Guiding students’ online complex learning-task behavior through representational scripting

Slof, B., Erkens, G., Kirschner, P. A., Jaspers, J. G. M., & Janssen, J. (2010). Guiding students’ online complex learning-task behavior through representational scripting. Computers in Human Behavior, 26(5), 927-939. doi:10.1016/j.chb.2010.02.2007This study investigated the effects of representational scripting on students’ collaborative performance of a complex business-economics problem. The scripting structured the learning-task into three part-tasks, namely (1) determining core concepts and relating them to the problem, (2) proposing multiple solutions to the problem, and (3) coming to a final solution to the problem. Each provided representation (i.e., conceptual, causal, or simulation) was suited for carrying out a specific part-task. It was hypothesized that providing part-task congruent support would guide student interaction towards better learning-task performance. Groups in four experimental conditions had to carry out the part-tasks in a predefined order, but differed in the representation they received. In three mismatch conditions, groups only received one of the representations and were, thus, only supported in carrying out one of the part-tasks. In the match condition, groups received all three representations in the specified order (i.e., representational scripting). The results indicate that groups in the match condition had more elaborated discussions about the content of the knowledge domain (i.e., concepts, solutions and relations) and were better able to share and to negotiate about their knowledge. As a consequence, these groups performed better on the learning-task. However, these differences were not obtained for groups receiving only a causal representation of the domain

Fostering complex learning-task performance through scripting student use of computer supported representational tools

This study investigated whether scripting student use of computer supported representational tools fostered students’ collaborative performance of a complex business-economics problem. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely (1) determining core concepts, (2) proposing multiple solutions, and (3) coming to a final solution. The representational tools facilitated students in constructing specific representations of the domain (i.e., conceptual, causal, or mathematical) and were each suited for carrying out the part-task demands of a specific phase. Student groups in four experimental conditions had to carry out all part-tasks in a predefined order, but differed in the representational tool(s) they received during their collaborative problem-solving process. In three mismatch conditions, student groups received either a conceptual, causal, or simulation representational tool which supported them in only carrying out one of the three part-tasks. In the match condition, student groups received the three representational tools in the specified order, each matching the part-task demands of a specific problem phase. The results revealed that student groups in the match condition constructed more task-appropriate representations and had more elaborated and meaningful discussions about the domain. As a consequence, those student groups performed better on the complex learning-task. However, similar results were obtained by student groups who only received a representational tool for constructing causal representations for all part-tasks

Fostering complex learning-task performance through scripting student use of computer supported representational tools

Slof, B., Erkens, G., Kirschner, P. A., Janssen, J., & Phielix, C. (2010). Fostering complex learning-task performance through scripting student use of computer supported representational tools. Computers & Education, 55(4), 1707-1720.This study investigated whether scripting student use of computer supported representational tools fostered students’ collaborative performance of a complex business-economics problem. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely (1) determining core concepts, (2) proposing multiple solutions, and (3) coming to a final solution. The representational tools facilitated students in constructing specific representations of the domain (i.e., conceptual, causal, or mathematical) and were each suited for carrying out the part-task demands of a specific phase. Student groups in four experimental conditions had to carry out all part-tasks in a predefined order, but differed in the representational tool(s) they received during their collaborative problem-solving process. In three mismatch conditions, student groups received either a conceptual, causal, or simulation representational tool which supported them in only carrying out one of the three part-tasks. In the match condition, student groups received the three representational tools in the specified order, each matching the part-task demands of a specific problem phase. The results revealed that student groups in the match condition constructed more task-appropriate representations and had more elaborated and meaningful discussions about the domain. As a consequence, those student groups performed better on the complex learning-task. However, similar results were obtained by student groups who only received a representational tool for constructing causal representations for all part-tasks

Towards a Framework for CSCL Research

Although collaborative learning, often supported by computer networks (widely called computer supported collaborative learning, or CSCL) is currently being implemented at all levels of education, it has not always proven to be the wonder-tool that educators envisioned and has often not lived up to the high expectations that educators had for it. In this introduction to the special issue on computer supported collaborative learning (CSCL), a framework for research on CSCL is presented. This framework is presented in the form of a 3 X 3 X 3 cube, with the dimensions Level of Learning (cognitive, social, and motivational), Unit of Learning (individual, group/team, and community) and Pedagogical measures (interactive, representational, and guiding). Based on this framework, the different contributions are discussed, and the empty cells - which should form the basis for further theoretical research – become evident

Toward a script theory of guidance in computer-supported collaborative learning

This article presents an outline of a script theory of guidance for computer-supported collaborative learning (CSCL). With its four types of components of internal and external scripts (play, scene, role, and scriptlet) and seven principles, this theory addresses the question how CSCL practices are shaped by dynamically re-configured internal collaboration scripts of the participating learners. Furthermore, it explains how internal collaboration scripts develop through participation in CSCL practices. It emphasizes the importance of active application of subject matter knowledge in CSCL practices, and it prioritizes transactive over non-transactive forms of knowledge application in order to facilitate learning. Further, the theory explains how external collaboration scripts modify CSCL practices and how they influence the development of internal collaboration scripts. The principles specify an optimal scaffolding level for external collaboration scripts and allow for the formulation of hypotheses about the fading of external collaboration scripts. Finally, the article points towards conceptual challenges and future research questions

Successfully carrying out complex learning tasks through guiding teams’ qualitative and quantitative reasoning

Slof, B., Erkens, G., Kirschner, P. A., Janssen, J., & Jaspers, J. G. M. (2012). Successfully carrying out complex learning tasks through guiding teams' qualitative and quantitative reasoning. Instructional Science, 40, 623-643. DOI: 10.1007/s11251-011-9185-2This study investigated whether and how scripting learners’ use of representational tools in a Computer Supported Collaborative Learning (CSCL)-environment fostered their collaborative performance on a complex business-economics task. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely defining the problem and proposing multiple solutions, followed by determining suitability of the solutions and coming to a definitive problem solution. Two tools facilitated construction of causal or mathematical domain representations. Each was suited for carrying out the part-task demands of one specific problem-solving phase; the causal was matched to problem-solution phase and the mathematical (in the form of a simulation) to the solution-evaluation phase. Teams of learners (N = 34, Mean age = 15.7) in four experimental conditions carried out the part-tasks in a predefined order, but differed in the representational tool/tools they received during the collaborative problem-solving process. The tools were matched, partly matched or mismatched to the part-task demands. Teams in the causal-only (n = 9) and simulation-only (n = 9) conditions received either a causal or a simulation tool and were, thus, supported in only one of the two part-tasks. Teams in the simulation-causal condition (n = 9) received both tools, but in an order that was mismatched to the part-task demands. Teams in the causal-simulation condition (n = 7) received both tools in an order that matched the part-task demands of the problem phases. Results revealed that teams receiving part-task congruent tools constructed more task-appropriate representations and had more elaborated discussions about the domain. As a consequence, those teams performed better on the complex learning-task

The Effects of constructing domain-specific representations on coordination processes and learning in a CSCL-environment

Slof, B., Erkens, G., & Kirschner, P. A. (2012). The effects of constructing domain-specific representations on coordination processes and learning in a CSCL-environment. Computers in Human Behavior, 28, 1478-1489. doi:10.1016/j.chb.2012.03.011This study examined the effects of scripting learners’ use of two types of representational tools (i.e., causal and simulation) on their online collaborative problem-solving. Scripting sequenced the phase-related part-task demands and made them explicit. This entailed (1) defining the problem and proposing multiple solutions (i.e., problem-solution) and (2) evaluating solutions and coming to a definitive solution (i.e., solution-evaluation). The causal tool was hypothesized to be best suited for problem solution and the simulation tool for solution evaluation. Teams of learners in four experimental conditions carried out the part-tasks in a predefined order, but differed in the tools they received. Teams in the causal-only and simulation-only conditions received either a causal or a simulation tool for both part-tasks. Teams in the causal-simulation and simulation-causal conditions received both tools in suited and unsuited order respectively. Results revealed that teams using the tool suited to each part-task constructed more task appropriate representations and were better able to share and negotiate knowledge. As a consequence, they performed better on the complex learning-task. Although all learners individually gained more domain knowledge, no differences were obtained between conditions

Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment

This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs. online discussion) in computer-supported collaborative learning (CSCL) on student knowledge construction. Students (N = 87) were first randomly assigned to the two information presentation conditions to work individually on a case-based assignment in IDLM. Students who received information during learning task clusters tended to show better results on knowledge construction than those who received information only before each cluster. The students within the two separate information presentation conditions were then randomly assigned to pairs to discuss the outcomes of their assignments under either the personal discussion or online discussion condition in CSCL. When supportive information had been presented before each learning task cluster, online discussion led to better results than personal discussion. When supportive information had been presented during the learning task clusters, however, the online and personal discussion conditions had no differential effect on knowledge construction. Online discussion in CSCL appeared to compensate for suboptimal timing of presentation of supportive information before the learning task clusters in IDLM