Computers & Education 58 (2012) 12–20

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Computers & Education

journal homepage: www.elsevier .com/locate/compedu

E-tutorial support for collaborative online learning: An explorative studyon experienced and inexperienced e-tutors

Birgitta Kopp a,*, Maria Cristina Matteucci b,1, Carlo Tomasetto b,2

aDepartment Psychology, Ludwig-Maxilimans-University, Leopoldstaße 13, 80802 Munich, GermanybDepartment of Education, Alma Mater Studiorum, University of Bologna, Piazza Aldo Moro 90, I-47023 Cesena, Italy

a r t i c l e i n f o

Article history:Received 14 March 2011Received in revised form12 July 2011Accepted 10 August 2011

Keywords:Cooperative/collaborative learningE-tutoringExperience of e-tutors

* Corresponding author. Tel.: þ49 89 2180 3779; faE-mail addresses: birgitta.kopp@psy.lmu.de (B. Ko

1 Tel.: þ39 0537 339838; fax: þ39 0547 339819.2 Tel.: þ39 0547 339847; fax: þ39 0547 339819.

0360-1315/$ – see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.compedu.2011.08.019

a b s t r a c t

The e-tutor plays a major role in supporting virtual collaborative learning, as he/she supervises learnersin collaboratively solving tasks, acquiring new skills, and applying new knowledge. This study is aimed atgaining further insights into the daily support practices of e-tutors. Seventy-six e-tutors from 17 differentEuropean countries were invited to fill in an online questionnaire to evaluate collaborative activities, andto answer yes/no-questions regarding their intervention to support these collaborative activities. Acluster analysis identified two profiles of e-tutors according to the importance ascribed to collaborativeactivities, and to the number of times they intervened to foster such activities. The cluster validationrevealed a difference between experienced and inexperienced European e-tutors in their support ofonline collaboration: e-tutors with experience considered specific cognitive activities to be moreimportant for effective online collaboration, and they seemed to be more familiar in detecting andadequately intervening to avoid dysfunctional social phenomena. Thus, experience in supporting onlinecollaboration seems to be a useful precondition for successfully intervening to stimulate necessarylearning activities and to avoid dysfunctional collaborative activities.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Virtual collaborative learning is being used increasingly in different contexts: in schools, in universities, in higher or in further education.This is due to the fact that collaborative learning has several benefits, e.g. fostering individual knowledge acquisition (Lou et al., 1996),supporting knowledge application (De Corte, 2003), and encouraging the acquisition of social competencies (Cohen, 1994).

But collaborative learning per se is not successful without adequate support (Kirschner, Sweller, & Clark, 2006). Furthermore, virtualcollaborative learning is even more demanding for learners as the virtual context involves new ways of communication and collaboration.For example, since this context lacks non-verbal cues (O’Connaill, Whittaker, &Wilbur, 1993), learners often do not know how to collaborateadequately (Kopp & Mandl, 2011). Therefore, in collaborative learning situations, it is necessary to provide support.

Support for virtual collaboration and collaborative learning is often realized by the e-tutor. There is awide range of different names for e-tutors that are used interchangeably, e.g. tele-tutor, online coach, e-moderator, tele-teacher, online facilitator or e-trainer (Rautenstrauch,2001). All these names are used to describe the same phenomenon, namely the support of e-learners, even though the range of tasks maydiffer somewhat depending on the respective name. For the purpose of this article, e-tutors are defined according to their main function,which is to supervise and to support learners (Kopp, Germ, & Mandl, 2010). According to this perspective, e-tutoring comprises all theactivities of a teacher that support a learner in constructively and actively handling the learning environment (Kopp et al., 2010). In thiscontext, the focus is on the importance of socially shared activities which are essential for individual cognitive development and for learning(Larreamendy-Joerns & Leinhardt, 2006). Thus, the interaction between learners and between e-tutors and learners is important forpromoting the activities necessary for the acquisition and application of knowledge.

x: þ49 89 2180 99 3779.pp), mariacristina.matteucci@unibo.it (M.C. Matteucci), carlo.tomasetto@unibo.it (C. Tomasetto).

ll rights reserved.

B. Kopp et al. / Computers & Education 58 (2012) 12–20 13

The major collaborative activities in virtual learning environments which should be supported by e-tutors are specifically (a) content-specific cognitive activities (b) social activities (Kopp & Mandl, 2011), and (c) meta-cognitive activities.

a) Cognitive activities include knowledge exchange (dissemination of shared and unshared knowledge between group members), onlinediscussion (discussing different points of view in depth), argumentation,3 collaborative problem solving, and considering differentperspectives (actively thinking about the opinions of collaborating partners).

b) Social activities which need to be supported by e-tutors include the motivation of the group members, interpersonal interaction, socialinfluence processes and information processing. As to groupmotivation, achievement goals should be taken into account – i.e., whetherlearners are more interested in their performance or in mastery (Elliot & McGregor, 2001) – as well as competition. Competition resultsfrom different competing motives of the group members (Deutsch, 1949). With respect to interpersonal interaction, conflicts, balancedparticipation and lack of responsibility are of importance. Social influence processes include activities such as ignoring minorities(Nemeth, 1986) or imposing conformity upon other group members. Information processing includes superficial discussion in order toprevent conflicts, but simultaneously deprives the group members of intensive cognitive processing (De Lisi & Goldbeck, 1999).Information processing also includes addressing the e-tutors rather than group members.

c) Meta-cognitive activities are essential for self-guided collaborative learning. In this context, planning/organizing, monitoring andregulating collaborative learning are the main meta-cognitive strategies that support the guidance of the group work. Planning andorganizing collaborative activities includes choosing specific strategies and often takes place at the beginning of collaboration before themain learning process starts (Schiefele & Pekrun, 1996). In contrast, monitoring and regulating collaborative activities are essentialduring the collaboration process. With help of a target-performance-comparison, the group’s learning success could be evaluated(Schreblowski & Hasselhorn, 2006). Based on the results of this evaluation process, learners may regulate their activities whennecessary.

Even though specific cognitive activities, social activities, and meta-cognitive activities are essential for effective collaborative learning,the question is how e-tutors support such activities in practice. On a theoretical basis, there are two methods of supporting onlinecollaboration: providing specific structures in the virtual learning environment (such as scripts) or directly intervening during thecollaborative learning process (Kopp & Mandl, 2011).

1.1. Providing feedback in collaborative learning

In e-tutoring, it is highly important to utilize direct intervention with feedback. Providing feedback “is conceptualized as informationprovided by an agent (e.g., teacher, peer, book, parent, self, experience) regarding aspects of one’s performance or understanding” (Hattie &Timperley, 2007, p. 81). Feedback is very helpful for preventing the student’s sense of being totally alone and unguided (Schweizer, Paechter,& Weidenmann, 2001) and specifically focuses on the problems of the learners. The objective of giving feedback is to reduce thediscrepancies between the student’s current understanding and the desired goal. There are four main kinds of feedback: feedback (1) on thetask level, (2) on the process level, (3) on the self-regulation level, and (4) on the self level. Most often, feedback is given on the task and onthe process level. Feedback on the task includes howwell the tasks are understood or performed (Hattie & Timperley, 2007). This correctivefeedback is related to the concrete accomplishment of the task. Meta-analyses of task feedback are highly related to fostering performance(e. g. Tenenbaum & Goldring, 1989; Walberg, 1982). Feedback about the processing of the task refers to the processes that take place duringthe task-solving and specifically involves aspects of deep understanding (Hattie & Timperley, 2007). In this context, it is important to providestrategies for error detection that provide information on how to improve and change activities to solve a task correctly or to utilize cueingmechanisms that provide information about the processes underlying the task. Providing learners with such feedback is one of the maintasks of e-tutors (Kopp et al., 2010).

1.2. E-tutoring

Since e-tutoring is defined as all activities that support learners in their learning process, it seems necessary for e-tutors themselves to beequipped with an appropriate set of skills and attributes in addition to subject matter expertise (McPherson & Nunes, 2004). E-tutoringspecifically differs from face-to-face tutoring in number of ways. For example, e-tutoring uses writtenmessages more often andwith a moreformal tone, promotes multiple conversations and communication between several learners, and requires teachers to assess the worth ofonline contributions and to develop newways of encouraging participation (McPherson & Nunes, 2004). Therefore, it seems necessary for e-tutors to be experienced in fostering online learning. In addition to discipline expertise, it is necessary for e-tutors to possess pedagogical,communicational and technological competencies (Denis, Watland, Pirotte, & Verday, 2004). In previous research, a number of differentpreconditions have been linked to specific competencies. In summary, we essentially have three kinds of knowledge (Lepper, Drake, &O’Donnell-Johnson, 1997; Salmon, 2000; Schmidt & Moust, 1995): (a) content-specific knowledge to support cognitive activities, (b)pedagogical knowledge to initiate and sustain adequate learning processes on a motivational and meta-cognitive level, and to adequatelycope with difficulties of the learners on a social level (social activities), and (c) technical knowledge about the functioning of the Internet,technical skills and knowledge on net-based communication. This kind of knowledge reflects the “e” in the term “e-tutor”. Furthermore, thee-tutor needs a wide range of tutoring skills in order to assume different roles, such as content facilitator, metacognition facilitator, processfacilitator, advisor/counselor, assessor, technologist, resource provider, etc. (e.g. Denis et al., 2004; McPherson & Nunes, 2004). Because ofthis diverse range of different competencies, skills and roles, e-tutors must adequately prepare for their work in facilitating onlinecollaboration (Banks, Denis, Fors, & Pirotte, 2004).

3 Argumentation is “a verbal and social activity of reason aimed at increasing (or decreasing) the acceptability of a controversial standpoint for the listener or reader, byputting forward a constellation of propositions intended to justify (or refute) the standpoint before a rational judge” (Van Eemeren, Grootendorst, & Henkemans, 1996, p. 5).

B. Kopp et al. / Computers & Education 58 (2012) 12–2014

1.3. E-tutors’ expertise

When taking a closer look at the connection between e-tutors’ expertise and competencies, the question arises whether e-tutors withexpertise differ from e-tutors without expertise in their specific daily practices. Research literature on the topic of expertise in other domainsshows us that experts differ from novices in theway that they deal with problems (Jacobson, 2001). Beyond that, studies on the e-tutors’ roleshowed that experience of the e-tutors seems to be important to how they handle their online courses. With respect to the course orga-nization, e-tutors with experience need less time for their online courses and have better time management than e-tutors without expe-rience (Hemsing, 2008). Regarding interaction, experienced e-tutors post more contributions in their courses than novice instructors,ranging from an average of 19 postings per course for novice instructors to an average of 193 postings per course by experienced instructors(Morris, Xu, & Finnegan, 2005). This finding was closely mirrored in the study of Goold, Coldwell, and Craig (2010). They showed that theexperienced e-tutor had 129 postings and 215 postings in two different modules, compared to 109 postings when the module wasmoderated by a novice e-tutor. Looking at the kind of postings in more detail, further differences between experienced and novice e-tutorswere identified: experienced e-tutors give more direct instructions and feedback, and the postings more often involve pedagogicalknowledge than the postings of novice tutors (Maor, 2008). Furthermore, experienced e-tutors mainly encouraged the students to engagewith the learning resources and activities beyond what was specified in order to promote deep learning (Goold et al., 2010). Therefore,experienced e-tutors appeared to be able to provide learners with the scaffolding they needed to understand the content, while novice e-tutors were not. Based on this research, we define experience of e-tutors on the basis of their competencies in fostering learning activitieswith specific support methods. In the existing research, expertise of e-tutors seems tomake a difference in their support behavior, but thereis no data about how experienced e-tutors differ from novice e-tutors in how they handle problems when fostering online collaboration.Therefore, in this study, we investigated the differences between experienced and inexperienced e-tutors when supporting onlinecollaboration in practice.

2. Research question

Support for collaborative online learning is essential for effective learning. E-tutors may intervene to promote cognitive, social, andmeta-cognitive activities in order to help learners in their learning efforts. Literature suggests the importance of the past experiences of e-tutorson the development of their competence in adequately supporting learners. Therefore, the question is whether experienced e-tutors differfrom inexperienced e-tutors in their support behaviors. Therefore, we addressed the following research question:

Do experienced and inexperienced e-tutors differ in how they support online collaboration?We expect to find two clusters: one cluster with more experienced e-tutors and one cluster with novice e-tutors. We assume that these

two clusters will have differences in support behavior with experienced e-tutors supporting their learners more often and in more differentways than e-tutors without experience.

3. Method

3.1. Participants

The sample comprised a total of 78 online courses from 17 different European countries described by e-tutors (2 from Austria, 3 fromBelgium, 15 from Finland, 16 from France, 13 from Germany, 15 from Italy, 4 from Switzerland, and 1 each from Denmark, Greece, Hungary,Ireland, Latvia, Netherlands, Poland, Romania, Spain, United Kingdom). Courses at the higher education or university level comprised 74.4%of the sample, while lifelong learning courses comprised 19.2%. Specifically, 58 online courses (74.4%) were offered at universities for onesemester, 14 courses (17.9%) were offered in vocational trainings as a single learning opportunity, and 6 (7.7%) were offered at institutes aspart of their curriculum. Inclusion criteriawere as follows: (a) the respondent is a teacher, an instructor, or a tutor of an e-learning course (orthe individual knows the experience sufficiently to provide details about it); (b) the course is ongoing or has been delivered recently; (c) thecourse includes online social interaction activities/collaborative learning activities (such as discussions, cooperation, collaboration, etc.); (d)

Fig. 1. Example of the online questionnaire for e-tutors.

Table 1Results of e-tutor’s evaluation of cognitive activities.

Clustering items Cluster 1 (n ¼ 51) Cluster 2 (n ¼ 24)

How important was it for you. M SD z M SD z

. to have participants exchange their knowledge? 5.41 (.90) .14 4.83 (1.76) �.29

. to have participants involved in content-related online discussion?* 5.47 (.09) .20 4.58 (1.95) �.44

. to have participants involved in argumentation?** 5.33 (1.07) .26 4.08 (2.00) �.54

. to have participants work together online on problems and cases? 4.90 (1.58) .10 4.13 (2.27) �.31

. to have participants integrate their different perspectives? 5.25 (1.00) .13 4.63 (2.28) �.27

*t(73) ¼ 2.70, p ¼ .009; **t(73) ¼ 3.53, p ¼ .001.

B. Kopp et al. / Computers & Education 58 (2012) 12–20 15

collaboration in the course as well as e-tutors’ interventions occur in full-distance modality. Participants were asked to complete onequestionnaire for each e-learning course, permitting more than one questionnaire to be answered by an individual. Only two e-tutorscompleted the questionnaire for two different e-learning experiences. Therefore, in total, 76 e-tutors took part in the study.

3.2. Procedure

In the framework of a co-financed European project (Socrates Minerva Programme), a research team including researchers from fiveEuropean countries (Italy, Germany, Finland, Switzerland, and France) contacted colleagues whowere involved in e-learning experiences viae-mail or telephone, and invited them to complete a questionnaire on their e-learning experiences (see below). A chain-referral samplingmethod (Bogdan & Biklen, 1992) was used in order to involve this small and hard-to-reach segment of the general population involved in e-learning experiences. Through a snowball technique, each contact was asked to propagate the questionnaire to other online instructors whomight meet the above-mentioned inclusion criteria. To reduce the sampling bias, we included e-tutors from different countries and withdifferent languages. Furthermore, the five investigation coordinators were researchers from different scientific fields (psychology, pedagogyand education science, engineering, communication sciences), thus improving the effectiveness of the recruitment, with an increasedpossibility of recruiting experiences from different populations.

The survey took place from June 15th, 2007 to October 1st, 2007. In each of the five countries, there was one coordinator who wasresponsible for data collection. These five coordinators sent a letter to colleagues asking them to participate in a survey for e-tutors.Participants had four weeks to answer the questionnaire. All data were centrally saved on the server of the one coordinator for the wholeresearch team. This individual was also responsible for the delivery of the questionnaire.

3.3. Instrument

An online questionnaire was created in order to gain further insights into how e-tutors support online collaboration (see Fig. 1). In thequestionnaire, we first collected general information about the e-tutors and the e-learning experiences. In this context, we especially askedfor the experience of e-tutors in designing and/or realizing online courses (using a “yes” or, “no” question, e.g.: “Do you have experience indesigning and/or realizing e-learning experiences?”). The second part surveyed collaborative activities, especially items concerning content-specific cognitive aspects (5 items), social aspects (11 items), and meta-cognitive aspects of collaboration (2 items), as well as givingfeedback (3 items) including evaluation criteria (7 criteria to choose), and evaluation procedures (5 procedures to choose from). Regardingthe content-specific cognitive aspects of collaboration, the questionnaire included five items concerning the following activities: knowledgeexchange, online discussion, argumentation, collaborative problem solving, and considering different perspectives. In each item, e-tutorswere first asked on a six-point Likert scale (ranging from 1, not important, to 6, very important) to evaluate certain aspects of collaborativeonline learning. In the second step, e-tutors were asked whether they intervened to foster the specific collaborative activity. In a third step,they were asked through open-format questions either how they intervened or why they did not intervene.

The questionnaire also asked e-tutors to evaluate social activities, namely the motivation of the group members, interpersonal inter-action, social influence processes, and information processing. Regarding motivational aspects, two dimensions were investigated, namelydifferent group goals (2 items), and dysfunctional competition (1 item). Interpersonal interaction included items concerning phenomenasuch as dysfunctional interpersonal conflicts (1 items), balanced participation (1 item), and diffusion/lack of responsibility, for a total of 5items. Social influencing factors were ignoringminorities (1 item), and putting pressure on groupmembers (1 item). Information processingincluded the sub-dimensions of superficial discussion to avoid conflicts (1 item), and addressing the e-tutor rather than group members (2items). Each item asked e-tutors first whether they intervened to prevent dysfunctional phenomena. If they answered yes, they were askedhow they intervened, and if they answered no, they were asked why they did not intervene.

Table 2E-tutors’ intervention rates for cognitive activities.

Clustering items Cluster 1 (n ¼ 51) Cluster 2 (n ¼ 24)

Did you intervene to promote. Yes No Yes No

. the exchange of knowledge/information? 46 5 10 14

. content-related online discussion?* 48 3 14 10

. argumentation?* 46 5 13 11

. collaboration in problem solving?* 48 3 14 10

. the integration of different perspectives?* 40 11 8 16

*p < .05 according to Chi-Square Test.

Table 3E-tutors’ intervention rates for social activities.

Clustering items Cluster 1 (n ¼ 51) Cluster 2 (n ¼ 24)

Did you intervene to prevent such a phenomenon. Yes No Yes No

. individual goals (different group goals I)* 21 30 1 23

. outcome than process (different group goals II)* 23 28 0 24

. dysfunctional competition* 16 35 2 22

. dysfunctional conflicts 21 30 5 19

. balanced participation* 30 21 7 17

. lack of responsibility* 27 24 2 21

. ignoring minorities 20 31 6 18

. putting pressure on group members* 21 30 1 23

. superficial discussion (in order to preserve positive relationships)* 24 27 5 19

. content-related questions to elicit a response (addressing to the e-tutor I)* 35 16 3 21

. turning to e-tutor (addressing to the e-tutor II)* 38 13 4 20

*p < .05 according to Chi-Square Test.

B. Kopp et al. / Computers & Education 58 (2012) 12–2016

Meta-cognitive activities included the planning (1 item), and organization (1 item) of groupwork. In this context, e-tutors were first askedhow important they considered these activities for collaboration, and secondly, whether they intervened to support these activities or not. Ifthey answered yes, they were asked how they intervened, and if no, they were asked why they did not intervene.

According to the theoretical introduction on providing feedback, feedback was differentiated between feedback on the task level and onthe process level. Feedback on the task level included the final product of the collaborative work (1 item), while feedback on the processincluded two questions; one on content-related feedback, and one on feedback about group activities. Again, e-tutors were asked how theyevaluated the respective feedback, and whether they provided such kind of feedback in their respective e-learning course. Furthermore, e-tutors were asked how they evaluated the final product. Seven criteriawere used: knowledge gain, knowledge application, understanding ofthe content, creativity, ability to collaborate, mastery/skillfulness, and effort. In addition, e-tutors were asked which procedure they used forevaluation, namely tests, essays, collection of documents, quality of online participation, and observation of collaboration.

3.4. Data analyses

Cluster analysis is useful in identifying homogenous groups of individuals or clusters based on the characteristics they possess that aresimilar to each other, but different from individuals in other groups. Cluster analysis methods organize the observations into smallernumbers of groups that account for most variance based on the clustering algorithm chosen (Meece & Holt, 1993). In this way, rather thangrouping similar variables, as is the case in factor analysis, cluster analysis groups similar people. The cluster analysis is well suited for thepresent study as we seek to profile e-tutors’ characteristics. Specifically, we used the TwoStep cluster methodology in order to identifygroups of e-tutors that responded similarly in terms of practices used when tutoring/supporting learners. This methodology is convenientfor dealing with a mix of quantitative and qualitative variables and provides a classification model which is optimized through the use of aninformation criterion such as Schwarz’s Bayesian Information Criterion (BIC). The SPSS 17.0 two-step clusteringmodel was used, based on analgorithm using a decrease in the log-likelihood for combining clusters as distance measure. The algorithm selects the optimal number inclusters based on either the Schwarz Bayesian Criterion or the Akaike Information Criterion.

If the sample size is small, one proficient approach for validating the cluster solution is external validation; that is to evaluate the resultswith respect to a pre-specified structure. This is a procedure based on measuring the extent to which clusters match external variable(s)which are not used to form the clusters (Aldenderfer & Blashfield, 1985). In this study, clusters were built on the basis of several variables ofthe questionnaire, namely on the evaluation of cognitive activities, as well as on the intervention rate regarding cognitive, social, and meta-cognitive activities (see Table 1 to 4). Once clusters were established, we used the following items of the questionnaire to validate theanalyses: the e-tutor’s experience, the scale on providing feedback, the criteria how the final product was evaluated, as well as theprocedures used for evaluationwere used for cluster validation (see Table 5 to 7). Separate t-tests and chi-square tests were used to test thevalidity of the cluster solution comparing e-tutors intervention clusters in terms of past experience of e-tutors and the feedback andevaluation practices. Before the cluster analysis was performed, all variables were standardized using z-scores (mean of 0, and a standarddeviation of 1).

4. Results

4.1. Identification of e-tutors’ profiles

Based on the research summarized above on support methods, we expected that the cluster analysis would identify theoreticallymeaningful profiles of e-tutors based on their reports of support methods.

Table 4E-tutors’ intervention rates for meta-cognitive activities.

Clustering Items Cluster 1 (n ¼ 51) Cluster 2 (n ¼ 24)

Yes No Yes No

Did you intervene to promote long-term planning?* 35 16 3 21Did you intervene to support participants in the organization of group work? 34 17 11 13

*p < .05 according to Chi-Square Test.

Table 5Cross-tabulation of cluster allocation and experience of e-tutors.

Cluster E-tutors c2 F

Experienced Inexperienced

1 (n ¼ 51) 44 (3.4) 7 (�3.4) 11.35*** .392 (n ¼ 24) 12 (�3.4) 12 (3.4)

Note. *** ¼ p < 0.001. Adjusted standardized residuals appear in parentheses below group frequencies.

B. Kopp et al. / Computers & Education 58 (2012) 12–20 17

The automatic clustering suggested two clusters whose stability was ascertained until 96.2% of the sample size (3.8% of excluded cases).Using a theoretically meaningful distinction, we reported the data on content-specific cognitive activities, on social activities, and on meta-cognitive activities. Cluster 1 (n ¼ 51; 65.4%) comprised e-tutors whose responses were generally in the highest part of the rating scalesregarding the importance of fostering relevant collaborative activities for the acquisition and application of knowledge (content-specificcognitive activities), and who affirmed having intervened in most cases to foster content-specific cognitive, social and meta-cognitiveactivities. Cluster 2 (n ¼ 24; 30.8%) included e-tutors whose responses were not concentrated around a specific point of the ratingscales, but which principally ranged considerably around the means of responses concerning the importance of fostering relevantcollaborative activities. In this cluster, most e-tutors reported that they did not intervene to support the cognitive, social and meta-cognitiveactivities which were investigated.

Regarding cognitive activities, e-tutors in cluster 1 significantly differed from e-tutors in cluster 2 in how they evaluated the importanceof online discussion and argumentation. Both activities received higher ratings by e-tutors in cluster 1 (see Table 1).

Regarding the intervention rate for cognitive activities, e-tutors in cluster 1 reported, that they intervened significantlymore often in fourout of five activities in comparison to e-tutors in cluster 2. These activities included online discussion, argumentation, problem solving, andintegrating different perspectives (see Table 2).

When looking at social activities, e-tutors in cluster 1 declared that they intervened significantly more often in 9 out of 11 dimensionsthan e-tutors in cluster 2 (see Table 3).

In the context of meta-cognitive activities, e-tutors in cluster 1 indicated they intervened significantly more often in long-term planningthan e-tutors in cluster 2 (see Table 4).

4.2. Cluster validation

The validity of the two cluster solution was evaluated by testing group differences on variables that were theoretically or empiricallyrelated to each cluster, but which were not included in the previous analysis to form the clusters. In particular, we used the following items:1. one item concerning the past experience of the e-tutors in designing and/or realizing online courses, 2. items pertaining to providingfeedback, and 3. items regarding the evaluation criteria and the evaluation procedures. We expected e-tutors with experience to be over-represented in cluster 1, which was confirmed by a chi-square analysis (see Table 5). The great majority of e-tutors with past experience indesigning and/or realizing e-learning courses belonged to cluster 1, while only few inexperienced e-tutors belonged to this cluster.

Supplementary analyses were conducted for the 7 experiences included in cluster 1 (experienced e-tutors), but whose e-tutors claimedto be inexperienced, and the 12 experiences of cluster 2 (inexperienced e-tutors), but whose e-tutors perceived themselves as experienced.The findings revealed that e-tutors of cluster 1 who evaluated themselves as inexperienced behaved fairly similarly in their daily practice toe-tutors of cluster 1 – who described themselves as experienced – with respect to evaluating the importance of fostering collaborativeactivities for the acquisition and application of knowledge. They also behaved similarly with respect to their intervention rate in fosteringcontent-specific cognitive, social and meta-cognitive activities. A similar pattern emerged also for the 12 e-tutors of cluster 2 who perceivedthemselves as experienced and who behaved similarly to e-tutors of their belonging cluster.

Furthermore, we examined the e-tutor’s feedback by looking at the feedback rate (see Table 6), at the final product used to evaluate thecollaborative work of the participants, and at the procedure used for evaluation using t-test analyses. E-tutors classified in cluster 1 reportedto provide significantly more feedback on the process and on the task solution than e-tutors without experience.

Regarding the criteria and procedure for giving feedback, therewere significant differences between the two clusters with respect to fourdependent variables (see Table 7). As expected, e-tutors in cluster 1 evaluated participants’ contributions during collaboration as a key factorof evaluation. Moreover, compared to e-tutors in cluster 2, they agreed significantly more often with the evaluation criteria based onmastery or skillfulness of learners, and on focusing effort on learning activities.

5. Discussion

On the basis of the cluster analysis, it was possible to build two clusters which differ in various dimensions concerning the daily practiceof e-tutors interviewed. E-tutors in cluster 1 are those who regard specific collaborative activities and feedback in online learning as

Table 6Results of e-tutor’s evaluation of intervention rate for feedback activities.

Clustering Items Cluster 1 (n ¼ 51) Cluster 2 (n ¼ 24)

Yes No Yes No

Did you evaluate or rate the ongoing activities of the collaborative work?* 35 16 2 22Did you give feedback related to group activities?* 40 11 8 16Did you evaluate or rate the final product of the collaborative work?* 42 9 13 11Did you give content-related feedback to your participants?* 48 3 19 5

*p < 0.05 according to Chi-Square Test.

Table 7Differences between clusters regarding evaluation criteria and evaluation procedures.

Cluster t df

1 2

Items on evaluation criteriaKnowledge gain 4.93 (1.40) 4.54 (1.56) 0.85 53Knowledge application 5.67 (.75) 5.46 (.66) 0.88 53Understanding of the content 5.45 (.77) 4.62 (1.50) 1.93 14Creativity 4.86 (1.26) 3.69 (2.25) 1.78 14Ability to collaborate 4.48 (1.31) 3.69 (1.75) 1.73 53Mastery/skillfulness 5.26 (1.08) 4.38 (1.66) 2.23 53*Effort 4.95 (1.15) 3.54 (1.98) 2.44 14*Items on the evaluation procedureTests 3.57 (2.44) 3.85 (2.58) �0.35 53Essays 5.38 (1.34) 5.00 (1.91) 0.80 53Collection of documents 4.62 (1.90) 4.69 (2.06) �0.12 53Quality of online participation 4.69 (1.46) 3.62 (2.02) 2.11 53*Observation of collaboration 4.55 (1.48) 2.92 (2.14) 3.09 53*

Note. * ¼ p < .05.Standard Deviations appear in parentheses.

B. Kopp et al. / Computers & Education 58 (2012) 12–2018

extremely important. These tutors also report to intervene more frequently to promote specific activities to avoid dysfunctional groupphenomena and report to give feedback on the collaborative process and on the task solutions. The cluster validation procedure revealedthat e-tutors in cluster 1 are for the most part those who claimed to have past experience in designing and/or realizing e-learning expe-riences. Thus, according to the data analysis, it seems that e-tutors who perceive themselves as experts are more sensitive to the problemsand pitfalls of virtual collaboration than e-tutors without experience.

With respect to content-specific cognitive activities, e-tutors in cluster 1 (which includes the majority of e-tutors with experience)evaluated these core activities as more important, and they reported to support these activities more frequently. Even though the inter-vention rate for social activities is less than 50% overall, and thus much lower than for cognitive activities, it seems that e-tutors in cluster 1intervene much more often than e-tutors in cluster 2. This may be due to the fact that more experienced e-tutors are more able to detectdysfunctional social phenomena in online collaboration. As such phenomena are crucial for effective interaction in computer-supportedlearning environments (e.g., Mandl, Ertl, & Kopp, 2006), it seems necessary that e-tutors themselves receive more support in detectingproblems in group work, especially with regard to social activities.

Meta-cognitive activities, namely the organization and long-term planning of groupwork before themain collaboration process are ratedas more relevant by e-tutors in cluster 1 than by e-tutors in cluster 2. This is evidenced by the higher intervention rate. Again, it seems thatexperience in supporting online collaboration makes e-tutors sensitive to fostering meta-cognitive activities, as such planning mainlydetermines the subsequent procedures and strategies used by the groups. These activities of planning and organizing collaboration aremuch more demanding in virtual learning environments. E-tutors need to work to prevent social loafing, or a lack or diffusion ofresponsibility, because virtual learning is more anonymous than face-to-face learning.

A key predictor for high group performance and deep understanding is providing feedback not only on the task level (e.g. task solutionsand performance of the groups), but also on the process level (e.g. collaboration process) (Hattie & Timperley, 2007; Tenenbaum & Goldring,1989). As collaborative learning in virtual environments is more demanding than in usual environments (e.g., a different kind ofcommunication caused by missing non-verbal cues which learners are not familiar with), continuous feedback is even more important forthe learner’s success. E-tutors included in cluster 1, composed predominantly of experienced e-tutors, provided more feedback on the taskand on the process level to continuously improve learners’ collaboration processes and performance. This is in line with a previous studyshowing that experienced e-tutors are able to provide their learners with the scaffolds necessary to foster their understanding of the content(Goold et al., 2010).

According to the self-reported results of the e-tutors, we might make the general presumption that e-tutors with experience have moreknowledge about collaboration processes, problems and pitfalls. Therefore, e-tutors in cluster 1 rated the importance of specific collabo-ration activities as more important and intervened more frequently in order to foster desired activities and outcomes, and to avoid diffi-culties. To guarantee adequate support and supervision in online collaboration, it seems to be necessary that all e-tutors are trained for theirspecific competencies, skills and roles as e-tutors. Also, previous authors suggest a training in online facilitation for tutors, because there isa need to prepare teachers for the online environment (Goold et al., 2010; see alsoWilson & Stacey, 2004; McDonald & Reushle, 2002; Harris& Sandor, 2007).

Finally, the analysis of the outlier experiences included in both clusters revealed that, in a minority of cases, the perceived experience ofe-tutors is not fundamental to determining their practice in fostering collaboration strategies. In those cases, they behave similarly to theirallocated cluster. This suggests the need for further investigation on e-tutors competencies and on the role of specific training asa prerequisite for fostering online collaboration activities. Furthermore, it suggests that the perceived experience of e-tutors has no impacton their expectations of their role as an e-tutor.

6. Conclusions

This study showed differences in e-tutors’ support behavior based on their previous experience. These results indicate that experience indesigning and/or realizing e-learning courses is a critical precondition for giving the proper weight and importance to collaborativeactivities, and in adequately intervening to foster the interaction between group members. Thus, experience has an impact on the way e-tutors support virtual online collaboration. This is in line with previous studies that investigated e-tutors’ course organization, their time

B. Kopp et al. / Computers & Education 58 (2012) 12–20 19

management, and the number of posted contributions that provide more direct instructions and feedback (Goold et al., 2010; Hemsing,2008; Maor, 2008; Morris et al., 2005).

Even though this study gives us initial indications of the importance of the e-tutor’s experience for supporting online collaboration, and isa starting point for gaining further insights into the daily practice of e-tutors, there are several limitations. First of all, the data sources arelimited, as we only refer to subjective perceptions of the e-tutors regarding their past experience and how they support online collaboration.Future studies should investigate how e-tutors intervene in their actual practices using different and more objective measures for e-tutor’ssupport behavior (such as those based on log-files or on content analysis of the contributions in the forums). In line with this is a furtherlimitation of the study that concerns the sampling technique. The snowball technique, like other respondent-driven sampling (RDS), isa network-based technique used especially in hard-to-reach populations (Salganik & Heckathorn, 2004). In recent years, RDS has been usedin more than 120 studies in more than 20 countries, but despite the widespread use and growing popularity of this technique, this samplingmethodology is substantially less accurate than the standard one (Goel & Salganik, 2010). The accuracy of RDS estimates is affected by thestructure of the underlying social network, the distribution of traits within the network, and the recruitment dynamics, thus leading to thepossibility of an under-coverage, non-response or voluntary response bias.

Second, based on this and previous studies, we still do not know whether experienced e-tutors just intuitively support online collab-oration based on their naïve beliefs and on the functioning of virtual collaboration in their daily practice, or whether they have thetheoretical and empirical knowledge as a basis onwhich to act in a reflective and profound manner. This latter possibility could explain thepresence of e-tutors without experience in cluster 1 – but who behaved similarly to experienced e-tutors – and vice versa.

Third, it would be of interest to examine the effects of such behavior on the learning process and learning performance of the students.Regarding practical implications of the study, it seems necessary to prepare tutors for their tasks in online learning environments withadequate trainings which sensitize them to the specific phenomena involved in collaboration (e.g. Harris & Sandor, 2007; McDonald &Reushle, 2002; Wilson & Stacey, 2004).

Acknowledgment

This project (“Social networks and knowledge construction promotion in e-learning contexts” http://minerva.ing2.unibo.it) has beenfunded with support from the European Commission. This publication reflects the views only of the authors, and the Commission cannot beheld responsible for any use which may be made of the information contained therein. Authors acknowledge the project partners and theEACEA.

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