Skip to main content
SpringerLink
Log in

Improving collaborative learning in the classroom: Text mining based grouping and representing

  • Published:
International Journal of Computer-Supported Collaborative Learning Aims and scope Submit manuscript

Abstract

Orchestrating collaborative learning in the classroom involves tasks such as forming learning groups with heterogeneous knowledge and making learners aware of the knowledge differences. However, gathering information on which the formation of appropriate groups and the creation of graphical knowledge representations can be based is very effortful for teachers. Tools supporting cognitive group awareness provide such representations to guide students during their collaboration, but mainly rely on specifically created input. Our work is guided by the questions of how the analysis and visualization of cognitive information can be supported by automatic mechanisms (especially using text mining), and what effects a corresponding tool can achieve in the classroom. We systematically compared different methods to be used in a Grouping and Representing Tool (GRT), and evaluated the tool in an experimental field study. Latent Dirichlet Allocation proved successful in transforming the topics of texts into values as a basis for representing cognitive information graphically. The Vector Space Model with Euclidian distance based clustering proved to be particularly well suited for detecting text differences as a basis for group formation. The subsequent evaluation of the GRT with 54 high school students further confirmed the GRT’s impact on learning support: students who used the tool added twice as many concepts in an essay after discussing as those in the unsupported group. These results show the potential of the GRT to support both teachers and students.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • AlSumait, L., Wang, P., Domeniconi, C., & Barbará, D. (2010). Embedding semantics in LDA topic models. In M. W. Berry & J. Kogan (Eds.), Text mining: applications and theory (pp. 183–204). Sussex, UK: John Wiley & Sons, Ltd.. doi:10.1002/9780470689646.ch10.

    Chapter  Google Scholar 

  • Aronson, E., Blaney, N., Stephan, C., Sikes, J., & Snapp, M. (1978). The jigsaw classroom. Beverly Hills, CA: Sage Publications.

    Google Scholar 

  • Berger, A., Moretti, R., Chastonay, P., Dillenbourg, P., Bchir, A., Baddoura, R., et al. (2001). Teaching community health by exploiting international socio-cultural and economical differences. In P. Dillenbourg, A. Eurelings, & K. Hakkarainen (Eds.), Proceedings of the first European Conference on Computer Supported Collaborative Learning (pp. 97–105) .Retrieved from http://www.eculturenet.org/mmi/euro-cscl/Papers/14.pdf

    Google Scholar 

  • Berlyne, D. (1966). Notes on intrinsic motivation and intrinsic reward in relation to instruction. In J. Bruner (Ed.), Learning about learning (Cooperative Research Monograph No. 15). Washington, DC: Department of Health, Education, and Welfare, Office of Education.

    Google Scholar 

  • Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84. doi:10.1145/2133806.2133826.

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, I. J. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. doi:10.1162/jmlr.2003.3.4-5.993.

    Google Scholar 

  • Bodemer, D. (2011). Tacit guidance for collaborative multimedia learning. Computers in Human Behavior, 27(3), 1079–1086. doi:10.1016/j.chb.2010.05.016.

    Article  Google Scholar 

  • Bodemer, D., & Buder, J. (2006). Supporting collaborative learning with augmented group awareness tools. In R. Sun & N. Miyake (Eds.), Proceedings of the twenty-eighth annual conference of the cognitive science society (pp. 77–82). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Bodemer, D., & Dehler, J. (2011). Group awareness in CSCL environments. Computers in Human Behavior, 27(3), 1043–1045. doi:10.1016/j.chb.2010.07.014.

    Article  Google Scholar 

  • Bodemer, D., & Scholvien, A. (2014). Providing knowledge-related partner information in collaborative multimedia learning: Isolating the core of cognitive group awareness tools, In Proceedings of the 22nd International Conference on Computers in Education ICCE 2014 (pp. 171–179). Japan: Nara.

    Google Scholar 

  • Buder, J., & Bodemer, D. (2008). Supporting controversial CSCL discussions with augmented group awareness tools. International Journal of Computer-Supported Collaborative Learning, 3(2), 123–139. doi:10.1007/s11412-008-9037-5.

    Article  Google Scholar 

  • Daems, O., Erkens, M., Malzahn, N., & Hoppe, H. U. (2014). Using content analysis and domain ontologies to check learners’ understanding of science concepts. Journal of Computers in Education, 1(2–3), 113–131. doi:10.1007/s40692-014-0013-y.

    Article  Google Scholar 

  • Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407.

    Article  Google Scholar 

  • Dehler, J., Bodemer, D., Buder, J., & Hesse, F. W. (2011). Guiding knowledge communication in CSCL via group knowledge awareness. Computers in Human Behavior, 27(3), 1068–1078. doi:10.1016/j.chb.2010.05.018.

    Article  Google Scholar 

  • Dillenbourg, P. (2002). Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In P. A. Kirschner (Ed.), Three worlds of CSCL. Can we support CSCL (pp. 61–91). Heerlen, NL: Open Universiteit Nederland.

    Google Scholar 

  • Dillenbourg, P., & Betrancourt, M. (2006). Collaboration load. In J. Elen & R. E. Clark (Eds.), Handling complexity in learning environments: research and theory (pp. 142–163). Amsterdam, NL: Elsevier.

    Google Scholar 

  • Dillenbourg, P., & Jermann, P. (2007). Designing integrative scripts. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting Computer-Supported Collaborative Learning (pp. 275–301). Springer US.

  • Dillenbourg, P., & Tchounikine, P. (2007). Flexibility in macro-scripts for computer-supported collaborative learning. Journal of Computer Assisted Learning, 23(1), 1–13. doi:10.1111/j.1365-2729.2007.00191.x.

    Article  Google Scholar 

  • Dillenbourg, P., Järvelä, S., & Fischer, F. (2009). The evolution of research on computer-supported collaborative learning. In D. N. Balacheff, D. S. Ludvigsen, D. T. de Jong, D. A. Lazonder, & D. S. Barnes (Eds.), Technology-Enhanced Learning (pp. 3–19) .Springer Netherlands

    Google Scholar 

  • Dimitriadis, Y. A. (2012). Supporting teachers in orchestrating CSCL classrooms. In A. Jimoyiannis (Ed.), Research on e-Learning and ICT in Education (pp. 71–82). Springer .Retrieved from http://link.springer.com/chapter/10.1007/978-1-4614-1083-6_6

  • Doise, W., & Mugny, G. (1984). The social development of the intellect. (W. Doise & G. Mugny, Eds.) (Vol. 10). Amsterdam, Netherlands: Pergamon. Retrieved from http://www.sciencedirect.com/science/article/pii/B978008030215750010X.

  • Engelmann, T., & Hesse, F. W. (2010). How digital concept maps about the collaborators’ knowledge and information influence computer-supported collaborative problem solving. International Journal of Computer-Supported Collaborative Learning, 5(3), 299–319. doi:10.1007/s11412-010-9089-1.

    Article  Google Scholar 

  • Erkens, M., Schlottbom, P., & Bodemer, D. (2016). Qualitative and quantitative information in cognitive group awareness tools: Impact on collaborative learning. In U. Cress (Ed.), Transforming learning, empowering learners: the international conference of the learning sciences (ICLS) 2016. International Society of the Learning Sciences: Singapore.

    Google Scholar 

  • Feinerer, I., & Hornik, K. (2013). Tm: text mining package. A framework for text mining applications within R. Retrieved from http://tm.r-forge.r-project.org/.

  • Fischer, F., Kollar, I., Stegmann, K., & Wecker, C. (2013). Toward a script theory of guidance in computer-supported collaborative learning. Educational Psychologist, 48(1), 56–66. doi:10.1080/00461520.2012.748005.

    Article  Google Scholar 

  • Flavell, J. H., & Botkin, P. T. (1968). The development of role-taking and communication skills in children. Wiley.

  • Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H. H., Ventura, M., Olney, A., & Louwerse, M. M. (2004). AutoTutor: A tutor with dialogue in natural language. Behavior Research Methods, Instruments, & Computers, 36(2), 180–192. doi:10.3758/BF03195563.

    Article  Google Scholar 

  • Hesse, F. (2007). Being told to do something or just being aware of something? An alternative approach to scripting in CSCL. In F. Fischer, I. Kollar, H. Mandl, & J. Haake (Eds.), Scripting computer-supported communication of knowledge-cognitive, computational and educational perspectives (pp. 91–98). New York, NY: Springer.

    Google Scholar 

  • Hung, J. (2012). Trends of e-learning research from 2000 to 2008: Use of text mining and bibliometrics: Research trends of e-learning. British Journal of Educational Technology, 43(1), 5–16. doi:10.1111/j.1467-8535.2010.01144.x.

    Article  Google Scholar 

  • Janssen, J., & Bodemer, D. (2013). Coordinated computer-supported collaborative learning: Awareness and awareness tools. Educational Psychologist, 48(1), 40–55. doi:10.1080/00461520.2012.749153.

    Article  Google Scholar 

  • Johnson, D. W. (1970). The social psychology of education. New York, NY: Holt, Rinehart and Winston.

    Google Scholar 

  • Johnson, D. W. (1980). Group processes: influences of student-student interaction on school outcomes. In J. McMillan (Ed.), The social psychology of school learning (pp. 123–168). New York, NY: Academic Press.

    Google Scholar 

  • Johnson, D. W., & Johnson, R. T. (1979). Conflict in the classroom: controversy and learning. Review of Educational Research, 49(1), 51–69. doi:10.3102/00346543049001051.

    Article  Google Scholar 

  • Johnson, D. W., & Johnson, R. T. (2009). Energizing learning: the instructional power of conflict. Educational Researcher, 38(1), 37–51. doi:10.3102/0013189X08330540.

    Article  Google Scholar 

  • Johnson, D. W., Johnson, R. T., & Smith, K. A. (1998). Cooperative learning returns to college what evidence is there that it works? Change: The Magazine of Higher Learning, 30(4), 26–35. doi:10.1080/00091389809602629.

    Article  Google Scholar 

  • Kobbe, L., Weinberger, A., Dillenbourg, P., Harrer, A., Hämäläinen, R., Häkkinen, P., & Fischer, F. (2007). Specifying computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2(2–3), 211–224. doi:10.1007/s11412-007-9014-4.

    Article  Google Scholar 

  • Kohlberg, L. (1969). Stage and sequence: the cognitive-developmental approach to socialization. Chicago, IL: Rand McNally.

    Google Scholar 

  • Kollar, I., Fischer, F., & Hesse, F. W. (2006). Collaboration scripts – a conceptual analysis. Educational Psychology Review, 18(2), 159–185. doi:10.1007/s10648-006-9007-2.

    Article  Google Scholar 

  • Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: a review of the research. Computers in Human Behavior, 19(3), 335–353. doi:10.1016/S0747-5632(02)00057-2.

    Article  Google Scholar 

  • Lee, Y., & Nelson, D. (2004). Instructional use of visual representations of knowledge. In R. Ferdig, C. Crawford, R. Carlsen, N. Davis, J. Price, R. Weber, & D. Willis (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2004 (pp. 2371–2378). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE).

    Google Scholar 

  • Leydesdorff, L., & Hellsten, I. (2006). Measuring the meaning of words in contexts: An automated analysis of controversies about “Monarch butterflies,” “Frankenfoods,” and “stem cells.”. Scientometrics, 62(2), 231–258. doi:10.1556/Scient.67.2006.2.6.

    Article  Google Scholar 

  • Mäkitalo-Siegl, K., & Kollar, I. (2012). Collaboration scripts. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 628–631). New York, Dordrecht: Springer US.

    Google Scholar 

  • Manske, S., Hecking, T., Chounta, A., Werneburg, S., & Hoppe, H. U. (2015). Using differences to make a difference: A study on heterogeneity of learning groups. In O. Lindwall, P. Häkkinen, T. Koschmann, P. Tchounikine, & S. Ludvigsen (Eds.), Exploring the material conditions of learning: the computer supported collaborative learning (CSCL) conference 2015 (Vol. 1, pp. 182–189). Gothenburg: The International Society of the Learning Sciences.

    Google Scholar 

  • Miller, T. W. (2005). Data and text mining: a business applications approach. Upper Saddle River, N.J: Prentice Hall.

    Google Scholar 

  • Miner, G., Elder, J., Fast, A., Hell, T., Nisbet, B., & Delen, D. (2012). Practical text mining and statistical analysis for non-structured text data applications. Boston: Academic Press.

    Google Scholar 

  • O’Donnell, A. M., & O’Kelly, J. (1994). Learning from peers: Beyond the rhetoric of positive results. Educational Psychology Review, 6(4), 321–349. doi:10.1007/BF02213419.

    Article  Google Scholar 

  • Piaget, J. (1950). The psychology of intelligence. New York, NY: Harper.

    Google Scholar 

  • Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137.

    Article  Google Scholar 

  • Rajman, M., & Vesely, M. (2004). From text to knowledge: Document processing and visualization: A text mining approach. In S. Sirmakessis (Ed.), Text mining and its applications (Vol. 138, pp. 7–24). Springer: Berlin, Heidelberg. doi:10.1007/978-3-540-45219-5_2.

    Chapter  Google Scholar 

  • Rosé, C., Wang, Y.-C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., & Fischer, F. (2008). Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. International Journal of Computer-Supported Collaborative Learning, 3(3), 237–271. doi:10.1007/s11412-007-9034-0.

    Article  Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2006). Self-regulation and the problem of human autonomy: Does psychology need choice, self-determination, and will? Journal of Personality, 74(6), 1557–1586. doi:10.1111/j.1467-6494.2006.00420.x.

    Article  Google Scholar 

  • Sangin, M., Molinari, G., Nüssli, M.-A., & Dillenbourg, P. (2011). Facilitating peer knowledge modeling: Effects of a knowledge awareness tool on collaborative learning outcomes and processes. Computers in Human Behavior, 27(3), 1059–1067. doi:10.1016/j.chb.2010.05.032.

    Article  Google Scholar 

  • Scardamalia, M., & Bereiter, C. (2014). Smart technology for self-organizing processes. Smart Learning Environments, 1(1), 1–13. doi:10.1186/s40561-014-0001-8.

    Article  Google Scholar 

  • Schittekatte, M., & Hiel, A. V. (1996). Effects of partially shared information and awareness of unshared information on information sampling. Small Group Research, 27(3), 431–449. doi:10.1177/1046496496273006.

    Article  Google Scholar 

  • Sherin, B. (2012). Using computational methods to discover student science conceptions in interview data. In LAK ‘12 proceedings of the 2nd international conference on learning analytics and knowledge (pp. 188–197). New York, NY: ACM. doi:10.1145/2330601.2330649.

    Chapter  Google Scholar 

  • Southavilay, V., Yacef, K., Reimann, P., & Calvo, R. A. (2013). Analysis of collaborative writing processes using revision maps and probabilistic topic models. In Proceedings of the third international conference on learning analytics and knowledge (pp. 38–47). New York, NY: ACM. doi:10.1145/2460296.2460307.

    Chapter  Google Scholar 

  • Springer, L., Stanne, M. E., & Donovan, S. S. (1999). Effects of small-group learning on undergraduates in science, mathematics, engineering, and technology: A meta-analysis. Review of Educational Research, 69(1), 21–51. doi:10.3102/00346543069001021.

    Article  Google Scholar 

  • Suthers, D. (2001). Towards a systematic study of representational guidance for collaborative learning discourse. Journal of Universal Computer Science, 7(3), 254–277.

    Google Scholar 

  • Suthers, D., & Hundhausen, C. D. (2003). An experimental study of the effects of representational guidance on collaborative learning processes. The Journal of the Learning Sciences, 12(2), 183–218. doi:10.1111/3.5739.

    Article  Google Scholar 

  • Tane, J., Schmitz, C., & Stumme, G. (2004). Semantic resource management for the web: An e-learning application. In Proceedings of the WWW conference (pp. 1–10). New York, NY: ACM.

    Google Scholar 

  • Villalon, J., Kearney, P., Calvo, R. A., & Reimann, P. (2008). Glosser: Enhanced feedback for student writing tasks. In In Eighth IEEE International Conference on Advanced Learning Technologies, 2008. ICALT ‘08 (pp. 454–458). Santander, Cantabria: IEEE. doi:10.1109/ICALT.2008.78.

    Chapter  Google Scholar 

  • Weinberger, A., Stegmann, K., Fischer, F., & Mandl, H. (2007). Scripting argumentative knowledge construction in computer-supported learning environments. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting Computer-Supported Collaborative Learning (pp. 191–211). New York, NY: Springer. doi:10.1007/978-0-387-36949-5_12 .

Download references

Acknowledgments

We thank the gymnasium of Pesch in Germany for the excellent cooperation.

Author information

Authors and Affiliations

  1. University of Duisburg-Essen (Media-Based Knowledge Construction), Lotharstr. 65, 47057, Duisburg, Germany

    Melanie Erkens & Daniel Bodemer

  2. University of Duisburg-Essen (Collaborative Learning in Intelligent Distributed Environments), Lotharstr. 63, 47057, Duisburg, Germany

    H. Ulrich Hoppe

Authors
  1. Melanie Erkens
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Bodemer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Ulrich Hoppe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Melanie Erkens or Daniel Bodemer.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Erkens, M., Bodemer, D. & Hoppe, H.U. Improving collaborative learning in the classroom: Text mining based grouping and representing. Intern. J. Comput.-Support. Collab. Learn 11, 387–415 (2016). https://doi.org/10.1007/s11412-016-9243-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11412-016-9243-5

Keywords

Search

Navigation