Design of collaborative learning environments

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Computers in Human Behavior 17 (2001) 507–516 www.elsevier.com/locate/comphumbeh

Design of collaborative learning environments J. Lowyck*, J. Po¨ysa¨ Centre for Instructional Psychology and Technology (CIP & T) University of Leuven, Vesaliusstraat 2, B-3000 Leuven, Belgium

Abstract Designing collaborative learning environments is dependent upon the descriptive knowledge base on learning and instruction. Firstly, the evolution in conceptions of design towards collaborative learning is described, starting from designing as an intuitive behaviour. Secondly, collaborative learning is described from different angles, like individuals-in-context, learner communities, including motivational factors and distributed cognition. It is evidenced that the adequate use of collaborative learning settings may contribute to the learning quality. Thirdly, the implications of collaborative theories on instructional design are outlined, centred around: student, knowledge, assessment and community. The interplay between these perspectives is challenged in new models of (co) design. In the conclusion, an interactive approach of designing environments is advocated. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Instructional design; Collaborative learning; Learning community; Learning environments; Co-design

1. Introduction Designing refers to systematic choices and use of procedures, methods, prescriptions and devices in order to bring about effective, efficient and productive learning. The outcome of any design activity is a plan or scenario that defines the format, content and structure of the environment, the delivery systems and implementation strategies (Reigeluth, 1983). With the rise of more open, electronic learning environments, these definitions undoubtedly will need adaptation (Hannafin & Land, 1996), since increased environmental complexity and learners’ concomitant ‘higherorder’ learning call for more sophisticated models of design. Designing is no more * Corresponding author. Tel.: +32-16-32-62-44; fax: +32-16-32-62-74. E-mail addresses: [email protected] (J. Lowyck), [email protected]. be (J. Po¨ysa¨). 0747-5632/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S0747-5632(01)00017-6

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an intuitive endeavour with a lot of instability and variability in its knowledge-base, as reflected in Montaigne’s four centuries old adage: ‘du bon coeur, du bon sens et quelques petits trucs’ (a warm heart, common sense, and some handy tricks). In this approach, the ceiling effect is the designers’ individual competence in using recipes that only work in contexts that are identical to those in which the recipes were developed. Gradually, recipes were replaced by more systematic procedures developed within a ‘systems approach’. It consists of task-analysis, problem-solving and testing by a team of experts in complex domains. Instructional knowledge was documented and put into formal didactical models and procedures. Most models consist of predefined objectives (target position), description of trainee characteristics (actual position), methods and content to bridge the gap between both positions, and control of the outcomes (Andrews & Goodson, 1980). The quality of instructional design highly depends on the fit between the design model and its ‘intelligent’ use by a designer. In this model external, programmed control, decomposition of complexity, focus on content or subject matter, and ‘simple’ learning principles are predominant. Designers entirely define and produce instruction, while teachers and learners are consumers of rather alienating design products at the end of the chain. Later on, a more cognitive position on design is taken, based on outcomes of research on cognitive processing (Lowyck & Elen, 1993). Learning is an active, goaloriented and self-regulated process during which the learner continuously constructs meaning out of the environmental stimuli. The design process is aimed at support of the learners’ self-control (Merrill, Li, & Jones, 1990; Tennyson, 1992): learning environments aim now at enhancing cognitive and meta-cognitive processes. Since learning as a process is mainly the transition from a novice position towards that of a (semi-) expert, instructional design is tailored to the idiosyncratic characteristics of the learner in terms of both domain knowledge, (meta-) cognitive strategies and motivation. Processes like ‘scaffolding’ and ‘fading’ that enhance the self-regulating capacity of learners become central in the design agenda. This longitudinal support of learning puts designing on a developmental line and it creates links with curriculum design. While most theories on instructional design refer to the optimal adaptation of an environment to the individual learner (see ‘intelligent tutoring systems’), the rise of collaborative learning theories results in team learning design (Collis, 1994). This refers to socio-constructivist theories of learning and design, where learners coconstruct knowledge and co-design their learning environments. Design is now a non-linear, cyclical and iterative process. It starts from a rough prototype, which is gradually refined through feedback of users. In addition, the expansion of Internet as an encompassing technology supports this fundamental shift towards collaboration since learning takes place in a distributed knowledge environment (Dillenbourg, 1996) in combination with information and communication technologies. Instructional design is not restricted to the mere delivery and pacing of information, but learners are collaborating in a continuous flow of information and communication actually available on the Internet. However, as Van Merrie¨nboer (1999)

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contends, there is no direct link between content-driven web-based instruction and the quality of (constructive) learning. Indeed, there is no automatic learning effect to be expected from a mere technology.

2. Learning apart together: collaborative learning Any instructional design rests upon a knowledge base in which outcomes of research are represented in a given time and space dependent on the ‘Zeitgeist’. The recent breakthrough of telecommunications in education (e-learning) as well as evolutions in socio-cognitive and socio-cultural learning theories directed interest towards collaborative learning. It should be remembered that the state-of-the-art of a certain knowledge domain is rapidly changing, and that therefore any meta-review shows severe limitations. The primary aim of research on collaborative learning was to investigate whether this way of learning was more effective than solitary learning (Littleton & Ha¨kkinen, 1999). Other studies stress possible effects of different variables (e.g. task, medium, group structure) on learning and the intersection between collaborative (peer) learning in classrooms and learning with some types of computer software. Recent research in collaborative learning analyses effective collaborative activity instead of focusing on products of collaboration. For an overview of developments in the field of collaborative learning (CL), computer-supported collaborative learning (CSCL) and computer-supported co-operative work (CSCW), see Lehtinen, Hakkarainen, Lipponen, Rahikainen, and Muukkonen, 1998. In the next section, collaborative learning is described from different angles: (1) the individual in a social context; (2) motivational aspects; (3) distributed cognition; and (4) learning community. (1) In the broad framework of a socio-cultural approach, human activities in general are seen as socially mediated. Consequently, learning is embedded in a social process of knowledge construction rather than being a solitary endeavour (Vygotsky, 1978). Indeed, individual knowledge results from internalisation processes of information from the surrounding culture or, in other words, internal cognitive behaviour gradually results from external overt behaviour. When an individual participates in a social system, both culture and communication tools, especially language, shape the individual’s cognition as a source of learning and development. Knowledge emerges through the network of interactions and is distributed among humans and tools that interact. (2) Learning under ‘positive contact conditions’ can facilitate interpersonal relationships, which in turn can affect social-affective characteristics, like student’s motivation and self esteem (Nastasi & Clements, 1991). Collaborative learning influences student motivation in terms of increased students’ self-efficacy, learning goal orientation, and intrinsic valuing of the learning task. A first factor that accounts for these effects is the positive motivational impact of peer support for learning (Slavin, 1990). When peers recognise that success in learning depends upon the success of their peers, they are more likely to provide emotional and tutorial support for learning. A second factor is the support of the group for facing the

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perceived task difficulty. Collaborative groups have higher levels of self-efficacy regarding the achievement task because they are challenged by group members to cope with difficulties and to persevere as well. A third factor is that group activities encourage students to display greater intrinsic value of the subject matter or the task to be fulfilled, like problem solving and discussion of competing hypotheses. A fourth factor is the need to make one’s own knowledge explicit and hence disputable by other members of the group. Increased motivation will also increase timeon-task, one of the variables most clearly influencing learning outcomes. (3) Recently, the notion of distributed cognition attracts a lot of research attention. Knowledge and cognition do not reside in the head of each individual, but cognition is distributed over both individuals and their surrounds (Dillenbourg, 1996). Any human activity is affected by contextual affordances, which include both people and cultural artefacts. According to Pea (1993), the use of socio-culturally developed cognitive tools, external representations and other artefacts can reduce cognitive processing load and let solve more complex problems than would be possible otherwise. The cognitive significance of distributed cognition is based on the fact that human beings’ cognitive resources, like time, memory, or computational power, are limited. (Hakkarainen, 2001). This ‘distributed’ knowledge becomes predominant in multimedia and telecommunication environments, in which most information is distributed over different resources. (4) If learning is defined as a process, which takes place in a participation framework, it is the community, or at least the participants in the learning context, who learn under this definition (Lave & Wenger, 1991). The development of expertise is not only related to the nature of an individual’s knowledge structures, but also to that person’s access to relevant formal and informal cultural knowledge through participating in an expert community or network. Until now, schools, homes, and workplaces are isolated from each other and they mostly function because they are geographically connected or linked by accident or circumstances, but seldom by common purpose and deliberate collaborative action (Center for Technology in Learning, 1994). E-learning with different interaction tools offers ample opportunities for learners to collaborate with all kinds of people: peers, tutors, experts, professionals, and parents. Intensive electronic networking can offer added value to the existing networks and collaboration facilities, since schools can be electronically linked with the broader community. The specific properties of a technology determine both the kind of information that can be exchanged and the easiness of the communication process, though its effectiveness highly depends on how the properties are used. Numerous studies have been carried out that investigate the conditions for successful use (for a review: Wells, 1992). Effectiveness depends on the educational level of students, the amount of time needed to participate in the interaction and the extent to which the environment is perceived to be interactive. Especially when collaboration at a distance is aimed at, pacing the work becomes highly important. Marttunen (1996) characterises computer-mediated communication (CMC) as follows. Learning largely depends on students’ activities, especially on self-direction among students. Students are forced to make own ideas explicit and to critically

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argue, while confrontation with a variety of ideas and arguments enhances deep reflection and explicit account, due to an increased visibility of different ideas (Scardamalia & Bereiter, 1993). Moreover, groups of students may see how their understanding of a particular problem or aspect of reality changes. In other words, the flow of the learning process becomes more visible too. Technologies used in learning settings can be situated on various dimensions. Each dimension triggers a decision from the instructional designer, while the combination of decisions determines the outlook of the instructional support delivered. Design decisions should be made according to information modality, (non)linearity, type of interaction (human–human, human–machine, human–machine–human), number of participants, time (in)dependency, immediacy and place (in) dependency (see Dillemans, Lowyck, Van der Perre, Claeys, & Elen, 1998). This complexity undoubtedly will require new and powerful types of organisation, redefinition of the actors’ roles, in-depth analysis of the tasks and management of the intensive flow of information.

3. Implications of instructional design models on learners and designers The evolution in both the nature and the function of instructional design challenges the search for adequate interventions in order to enhance efficient and effective learning. This brings about the need for understanding the ‘conditions of learning’ instructional agents are confronted with in order to build interactions between learners and their environments. Taking an interaction perspective seems to be the crux of any design of powerful learning environments which are student centred, knowledge centred, assessment centred and community centred (Bransford, Brown, & Cocking, 1999). Design is no more a linear, externally controlled endeavour, but an adaptive and iterative process in which all agents play their role: learners, peers, tutors, teachers, and parents. 3.1. Student centred design Orientation in learning environment design focuses on several inputs from the learner. Essential in the design of collaborative learning environments is the position of the learner in his/her interaction with the environment. Not the designer, but the complex interaction between instructional and learning agents is the paramount object of designing. Clarebout et al. (1998) point to the following issues: (1) the goal of designing is to support and not to withdraw cognitive processing of learners: the learner has to be engaged in specific learning activities and processes, (2) learners will engage only if well adapted support is offered, and (3) support is not an objective nor external measure, but it is mediated by many learners’ processes in terms of perceptions, interpretations, and function attributions of the environmental characteristics (Elen & Lowyck, 1998). This means that learners need to have access to the different functions embedded in a collaborative learning environment. Depending on their epistemological beliefs and instructional or learning experiences,

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nominal stimuli in the environment will be (or not be) activated into effective stimuli. Crucial is the balance between self-regulation by the learner and external support. Research on learner control stems mainly from interactivity in multimedia settings (Chung & Reigeluth, 1992). They differentiate learner control in (1) content control, (2) sequence control, (3) pace control, (4) display control, and (5) advisor strategy. Research outcomes show learner control to be a highly complex variable. It interacts with other variables such as learner characteristics (prior knowledge, ability, motivation, aptitude, task persistence, age, training level, locus of control, and readiness), learner control variables (content, sequence, pace, display or strategy, and internal processing), and programme variables (advisement, adaptability, and learning model). In general, learner control may increase motivation to learn but not achievement, can increase time spent in learning, and does not guarantee optimal decisions from the part of the learner. Considering the characteristics of learners, high achievers who are knowledgeable about an area of study can benefit from a high degree of learner control, while uninformed learners require structure, interaction, and feedback to perform well. As to the relationship between learner control and programme variables, learner control with advice seems superior to unstructured learner control for enhancing achievement and curiosity, promoting time-on-task, and stimulating self-challenge. It still needs to be investigated, if and to what degree learner control in a collaborative context may lead to similar conclusions, since the object of conrol could differ seriously. 3.2. Knowledge centred design Curricula are only partially fixed and result from a negotiation process between the (group) of learner(s) and instructional agents. It may be expected that with growing maturity, increased self-regulating skills and more elaborated goaldirectness of students, the negotiation process, will cover gradually more and more aspects of the learning environment. Knowledge-centred environments focus on the kinds of information and learning activities that help students develop an understanding of disciplines (Bransford et al., 1999). Knowledge-centred design has to support the learners to become meta-cognitive by expecting new information and asking for further clarification (sense making). It has been unclear until now how distributed information environments can be turned into knowledge-centred design endeavours. It surely refers to the notion of ‘deep understanding’ in relationship to ‘deep level’ structures of disciplines. Supporting students to grasp the essentials of a given subject matter domain is a paramount task of design. 3.3. Assessment centred design Assessment needs to reflect the basic philosophy of learning and instruction. In order to achieve a systemic design, authentic assessment is a major component of the learning environment, taking real-life problems into account (Glaser & Silver,

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1994). Instructional agents at the micro-level have the responsibility to prepare and coach students towards these external assessments. A distinction already established in some countries between the supporting instructional agent and the goal setting and assessing function of instruction will most probably become the rule not only at the secondary and tertiary education level but in all links of the educational chain. Assessment is in line with the characteristics of constructive learning and learner’s support. Students are responsible for their learning processes, including formative evaluation of both processes and outcomes. Especially for the assessment of performances in authentic situations, self-evaluation as well as peer assessment are highly complex in terms of setting the right criteria, judgement of performances and creation of meaningful feedback to control subsequent learning. Students need explicit training in self- and peer-assessment skills in order to reach an acceptable level of learning and performance (Sluijsmans, Dochy, & Moerkerke, 1999). 3.4. Community centred design Technology can drastically alter the social structure of schools. Computernetworked environments give opportunities for socially mediated and distributed learning and make it possible for students to interact and participate in more varying ways than in traditional classroom settings. Rather than organising the system like a factory with groups of students arriving all at the same time and getting the same kind of information to process, schools will be far more like a service-company with tasks distributed over its different members. According to Hakkarainen (2001), ‘There is a growing body of evidence that cognitive diversity and distribution of expertise promote knowledge advancement and cognitive growth. Distribution of cognitive efforts allows the community to be more flexible and achieve better results than otherwise would be possible’ (p. 9). Methodological differences and even functions between education and training, or learning in and out school will gradually fade. Kearsly and Schneiderman (1998) refer to ‘engaged learning’ that implies a group or team context, a project-based curriculum, and an outside (authentic) focus. Opening schools implies turning learning environments into community environments. Finding ways to construct and support teacher communities and wider educational professional communities, including parents, experts, principals, (Dillemans et al., 1998) needs investigating community building processes. Schwier (1999) states: ‘‘Emerging approaches to developing rich learning environments combine multimedia, computer mediated communication, and a host of interactive strategies to connect people in varied and robust ways. But traditional understandings of learning environments and interaction usually stop short of the kind of engagement that will allow learning communities to form’’ (p. 282). Dillenbourg (2000) proposes ‘the culture’ as a key answer. In recent theories, learning is described as the process of becoming a member of the community and acquiring skills to communicate and act according to its socially negotiated forms (Lave & Wenger, 1991). Learning takes place in a participation framework and is

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distributed among co-participants. The development of competencies is not only related to the nature of an individual’s knowledge structure but also to relevant formal and informal cultural knowledge in an expert community or network.

4. Conclusion There is a clear evolution in the domain and processes of design. While in earlier years educational methods and later on audio-visual media were targets of design, the rise of computers necessitated profound adaptation. As evidenced by the stateof-the-art in research, computers in education were designed to adapt instructional objectives, content and methods to the individual learner. Intelligent Tutoring Systems (ITS) were the ultimate tools for individualisation, though their design, development and implementation were less successful than expected (De Corte, Verschaffel, & Lowyck, 1996). The ‘personal’ computer was shortening days. The interplay between theories of collaborative learning, telecommunication, and community-based education paved the way for the design and organisation of more hybrid, collaborative learning environments. This mix of ambition and complexity, of external structure and self-regulation, of curriculum and co-construction of knowledge necessitates brand new visions on and approaches of design. However, in line with the recent theories of collaborative learning, designers do not focus directly on programmes, methods or tools, but rather on more complex realities, like learning environments. Recently, this concept has begun opening and broadening into the direction of learning ‘communities’ (Schwier, 1999) and ‘virtual’ environments are considered as a new generation of computer-based educational systems (Dillenbourg, 2000). The challenge in the design of virtual environments is to explore, understand and integrate different new communication functions in a pedagogically relevant way. Recent models of design reveal an increased complexity at the meso-level of design. This means that design activities and procedures are focused on synergy between learning theories, learning environment components and actors. Consequently, learning environments need to be designed as a package, not as a cluster of isolated factors. The trend is toward integration of different tools. It might be expected for instance that each student will have its own laptop that can be logged to the network at school and in the home. This implies that the issue of technology as such will gradually disappear, since technology will be embedded in the environment as a natural and not as a dominant component. Not the technology itself, but its educational use becomes predominant as has been documented through the history of educational technology. In the beginning of the art of printing or of the appearance of desk calculators, possible negative or positive effects of using these technologies were questioned. After a period of adaptation the use of these technologies rather than the technology itself became the important research topic. The basic idea will be that whatever we do or want to do in an educational setting, some kind of technology can be used. Technology becomes a mere tool for learning, embedded in sound methods and suitable content.

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Along with the increasing complexity of learning environments or even learning communities, all actors play an important role. While in earlier design endeavours the teacher, instructional designer or instructional agent was a central actor, guiding the activities of learners from the perspective of an external instance, nowadays the interplay between all actors is crucial: teachers, designers, peers, teams. Design is at the organisational level, managing the complexity of learning with the help of design strategies in which the learner plays an increasingly important role. Participatory design reflects a systemic approach to designing (Wilson, 1999) while the boundaries between users and designers seem to blur. The former ‘end-users’ become codesigners of their learning environment, which calls for a systematic knowledge about learning and instruction. Instructional design changed interest from a linear and externally controlled design, development and implementation towards an iterative, self-regulated and systemic endeavour. To alter the field of ‘instructional’ design into validated strategies for building a ‘learning community’ seems to be the challenge of further research. The building blocks are already partially available and they need to be integrated into a new systems design or, in other words, into a new architecture of learning and learning support.

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