition lines. The scalability of the methodology opens the door for future research efforts directed towards understanding, and creating more equitable and inclusive online peer-interactions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bernard, R.M., et al.: A meta-analysis of three types of interaction treatments in distance education. Rev. Educ. Res. 79(3), 1243–1289 (2009)
Borokhovski, E., Tamim, R., Bernard, R.M., Abrami, P.C., Sokolovskaya, A.: Are contextual and designed student-student interaction treatments equally effective in distance education? Distance Educ. 33(3), 311–329 (2012)
Bransford, J.D., Brown, A.L., Cocking, R.R.: How People Learn: Brain, Mind, Experience, and School (Exp Sub edition). National Academies Press, Washington (2000)
Çakır, M.P., Zemel, A., Stahl, G.: The joint organization of interaction within a multimodal CSCL medium. Int. J. Comput.-Support. Collaborative Learn. 4(2), 115–149 (2009)
Chopade, P., Stoeffler, K., M Khan, S., Rosen, Y., Swartz, S., von Davier, A.: Human-agent assessment: interaction and sub-skills scoring for collaborative problem solving. In: Penstein RosĂ©, C., MartĂnez-Maldonado, R., Hoppe, H.U., Luckin, R., Mavrikis, M., Porayska-Pomsta, K., McLaren, B., du Boulay, B. (eds.) AIED 2018. LNCS (LNAI), vol. 10948, pp. 52–57. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93846-2_10
Dasgupta, N., Scircle, M.M., Hunsinger, M.: Female peers in small work groups enhance women’s motivation, verbal participation, and career aspirations in engineering. Proc. Natl. Acad. Sci. 112(16), 4988–4993 (2015)
Dich, Y., Reilly, J., Schneider, B.: Using physiological synchrony as an indicator of collaboration quality, task performance and learning. In: Penstein RosĂ©, C., MartĂnez-Maldonado, R., Hoppe, H.U., Luckin, R., Mavrikis, M., Porayska-Pomsta, K., McLaren, B., du Boulay, B. (eds.) AIED 2018. LNCS (LNAI), vol. 10947, pp. 98–110. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93843-1_8
Ding, N., Bosker, R.J., Harskamp, E.G.: Exploring gender and gender pairing in the knowledge elaboration processes of students using computer-supported collaborative learning. Comput. Educ. 56(2), 325–336 (2011)
Dowell, N.M., Cade, W.L., Tausczik, Y., Pennebaker, J., Graesser, A.C.: What works: creating adaptive and intelligent systems for collaborative learning support. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 124–133. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07221-0_15
Dowell, N.M., Graesser, A.C.: Modeling learners’ cognitive, affective, and social processes through language and discourse. J. Learn. Anal. 1(3), 183–186 (2015)
Dowell, N.M., Nixon, T., Graesser, A.C.: Group communication analysis: a computational linguistics approach for detecting sociocognitive roles in multi-party interactions. Behav. Res. Methods 51, 1007–1041 (2019)
Dowell, N. M., Poquet, O., Brooks, C.: Applying group communication analysis to educational discourse interactions at scale. In: Proceedings of the 13th International Conference on the Learning Sciences. London, England (2018)
Eddy, S.L., Brownell, S.E., Thummaphan, P., Lan, M.-C., Wenderoth, M.P.: Caution, student experience may vary: social identities impact a student’s experience in peer discussions. CBE Life Sci. Educ. 14(4), ar45 (2015)
Fitzpatrick, H., Hardman, M.: Mediated activity in the primary classroom: girls, boys and computers. Learn. Instr. 10(5), 431–446 (2000)
Freeman, S., et al.: Active learning increases student performance in science, engineering, and mathematics. Proc. Natl. Acad. Sci. 111(23), 8410–8415 (2014)
Goldstone, R.L., Lupyan, G.: Discovering psychological principles by mining naturally occurring data sets. Top. Cognitive Sci. 8(3), 548–568 (2016)
Graesser, A.C., Dowell, N., Clewley, D.: Assessing collaborative problem solving through conversational agents. In: von Davier, A.A., Zhu, M., Kyllonen, P.C. (eds.) Innovative Assessment of Collaboration. MEMA, pp. 65–80. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-33261-1_5
Hu, X., Cai, Z., Wiemer-Hastings, P., Graesser, A.C., McNamara, D.S.: Strengths, Limitations, and Extensions of LSA. The Handbook of Latent Semantic Analysis, pp. 401–426 (2007)
Landauer, T.K., McNamara, D.S., Dennis, S., Kintsch, W. (eds.): Handbook of Latent Semantic Analysis. Psychology Press (2013)
Inzlicht, M., Ben-Zeev, T.: A threatening intellectual environment: why females are susceptible to experiencing problem-solving deficits in the presence of males. Psychol. Sci. 11(5), 365–371 (2000)
Johnson, D.R.: Campus racial climate perceptions and overall sense of belonging among racially diverse women in STEM majors. J. Coll. Student Dev. 53(2), 336–346 (2012)
Johnson, D.W., Johnson, R.T.: Learning Together and Alone. Cooperative, Competitive, and Individualistic Learning, 4th edn. Allyn and Bacon, Needham Heights (1994)
Joiner, R., Messer, D., Littleton, K., Light, P.: Gender, computer experience and computer-based problem solving. Comput. Educ. 26(1), 179–187 (1996)
Joksimović, S.A., Gašević, D.A., Loughin, T.M.C., Kovanović, V.B., Hatala, M.D.: Learning at distance: effects of interaction traces on academic achievement. Comput. Educ. 87, 204–217 (2015)
Kessels, U., Hannover, B.: When being a girl matters less: accessibility of gender-related self-knowledge in single-sex and coeducational classes and its impact on students’ physics-related self-concept of ability. Br. J. Educ. Psychol. 78(Pt 2), 273–289 (2008)
Kreijns, K., Kirschner, P.A., Jochems, W.: Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: a review of the research. Comput. Hum. Behav. 19(3), 335–353 (2003)
National Science Foundation: Science and Engineering Indicators 2018 (2018). https://www.nsf.gov/statistics/2018/nsb20181/report/sections/higher-education-in-science-and-engineering/graduate-education-enrollment-and-degrees-in-the-united-states
Office of Science and Technology Policy: STEM depiction opportunities. The White House President Barack Obama (2016). https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/imageofstemdepictiondoc_02102016_clean.pdf
Perera, N., Wise, A.F.: Beyond Demographic Boxes: Relationships Between Students’ Cultural Orientations and Collaborative Communication. In: Smith, B.K., Borge, M., Mercier, E., Lim, K.Y. (eds.). Presented at the Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL), Philadelphia, PA: International Society of the Learning Sciences (2017). http://www.diva-portal.org/smash/get/diva2:1172562/FULLTEXT01.pdf#page=187
Prinsen, F.R., Volman, M.L.L., Terwel, J.: Gender-related differences in computer-mediated communication and computer-supported collaborative learning. J. Comput. Assist. Learn. 23(5), 393–409 (2007)
Reimann, P.: Time is precious: variable- and event-centred approaches to process analysis in CSCL research. Int. J. Comput.-Support. Collaborative Learn. 4(3), 239–257 (2009)
Savicki, V., Kelley, M.: Computer mediated communication: gender and group composition. Cyberpsychol. Behav. Impact Internet Multimedia Virtual Reality Behav. Soc. 3(5), 817–826 (2000)
Stahl, G.: Group practices: a new way of viewing CSCL. Int. J. Comput.-Support. Collaborative Learn. 12(1), 113–126 (2017)
Stahl, G., Koschmann, T., Suthers, D.D.: Computer-supported collaborative learning: an historical perspective. In: Sawyer, R.K. (ed.) Cambridge Handbook of the Learning Sciences, pp. 409–426. Cambridge University Press, Cambridge (2006)
Stahl, G., Rosé, C.P.: Theories of Team Cognition: Cross-Disciplinary Perspectives. In: Salas, E., Fiore, S.M., Letsky, M.P. (eds.), pp. 111–134. Routledge, New York (2013)
Stewart, A., D’Mello, S.K.: Connecting the dots towards collaborative AIED: linking group makeup to process to learning. In: Penstein RosĂ©, C., MartĂnez-Maldonado, R., Hoppe, H.U., Luckin, R., Mavrikis, M., Porayska-Pomsta, K., McLaren, B., du Boulay, B. (eds.) AIED 2018. LNCS (LNAI), vol. 10947, pp. 545–556. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93843-1_40
Suthers, D.D., Dwyer, N., Medina, R., Vatrapu, R.: A framework for conceptualizing, representing, and analyzing distributed interaction. Int. J. Comput.-Support. Collaborative Learn. 5(1), 5–42 (2010)
Theobald, E.J., Eddy, S.L., Grunspan, D.Z., Wiggins, B.L., Crowe, A.J.: Student perception of group dynamics predicts individual performance: comfort and equity matter. PLoS ONE 12(7), e0181336 (2017)
Underwood, J., Underwood, G., Wood, D.: When does gender matter?: interactions during computer-based problem solving. Learn. Instr. 10(5), 447–462 (2000)
Walton, G.M., Cohen, G.L., Cwir, D., Spencer, S.J.: Mere belonging: the power of social connections. J. Pers. Soc. Psychol. 102(3), 513–532 (2012)
Wang, S.-L., Lin, S.S.J.: The effects of group composition of self-efficacy and collective efficacy on computer-supported collaborative learning. Comput. Hum. Behav. 23(5), 2256–2268 (2007)
Wunnasri, W., Pailai, J., Hayashi, Y., Hirashima, T.: Reciprocal kit-building of concept map to share each other’s understanding as preparation for collaboration. In: Penstein RosĂ©, C., MartĂnez-Maldonado, R., Hoppe, H.U., Luckin, R., Mavrikis, M., Porayska-Pomsta, K., McLaren, B., du Boulay, B. (eds.) AIED 2018. LNCS (LNAI), vol. 10947, pp. 599–612. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93843-1_44
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Dowell, N., Lin, Y., Godfrey, A., Brooks, C. (2019). Promoting Inclusivity Through Time-Dynamic Discourse Analysis in Digitally-Mediated Collaborative Learning. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-23204-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23203-0
Online ISBN: 978-3-030-23204-7
eBook Packages: Computer ScienceComputer Science (R0)