Shared and personal learning spaces: Challenges for pedagogical design

Internet and Higher Education 15 (2012) 231–236

Contents lists available at SciVerse ScienceDirect

Internet and Higher Education

Shared and personal learning spaces: Challenges for pedagogical design Päivi Häkkinen ⁎, Raija Hämäläinen 1 University of Jyväskylä, Finnish Institute for Educational Research, P.O. Box 35, 40014 University of Jyväskylä, Finland

a r t i c l e

i n f o

Available online 24 September 2011 Keywords: Collaborative learning Pedagogical design Shared and personal learning spaces

a b s t r a c t The development of new tools for collaboration, such as social software, plays a crucial role in leisure time and work activities. The aim of this article is to summarize the research in the field of computer-supported collaborative learning (CSCL). This is done particularly from the perspective of the blurred line between individual (personal) and group-level (shared) learning that the use of the new tools has forced us to re-think. First, individual and group-level perspectives to learning are discussed to make sense of the major notions of how learning is understood in CSCL research. Second, based on this theoretical grounding, it will be further elaborated what this means to the pedagogical design of educational practices utilizing emerging technological landscapes. And third, two different empirical examples will be presented to illustrate the variety of emerging technological landscapes meeting the needs of future learning. © 2011 Elsevier Inc. All rights reserved.

1. Introduction The recent developments in technological landscapes have a crucial influence on the future of schooling in the knowledge society (Hargreaves, 2003). On the one hand, advances in mobile communication and social media change and challenge the educational practices of the knowledge society. On the other hand, learning technologies can also provide aids for individual thinking and collaborative knowledge construction, and, thus, address the challenges of the 21st century. Namely, a knowledge-intensive and networked working life demands facilities for lifelong and life-wide learning to tackle the complex problems being faced (Billett, 2008). There is also a need to communicate effectively in teams, to search and manage information as well as to produce new knowledge for everyday purposes. As networked technologies have become broadly available at the beginning of the 21st century, people also tend to access information and build knowledge in more diverse ways than previously. However, nowadays there are also critical discussions about whether this trend has reached our educational settings in a satisfactory way. Many educational settings are still far from implementing innovative knowledge working practices to support learning. Tools for collaboration have dramatically changed during the last few years. The development of social media and Web 2.0 applications such as blogs, wikis, and different community services play a crucial role in leisure time and work activities (Cress & Kimmerle, 2007; Franklin & Van Harmelen, 2007). The emphasis on participation, networking, and shared creation of content and knowledge in Web 2.0 practices offers possibilities for actively engaging the learner (Bonderup Dohn, 2009). In addition to ⁎ Corresponding author. Tel.: + 358 40 584 3325; fax: + 358 14 260 3201. E-mail addresses: paivi.m.hakkinen@jyu.fi (P. Häkkinen), raija.h.hamalainen@jyu.fi (R. Hämäläinen). 1 Tel.: + 358 40 805 4250; fax: + 358 14 260 3201. 1096-7516/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.iheduc.2011.09.001

software, mobile technology and context-aware devices have enabled contextualized learning experiences by adapting information they provide to the context of the person. These technologies make information available to each learner at any time and place, and hence, broaden the learning context beyond the institutionalized educational settings such as classrooms. In addition, there is a growing interest on self-initiated and interest-driven learning environments also outside formal education (Barron, 2006). Related to growing interest, there is also a lot of hype and overly optimistic notions regarding the possibilities of recent technological developments. In fact fairly little empirical research on learning processes and the effects in these environments has been conducted. Furthermore, for some communication practices (e.g., Google+, Facebook, and Second Life), there is a strong emphasis on participation and social relations for the sake of themselves, but advanced cognitive and pedagogical practices utilizing these environments are still to come. Research has indicated that collaborative learning—with or without technology—is a tempting phenomenon, but high-level collaboration among students in real-life learning settings (e.g., in classrooms) is more difficult to realize than previously thought (Häkkinen, Arvaja and Mäkitalo, 2004; Häkkinen & Järvelä, 2006; Schellens & Valcke, 2005; Vonderwell, 2003). For example, in Finnish schools mobile devices and technical infrastructure have provided contexts for learning and collaboration that go beyond classrooms to surrounding society (e.g. to museums). However, utilization of these facilities varies substantially between schools. The majority of schools in Finland are at a modest level in using information and communication technology (ICT) for learning purposes, and some teachers do not use ICT at all in their teaching (Kankaanranta & Puhakka, 2008; Kankaanranta & Vahtivuori-Hänninen, 2011). In particular, the use of collaborative technology, such as social software or learning platforms for joint knowledge construction, in a pedagogically meaningful way is still rare among the majority of Finnish schools.

232

P. Häkkinen, R. Hämäläinen / Internet and Higher Education 15 (2012) 231–236

This article will draw on our earlier research on collaborative learning (Arvaja, 2007; Arvaja, Salovaara, Häkkinen, & Järvelä, 2007; Häkkinen & Järvelä, 2006; Häkkinen et al., 2010; Hämäläinen, 2008; Hämäläinen & Arvaja, 2009; Hämäläinen & Häkkinen, 2010). Based on this research, the quality of interaction and learning with current collaboration technologies varies significantly. Some students and groups perform better than others; some learning environments produce ideal learning experiences and some do not. For example, asynchronous learning platforms (e.g., Moodle) utilized in many distance learning courses offer fairly open spaces for collaborative knowledge construction. At their best, they enable sharing of different perspectives and engaging in critical analysis. However, despite apparently similar instructional support, the level and quality of collaborative activities vary drastically among groups, especially in terms of the number, length, and content of interactions (see also, Hämäläinen & Häkkinen, 2010). The production of descriptive and surface-level knowledge, the difficulty in creating explanation-seeking questions, the reaching of mutual understanding among participants, and uneven participation are some of the main challenges that exist in computer-supported collaborative learning settings (Häkkinen & Järvelä, 2006; Lipponen, Hakkarainen, & Paavola, 2004). The challenges of earlier research call for better understanding of the forms of individual and collaborative activities in the learning environments utilizing different emerging technologies. It is important to find out what tends to make some interactions and environments successful, and under which circumstances, but not others. The specific relation between individual (personal) and group-level (shared) dimensions of collaborative learning, the blurring of which is particularly evident for learning contexts in which learners utilize many tools and applications (e.g. services of social media) simultaneously as part of their everyday activities, needs to be investigated. The fundamental questions related to this area as follows: What is the interplay of individual and social dimensions of collaboration in a variety of contexts? In what ways are individual and group-level learning activities intertwined in productive collaboration and learning? How are team members' personal perspectives intertwined in an effective shared network of perspectives on task-relevant information? What needs to be understood in order to design support for productive learning activities in the environments that integrate shared and personal learning spaces? In light of the questions above, the aim of this article is to summarize the research in the field of computer-supported collaborative learning (CSCL), particularly from the perspective of the blurred line between individual (personal) and group-level (shared) learning. First, individual and group-level perspectives to learning are discussed to make sense of the major notions of how learning is understood in CSCL research. Second, based on this theoretical grounding, it will be further elaborated what this means to the pedagogical design of educational practices utilizing emerging technological landscapes. And third, two different empirical examples will be presented to illustrate the variety of emerging technological landscapes meeting the needs of future learning. The two examples differ from each other particularly in terms of pedagogical design. In the first example, the focus is on facilitating a particular kind of collaboration (complementary knowledge construction) in an asynchronous learning platform with the aid of a pedagogical design and structure. The second example, on the other hand, leans on learner-centered, self-initiated, and interest-driven learning that utilizes multiple tools (e.g., social software) as part of one's personal learning environment. 2. Challenges for theory: individual and group-level dimensions of learning In this section, the research in the field of computer-supported collaborative learning will be shortly summarized, particularly from the perspective of the blurred line between individual (personal)

and group-level (shared) learning. Although tools for learning have dramatically changed over recent years, the basic mechanisms of learning are still very much the same. Typical features of the learning environments of the 21st century are inquiry approaches, strategic skills, and self-regulated learning, as well as a focus on collaboration and social forms of learning education (Assessment & Teaching of 21st Century Skills, 2011). Furthermore, socially organized activities can be realized in small study groups and work teams or in broader social networks and communities. To succeed in a knowledge society, learners and knowledge workers need to combine originally divergent perspectives and complementary expertise in solving complex problems and creating new knowledge. Next, the research on collaborative learning is briefly surveyed, with a particular emphasis on the individual and group-level dimensions of learning. In the research field of computer-supported collaborative learning (CSCL), there is tension between two major notions of how learning is understood (Clara & Mauri, 2009; Dillenbourg, 2006; Goodyear & Zenios, 2007). In one extreme, learning is understood as an individual phenomenon and a construction of one's own knowledge. An example of this type of research is the design of support structures (e.g., scripts) for collaboration and, subsequently, individual knowledge acquisition (e.g., Schrire, 2004; Weinberger, Ertl, Fischer, & Mandl, 2005). In other words, collaboration is valued only in terms of individual learning outcomes. The other extreme, in contrast, considers group cognition and inter-mental activity as the main agent of learning (Stahl, 2006). In this view, the core of collaborative learning is that through co-ordination of different perspectives, commitment to joint goals, and the joint evaluation of group activities, a group can go beyond individual thinking (Dillenbourg, 1999; Stahl, 2006). In intermediate positions, the individual and social processes of learning are seen as intertwined (Stahl, 2006). They acknowledge the benefits at the group and individual levels in parallel to reciprocal influences (Clara & Mauri, 2009). The socially shared learning approach describes the group life as depending on individual participation, while individual life depends on the impact of groups (Levine, Resnick, & Higgins, 1993). According to Suthers (2005), knowledge construction recognises that individuals create their worldview; this meaning-making is more specifically located in a group context in collaborative knowledge construction (see Arvaja et al., 2007). Furthermore, the process of meaning-making is itself constituted of social interactions (Suthers, 2005). Also Stahl (2006) suggests that collaborative learning takes place through processes of shared meaning-making when there is a dynamic relationship between shared meanings and individual interpretations. Through this process, learners verify and negotiate their individual views so as to reach shared understanding to be used as a resource for constructing further understanding (Stahl, 2006). Furthermore, when group members engage in the intentional process of group-based meaningmaking, it is referred to as knowledge building that can benefit the whole community of practice by developing cultural artifacts like theories (Scardamalia & Bereiter, 2006). According to Enedy and Hoadley (2006), discourse progresses as individuals take up what is said by others, compare it to their own understanding, and respond to ideas. This way collective discourse goes forward and extends one's own thinking. In other words, learning can be seen as a byproduct of participating in a series of linked dialogues and monologues. Especially in the context of emerging technological landscapes, such as social media, how collective discourse progresses from dialogue to monologue and back again needs to be understood. This is particularly important as our current learning trajectories include a variety of different contexts (e.g. formal – informal, physical – virtual), some of which are supported by dialogic, communication media, whereas others are supported by monologic, informational media. To sum up, individuals' divergent ideas have a crucial role in collaborative interactions, but the challenge is to merge them into shared knowledge construction. Understanding each other and

P. Häkkinen, R. Hämäläinen / Internet and Higher Education 15 (2012) 231–236

establishing shared knowledge are not trivial even in face-to-face groups, not to even mention when collaboration is virtual or computersupported. Furthermore, in the emerging technological landscapes based on, for example, social media, the line between individual (personal) and group-level (shared) activities is becoming increasingly blurred as individuals' interest-driven and self-initiated activities face the social networks and communities of interest. Students collaborate at multiple social levels, including working individually, working in small groups as well as working in multiple communities at the same time. As students also utilize multiple social and material resources that are distributed across different settings that go beyond formal schooling, there is also the challenge of reconceptualizing the notion of context. For example, the role of technology has often been interpreted as one variable that influences learning, whereas nowadays it is seen more as a part of the context that is intrinsic and a constituent of the learning process (Clara & Mauri, 2009). 3. Challenges for pedagogical design of shared and personal learning spaces Based on the theoretical grounding above, the challenges for pedagogical design of shared and personal learning spaces are next shortly explored. Traditional instructional design (ID) models are used to create an understanding of the conditions and attempted outcomes of instruction, and to use this knowledge in specifying methods of instruction (Reigeluth, 1983). These approaches are externally directed, contentdriven, and based on clear pre-structuring of activities (e.g., Hannafin, 1993; Häkkinen, 2002). However, learning goals and activities are nowadays often less explicit and identifiable in a simple way. Instead, the focus has clearly moved to the design of more complex realities and open-ended learning environments instead of programs, methods, or tools (Hannafin & Land, 1996; Lowyck & Pöysä, 2001). Especially the focus on the social dimension of learning confronts instructional designers with the challenge of building interactions between learners and their environment. In addition, the concept of designing for learning communities has become more common (Lowyck & Pöysä, 2001). As the concept of instructional design is often associated with the traditional ID models, in this article the concept of pedagogical design is preferred in order to refer to more indirect approach to design. This means focusing on setting up favorable situations for collaboration without interfering with detailed interaction processes. Socially organized activities cannot be directly predicted. Pedagogical design should be interpreted as resources to support group interactions and knowledge construction activities instead of giving prescriptive action plans (Dillenbourg & Tchounikine, 2007; Häkkinen et al., 2010). Today's learning environments, utilizing tools such as social software and mobile devices, are often fairly open and loosely structured, allocating the learner a lot of responsibility for monitoring his or her own learning. With adequate skills or insufficient guidance, students might not reach productive collaborative activities such as questioning, explaining, elaborating, or arguing (Kollar, Fischer, & Slotta, 2008). To facilitate these knowledge-generative activities, the pedagogical design can aim to structure social interactions in a way that makes productive collaboration more likely (Kobbe et al., 2007). This can be done by designing a particular kind of collaborative activities that would not probably occur otherwise (e.g., argumentation − N cognitive conflicts) (Dillenbourg, 2002). In this kind of pedagogical design, the resources for collaboration can be either internal (e.g. peer students with their contradictory opinions), external (e.g. study material aimed at triggering contradictory perspectives) or integrated (e.g. pictures that ground knowledge construction and represent personal experience) (see also, Arvaja, 2007; Hämäläinen & Vähäsantanen, accepted for publication; Jeong & Hmelo-Silver, 2010). For the pedagogical design of today's learning environments, it is crucial to realize that there are qualitatively distinct ways in which individual students participate in social practices (Cobb & Bowers, 1999). For

233

example, different backgrounds of group members influence how the collaborative context is created and the learning task is interpreted (Arvaja, 2007). For example, pre-service teachers analyze the same learning tasks from a different viewpoint than experienced teachers in the field. A particular challenge in these new ecologies of learning and collaboration is to bring team members' divergent meanings and personal perspectives into an effectively shared network of viewpoints on task-relevant information (Stahl & Hesse, 2009). As students are also seen as active agents in collaboration, one of the main roles of design is to offer them resources to support their joint knowledge construction activities. In terms of the pedagogical design of emerging technological landscapes, one of the most crucial questions is the balance between activities designed beforehand (e.g., task design) and monitoring (e.g., by teacher) reactively during the learning process. In both, the teacher is the key player (Dillenbourg, Järvelä, & Fisher, 2009). However, researchers claim that current educational settings often do not produce the desired results in learner-centered settings (e.g., Alvarez, Guascj, & Espasa, 2009; Downes, 2010; Minocha, Schroeder, & Schneider, 2011). Drexler (2010) and McLoughlin and Lee (2010) have also claimed that the easy availability of emerging learning technologies, such as social software, has caused challenges for teachers. For example, there is increasing demand to provide personalized learning experiences to students that cultivate their independent learning skills as well as scaffold the learners' reflective activities and the development of generic competencies. However, at the same time these environments typically offer fairly poor support for teachers at monitoring learning activities (Arvaja, Hämäläinen, & Rasku-Puttonen, 2009). No matter how self-directed the students are, the teacher has to deal with issues such as group formation (e.g., heterogeneous vs. homogeneous groups), the focus of different social levels (individual – group – classroom – community) in task design as well as data flow between these levels. Furthermore, in institutionalized educational settings, the teacher typically chooses the learning material and technology used as well as the ways of monitoring and scaffolding the students. As a whole, the teacher integrates all of these elements into the overall classroom activity by coordinating supportive interventions across multiple learning activities (Dillenbourg & Jermann, 2010; Dillenbourg et al., 2009). Puntambekar and Kolodner (2005) have also referred to the notion of distributed scaffolding, meaning the kind of support for students that is distributed across various resources constituting students' learning environment. These tools might include task structure, learning material, social arrangements, technology, and the teacher's role (see also Lakkala, Muukkonen, Paavola, & Hakkarainen, 2008). 4. Empirical examples of different pedagogical scenarios and emerging technological landscapes In this section, recent studies are referred to in order to illustrate the challenges of pedagogical design addressed above. The nature of learning tasks, forms of collaboration as well as the integration of individual and collaborative activities vary in these examples. In the first example, the focus was on facilitating particular kind of collaboration (complementary knowledge construction) in an asynchronous learning platform with the aid of a pedagogical design and structure. The second example, on the other hand, leans on learner-centered, self-initiated, and interest-driven learning that utilizes multiple tools (e.g., social software) as a part of one's personal learning environment. Table 1 illustrates some of the main differences in these two cases. 4.1. Pedagogical design and collaborative use of technology in highereducation context In several research projects, collaborative interactions in pedagogically structured web-based environments have been studied in order to understand and facilitate high-level knowledge construction activities (Arvaja, 2007; Arvaja et al., 2007; Häkkinen & Järvelä, 2006;

234

P. Häkkinen, R. Hämäläinen / Internet and Higher Education 15 (2012) 231–236

Table 1 Two cases of different pedagogical scenarios and emerging technological landscapes.

Social level Technology Pedagogical rationale

Pedagogical design and collaborative use of technology in higher-education context

Personal and shared learning spaces for self-initiated and interest-driven learning

Starting point in group/ collaboration Asynchronous learning platform Pedagogical design aiming to facilitate particular kind of collaboration (complementary knowledge construction)

Starting point in individuals' personal interests Mixture of different tools tailored for personal purpose Self-initiated and interest-driven learning, learner-centeredness

Hämäläinen & Arvaja, 2009: Hämäläinen & Häkkinen, 2010). The interventions have typically included structuring the collaborative actions with different pedagogical designs while students work in small groups in an asynchronous, mainly text-based web environment. The focus of design in many of our own projects has been on designing the learning task and working phases in a way that aims to trigger deep-level discussion from different viewpoints (complementary knowledge construction). The learning tasks have been designed in a way that they presuppose integration of individual (e.g., reading) and group-level activities (e.g., discussing, building a joint concept map integrating complementary perspectives). Hence, a crucial point in the pedagogical design is that a series of linked individual and collaborative phases enable discourse to progress as individuals take up what is said by others and contribute to it. In the context of these kinds of distance learning courses, technology has been used for collaborative work, but not specifically designed to scaffold collaboration and learning, for example, with the aid of prompts or other tools. Shared workspaces and asynchronous communication tools, at their best, can provide a setting for explanation, knowledge articulation, argumentation, and other advanced collaborative activities necessary for knowledge work and learning. These environments can also store the history of the knowledge construction process for revisions and future use. Also collaborative knowledge building that can benefit the community of learners (Goodyear & Zenios, 2007) can occur in these environments, but there are also several challenges. According to the research findings, the pedagogical designs can guide the majority of students to a sufficient degree (Häkkinen et al., 2010; Hämäläinen & Arvaja, 2009). However, some groups perform better than others. In many cases, students engage in surfacelevel interaction and cannot generate explanation-seeking questions. Students might also share or construct knowledge from similar perspectives, instead of utilizing their complementary perspectives in constructing new knowledge (Arvaja, 2007; Hämäläinen & Arvaja, 2009). What, then, makes some students and some groups engage in high-level collaboration processes while others do not? Why do ideal learning experiences occur in some contexts but not in others? The effectiveness of networked learning is influenced by several elements of the learning environment that have combined effects on the quality of collaboration. This complex set of variables includes things such as learning tasks, individual and joint goals, technological affordances, the teacher's role, classroom culture, etc. In terms of pedagogical design, a particular challenge is to respond to the different needs of individuals' backgrounds and to utilize these backgrounds in constructing new knowledge. Subsequently, with the aid of task design and group composition, team members' personal perspectives are turned into a shared network of perspectives utilized in knowledge construction.

collaboration, a fairly different focus is taken in learner-centered environments that start from the individual's interests and lean on learners' self-initiated activities. Many of the recent views have emphasized that learners should be allowed to construct their own learning environments by setting their own learning goals, selecting working methods and tools, building groups and communities of interest, creating and sharing resources as well as monitoring the visible products of learning activities and processes (Attwell, 2007; Wilson et al., 2007). In these views, learning is seen as a continuous and ongoing process taking place in different contexts and being provided by multiple tools (e.g., social software) and individuals, with the help of knowledgeable peers, mentors, or teachers (Drexler, 2010; McLoughlin & Lee, 2010). Learner-centered environments have the potential to integrate informal and formal learning experiences and resources as well as facilitate lifelong and life-wide learning (Barrett & Garrett, 2009). One example of the realization of learner-centeredness is the concept of a personal learning environment (PLE) that has emerged as a topical research and development issue (Attwell, 2007; Wilson et al., 2007). A PLE is an environment where people and communities, and tools and resources, interact flexibly. In addition to the emphasis on learner-centeredness, the aim of a PLE is to offer personalized, customized, and modular solutions to integrate personal and shared learning spaces. Therefore, a PLE is located at the crossroads of individual (personal) and group-level (shared) dimensions of learning. Students' PLEs can include tools to support individual access, manipulation, synthesis, and analysis of information as well as communication tools to support interaction between people. The PLEs of individual students can also form emerging and growing networks that can be further integrated into large communities on the Internet (see also Wilson et al., 2007). From the technological viewpoint, the PLE concept challenges the traditional learning management systems (LMS) that are typically organization- or institution-centered and, therefore, do not allow learners to choose the tools (Laakkonen & Juntunen, 2009). For some people, a PLE means a set of tools (e.g., collection of social software) that the learner uses to organize and share his or her material and learning experiences, whereas for others, a PLE is more like the ideology emphasizing learner-centeredness and one's responsibility for personal learning goals (Taalas & Laakkonen, 2010). The aim of PLE ideology is to value the learners' ability to make meaning of their own experiences (Wilson et al., 2007). However, the other side of the coin is that if learners have access to large repositories of information, they might have challenges in creating meaning from this information as well as in organizing and sharing meaningful content. The major challenge is that this kind of learnercentered culture of learning is not realized in a moment or without problems. The traditions, structures, and processes of formal education do not support learning from doing complex projects in a selfinitiated and inquiry way and as part of social networks. This requires resourcefulness and planning by the student, a new role for the teacher, re-thinking of the assessment, and expanded mechanisms for collaboration and communication. Fairly little is known so far about the support structures that are needed in personal learning environments. Self-directed students can manage with minimalist support, but how about the others? What are the pre-conditions that can be organized to support learner-centered, personal inquiry? And what is the role of the teacher in personal learning environments (Shaikh & Khoja, 2011)? 5. Conclusions

4.2. Personal and shared learning spaces for self-initiated and interestdriven learning Compared to asynchronous learning environments described above, in which the aim has been to facilitate a particular kind of

The developments in the field of technology-enhanced learning over the last few years have indicated that there is a need to work on how people establish or design spaces for individual and collaborative activities within new architectures for educational activities, in addition to the kind of structure and support that are needed.

P. Häkkinen, R. Hämäläinen / Internet and Higher Education 15 (2012) 231–236

The nature of computer-supported collaborative learning environments has dramatically changed over the last few decades. Instead of emphasizing single applications of educational technology, the focus is more on integrating physical and virtual (Web 2.0 & 3D), personal and collective, local and global, as well as formal and informal learning spaces. In other words, future learning environments are complex entities in which learners are surrounded by a variety of resources (e.g., people, artifacts) that are utilized in collaborative meaning making (Arvaja, 2007; Linell, 1998). In terms of technological resources, students need tools for individual access, manipulation, and analysis of information as well as for communication, sharing and joint knowledge construction with other people. It is also typical of today's learning contexts that learners utilize multiple tools and applications simultaneously as part of their everyday activities. On the one hand, this use of technology in a ubiquitous way enables contextualized learning experiences by making information available at any time and place. On the other hand, multi-tasking of information resources might also lead to an increase of cognitive load and surface-level processing of information. Skills and strategies for finding, handling, and producing information play a crucial role in the emerging learning environments. Learning with understanding and high-level collaboration is not just something that takes place whenever learners come together. Rather, various cognitive, social and motivational challenges must be overcome. Furthermore, a challenge is to find a delicate balance between the benefits of openness and self-initiated activity in these environments and the appropriate amount of structure and support necessary for the learning process. It is evident that general guidelines for designing future learning environments cannot be drawn. Also our two case examples presented in this article demonstrate that it is impossible to talk about any particular kind of process when referring to computer-supported collaborative learning. When comparing different pedagogical scenarios enabled by emerging technological landscapes, differences can be found in several elements such as in learning tasks, pedagogical rationales, forms of collaboration (synchronous – asynchronous), support structures (e.g. teacher's role), technology used, etc. It is difficult to see the effects of any single element alone, but rather a wholeness of these interrelated elements. Furthermore, although much on CSCL research has focused on tracing high-level dialogue, it is often only one visible part in the series of intellectual activities, some of which happen individually and some collaboratively. Researchers in fields such as CSCL aim to develop methodologically justifiable measures for collaborative knowledge construction. Wegerif (2006) has argued that some of the underlying assumptions behind CSCL pedagogies are mainly based on the industrial age and focus too much on individual skills. However, what matters is not only the final result of collaborators but also the intensity of the interactions required for detecting and repairing misunderstandings as well as for constructing shared understanding (Dillenbourg, 2002; Schwartz, 1995). The particular methodological challenges are related to research on emerging technological landscapes, in which students utilize multiple resources that are distributed across different settings going beyond formal schooling. Capturing the processes of learning and collaboration at multiple levels including working individually, working in small groups as well as working in multiple communities at the same time is a challenge for both data collection and analysis. One of the reasons why new models of learning are rare in institutional educational settings is that traditional assessments are inadequate for measuring the outcomes related to self-regulated and collaborative learning. As the assessment practices have a strong guiding influence on education, the most powerful way of changing educational practices is to change the assessment. We should move away from assessing operationalized, routine kinds of tasks and easily measurable knowledge and skills, and focus on assessing how students use technologies as thinking tools in order to search, produce, manage, analyze, and share knowledge as well as solve complex

235

problems individually and collaboratively. This also means an emphasis on assessing cross-curricula and cross-disciplinary skills that are needed in one's future working life. Furthermore, Barron (2006) refers to the importance of self-initiated and interest-driven learning that takes place across formal and informal learning contexts. This should also be recognized as an important element of assessment. For example, students might develop an interest in some topic or knowledge in formal education and develop it further in informal contexts such as at home or in some other community affording knowledge about the topic. Further on, in formal education, this interest could result in expertise on the topic recognized by peers or teachers (Barron, 2006). To conclude, technology is an integrated part of today's schooling and everyday practice. Powerful computer-supported collaborative environments can be seen as essential elements in the re-structuring of social interaction and knowledge creation. This way, they can be an agent for change that is strongly associated with the creation of a new kind of learning culture. If computer-supported collaborative learning environments can contribute to pedagogical practices that inspire students to reveal their complex thinking processes and to evaluate and critically discuss their own learning processes, these environments can play a crucial role in renewing teaching and learning practices. Acknowledgements This research was supported by the Academy of Finland (projects no. 121097 and no. 135939). We thank the members of the research group on ICT in Learning and Working Environments. References Alvarez, I., Guascj, T., & Espasa, A. (2009). University teacher roles and competencies in online learning environments: A theoretical analysis of teaching and learning practices. European Journal of Teacher Education, 32(3), 321–336. Arvaja, M. (2007). Contextual perspective in analysing collaborative knowledge construction of two small groups in Web-based discussion. International Journal of Computer-Supported Collaborative Learning, 2(2/3), 133–158. Arvaja, M., Hämäläinen, R., & Rasku-Puttonen, H. (2009). Challenges for the teacher's role in promoting productive knowledge construction in computer-supported collaborative learning contexts. In J. O. Lindberg, & A. D. Olofsson (Eds.), Online learning communities and teacher professional development: Methods for improved education delivery (pp. 263–280). Hersey: IGI Global. Arvaja, M., Salovaara, H., Häkkinen, P., & Järvelä, S. (2007). Combining individual and group-level perspectives for studying collaborative knowledge construction in context. Learning and Instruction, 17(4), 448–459. Assessment & Teaching of 21st Century Skills (2011). Purpose. (Retrieved from). http://atc21s.org Attwell, G. (2007). Personal learning environments – the future of eLearning? eLearning papers, 2(1) (Retrieved from). http://www.elearningeuropa.info/files/media/ media11561.pdf Barrett, H., & Garrett, N. (2009). Online personal learning environments: Structuring electronic portfolios for lifelong and life-wide learning. On the Horizon, 12(2), 142–152. Barron, B. (2006). Interest and self-sustained learning as catalysts of development: A learning ecologies perspective. Human Development, 49, 193–224. Billett, S. (2008). The workplace as learning environment: Introduction. International Journal of Educational Research, 47(4), 209–212. Bonderup Dohn, N. (2009). Web 2.0: Inherent tensions and evident challenges for education. International Journal of Computer-Supported Collaborative Learning, 4(3), 343–363. Clara, M., & Mauri, T. (2009). Toward a dialectic relation between the results in CSCL: Three critical methodological aspects of content analysis schemes. International Journal of Computer-Supported Collaborative Learning, 5(1), 117–136. Cobb, P., & Bowers, J. S. (1999). Cognitive and situated learning perspectives in theory and practice. Educational Researcher, 28(2), 4–15. Cress, U., & Kimmerle, J. (2007). A systemic and cognitive view on collaborative knowledge building with wikis. International Journal of Computer-Supported Collaborative Learning, 3(2), 105–122. Dillenbourg, P. (1999). Introduction: What do you mean by collaborative learning? In P. Dillenbourg (Ed.), Collaborative learning: Cognitive and computational approaches (pp. 1–19). Oxford, England: Pergamon. Dillenbourg, P. (2002). Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In P. A. Kirschner (Ed.), Three worlds of CSCL. Can we support CSCL (pp. 61–91). Heerlen: Open Universiteit Nederland. Dillenbourg, P. (2006). The solo/duo gap. Computers in Human Behavior, 22(1), 155–159.

236

P. Häkkinen, R. Hämäläinen / Internet and Higher Education 15 (2012) 231–236

Dillenbourg, P., Järvelä, S., & Fisher, F. (2009). The evolution of research on computersupported collaborative learning: From design to orchestration. In N. Balacheff, S. Ludvigsen, T. de Jong, T. Lazonder, & S. Barnes (Eds.), Technology enhanced learning: Principles and products (pp. 3–19). Dordrecht, Netherlands: Springer. Dillenbourg, P., & Jermann, P. (2010). Technology for classroom orchestration. In M. S. Khine, & I. M. Saleh (Eds.), New science of learning (pp. 525–552). New York, NY: Springer Science+Business Media. Dillenbourg, P., & Tchounikine, P. (2007). Flexibility in macro-scripts for computersupported collaborative learning. Journal of Computer Assisted Learning, 23(1), 1–13. Downes, S. (2010). The role of educator in a PLE world. (Retrieved from). http://www. downes.ca/post/54312 Drexler, W. (2010). The networked student model for construction of personal learning environments: Balancing teacher control and student autonomy. Australasian Journal of Educational Technology, 26(3), 369–385. Enedy, N., & Hoadley, C. (2006). From dialogue to monologue and back: Middle spaces in computer-mediated learning. International Journal of Computer-Supported Collaborative Learning, 1(4), 413–440. Franklin, T., & Van Harmelen, M. (2007). Web 2.0 for content for Learning and Teaching in Higher Education. Bristol: JISC (Retrieved from). http://www.jisc.ac.uk/media/ documents/programmes/digital_repositories/web2-content-learning-and-teaching.pdf/ Goodyear, P., & Zenios, M. (2007). Discussion, collaborative knowledge work and epistemic fluency. British Journal of Educational Studies, 55(4), 351–368. Häkkinen, P. (2002). Challenges for design of computer-based learning environments. British Journal of Educational Technology, 33(4), 465–474. Häkkinen, P., Arvaja, M., Hämäläinen, R., & Pöysä, J. (2010). Scripting computersupported collaborative learning: A review of SCORE studies. In B. Ertl (Ed.), E-collaborative knowledge construction: Learning from computer-supported and virtual environments (pp. 180–194). New York, NY: IGI Global. Häkkinen, P., Arvaja, M., & Mäkitalo, K. (2004). Prerequisites for CSCL: Research approaches, methodological challenges and pedagogical development. In K. Littleton, D. Faulkner, & D. Miell (Eds.), Learning to collaborate and collaborating to learn (pp. 161–175). New York, NY: Nova Science. Häkkinen, P., & Järvelä, S. (2006). Sharing and constructing perspectives in web-based conferencing. Computers in Education, 47(1/2), 433–447. Hämäläinen, R. (2008). Designing and evaluating collaboration in a virtual game environment for vocational learning. Computers & Education, 50(1), 98–109. Hämäläinen, R., & Arvaja, M. (2009). Scripted collaboration and group-based variations in a higher education CSCL context. Scandinavian Journal of Educational Research, 53(1), 1–16. Hämäläinen, R., & Häkkinen, P. (2010). Teachers' instructional planning for computersupported collaborative learning: Macro-scripts as a pedagogical method to facilitate collaborative learning. Teaching and Teacher Education, 26(4), 871–877. Hämäläinen, R., & Vähäsantanen, K. (accepted for publication). Theoretical and pedagogical perspectives on orchestrating creativity and collaborative learning. Educational Research Review. Hannafin, M. J. (1993). Emerging technologies, ISD, and learning environments: Critical perspectives. Educational Technology Research and Development, 40(1), 49–63. Hannafin, M. J., & Land, S. (1996). A conceptual framework for the development of theories-in-action with open-ended learning environments: A qualitative study. Educational Technology Research and Development, 45(2), 47–73. Hargreaves, A. (2003). Teaching in the knowledge society: education in the age of insecurity. New York, NY: Teachers College Press. Jeong, H., & Hmelo-Silver, C. E. (2010). Productive use of learning resources in an online problem-based learning environment. Computers in Human Behavior, 26(1), 84–99. Kankaanranta, M., & Puhakka, E. (2008). Kohti innovatiivista tietotekniikan opetuskäyttöä. Results of International SITES 2006 Study. Jyväskylä: University of Jyväskylä, Finnish Institute for Educational Research. Kankaanranta, M., & Vahtivuori-Hänninen, S. (2011). Opetusteknologia koulun arjessa II. Jyväskylä: University of Jyväskylä, Finnish Institute for Educational Research. Kobbe, L., Weinberger, A., Dillenbourg, P., Harrer, A., Hämäläinen, R., Häkkinen, P., et al. (2007). Specifying computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2(2–3), 211–224. Kollar, I., Fischer, F., & Slotta, J. (2008). Internal and external scripts in computersupported collaborative inquiry learning. Learning and Instruction, 17(6), 708–721. Laakkonen, I., & Juntunen, M. (2009). Tulevaisuuden oppimisympäristöt? – Henkilökohtaiset ja avoimet oppimisen tilat. In J. Viteli, & A. Östman (Eds.), Articles of the

research meeting at the Interactive Technology in Education Conference. Research of Interactive Media, 2. (pp. 69–83) Tampere, Finland: Tampere University Press. Lakkala, M., Muukkonen, H., Paavola, S., & Hakkarainen, K. (2008). Designing pedagogical infrastructures in university courses for technology-enhanced collaborative inquiry. Research and Practice in Technology Enhanced Learning, 3(1), 33–64. Levine, J., Resnick, L., & Higgins, E. (1993). Social foundations of cognition. Annual Review of Psychology, 44, 585–612. Linell, P. (1998). Approaching dialogue. Talk, interaction and contexts in dialogical perspectives. Amsterdam, the Netherlands: Benjamins. Lipponen, L., Hakkarainen, K., & Paavola, S. (2004). Practices and orientations of computer-supported collaborative learning. In J. Strijbos, P. Kirschner, & R. Martens (Eds.), What we know about CSCL, and implementing it in higher education (pp. 31–50). Boston, MA: Kluwer. Lowyck, J., & Pöysä, J. (2001). Design of collaborative learning environments. Computers in Human Behavior, 17, 507–516. McLoughlin, C., & Lee, M. (2010). Personalised and self-regulated learning in the Web 2.0 era: International exemplars of innovative pedagogy using social software. Australasian Journal of Educational Technology, 26(1), 28–43 (Retrieved from) http://www.ascilite.org.au/ajet/ajet26/mcloughlin.html Minocha, S., Schroeder, A., & Schneider, C. (2011). Role of the educator in social software initiatives in further and higher education: A conceptualisation and research agenda. British Journal of Educational Technology, 42, doi:10.1111/j.1467-8535.2010.01131.x. Puntambekar, S., & Kolodner, J. (2005). Toward implementing distributed scaffolding: Helping students learn science by design. Journal of Research in Science Teaching, 42(2), 185–217. Reigeluth, C. M. (Ed.). (1983). Instructional design theories and models: An overview of their current status. Hillsdale, NJ: Erlbaum. Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (pp. 97–118). New York: Cambridge University Press. Schellens, T., & Valcke, M. (2005). Collaborative learning in asynchronous discussion groups: What about the impact on cognitive processing? Computers in Human Behavior, 21, 957–975. Schrire, S. (2004). Interaction and cognition in asynchronous computer conferencing. Instructional Science, 32, 475–502. Schwartz, D. (1995). The emergence of abstract representations in dyad problem solving. The Journal of the Learning Sciences, 4(3), 321–354. Shaikh, Z., & Khoja, S. (2011, July). Role of teacher in Personal Learning Environments. Paper presented at the PLE Conference, Southampton, England. Stahl, G. (2006). Group cognition: Computer support for building collaborative knowledge. Cambridge, MA: MIT Press. Stahl, G., & Hesse, F. (2009). Paradigms of shared knowledge. International Journal of Computer-Supported Collaborative Learning, 4(4), 365–369. Suthers, D. (2005). Technology affordances for intersubjective learning: A thematic agenda for CSCL. In T. Koschmann, D. Suthers, & T. W. Chan (Eds.), Proceedings of computer supported collaborative learning 2005: The next 10 years! (pp. 662–671). Mahwah, NJ: Lawrence Erlbaum Associates. Taalas, P., & Laakkonen, I. (2010, September). Informal, collaborative and learnercentered? Developing a network of personal learning environments (PLEs) for formal HE language learning. Paper presented at the EUROCALL conference, Bordeaux, France. Vonderwell, S. (2003). An examination of asynchronous communication experiences and perspectives of students in an online course: A case study. Internet and Higher Education, 6(1), 77–90. Wegerif, R. (2006). A dialogical understanding of the relationship between CSCL and teaching thinking skills. International Journal of Computer-Supported Collaborative Learning (ijCSCL), 1(1), 143–157. Weinberger, A., Ertl, B., Fischer, F., & Mandl, H. (2005). Epistemic and social scripts in computer-supported collaborative learning. Instructional Science, 33(1), 1–30. Wilson, S., Liber, O., Johnson, M., Beauvoir, P., Sharples, P., & Milligan, C. (2007). Personal Learning Environments: Challenging the dominant design of educational systems. Journal of e-Learning and Knowledge Society, 3(2), 27–38.