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Investigating Help-Giving Behavior in a Cross-Platform Learning Environment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11625)

Abstract

A key promise of adaptive collaborative learning support is the ability to improve learning outcomes by providing individual students with the help they need to collaborate more effectively. These systems have focused on a single platform. However, recent technology-supported collaborative learning platforms allow students to collaborate in different contexts: computer-supported classroom environments, network based online learning environments, or virtual learning environments with pedagogical agents. Our goal is to better understand how students participate in collaborative behaviors across platforms, focusing on a specific type of collaboration - help-giving. We conducted a classroom study (N = 20) to understand how students engage in help-giving across two platforms: an interactive digital learning environment and an online Q&A community. The results indicate that help-giving behavior across the two platforms is mostly influenced by the context rather than by individual differences. We discuss the implications of the results and suggest design recommendations for developing an adaptive collaborative learning support system that promotes learning and transfer.

Keywords

Adaptive collaborative learning support Intelligent collaborative support Help-giving-behavior Motivation 

Notes

Acknowledgements

This work is supported by the National Science Foundation under Grant No 1736103.

References

  1. 1.
    Baghaei, N., Mitrovic, A., Irwin, W.: Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams. Int. J. Comput. Support. Collaborative Learn. 2(2–3), 159–190 (2007)CrossRefGoogle Scholar
  2. 2.
    Bandura, A.: Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84(2), 191 (1977)CrossRefGoogle Scholar
  3. 3.
    Berger, J.L., Karabenick, S.A.: Motivation and students’ use of learning strategies: evidence of unidirectional effects in mathematics classrooms. Learn. Instr. 21(3), 416–428 (2011)CrossRefGoogle Scholar
  4. 4.
    Davidson-Shivers, G.V., Muilenburg, L.Y., Tanner, E.J.: How do students participate in synchronous and asynchronous online discussions? J. Educ. Comput. Res. 25(4), 351–366 (2001)CrossRefGoogle Scholar
  5. 5.
    Duncan, T.G., McKeachie, W.J.: The making of the motivated strategies for learning questionnaire. Educ. Psychol. 40(2), 117–128 (2005)CrossRefGoogle Scholar
  6. 6.
    Gweon, G., Rose, C., Carey, R., Zaiss, Z.: Providing support for adaptive scripting in an on-line collaborative learning environment. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 251–260. ACM, April 2006Google Scholar
  7. 7.
    Jackson, J., Dukerich, L., Hestenes, D.: Modeling instruction: an effective model for science education. Sci. Educ. 17(1), 10–17 (2008)Google Scholar
  8. 8.
    Kumar, R., Rosé, C.P., Wang, Y.C., Joshi, M., Robinson, A.: Tutorial dialogue as adaptive collaborative learning support. Front. Artif. Intell. Appl. 158, 383 (2007)Google Scholar
  9. 9.
    Liu, X., Koirala, H.: The effect of mathematics self-efficacy on mathematics achievement of high school students (2009)Google Scholar
  10. 10.
    Magnisalis, I., Demetriadis, S., Karakostas, A.: Adaptive and intelligent systems for collaborative learning support: a review of the field. IEEE Trans. Learn. Technol. 4(1), 5–20 (2011)CrossRefGoogle Scholar
  11. 11.
    Ogan, A., Finkelstein, S., Walker, E., Carlson, R., Cassell, J.: Rudeness and rapport: insults and learning gains in peer tutoring. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 11–21. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-30950-2_2CrossRefGoogle Scholar
  12. 12.
    Oztok, M., Zingaro, D., Brett, C., Hewitt, J.: Exploring asynchronous and synchronous tool use in online courses. Comput. Educ. 60(1), 87–94 (2013)CrossRefGoogle Scholar
  13. 13.
    Paramythis, A.: Adaptive support for collaborative learning with IMS learning design: are we there yet. In: Proceedings of the Workshop on Adaptive Collaboration Support, Held in Conjunction with the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, Hannover, Germany, pp. 17–29, July 2008Google Scholar
  14. 14.
    Pintrich, P.R., De Groot, E.V.: Motivational and self-regulated learning components of classroom academic performance. J. Educ. Psychol. 82(1), 33 (1990)CrossRefGoogle Scholar
  15. 15.
    Rienties, B., Tempelaar, D., Van den Bossche, P., Gijselaers, W., Segers, M.: The role of academic motivation in computer-supported collaborative learning. Comput. Hum. Behav. 25(6), 1195–1206 (2009)CrossRefGoogle Scholar
  16. 16.
    Roscoe, R.D., Chi, M.T.: Understanding tutor learning: knowledge-building and knowledge-telling in peer tutors’ explanations and questions. Rev. Educ. Res. 77(4), 534–574 (2007)CrossRefGoogle Scholar
  17. 17.
    Schoor, C., Bannert, M.: Motivation in a computer-supported collaborative learning scenario and its impact on learning activities and knowledge acquisition. Learn. Instr. 21(4), 560–573 (2011)CrossRefGoogle Scholar
  18. 18.
    Tapia, M., Marsh, G.E.: An instrument to measure mathematics attitudes. Acad. Exch. Q. 8(2), 16–22 (2004)Google Scholar
  19. 19.
    Walker, E., Rummel, N., Koedinger, K.R.: Adaptive intelligent support to improve peer tutoring in algebra. Int. J. Artif. Intell. Educ. 24(1), 33–61 (2014)CrossRefGoogle Scholar
  20. 20.
    Webb, N.M., Farivar, S.: Promoting helping behavior in cooperative small groups in middle school mathematics. Am. Educ. Res. J. 31(2), 369–395 (1994)CrossRefGoogle Scholar
  21. 21.
    Webb, N.M., Mastergeorge, A.: Promoting effective helping behavior in peer-directed groups. Int. J. Educ. Res. 39(1–2), 73–97 (2003)CrossRefGoogle Scholar
  22. 22.
    Wigfield, A., Eccles, J.S.: The development of achievement task values: a theoretical analysis. Develop. Rev. 12(3), 265–310 (1992)CrossRefGoogle Scholar
  23. 23.
    Wu, D., Hiltz, S.R.: Predicting learning from asynchronous online discussions. J. Asynchronous Learn. Netw. 8(2), 139–152 (2004)Google Scholar
  24. 24.
    Xie, K., Ke, F.: The role of students’ motivation in peer-moderated asynchronous online discussions. Br. J. Educ. Technol. 42(6), 916–930 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of PittsburghPittsburghUSA
  2. 2.Arizona State UniversityTempeUSA
  3. 3.New York UniversityNew YorkUSA
  4. 4.STEMteachersPHXGilbertUSA

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