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协同学习的性别配对

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physics ,1994), Learning pr ether 1999), online communication style (Savicki, Kelley, Teasley,1995); b) the promising potential of computers can connect isolated learners in an innovative way (Stahl et al., 2006). Although both premises have already gained sufficient empirical support, both are controversial where gender is concerned. As for collaboration, although some researchers claim that it appeals to both male and female students (Heller Johnson Kahle Lakoff, 1973; Lay, 1992; Li, 2002) and a difference in gender pairing such as single and mixed-gender collaboration in terms of interaction and learning performance (Howe, Tolmie, Anderson, Stewart, Shields, Monolescu, Neuage,2002). Furthermore,Veermanand Veldhuis-Diemanse(2001) foundthatquestions posted in synchronous CSCL tended to be easily ignored. The synchronous interactions were fleeting with short contributions and numerous turns. Weisband (1992) claimed that computer-mediated communication reduces conformity and convergence. Therefore, CSCL may work out in different ways depending on how well students can communicate and elaborate their knowledge mutually. Koschmann et al. (2005) claim that in computer-supported collaborative learning, knowledge and meaning can be understood as jointly createdthroughinteractionwhichismediatedthroughcomputers.Incollaborativeproblemsolving,adyadcanbeviewedasaunitmadeupof two interdependent cognitive units (Dillenbourg, Baker, Blaye, Brown Toth, Suthers, 2) the reduced nonverbal N. Ding et al. / Computers and 3) the opportunities for students to regulate their learning processes. However, this is still a controversial claim, especially in a synchronous CSCL setting. Some studies have reported that ICT has a more positive effect on males by extending their interest span (Passey, Rogers, Machell, McHugh, Howe et al.,1992). Underwood et al., (2000) found that female and male students in the mixed-gender dyads did not engage in true collaboration because they were not jointly focused on the problem. Prinsen, Volman, and Terwel (2007) have reviewed thirteen studies on the gender problem in Computer-Mediated Communication (CMC) and CSCL. They focused on three aspects: degree of participation, kind of participation, and experience of participation. The review study reveals that in mixed-gender groups males tended to dominate the discourse and had a more positive group-work experience. Inzlicht and Ben-Zeev (2000) ascribed this to the stereotype threat. They found that the mere presence of a male can weaken females’ problem solving skills. Kessels and Hannover (2008) investigated around fourhundredeighth-graders in German schools. Students were randomlyassigned to single and mixed-gender physics classes. They found that females from single-gender physics classes outperformed females from mixed-gender classes, while male students achieved equally well in both kinds of classes. In other studies, however, researchers could not find a significant difference between males and females’ performance (Joiner, Messer, Littleton, Underwood, Jindal, Pheasy for the rest of the time, they remained parallel. Such patterns were categorized as “ambiguous patterns.” Table 3 Elaboration Values. Number Description Example þ1 on-task message elaborating on knowledge or contributing to the final solution. Student A: How many forces are applied on the box? Student B: I think, four 0 on-task message but no improvement in knowledge elaboration or problem solving (Student B: There are four forces being applied on the box.) Student A: OK. C01 off-task messages distracting the problem solving process Student B: What’s your guess about what will be in our next English test? Table 4 Mean numbers and standard deviations of text-based and pictorial messages per student for all six problems. Factors Female Male Mixed-Gender Text-based Messages Pictorial Messages Text-based Messages Pictorial Messages 458.76(113.03) 3.04(1.67) 434.84(151.43) 4.68(1.70) Single-Gender Text-based Messages Pictorial Messages Text-based Messages Pictorial Messages 509.25(127.67) 5.00(1.96) 513.64(167.61) 7.32(2.12) N. Ding et al. / Computers 474 were pictorial messages. Tables 4 and 5 summarize the mean numbers and standard deviations for all messages, on-task, off-task, text-based and pictorial messages for all six problems per student. It is notable that there was a significant gender difference in terms of pictorial messages (F (3,92) ¼ 18.47, p ¼ .02). Male students generated significantly more pictorial messages during problem solving than female students. This echoes our previous findings about a gender difference in terms of ways of representing knowledge in physics problem solving (Ding Snijders this effect remains significant. We explored the interaction effect of gender and the number of divergent patterns (Model 6), and of group gender and the number of divergent patterns (Model 7). The reduction of deviance for Model 6 was not significant (c 2 (1) ¼ 3.50, p ¼ .06), nor was it significant for Table 8 Summary of the model estimates for the two-level analyses of students’ posttest scores. Parameter Model 0 12345678 Fixed Intercept 72.65 (1.83) 8.10 (2.40) 8.03 (2.37) 8.80 (2.39) 8.24 (2.27) 17.80 (3.99) 17.36 (4.14) 17.31 (4.15) 20.01 (4.65) Pretest 0.95(0.03) 0.96 (0.04) 0.96 (0.04) 0.94 (0.03) 0.87 (0.05) 0.86 (0.05) 0.87 (0.05) 0.85 (0.05) Gender C01.37 (0.97) C01.41 (0.96) 1.77 (1.33) 2.38 (1.34) C00.38 (1.96) C00.44 (1.98) C03.10 (2.90) Groups C01.52 (0.94) 1.50 (1.28) 2.41 (1.33) 2.95 (1.34) 2.45 (2.45) 0.31 (2.97) Female in MG vs. others C05.90 (1.79) C06.34 (1.77) C07.29 (1.81) C07.25 (1.82) C02.87 (3.95) Divergent C00.93 (0.47) C01.65 (0.60) C01.73 (0.70) C02.45 (0.90) Gender * Divergent 1.20 (0.64) 1.20 (0.64) 2.37 (1.13) Groups * Divergent 0.17 (0.71) 1.03 (0.99) Female * Groups * Divergent C01.71 (1.37) Group Level 127.90 (33.63) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) Individual Level 66.88 (13.65) 21.97(3.17) 21.53 (3.11) 20.95 (3.02) 18.83 (2.72) 18.08 (2.61) 17.44 (2.52) 17.43 (2.52) 17.15 (2.48) Deviance (-2 Logliklihood) 751.45 569.06 567.09 564.48 554.21 550.36 546.86 546.80 545.25 Decrease in Deviance 182.39* 1.97 2.61 10.27* 3.86* 3.50 0.06 1.55 *p .05. 6. Conclusion and discussion References Arvaja, M., Salovaara, H., Hakkinen, P., & Jarvela, S. (2007). Combining individual and group-level perspectives for studying collaborative knowledge construction in context. Learning & Instruction, 17(4), 448–459. 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, New Jersey: Lawrence Erlbaum. Bruckman, A. (2000). Situated support for learning: Storm’s Weekend with Rachael. Journal of the Learning Sciences, 9(3), 329–372. Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: a practical guide. Journal of the Learning Sciences, 6,271–315. Theaimofthisstudywastwofold.First,itfocusedonthegenderdifferenceinlearningperformanceandexploredwhetherthesingleand mixed-gender dyads presented different pictures of knowledge elaboration in CSCL. Second, it investigated whether students’ gender, gender pairing, and knowledge elaboration processes had an effect on students’ learning achievement. From previous literature and empirical studies, we assumed that males’ learning was not influenced by computer-supported collaboration. But the results presented a different picture. In the mixed-gender dyads, our study indicated a proportionally much higher frequency of divergent patterns than cross or parallel patterns. It seems that in mixed-gender dyads, students’ knowledge elaboration processes are more inclined to diverge from each other. We also found that females in single-genderdyads significantlyoutperformed females in mixed-gender dyads in the posttest. For males, either this or a reverse pattern did not occur. Males in mixed-gender dyads did as well as males in single-gender dyads. The results of this study show that in a synchronous and text-based CSCL environment, females seem to profit more from single-gender collaboration than from mixed-gender collaboration. This result indicates that the gender problem that we were familiar within face-to-face collaborative learning appears to carry over into the CSCL setting. With respect to the question of whether students’ learning performance is related to their knowledge elaboration patterns, we resorted to a multilevel analysis. The analyses showed that the divergent pattern was a significant predictor for the posttest scores, but the disad- vantages of females in mixed-gender dyads remained. That notwithstanding, we found that the p value was .06, which indicates that the interaction effect between gender pairing and divergent patterns is worth deeper investigation. As Underwood and Underwood (1999) claimed, learning is at its best when the learners talk constructively together, and introduce and elaborate knowledge mutually. We used the “elaboration values” to visualize the students’ knowledge elaboration processes. The divergent patterns indicate that there is a gap in the students’ communication. Unlike the crossed patterns, in divergent patterns one student plays a dominant role while the interlocutor lags behind in knowledge elaboration. In addition, unlike in the parallel patterns, in the divergent patterns the gap between the two participating students grew larger and larger. One explanation for the high frequency of divergent patterns in the mixed-gender dyads may be found in the use of pictorial messages. As mentioned above, male students produced significantly more pictorial messages than female students did. In physics problem solving, graphicalrepresentationdoesnotmerelyreferto “drawingapicture.” Itisrecognizedasapowerfulstep(Kohl&Finkelstein,2006).Problem information needs to be accurately and pictorially reflected, while problem components need to be interrelated and categorized into an abstract representation. Examples of this are found in clarifying the direction or magnitude of a force pictorially, or using a triangular diagramtoanalyzerelatedforces.Formanyof thegeometricconceptsinphysics problems,graphicalrepresentationcanexpress amyriadof words in one economical form. In our study, it was found that males preferred illustrating the variables, drawing the relationships and mapping the solutions.In contrast,their female counterparts tendedtouse text-based messages toconvey their ideas. The differentways of representation ways in physics problem solving may result in an elaboration gap. An additional explanation for the frequent divergent patterns might be the communication styles. In physics collaborative problem solving, female and male students have different communication styles (Ding & Harskamp, 2006). Females tend to use questions to open or elicitthe discussion.Inprevious CSCLresearch, femaleswerealsofoundtoraise morequestions thanmales(Prinsen,Volman,Terwel,& Van den Eeden, 2009). However, in synchronous text-based CSCL, due to the lack of social cues, the questions are fleeting and often easily ignored. Our previous case studies suggested that the ignorance of each other’s questions was one of the mechanisms that led to divergent patterns (Ding, 2009). In future studies, it would be worth investigating whether ignorance of questions correlates with divergent patterns and the disadvantage of females in mixed-gender dyads. In CSCL research, gender studies have been relegated to a lower research priority for many years (Bruckman, 2000). In an attempt to remedy this, the current study explored the interaction effect of gender, gender pairing and collaboration patterns in a synchronous text- basedCSCLsetting.Wedidnotfindanysignificantinteractioneffect,however.Onepossibleexplanationmightbetherelativelyshortperiod of CSCL treatment. In total, students were involved in six CSCL sessions and none of them had ever used CSCL in formal problem solving learning before. For future study, a broad research agenda should be required to explore the different mechanisms that may cause female studentstolag behind in mixed-gendercollaboration. Theanswermight be found in the differentpatterns of interaction, and the difference inuseofverbalversusgraphicalrepresentationand/orthecommunicationstyleof thestudents.Thismighthelpusfindasolutiontoclosing the achievement gaps among students. Moreover, as Henderson and Dancy (2004) claimed, the best evidence of problem solving skills (as wellasan understandingofproblemprinciples)is astudent’s abilitytosolvenovelproblems inreallife. Therefore,adelayedposttestwould appear to be crucial if we are really interested in the sustained problem solving skills of the learners. Model 7 (c 2 (2) ¼ .06). As for the question of whether the divergent patterns were correlated with the females’ learning achievement in mixed-genderdyads, weconstructed Model 8. In thismodel, welooked intothe interaction effectof students’ gender, groupgenderand the number of divergent patterns. The results showed a reduction in deviance in comparison with Model 7, but this was not significant (c 2 (1)¼1.55). There was no significant interaction effect for gender, gender pairing and the frequency of divergent patterns on the posttest scores. N. Ding et al. / Computers & Education 56 (2011) 325–336334 Cress, U. (2008). 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