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协同学习可视化

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has Dillenbourg, 1999; Johnson in which individuals attempt to solve Teasley, 1995; Van Boxtel, 2000; Webb be a promising heuristic task (Heller, elaboration perspective, which stresses we found that group composition had lem-solving foci in the collaborative 0360-1315/$ - see front matter C211 2008 Elsevier Ltd. All doi:10.1016/j.compedu.2008.10.009 demonstrated better cognitive development for students than for those learning individually Johnson, 1994). Collaborative problem solving is a coordinated and synchronous activity with- through reflection, negotiation, correction and co-construction of meanings (Roschelle Lehtinen, 2003; Nelson, 1999; Teasley, 1995). Our study stems from the presence of detailed clarifications such as highly elaborated arguments. In our previous studies, influence on female students’ representation format, communication content and prob- process. What is still unclear is the precise nature of the students’ knowledge elaboration Visualizing the sequential process of knowledge elaboration in computer-supported collaborative problem solving N. Ding * Faculty of Behavioral and Social Sciences, University of Groningen, P.O. Box 1286, 9701 BG Groningen, The Netherlands article info Article history: Received 15 November 2007 Received in revised form 10 October 2008 Accepted 14 October 2008 Keywords: Computer-mediated communication Cooperative/collaborative learning Secondary education Gender studies abstract This case study illustrates the sequential process of the joint and individual knowledge elaboration in a computer-supported collaborative learning (CSCL) environment. The case comprised an Internet-based physics problem-solving platform. Six Dutch secondary school students (three males, three females) par- ticipated in the three-week experiment. They were paired based on self-selection. Each dyad was asked to collaborate on eight moderately structured problems concerning Newtonian mechanics. Their online interactions, including their textual and pictorial messages, were categorized and sequentially plotted. The three dyads showed three different collaboration patterns in terms of joint and individual knowledge elaboration. C211 2008 Elsevier Ltd. All rights reserved. Computers Howe, Tolmie, Duchak-Tanner, Harskamp this Elaboration of knowledge is the key factor for students’ effective problem solving and in how they learn scientific concepts (De Jong van Boxtel, 2000). To solve a problem collaboratively, highly elaborative messages are not only important for group success, but also for knowledge acquisition and expansion of the individual learner. There is much research that shows that, working in the collaboration, students’ learning is closely correlated with the elaborative explanations instead of simple forms of exchanges (Lemke, 1999; Van der Meijden Webb Suthers, 2006; Van der Meijden et al., 2005). To resolve the conflict, students may offer an explanation, argue or negotiate with each other (Brown Dillenbourg, Baker, Blay Van Boxtel, 2000; Webb the female–female dyad (Sandy and Carol) succeeded in solving Problems 4, 5, 6, 7, and 8; the mixed-gender (Jenny and Ralf) succeeded in solving Problems 2, 3, 4, 5, 6, 7, and 8. All the participants have been pseudonymous. 5.1.1. The male–male dyad: Henry and Peter Henry and Peter were the two male students. Their physics scores in the school exam were 7.4 and 7.5, respectively. At the beginning of Table 4 Sample of worked-out example. Worked-out example 1. 0–8 v t = 40 m/s v 0 = 0 m/s t =8s?a =(v t C0v o )/t = 40/8 = 5 m/s 2 F result = ma = 1500C35 = 7500 N F result = F push C0 F gravity 2. 8–24 v 0 = 40 m/s v t = 0 m/s t = (24–8) = 16 s?g =(v t C0 v 0 )/t = 40/16 = 2.5 m/s 2 F gravity = mg = 1500C32.5 = 3750 N F push = F result + F gravity =7500 + 3750 =11250 N H =½vt =½C340 C324 =480 m N. Ding/Computers 1391 pieces of verbal and visual ex- changes during the process of solving eight problems. Sandy–Carol generated 1147 pieces of exchanges. It was interesting to notice that Henry–Peter dyad generated the least verbal messages than did the Sandy–Carol and Jenny–Ralf dyads, but they exchanged a great many visual messages, around 232. 5.2. Joint and individual knowledge elaboration Table 5 Time spent and number of verbal and visual messages for all eight problems. P.1 P.2 P.3 P.4 P.5 P.6 P.7 P.8 Total Henry–Peter (male–male) Duration 25:34 19:36 23:13 18:45 16:43 18:57 27:44 23:34 172:06 Visual 27 28 33 22 24 29 37 32 232 Verbal 80 65 84 54 57 72 54 73 539 Total 107 93 117 76 81 101 91 105 771 Sandy–Carol (female–female) Duration 39:07 26:47 20:30 35:35 47:16 30:04 31:32 35:45 265.16 Visual 21 13 17 14 32 23 28 24 172 Verbal 130 96 93 113 153 132 149 109 975 Total 151 109 110 127 185 155 177 133 1147 Jenny–Ralf (mixed-gender) Duration 1:05:18 45:48 47:43 38:42 54:25 50:35 48:37 57:49 406.97 Visual 54 27 36 24 48 37 39 36 301 Verbal 150 148 156 106 146 120 157 107 1090 Total 204 175 192 130 194 157 196 143 1391 514 N. Ding/Computers others might result from the communication incoherence of CSCL. We found that mechanisms like argumentation contributed to both joint and individual knowledge elaboration. N. Ding/Computers parallel knowledge elaboration with two almost parallel elaboration curves; and divergent knowledge elaboration with two deviating elaboration curves. In the cross elaboration pattern, we found two mechanisms: cognitive difference and argumentation. Once Henry found that he had a different idea to Peter, he addressed the difference directly and used an argument to support himself. Although there was one instance of communicative incoherence, their discussion still went well and both participants reached an understanding. However, the Henry–Peter dyad had the most mixed elaboration patterns and they solved fewer problems in comparison than the other dyads. In this dyad, we found a large amount of off-task talk and low-level routine discussions without constructive thinking. The computer facilitated their Jenny–Ralf == * C2, cross pattern; =, parallel pattern; , divergent pattern. volved more dyads in the study. Secondly, it is hard to say that one pattern excelled the others. It depended largely on whether we took the involvement. In our future research, we will be investigating whether female students’ are generally at a disadvantage in terms of individ- ual knowledge elaboration, and we will explore whether the patterns are statistically related to students’ learning performances. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.compedu.2008.10.009. References Baker, M. (1999). Argumentation and constructive interaction. In P. Coirier & J. Andriessen (Eds.), Foundations of argumentative text processing (pp. 179–202). Amsterdam: Amsterdam University Press. Brown, A. L., & Palincsar, A. S. (1989). Guided cooperative learning and individual knowledge acquisition. In L. B. Resnick (Ed.), Knowing, learning and instruction: Essays in honor of Robert Glaser (pp. 395–451). Hillsdale, NJ: Lawrence Erlbaum. Chang, K. E., Sung, Y. T., & Lee, C. L. (2003). Web-based collaborative inquiry learning. Journal of Computer Assisted Learning, 19(1), 56–69. Cohen, E. G. (1994). Restructuring the classroom: Conditions for productive small groups. Review of Educational Research, 64(1), 1–35. De Corte, E., Verschaffel, L., Entwistle, N., & Van Merriënboer, J. (2003). Powerful learning environments: Unravelling basic components and dimensions. Amsterdam: Elsevier Science. De Jong, T., & Ferguson-Hessler, M. G. M. (1993). Het leren van exacte vakken. In W. Tomic & P. Span (Eds.), Onderwijspsychologie. Beïnvloeding, verloop en resultaten van leerprocessen (pp. 331–351). Utrecht: Lemma BV. Dillenbourg, P., Baker, M., Blay, A., & O’Malley, C. (1995). The evolution of research on collaborative learning. In H. Spada & P. Reimann (Eds.), Learning in humans and machines. Towards an interdisciplinary learning science (pp. 189–211). Oxford: Pergamon. Dillenbourg, P. (Ed.). (1999). Collaborative learning: Cognitive and computational approaches. Amsterdam: Pergamon, Elsevier. Ding, N., & Harskamp, E. (2006). How partner gender influences female students’ problem solving in physics education. Journal of Science Education and Technology, 15(5), 331–343. Dix, A., Finlay, J., Abowd, G., & Beale, R. (1998). Human–computer interaction (2nd ed.). Erkens, G. (1997). Coöperatief probleemoplossen met computers in het onderwijs. Dissertation. Utrecht University [Cooperative problem solving with computers in education]. Harskamp, E., & Ding, N. (2006). Structured collaboration versus individual learning in solving physics problems. International Journal of Science Education, 28(14), 1669–1688. Heller, P., Keith, R., & Anderson, S. (1992). Teaching problem solving through cooperative grouping. American Association of Physics Teachers, 60(7), 627–636. Hertz-Lazarowitz, R. (1992). Understanding students’ interactive behaviour: Looking at six mirrors of the classroom. In R. Hertz-Lazarowitz & N. Miller (Eds.), Interaction in cooperative groups: The anatomy of group learning (pp. 71–102). New York: Cambridge Press. Howe, C. J., Tolmie, A., Duchak-Tanner, V., & Rattray, C. (2000). Hypothesis testing in science. Group consensus and the acquisition of conceptual and procedural knowledge. Learning and Instruction, 10, 361–391. Johnson, D. W., & Johnson, R. T. (1994). Learning together and alone: Cooperative, competitive, and individualistic learning (4th ed.). Boston, MA: Allyn and Bacon. King, A. (1999). Discourse patterns for mediating peer learning. In A. O’Donnell & A. King (Eds.), Cognitive perspectives on peer learning (pp. 87–115). Mahwah, NJ: Lawrence Erlbaum Associates. Kumpulainen, K., & Mutanen, M. (1999). The situated dynamics of peer group interaction: An introduction to an analytic framework. Learning and Instruction, 9(5), 449–473. Lehtinen, E. (2003). Computer-supported collaborative learning: An approach to powerful learning environments. In Powerful learning environments: Unravelling basic components and dimensions (pp. 35–53). Pergamon. Lemke, J. L. (1990). Talking science: Language, learning and values. Norwood, NJ: Ablex. group or the individual as a unit of analysis. Due to the limited number of participants, we were also unable to correlate the elaboration patterns with the learning performance through CSCL as well. However, our microscopic analysis of the Ralf–Jenny dyad indicated why one student was put at a disadvantage while the group succeeded. Even if consensus was reached, there was always the possibility that one student might revert to no cognitive communication and co-construction of knowledge, but sometimes they worked on the problem too rashly because it was too convenient to submit and check their answers. In the parallel elaboration pattern, mechanisms such as cognitive difference and explanation were found. While Henry had a different problem-solving approach to Peter, Carol had not even conceived of a concrete approach yet. She used several short and simple questions revealing what she did not know. Responding to her questions, Sandy explained step by step, although some explanations appeared to be rather simple and haphazard. There also were incoherencies in communication. Still, their interaction was generally effective. However, in line with our previous findings (2006) that female students’ tend to express their ideas verbally, in the Sandy–Caroldyad we found the least amount of visual representations. In the physics problem-solving tasks, many geometric concepts such as schemas or graphs can express myriad words in economical form. A lack of visual representation runs the risk of curtailing problem-solving effectiveness. For the Ralf–Jenny dyad, the divergent elaboration pattern was dominant. However, the dyad was productive, since we took the group as a unit of analysis. From Table 6, we knew that the mixed-gender dyad, the Jenny–Ralf dyad, seemed to be the most productive dyad. They solved the most problems, exchanged the most visual and verbal messages and spent the most time on problem-solving tasks during the experiment. However, this came at the cost of a deviation in Jenny’s individual knowledge elaboration. The mechanisms that resulted in the divergent individual elaboration were Ralf’s ignoring-questions and Jenny’s stop-asking activities. In a computer-mediated distance learning setting, the lack of a shared context and co-present cues may inhibit students’ communication and knowledge elaboration (Stahl, 2006), at least as far as the individual knowledge elaboration is concerned. Our case study has the potential to shed light on research on collaborative learning as a group process versus an individual process, which is ‘‘a tension at the heart of CSCL” (Stahl et al., 2006). First, we took a ‘‘close-up” view of the process involved in the students’ cog- nitive elaborations. We were interested in questions such as how they responded to their partner’s message, how they process the received information cognitively, how they elaborate the knowledge jointly and individually, and what the difference was between joint and indi- vidual knowledge elaboration. Second, we used the elaboration values to evaluate each message and visualize the process of elaboration by plotting the values along a timeline. Such visualizations offered us a direct impression of the difference between the different dyads and the difference between the participants within a dyad. Third, we differentiated the joint cognitive activity from individual cognitive activities. Doing so helped us explain the dilemma of one group succeeding at the cost of one of the individuals. Still, there are two points that should be pointed out. First, more patterns or more mechanisms may have been revealed if we had in- 518 N. Ding/Computers & Education 52 (2009) 509–519 Miyake, N. (2006). Designed collaboration as a scaffold for schematic knowledge integration. In Mizoguchi, R., Dillenbourg, P., & Zhu, Z. (Eds.), Learning by effective utilization of technologies facilitating intercultural understanding. Proceeding of international conference on computers in education (ICCE 2006) (pp. 15–20). Beijing, China. Nelson, M. L. (1999). Collaborative problem solving. In C. Reigeluth (Ed.). Instructional design theories and models: A new paradigm of instructional theory (Vol. II, pp. 241–267). Mahwah, NJ: Lawrence Erlbaum Associates Inc Roschelle, J., & Teasley, S. D. (1995). The construction of shared knowledge in collaborative problem solving. In C. E. O’Malley (Ed.), Computer supported collaborative learning (pp. 69–197). Berlin: Springer-Verlag. Sizmur, S., & Osborne, J. (1997). Learning processes and collaborative concept mapping. International Journal of Science Education, 19(10), 1117–1135. Stahl, G. (2006). Group cognition: Computer support for building collaborative knowledge. Cambridge, MA: MIT Press. Available from http://www.cis.drexel.edu/faculty/gerry/ mit/. Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: An historical perspective. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences. Cambridge, UK: Cambridge University Press. Suthers, D. D. (2006). Technology affordances for intersubjective meaning making: A research agenda for CSCL. International Journal of Computer-Supported Collaborative Learning, 1(3), 315–337. Teasley, S. (1995). The role of talk in children’s peer collaboration. Developmental Psychology, 3(2), 207–220. Van Boxtel, C. (2000). Collaborative concept learning. Collaborative learning task, student interaction, and the learning of physics concepts. Doctoral dissertation. Utrecht, The Netherlands: Print Partners Ipskamp. Van Boxtel, C. (2004). Studying peer interaction from three perspectives. In J. L. van der Linden & P. Renshaw (Eds.), Dialogic learning: Shifting perspectives to learning, instruction, and teaching (pp. 125–143). Dordrecht: Kluwer. Van der Meijden, H., & Veenman, S. (2005). De invloed van groepsgrootte en schooltype op het elaboratiegedrag van leerlingen in een CSCL-omgeving. Paper presented at the 32nd Education Research Day 2005, Gent, Belgium, 30 May–1 June, 2005. Journal of Applied Social Psychology, 28(22), 2049–2067. Webb, N. M. (1995). Testing a theoretical model of student interaction and learning in small groups. In R. Hertz-Lazarowitz & N. Miller (Eds.), Interaction in cooperative groups: The theoretical anatomy of group learning. NY: Cambridge University Press. Webb, N. M., & Farivar, S. (1999). Developing productive group interaction in middle school mathematics. In A. O’Donnell & A. King (Eds.), Cognitive perspectives on peer learning (pp. 117–149). Mahwah, NJ: Lawrence Erlbaum Associates. N. Ding/Computers & Education 52 (2009) 509–519 519
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