Students’ experiences with collaborative learning in asynchronous Computer-Supported Collaborative Learning environments

  • Published on
    04-Sep-2016

  • View
    212

  • Download
    0

Transcript

Abstract* Corresponding author.E-mail address: silvia.dewiyanti@ou.nl (S. Dewiyanti).Computers in Human Behavior 23 (2007) 496514www.elsevier.com/locate/comphumbehComputers inHuman Behavior0747-5632/$ - see front matter 2004 Elsevier Ltd. All rights reserved.This paper describes an explorative study carried out to gain response from distance stu-dents on their experiences with collaborative learning in asynchronous computer supported col-laborative learning (CSCL) environments. In addition, this study also attempts to have a goodgrip of crucial aspects concerning collaborative learning. The study was undertaken among dis-tance learners from the Open University of the Netherlands who were working in groups of 411 persons. During and after the course students experiences with collaborative learning weremeasured and after the course also students satisfaction with collaborative learning wasassessed. The nding revealed that distance learners appreciate the opportunities to work col-laboratively. They show positive experiences and are quite satised with collaborative learning.This study also sought to explore individuals as well as course characteristics that inuencedaspects of collaborative learning, and to search aspects of collaborative learning that inuencedstudents satisfaction. The ndings suggested that a group product inuences group processregulation and group cohesion inuences students satisfaction with collaborative learning. 2004 Elsevier Ltd. All rights reserved.Students experiences with collaborative learningin asynchronous Computer-SupportedCollaborative Learning environmentsSilvia Dewiyanti a,*, Saskia Brand-Gruwel a, Wim Jochems a,Nick J. Broers ba Open University of the Netherlands, Educational Technology Expertise Center (OTEC), P.O. Box 2960,NL-6401 DL Heerlen, The Netherlandsb University of Maastricht, The NetherlandsAvailable online 18 November 2004doi:10.1016/j.chb.2004.10.021chronous CSCL environment because it oers exibility in time to read, to reectand to compose the responses (Abrami & Bures, 1996).S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514 497Students participation in collaborative learning is seen as the interaction and thecontribution of group members when they are collaborating to solve a problem or toaccomplish a task. Various elements in an asynchronous CSCL environment mayKeywords: Asynchronous CSCL environment; Collaborative learning; Distance education; Multilevelanalysis1. IntroductionNowadays computer supported collaborative learning (CSCL) environments areviewed as an important electronic learning medium for distance education. CSCLenvironments can be described as a context where the computer facilitates interac-tions among learners for acquisition of knowledge, skills and attitudes (Dillenbourg,1999; Kaye, 1992; Koschman, 1996). Working together while accomplishing a task isseen as a characteristic of a powerful learning environment, aiming at active con-struction of knowledge (Van Merrienboer & Paas, 2003). Through a process of inter-action and negotiation students have an active and constructive role in the learningprocess.Research in recent years has shown that CSCL environments have been used suc-cessfully to promote learning achievements in distance education. Harasim (1989)described the social, aective and cognitive benets of collaborative group workfor distance learners. From her study, she concluded that collaborative learning pro-motes more active and more eective learning for distance education. Hiltz (1995)also reported that students in collaborative learning conditions had more construc-tive learning processes and attained higher grades than students in other conditions.These environments provide distance learners the opportunity to work togetherand to practice critical reection, conict negotiation, and consensus building asin face-to-face learning environments. Besides, students are encouraged to exchangeideas, to share perspectives and arguments, and to use previous knowledge or expe-rience in order to decide on the best solution for the problem to be solved. So, the useof CSCL environments can both help to overcome physical isolation betweenstudents and teachers, and help to improve learning.CSCL environments are often promoted as an open, safe, and trustable learningenvironment that allows equal opportunities for learners to participate without thelimitation on knowledge levels (Scardamalia & Bereiter, 1994). These learning envi-ronments stimulate students to express their ideas and arguments without any feelingto be penalised or ridiculed (Rowntree, 1992). In a CSCL environment students haveopportunity to take over some control of their own learning and to be active learnerswho are not only absorbing information but also connecting previous knowledgeand new information to gain a deeper level of understanding. The use of an asyn-chronous CSCL environment is recommended for distance education above a syn-inuence students participation. The important elements are course characteristics,498 S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514individual characteristics, dierent aspects of collaborative learning process andsatisfaction.Course characteristics. Group size and the type of product students must deliver(individual or group product) are considered to be essential characteristics of coursesin CSCL environments. Group size inuences students participation in collaborationsubstantially (Johnson & Johnson, 1994; Shaw, 1981). Collaborating in small groupsmakes it easier to stimulate non-active participants, promotes a higher sense ofpresence and engagement, and increases the individual contributions (Bates, 1995;Hammond, 2000; Kaye, 1992; Wegerif, 1998). Regarding the type of product, Cohen(1994) argued that the task assigned to a group determines how group membersinteract. Courses that encourage collaboration in general show that the students be-come more active participants in the learning process when the task requires a highlevel of collaboration. A high-level collaborative task, for example requesting agroup product, requires group members not only to share information or to deter-mine how to divide their labors, but also to discuss how to proceed as a group.On the contrary, a task with low level of collaboration, for example, requesting sub-mission of individual report, lacks of group interdependency that might hindergroup members to collaborate while accomplishing the task (Johnson & Johnson,1994).Individual characteristics. Individual characteristics such as students ideas aboutcollaborative learning and students experience with the use of technology might in-hibit or promote their participation in the collaborative learning processes (Kagan,1994). For example, in CSCL environments students are required to communicate byusing text-based communication tool. A lack of experience of using text-based com-munication might inuence students participation in their groups (Ross, 1996;Zafeiriou, Nunes, & Ford, 2001).Collaboration process. The process of collaboration itself is the heart of CSCL.Collaboration refers to activities that are related to how the group is functioningin accomplishing a task. Within collaborative learning, the responsibility for learn-ing shifts from the teacher to the group members (Bruee, 1995). This provides anopportunity for the group members to regulate their collaboration process. As agroup, they should plan the working process together and make sure that theprocess will be goal directed. In order to achieve the learning goals group mem-bers need to support each other. They should discuss the learning content indepth and maintain the ongoing collaborative process. Determining strategy, con-tributing ideas, handling internal conicts and monitoring group processes areimportant aspects within the collaborative learning. Thus, in order to reach thelearning goals all group members have the responsibility to participate in collab-oration process.Satisfaction with collaborative learning. Students satisfaction with collaborativelearning can be described as the degree to which a student feels a positive associationwith his or her own collaborative learning experiences. Students satisfaction canhave repercussions on how students work together, such as whether everyone doeshis/her part of the work, whether group members can work with each other, whethergroup members remain on the task (no ghting, no fooling around or too much chat-1. How do distance students experience collaborative learning in asynchronousS. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514 499CSCL environments?2. Are distance students, who in general are unfamiliar to each other, satised withcollaborative learning in asynchronous CSCL environments?3. To what extent do the individual characteristics and the course characteristicsinuence students experiences with collaborative learning?4. What aspects with respect to collaboration do inuence students satisfaction?5. How do students actually collaborate in an asynchronous CSCL-environment?2. Method2.1. ParticipantsStudents from ve distance learning courses of the Open University of the Neth-erlands volunteered for this study. Participants were asked to complete three surveys(before, during and after the course). Respondents at the rst survey were 112 stu-dents (76 men and 36 women). Furthermore, 51 participants responded to the secondsurvey (34 men and 17 women). Finally, 67 participants (47 men and 20 women) res-ponded to the last survey. Table 1 summarises number of participants for eachcourses across surveys.2.2. Materials2.2.1. CoursesAll the courses required students to work in groups and to submit either a groupting), and whether there is a good working atmosphere in the group (Gunawardenaet al., 2001).Although several studies (Harasim, 2001; Hiltz, 1995) have reported the benetsof collaborative learning for distance learners, but still there are many questions sur-rounded the implementation of collaborative learning in distance education. Little isknown on students experiences during collaboration process in asynchronous CSCLenvironments. Understanding students experiences is important because this mighthelp designers to provide specic instructions to enhance the quality of learningprocess.This paper describes an explorative study carried out to gain response fromdistance learners on how they experience collaborative learning in asynchronousCSCL environments and attempts to have a good grip of crucial aspects concerningcollaborative learning. In the end, the ndings of this study should providepractical implications for supporting eective learning in asynchronous CSCLenvironments.The specic questions addressed in this study were as follows:product or an individual product. To facilitate communication within groups, all the500 S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514Table 1Number of participants for each course across the surveysCourse SurveysBefore the course During the course After the courseChange management 30 13 13Law 16 15 15Informaticsa 19 15Management science 33 8 16Environmental science 14 15 8a Because of the short duration of the informatics course, the participants from this course onlyresponsed at the rst and the third survey.Table 2Course characteristicsCourse Communication tool Number of studentsin a groupPeriod Type of productChange management Newsgroup 34 25 Weeks Individual productLaw Newsgroup 4 24 Weeks Group productInformatics Newsgroup 4 2 Weeks Group productManagement science Newsgroup 811 20 Weeks Individual productEnvironmental science E-room 4 17 Weeks Group productcourses used asynchronous computer mediated communication. The descriptions ofthe course characteristics are summarised in Table 2.2.2.2. Questionnaire on individual characteristicsThe individual characteristics questionnaire consists of ve variables. The rstvariable assesses student attitude towards collaboration (attitude towards collabora-tion, 12 items, Cronbachs a = 0.87), e.g., I nd that it is interesting to work to-gether in a group. The second variable gathers information about individualactivities in a group (group activity, 6 items, Cronbachs a = 0.82), e.g., I like totake the initiative. The third variable is intent on get information on students famil-iarity with text-based communication (perceived text-based communication, 4 items,Cronbachs a = 0.86), e.g., Discussion group is a pleasant way to communicate.The fourth variable aims at gaining information on student prior knowledge (priorknowledge, 4 items, Cronbachs a = 0.76), e.g., I can explain to other studentsabout this subject, and, the last variable assesses students opinion on using internet(opinion on using internet, 5 items, Cronbachs a = 0.75), e.g., Internet is a pleasantway to get information all over the world. The format of all items is a Likert-typescale, ranging from 1 (strongly disagree) to 5 (strongly agree).2.2.3. Questionnaire on collaborative learningThe students experiences with collaborative learning was assessed with six varia-bles (23 items all in) developed for the purpose of the present study and three existingS. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514 501variables. These six variables include (a) Monitoring Working Procedures (8 items,Cronbachs a = 0.87) e.g., I remind group member who do not work together prop-erly, (b) Participation (5 items, Cronbachs a = 0.85), e.g. All group members par-ticipate in discussions to reach a consensus, (c) Monitoring Group Progress (5items, Cronbachs a = 0.83) e.g., I have responsibility to maintain our plan, (d)Helping Each Other (3 items, Cronbachs a = 0.70), e.g., I help other group memberwho have diculty to understand the learning material (e) Giving Feedback (2items, Cronbachs a = 0.75) e.g., I constantly gave feedback to other group memberworks, and (f) Need to be Monitored (2 items, Cronbachs a = 0.68) e.g., I feelpleasant if someone reminds me about the deadline. Then, three existing variablesassess the Team Development, the Intra-group Conict and the Task Strategy. TheTeam Development is adapted from Savicki et al. (Savicki, Kelley, & Lingenfelter,1996) to assess the degree of cohesion that was achieved while group members havebeen working together (11 items, Cronbachs a = 0.91), e.g., All group membersunderstand the group goals and were committed to them. The Intra-group Conictconsists of seven items. Items in this variable are adapted from Saavedra et al. (Saav-edra, Early, & Van Dyne, 1993) and measure the degree of conicts in a group (7items, Cronbachs a = 0.72), e.g., There was a lot of tension among people in ourgroup. The Task Strategy is adapted from Saavedra et al. (Saavedra et al., 1993)and assesses the decisions and choices made by a group that performs the task (7items, Cronbachs a = 0.81), e.g., Our group developed a good strategy for doingthe tasks. The format of all items is a Likert-type scale, ranging from 1 (stronglydisagree) to 5 (strongly agree).2.2.4. Questionnaire on satisfaction with collaborative learningThis questionnaire consists of three variables that measure (a) satisfaction withgroup members attitudes (6 items, Cronbachs a = 0.86), e.g., All group memberscan get along well, (b) satisfaction with learning in the group (5 items, Cronbachsa = 0.87), e.g., I learn a lot from other group members, and (c) satisfaction withgroup working (4 items, Cronbachs a = 0.82), e.g., I feel pleasant to work togetherin the group to solve a task. In addition students satisfaction over their nal prod-uct was measured with a single item I am satised with the nal product. The for-mat of all items is a Likert-type scale, ranging from 1 (strongly disagree) to 5(strongly agree).2.2.5. Content analysisContent analysis is aimed to gain more detailed understanding of learners activ-ities during collaborative learning. Based on previous studies in analysing studentsmessages (Henri, 1992; Van Boxtel, van der Linden, & Kanselaar, 2000; Veerman,2000; Veldhuis-Diermanse, 2002), a coding scheme was developed to analyse stu-dents messages. The coding scheme consists of six functional dimensions and 19 spe-cic categories (Table 3).The Regulation dimension consists of contribution about coordinating activitiesof learners, e.g. I propose that we should nish the draft within two weeks. TheConsensus dimension consists approval expressions of an idea, e.g. Yes, I agree502 S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514of That is absolutely correct. The Conict dimension indicates diagreement oflearners activities, e.g. I do not like the way you work. The Content dimension in-cludes contributions about activities to gain domain knowledge, e.g. I do notunderstand what you mean. Can you explain it?. The Social dimension containsTable 3Coding schemeDimensions CategoriesRegulation OrientationPlanReectionMonitoring generalMonitoring working proceduresMonitoring working progressMonitoring participationConsensus Reach consensusTry to reach consensusConict ConictContent AskExplainArgueProductExternal resourcesSocial Negative emotion, positive emotion, o taskTechnology Technologyemotional expressions and non-task information, e.g. You did a great work orI had a nice weekend. The Technology dimension describes expressions aboutthe use of computer, e.g. How can I attach a document.In order to apply this coding scheme, each message was broken down into manage-able items, so-called units, for subsequent allocation into relevant categories. Eachunit was assigned only to one category. Because one message might contain more thanone topics, the base unit of analysis is sentences within one message. When two con-tinuous sentences dealt with the same topic, they were counted as one unit. And, whenone sentence contained two topics, it was counted as two separate units.Using this coding scheme, two raters independently segmented the messages andclassied the units into the appropriate category. If a unit could not be categorised(e.g. ambiguous statements) then the rest category was used.Coding messages was completed in two steps to establish a good reliability be-tween the raters. In the beginning, 10 postings transcripts were randomly selectedand were coded independently by the two raters. Then the codes were comparedto reach consensus on the use of the categories. This process allowed for the codingcategories to be further rened and for the raters to discuss ambiguity or disagree-ment until consensus was reached. The rst training session between two ratersacross all discourse categories reached a Cohens j value of .48. After an intensivetraining, Cohens j reached value of 0.62. Then the remaining discourse was codedby one rater.2.3. Design and procedureThe surveys were administered in the period of six months (dependent on thecourses starting dates and the duration of the courses involved). All surveys were dis-tributed via e-mail, regular mail or at a face-to-face meeting. Participants were askedto complete the survey individually and to return them to the researcher via elec-tronic mail or regular post. After one week a reminder was sent to the non-respondents.Three surveys concerning individual characteristics, experiences with collabora-tive learning and satisfaction were administered before, during and after the course.Table 4 provides an overview of the dierent measurements and moments of surveysadministration.The rst survey administered before the courses started was intended to get infor-3. ResultsS. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514 5033.1. Individual characteristicsBefore giving the results concerning the research questions, a closer look is takenat the characteristics of the students (the rst survey). Means and standard devia-tions on the individual characteristics variables are presented in Table 5.The means range from 3.32 to 4.03 indicating that students scored above midpointon all the scales. There were no signicant dierences on the individual characteris-Table 4Design of the studyCourse SurveysBefore During AfterChange management O1 O2 O2 + O3Law O1 O2 O2 + O3 + O4Informatics O1 O2 + O3Management science O1 O2 O2 + O3Environmental science O1 O2 O2 + O3O1 = individual characteristics.O2 = experience of collaborative learning processes.O3 = satisfaction.mation on students characteristics. The second survey was designed to retrieve infor-mation on students experiences with collaborative learning and was administeredhalfway the course. The third survey was designed to gain information on studentsexperiences with collaborative learning as well as on students satisfaction with col-laborative learning. This survey was administered after the course was completed. Inaddition, messages from one of ve groups from the Law course was analysed as asample to explore activities while students were working in the group.O4 = content analysis of one of the ve groups from the Law course.504 S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514tics variables across courses. It appears that collaborative learning was not a newlearning method for them. Students indicated their familiarity with using internetfor gaining resources, although their experience on communicating via text-basedmedium were quite varied (indicated by the high standard deviation). The resultsshow that students prior knowledge also vary substantially. The inuence of theindividual characteristics on the aspects of collaborative learning will be discussedlater on.Not all students respond to our survey completely, 50% students replied once,25% replied twice and 25% replied all the surveys. However, there were no signicantdierences between students who reply either once, twice or all surveys on the vari-ables of individual characteristics (all ps > 0.05).3.2. Students experiences with collaborative learningIn order to analyse the students experiences with collaborative learning the groupmeans were taken as the unit of analysis, because students worked in groups andinteracted with each other.Table 6 provides the group means and standard deviations with respect to the stu-dents experiences with collaborative learning during and after the course.The means range from 2.18 to 3.81 during the course and from 2.25 to 3.97Table 5Means and standard deviations on the variables of the individual characteristics questionnaireVariables n M SDAttitude towards collaboration 112 3.62 0.49Group activity 112 3.83 0.57Perceived text-based communication 110 3.46 0.70Prior knowledge 112 3.32 0.86Opinion on using Internet 112 4.03 0.55Note.Unit of analysis is the individual mean. The scale is ranging from 1 to 5, where 1 = strongly disagree,and 5 = strongly agree (3 = neutral).after the course. No extreme scores were found. The lowest score during andafter the course was for the variable Intra-group Conict. This indicates thatthere have been no serious conicts between group members while learning col-laboratively. On almost all the other variables the mean is above the midpoint.It can be concluded that students have quite positive experience with collabora-tive learning.Further analysis was conducted to examine whether the students experiences withcollaborative learning diered during and after the course. A paired sample t test wasused to examine students experiences with collaborative learning processes duringthe course as compared to after the course. However, only 23 groups had completedthe questionnaires for the second and the third survey. Results reveal that the vari-able Monitoring Working Procedures reached statistically signicance(t(22) = 3.58, p = 0.002) in the sense that students experienced better monitoringMonitoring working procedures 26 2.56 0.86 32 2.87 0.64S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514 505working procedures during the course. So, students paid more attention on monitor-ing their working procedures in the second half of the course.In addition, signicant dierences at a 10% level are found on the variablesGiving Feedback (t(22) = 1.92, p = 0.07) and Need to be Monitored(t(21) = 1.94, p = 0.07). So, it seems that students gave more feedback to eachother and that they needed more monitoring on group processes in the secondhalf of the course.Kruskal-Wallis analyses were used to compare across the ve courses. This non-parametric analysis was used because, using groups as units of analysis, we had arather small number of observations within each course. Results reveal that studentsParticipation 26 3.31 0.85 32 3.29 0.69Monitoring group progress 25 2.33 0.69 32 2.64 0.63Giving feedback 25 3.81 0.73 32 3.97 0.44Helping each other 25 3.40 0.76 32 3.39 0.58Need to be monitored 25 3.21 0.64 31 3.31 0.39Team development 26 3.47 0.59 32 3.39 0.63Task strategy 26 3.36 0.73 32 3.37 0.62Intra-group conict 26 2.18 0.44 32 2.25 0.49Note.Unit of analysis is the group mean. The scale is ranging from 1 to 5, where 1 = strongly disagree, and5 = strongly agree (3 = neutral).Table 6Means and standard deviations on the variables of the collaborative learning questionnaire during andafter the courseVariables During course After coursen M SD n M SDin the ve courses diered signicantly on Monitoring Working Procedure(v2 = 17.93, df = 3, p < 0.001), on Team Development (v2 = 8.05, df = 3, p < 0.05),and on Intra-group Conict (v2 = 14.23, df = 3, p < 0.01) during the course. Afterthe course a signicant dierence is found on Monitoring Working Procedures(v2 = 18.81, df = 4, p < 0.01). When we take a closer look at the mean scores acrossthe ve courses, the Management Science course had the lowest means on these var-iables. This course employs the largest group size (see Table 4) and requests studentto submit an individual product. Hence, group size and type of product might beimportant elements of asynchronous CSCL environments that inuence thecollaborative learning.3.3. Students satisfaction with collaborative learningTable 7 contains the group means and standard deviations on the satisfactionvariables.The means range from 3.31 to 3.97. These results indicate that the average scoresfor all satisfaction variables are above the midpoint. This means that students inTable 7Means and standard deviations on the variables of the satisfaction with collaborative learningquestionnaireVariables n M SDSatisfaction with other group members 32 3.52 0.53Satisfaction with learning in group 32 3.81 0.66Satisfaction with working in group 32 3.31 0.31Satisfaction with nal product 32 3.97 0.64Note.Unit of analysis is the group mean. The scale is ranging from 1 to 5, where 1 = strongly disagree, and5 = strongly agree (3 = neutral).506 S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514general were quite satised with learning collaboratively in an asynchronous CSCLenvironment.3.4. Individual and course characteristics that inuence aspects of collaborativelearningIn order to answer the questions concerning the inuence of individual and coursecharacteristics on aspects of collaborative learning and the inuence of collaborativelearning aspects on satisfaction, regression analyses were conducted. As the numberof potential predictors in the regression equations would be very large in comparisonto the number of observations, a factor analysis is conducted to reduce the numberof variables to be used in the regression analysis.Using principal axis factoring with oblique rotation, the ve variables of the indi-vidual characteristics questionnaire produced a two factor solutions explaining 70%of the total variance (see Table 8). Only variables with a factor loading greater than0.4 are shown. Factor 1 was labeled as Perceived Technology and factor 2 as Atti-tude Towards Group Work.We conducted two separate factor analyses on the collaborative learning varia-bles, respectively, on the data during and after the course in order to see whetherour variables in the second and the third survey have similar loading factor patterns.Many of the variables loaded on the same dimension; however few did not. In thesecond survey, one variable was loading below 0.40 on the appropriate dimension.In the third survey, all of the variables were loading above 0.40. Next, the variableTable 8Factor loadings of individual characteristics variablesVariables Factor1 2Attitude towards collaboration 0.429Group activity 0.782Perceived text-based communication 0.849 Prior knowledge Opinion on using internet 0.522 Giving Feedback which had a weak loading was excluded and the factor analyseson each separate surveys were re-run. A three-factor solution seems the best for boththe data during and after the course. The pattern of loadings was relatively similar.Table 9 displays the results. Only variables with a factor loading greater than 0.4 areshown.The second measurement (during the course) accounted for 72% of the variance inthe data and the third survey measurement (after the course) accounted for 79% ofthe variance in the data. The rst factor corresponds to group cohesion (COHES),factor two to group process regulation (PROCESS) and factor three to group sup-port (SUPPORT). These three factors were used as collaborative learning aspectsfor the regression analysis.Regression analyses with attitude towards group work, perceived technology,group size and type of product as independent variables and group process regula-tion, group cohesion and group support as dependent variables were conductedusing the backward elimination method. These explorative analyses yielded only asingle model where a signicant proportion of variation in the dependent variablecould be explained: the model containing group process regulation (PROCESS) asdependent variable and type of product (PRODUCT with values 0 in case of agroup product and 1 in case of an individual product) as independent variable(F(1,45) = 32.72, p < 0.001, R2 = 0.422). This OLS regression analysis ignores thefact that individuals were nested within study groups. A regression model that takesS. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514 507this nested structure into account is a multilevel model known as the random coef-cient model. Using multilevel analysis to re-analyse the regression model we foundTable 9Factor loadings of the collaborative learning variablesVariables Factor1 2 3During the courseMonitoring working procedures 0.566 0.676 0.570Participation 0.864 0.413Monitoring group progress 0.886 Helping each other 0.464Need to be monitored 0.754Team development 0.957 0.456Task strategy 0.871 Intragroup conict 0.628 After the courseMonitoring working procedures 0.937 Participation 0.921 0.467Monitoring group progress 0.837 Helping each other 0.488Need to be monitored 0.412Team development 0.876 Task strategy 0.804 0.682Intragroup conict 0.776 508 S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514with OLS regression yielded the following equation (with associated standard errorsbetween brackets): PROCESS = 0.548 (0.146) 1.248 (0.124) PRODUCT.This nding suggests that requiring a group product tends to stimulate groupmembers to regulate their group during collaborative learning processes.3.5. Aspects of collaborative learning that inuence satisfactionA regression analysis of group cohesion (COHES), group support (SUPPORT)and group process regulation (PROCESS) on satisfaction with other group mem-bers (SATOTHER) using the backward elimination method resulted in a regres-sion model that retained group cohesion and group support as statisticallysignicant predictors of satisfaction with other group members, F(2,44) = 13.852,p < 0.001, R2 = 0.386. This OLS regression analysis ignores the fact that individualsubjects were embedded within study groups, yielding dependency among scores.This problem can be tackled by using multilevel analysis instead of ordinaryregression analysis. Multilevel analysis permits the inclusion in the model of a ran-dom intercept that is allowed to vary among the study groups in which the indi-vidual subjects are embedded. Using multilevel analysis to test the model we hadfound with OLS regression, we found a result quite similar to that which was ob-tained with ordinary regression analysis. The random intercept model that was re-turned by the multilevel analysis was (with SEs reported between brackets):SATOTHER = 3.63 (0.08) + 0.29 (0.08) COHES + 0.18 (0.09) SUPPORT, showingboth group cohesion and group support to be signicant predictors of satisfactionwith others.Similarly, a regression analysis of group cohesion, group support and groupprocess regulation on satisfaction with learning in group (SATLEARN) using thesame backward elimination method yielded group cohesion and group support asstatistically signicant predictors of satisfaction with learning in group,F(2,44) = 31.137, p < 0.001, R2 = 0.586. Multilevel analysis returned the followingmodel: SATLEARN = 3.89 (0.08) + 0.39 (0.07) COHES + 0.17 (0.07) SUPPORT,showing both group cohesion and group support to be signicant predictors of sat-isfaction with learning in group.A third regression analysis of group cohesion, group support and group processregulation on satisfaction with working in group (SATGROUP) using backwardelimination resulted in a regression model that retained group cohesion and groupprocess regulation as statistically signicant predictors of satisfaction with workingin group, F(2,44) = 10.134, p < 0.001, R2 = 0.315. Multilevel analysis returned thefollowing model: SATWORK = 3.40 (0.05) + 0.22 (0.05) COHES 0.13 (0.05)PROCESS, showing both group cohesion and group process regulation to be signi-cant predictors of satisfaction with working in group.Finally, a regression analysis of group cohesion, group support and group processregulation on satisfaction with nal product (SATPROD) using the same backwardelimination method resulted in a regression model that only retained group cohesionas statistically signicant predictor of satisfaction with nal product,F(1,45) = 15.914, p < 0.001, R2 = 0.261. Multilevel analysis returned the followingmodel: SATPROD = 3.96 (0.14) + 0.46(0.10) COHES, showing only group cohesionto be a signicant predictor of satisfaction with nal product.Together, these analyses suggest that group cohesion is an important aspect ofcollaborative learning that inuences students satisfaction with collaborativelearning.3.6. Students activities when they collaborated in an asynchronous CSCL environmentMessages from one group from the Law course were analysed to give some insightinto how group members collaborated when they accomplished a task. The contentS. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514 509analysis was divided into two parts: part one contains data gathered from beginningthe course to survey 2 (period 1) and part two contains data collected from survey 2to the end of the course (period 2).To arrive at a balanced comparison between the number of units occurring in acategory during the rst period and the number of times these units were mentionedduring the second period, percentages of units are compared.When the course ended, students messages were obtained from the server. Thetranscipt corpus consists of 393 messages containing over 1009 units. In average,each group member posted 98 messages. Table 10 shows frequency and percentageof dimensions posted by students during period 1 and period 2.The overall amount of messages increased greatly as the course progressed from107 messages (293 units) during the rst period to 286 messages (716 units) duringthe second period. However, the percentage of units of all dimensions remain quitestable over both periods of the course. This result might suggest that group membersneed some time to adjust themselves with working together to complete a task. Inorder to get more insight in the collaboration process, we analysed six dimensionsinto detail: the Regulation, the Consensus, the Conict, the Content, the Social,and the Technology. Table 11 gives an overview of the number of units and the per-centages of the dierent categories within the ve dimensions.In the Regulation dimension Monitoring Working Procedures increased sharplyfrom the rst period (46%) to the second period (66%), Orientation declined from13% in the period 13% in the period 2, followed by the Monitoring Participationcategory which also dropped dramatically from 12% during the rst period to 2%in the second period. The increase of Monitoring Working Procedures indicates thatTable 10Number and percentage of units in all dimensions posted by students during period 1 and period 2Dimensions Period 1 Period 2Regulation 60 (20) 158 (22)Consensus 28 (10) 92 (13)Conict 4 (1) 8 (1)Content 117 (40) 284 (39)Social 20 (7) 37 (5)Technology 14 (5) 40 (6)Note: Value shown are numbers of units; percentages are in parantheses.510 S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514Table 11Number and percentage of units in dimensions: regulation, consensus, conict, content social andtechnologyDimensions Categories Period 1 Period 2Regulation Orientation 8 (13) 4 (3)Plan 12 (20) 28 (18)Reection 1 (2) 4 (3)Monitoring general 1 (2) 2 (1)Monitoring working procedures 28 (46) 105 (66)Monitoring working progress 3 (5) 12 (7)Monitoring participation 7 (12) 3 (2)Consensus Reach concensus 22 (79) 65 (71)Try to reach concensus 6 (21) 27 (29)Conict Conict 4 (100) 8 (100)Content Ask 18 (15) 49 (17)Explain 32 (27) 103 (37)Argue 27 (23) 49 (17)Product 37 (32) 72 (25)External resources 3 (3) 10 (4)Social Negative emotion 0 (0) 2 (5)group members paid more attention to monitor their working procedures during thesecond half of the course. Whereas the decline of Monitoring Participation mightsuggest that group members felt more responsibility for individual contribution aftera period of time. A slight increase was found at Reection, and Monitoring WorkingProgress, whereas Plan and Monitoring General remained almost the same through-out the course.Within the Consensus dimension the percentages increased in the second period.Try to reach consensus rose from 19% in the rst period to 27 % in the second per-iod. Also in the Conict dimension, the number of units inclined twice in the secondperiod than in the rst period. The results from both dimensions indicate that in thesecond period the group took more dierence perspectives and opinions intoconsiderations.The results in the Content dimension were quite varied. For instance, Explainingincreased from 27% to 37%, whereas Product decreased from 32% to 25%. Veryslight increases were found on Ask and Share External Resources. These results im-ply that group members were more active in gaining knowledge domain in the sec-ond period than in the rst period of the course.In the Social dimension, the results show that students made several comments onO-task category and exhibited a very small portion of Negative Emotion. The high-est percentage was reached by Positive Emotion. This result might indicate that stu-Positive emotion 15 (75) 24 (65)O task 5 (25) 11 (30)Technology Technology 14 (100) 40 (100)Note: Value shown are numbers of units; percentages are in parantheses.above the midpoint of the scale. Distance learning is often promoted to give exibi-lity for learners to manage their individual learning, collaborative learning limits theS. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514 511exibility of distance learners because it creates interdependence between the groupmembers. However, despite the fact that distance learners have less freedom in anasynchronous CSCL environment, the results in this study show that students werequite pleased with learning this way.The third issue examined whether individual and course characteristics inuenceddents showed their positive feelings and encouraged each other during collaborativelearning.The last dimension is Technology. The percentage of this dimension remained sta-ble during both periods. This stable percentage reected the students familiarity withcommunication via discussion group.4. Discussion and conclusionThe aim of this study was to explore students experiences and satisfaction withcollaborative learning in asynchronous CSCL environments. In order to have a goodgrip of crucial aspects during collaborative learning, the inuence of individual andcourse characteristics on aspects of collaboration learning and the inuence of theaspects of collaborative learning on satisfaction was determined. Also, students mes-sages from one group were analysed to gain more insight in how group members col-laborate while working on a task.The rst issue examined was students experiences with collaborative learning as aresult of participating in the courses with a collaborative learning method. In generalstudents show quite positive experiences with working in a CSCL environment bothduring and after the course. Only on the variable Monitoring Working Procedures asignicant dierence was found between the rst and the second half of the course.In the second half of the course students paid more attention to the procedures theyhad to follow to accomplish the task. It might indicate that group members involve-ment in regulating group processes might take some time to occur and does not hap-pen at the beginning of the collaboration automatically. Besides, this may be due tothe fact that working procedures must be more ecient in the second half of thecourse, because it was not allowed to exceed the deadline for accomplishing the task.Moreover, the scores on the group process regulation variables, namely MonitoringWorking Procedures and Monitoring Group Progress, were lower than on the othervariables both during and after the course. Students seemed not to pay much atten-tion to monitor their collaboration process. Hence, it is suggested to scaold groupmembers in regulating group process from the beginning of their collaboration.The second issue investigated was whether students were satised with workingand learning in an asynchronous CSCL environment. Consistent with previous stud-ies (Bures, Abrami, & Amundsen, 2000; Harasim, 2001), our results also indicatethat students are in general satised with working and learning in an asynchronousCSCL environment. On all the satisfaction variables the students mean scores arecollaboration process. It was expected that small-groups as well as a task which512 S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514requires a group product would stimulate student involvement in collaborativelearning. The result of the present study indicates that the type of product inuencesstudents regulation of group process. This nding shows that a group product sti-mulates students to regulate their group process because it involves all group mem-bers proceeding the task (Cohen, 1994; Johnson & Johnson, 1994). Thus, requiring agroup product not only enhance students to gain subject knowledge but also stimu-late students to develop group skills such as orienting, planning and monitoring.Although, the results of this study do not support the expectation that small groupsize stimulate group processes more than large groups, there is an indication thatparticipants from the course that used large groups (7 group members each group)scored lower on the experiences with collaborative learning than the participantsfrom the other courses. So, there is some evidence to conclude that the use of smallgroups is recommendable above larger groups. In addition, other studies (Ha-mmond, 2000; Kaye, 1992; Wegerif, 1998) also recommend to use small groupsrather than large groups.The fourth issues examined aspects of collaborative learning which inuence stu-dents satisfaction. The results reveal that group cohesion is an important aspect thatinuences students satisfaction. This nding is congruence with the work of Johnsonand Johnson (1994); they also underline the importance of group cohesion duringcollaboration to keep the group work together. Another nding is that group processregulation has a negative inuence on satisfaction with working in a group. Thisnding contradicts with result from other study (Gillies, 2003). In his study he re-ported that unstructured group process led to students became less positive abouttheir group experiences. A possible explanation for this nding that we should takeinto account that the participants are dierent. Our participants are distance learnerswho are adults and have to manage their time to study as well as to work, so that arigid group process regulation during collaborative learning might be dicult forthem. Although our nding shows that group process regulation inuences nega-tively on satisfaction with working in a group, we argue that group process regula-tion is needed during collaborative learning and is considered to be supportive in thelearning process. Lack of group process regulation may cause a group loss of controlin achieving their goal.The fth issue examined the collaborative activities within one group. The groupmembers discourse while completing a task was analysed. In general, most of thegroup communications discussed the learning content. Activities such as asking,arguing, explaining, and providing extra resources dominated more than regulativeactivities such as planning, monitoring and reecting. These ndings are in line withother results of studies on the collaborative learning in asynchronous CSCL environ-ments (Veerman, 2000; Veldhuis-Diermanse, 2002). The technology and socialdimension had the lowest percentage numbers throughout the course. It implied thatstudents were quite familiar with communication via the computer and indicatedthat group members did not spend much time to comment on unrelated task.Although, these ndings indicated that learning in an asynchronous CSCL environ-ment focused more at completing the task than other activities (such as talking aboutsocial life). It is important to notice that we analysed only discourse from one group.tasks which require a group product. Fourth, the use of small groups instead of largegroups is recommendable. Finally, it is recommended to use asynchronous CSCLThe authors thank Martine Coun, Ine Haaren-Dresens, Wilfried Ivens, Wim Jurg,S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514 513Wim de Kruij, Angelique Lansu, Frand de Langen, Jan Leppink, Hubert de Man,Peter Mijnheer, Wies Weterman from the Open University of the Netherlands fortheir helps.ReferencesAbrami, P. C., & Bures, E. M. (1996). Computer-supported collaborative learning and distance education.The American Journal of Distance Education, 10, 3741.Bates, A. W. (1995). Technology, open learning and distance education. London: Routledge.Bruee, K. A. (1995). Sharing our toys: cooperative learning versus collaborative learning. ChangeMagazine, 27, 1218.Bures, A. M., Abrami, P. C., & Amundsen, C. (2000). Student motivation to learn via computerconferencing. Research in Higher Education, 41, 593621.Cohen, E. G. (1994). Restructuring the classroom: conditions for productive small groups. Review ofEducational Research, 64, 135.Dillenbourg, P. (1999). Introduction: what do you mean by collaborative learning?. In P. Dillenbourg(Ed.), Collaborative learning: Cognitive and computational approaches. Amsterdam: Pergamon Press.Gillies, R. M. (2003). The behaviors, interactions, and perceptions of junior high school students duringsmall-group learning. Journal of Education Psychology, 95, 137147.Gunawardena, N. C., Nola, A. C., Wilson, P. L., Lopez-Islas, J. R., Ramrez-Angel, N., & Megchun-Alpzar, R. M. (2001). A cross-cultural study of group process and development in online conferences.environments as a medium to support collaborative learning form for distance edu-cation, because collaborative learning is seen as a didactical approach that stimulatesnew learning.AcknowledgementsTwo limitations of this study need to be acknowledged. One limitation of thisstudy is the sample size. Not all participants responded to our questionnaires. Num-ber of participants in the second and the third survey was among other things re-duced because of leaving the course and of time pressure. Another limitation ofthis study was that we focussed only partly on actual students behaviour. Due tothese limitations, the results of this study should be interpreted with caution.The results of this study have several important implications for practice. First, inorder to maintain group cohesion we might consider to ask students to reect ontheir group process. Hence, all group members should have the opportunity to reecton their group activities and on their gained knowledge in order to improve theirgroup performance. Second, the less experience of group process regulation mightbe tackled by providing specic guidelines on how to regulate the group. Besidesreection on group process can also be used to improve the group process regulation.Third, it is suggested to set tasks requiring high levels of collaboration; for instanceDistance Education, 22, 85121.Hammond, M. (2000). Communication within on-line forums: the opportunities, the constraints and thevalue of a communicative approach. Computer and Education, 35, 251262.Harasim, L. (1989). On-line education: a new domain. In R. Mason & A. Kaye (Eds.), Mindweavecommunication, computers and distance education. Oxford: Pergamon Press.Harasim, L. (2001). The virtual university: a state of the art 2 A powerful new phenomenon: onlinecollaborative learning in virtual classrooms. Advances in Computers, 55, 614.Henri, F. (1992). Computer conferencing and content analysis. In A. Kaye (Ed.). Collaborative learningthrough computer conferencing: The najaden papers. Berlin: Springer.Hiltz, S. R. (1995). The virtual classroom: Learning without limits via computer networks. Norwood: Ablex.514 S. Dewiyanti et al. / Computers in Human Behavior 23 (2007) 496514Johnson, D. W., & Johnson, F. P. (1994). Joining together: Group theory and group skills. Boston: Allynand Bacon.Kagan, S. (1994). Cooperative Learning. Wee co op: Resources for Teacher, Inc.Kaye, A. (1992). Learning together apart. In A. Kaye (Ed.). Collaborative learning through computerconferencing: The najaden papers. Berlin: Springer.Koschman, T. (1996). CSCL: Theory and practice of an emerging paradigm. New Jersey: LawrenceErlbaum.Ross, J. A. (1996). The inuence of computer communication skills in a computer conferencing course.Journal Educational Computing Research, 15, 3752.Rowntree, D. (1992). Exploring open and distance learning. London: Kogan Page.Saavedra, R. P., Early, P. C., & Van Dyne, L. (1993). Complex interdependence in task-performinggroups. Journal of Applied Psychology, 78, 6172.Savicki, V., Kelley, M., & Lingenfelter, D. (1996). Gender, group composition, and task type in small taskgroups using computer-mediated communication. Computers in Human Behavior, 12, 549565.Scardamalia, M., & Bereiter, C. (1994). Computer support for knowledge-building communities. TheJournal of The Learning Sciences, 3, 265283.Shaw, M. E. (1981). Group dynamics the psychology of small group behavior. New York: McGraw-Hill.Van Boxtel, C., van der Linden, J., & Kanselaar, G. (2000). Collaborative learning tasks and theelaboration of conceptual knowledge. Learning and Instruction, 10, 311330.Van Merrienboer, J. J. G., & Paas, F. (2003). Powerful learning and the many faces of instructional design:towards a framework for the design of powerful learning environments. In E. De Corte, L. Verschael,N. Enstwistle, & J. J. G. Van merrienboer (Eds.), Powerful learning environments: Unravelling basiccomponents and dimensions. Oxford: Elsevier Science.Veerman, A. (2000). Computer-supported collaborative learning through argumentation. Enschede: PrintPartners Ipskamp.Veldhuis-Diermanse, A. E. (2002). CSCLearning? participation, learning activities and knowledgeconstruction in computer-supported collaborative learning in higher education. Wageningen: GraschService Centrum Van Gils.Wegerif, R. (1998). The social dimension of asynchronous learning networks. Journal of AsynchronousLearning Networks, 2, 3449.Zafeiriou, G., Nunes, J. M. P., & Ford, N. (2001). Using students perceptions of participation incollaborative learning activities in the design of online learning environments. Education forInformation, 19, 83106.Students " experiences with collaborative learning in asynchronous Computer-Supported Collaborative Learning environmentsIntroductionMethodParticipantsMaterialsCoursesQuestionnaire on individual characteristicsQuestionnaire on collaborative learningQuestionnaire on satisfaction with collaborative learningContent analysisDesign and procedureResultsIndividual characteristicsStudents experiences with collaborative learningStudents satisfaction with collaborative learningIndividual and course characteristics that influence aspects of collaborative learningAspects of collaborative learning that influence satisfactionStudents activities when they collaborated in an asynchronous CSCL environmentDiscussion and conclusionAcknowledgementsReferences

Recommended

View more >