Research on computer-supported collaborative learning has shown that students need support to benefit from collaborative activities. While classical collaboration scripts have been effective in providing such support, they have also been criticized for being coercive and not allowing students to self-regulate their learning. Adaptive collaboration support, which would provide students with assistance when and where they need it, is a possible solution. However, due to limitations of natural language processing, the development of adaptive support based on an analysis of student dialogue is difficult. To facilitate the implementation of adaptive collaboration support, we propose to leverage existing intelligent tutoring technology to provide support based on student problemsolving actions. The present paper gives two examples that demonstrate this approach and reports first experiences from the implementation of the systems in real classrooms. We conclude the paper with a discussion of possible future developments in adaptive collaboration support.Keywords Collaboration scripts . Adaptive collaborative learning systems . Intelligent tutoring While research on collaborative learning has generally shown that student interaction can increase group performance and individual learning outcomes, these positive effects are not always found (cf. meta-analysis by Lou et al. 2001). Often students show unequal engagement in the collaborative learning activity; a few group members take responsibility for the problem-solving, while others engage in social loafing and are not motivated to interact with their partners (e.g. O'Donnell 1999). Even if students are engaged in the interaction, they might not show the type of collaborative behaviour that is positively related to learning. For instance, students often answer a partner's question by merely telling
Research on collaborative e-learning has often shown the effectiveness of students’ interaction on their group performance and their individual learning outcomes. However, these positive effects cannot always be found. Collaboration assistance such as pre-collaboration training and collaboration scripts have been shown to support student interaction and problem-solving. When developing assistance for collaboration, teachers and designers must make decisions concerning the processes the support should target, the timing of support, and the interplay of support on multiple levels. The framework we introduce in this book chapter describes these dimensions in detail. We present advantages and disadvantages of different design options, and give an example from our own research to exemplify the design of an e-learning environment that provides collaboration support. We discuss how the circumstances of any particular learning situation might influence which type of support is optimal, and conclude the book chapter with a discussion of possible future developments.
While research has generally shown that collaboration may facilitate student learning in mathematics, such positive effects are not always found. We argue that the effectiveness of collaboration may depend on the type of knowledge the instruction targets: The interaction with a partner can slow down students and may thus decrease the amount of practice necessary for procedural skill fluency. On the other hand, collaboration could be particularly useful for conceptual knowledge acquisition, as here, the elaborative meaningmaking activities ascribed to collaboration may facilitate learning. To evaluate the differential effects of collaborative learning, we compared four conditions: individual versus collaborative learning with procedural instruction, and individual versus collaborative learning with conceptual instruction. The study results support our hypotheses: Students who learned individually showed higher test scores in a procedural far transfer test. However, a combination test requiring both knowledge types revealed a positive impact of collaboration on students' conceptual knowledge acquisition.
CSCL includes a wide range of scenarios that integrate individual and collaborative learning. Scripts have repeatedly proven useful for guiding learners to engage in specific roles and activities in CSCL environments. The effective mechanisms of scripts in stimulating cognitive and collaborative processes, however, are not yet well understood. Moreover, scripts have been shown to be somewhat inflexible to variations in needs across individual learners, specific groups, and classroom constellations. In this symposium, we present research on how scripts impact sociocognitive processes. The symposium additionally focuses on how CSCL environments can be orchestrated through flexible scripts that adapt to meet the special requirements at the classroom, small group, and individual levels. Orchestrating learning activities on the social and the cognitive level to foster CSCL CSCL covers a range of scenarios in which learners both interact with each other supported by technology and engage in phases of individual learning activities, e.g., computer-mediated learners individually access specific resources before communicating through an asynchronous discussion board with each other (Dillenbourg & Fischer, 2006). But learners seem to rarely draw on CSCL's potential to engage in specific learning activities both on the cognitive and the social level. Hence, CSCL often benefits from socio-cognitive structuring, for example, in the form of scripts that guide learners' interactions (Fischer, Kollar, Mandl, & Haake, 2007). While scripts generally aim to facilitate specific socio-cognitive learning activities, scripts may have different foci and granularities leading researchers to distinguish between macro-and micro-scripts (e.g., Dillenbourg & Jermann, 2007; Kobbe et al., in press). Micro-scripts focus on specific activities of learners and may, for instance, prompt learners to build their arguments in a specific way or instruct students how to collaborate effectively. Macro-scripts rather support the teacher to implement CSCL scenarios within the classroom orchestrating individual and collaborative learning phases (e.g., by suggesting individual preparation before entering discussion). There is some need to better understand how micro-and macro-scripts can be tuned to orchestrate learning activities on the social and the cognitive level to foster CSCL. First, to understand how and when CSCL should encompass collaborative and individual learning activities, the effects of scripts on processes and outcomes of collaborative and individual computer-supported learning need to be investigated. Second, to understand how scripts should orchestrate learning activities on the social and the cognitive level, macro-scripts should be investigated that guide learners through the different individual and collaborative learning activities.
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