Succesvol probleemoplossen door deeltaakspecifieke ondersteuning [Succesfull problem-solving through providing part-task congruent support]

Slof, B., Erkens, G., & Kirschner, P. A. (2010, June). Succesvol probleemoplossen door deeltaakspecifieke ondersteuning [Succesfull problem-solving through providing part-task congruent support]. Proceedings van de 37e Onderwijs Research Dagen 2010, Enschede, Nederland.Deze studie richtte zich op het onderzoeken van de effecten van het aanbieden van deeltaakspecifieke ondersteuning op het gezamenlijk oplossen van een complex bedrijfseconomisch probleem. De ondersteuning structureerde de probleemoplossingtaak in drie deeltaken, namelijk (1) vaststellen van belangrijke concepten en het relateren hiervan aan het probleem, (2) formuleren van meerdere oplossingen voor het gestelde probleem en (3) komen tot een definitieve oplossing voor het probleem. Daarnaast werd iedere deeltaak voorzien van een domeinspecifieke visualisatie (i.e., conceptueel, causaal of mathematisch) welke ieder geschikt was voor het uitvoeren van een specifieke deeltaak. De verwachting was dat de deeltaakspecifieke ondersteuning de leerling-interactie en zodoende het probleemoplossingproces op een gunstige wijze zou gaan beïnvloeden. Alle leerling-groepen in de vier experimentele condities voerden de opeenvolgende deeltaken uit, maar verschilden in de visualisatie die zij ontvingen. In de drie mismatch condities ontvingen de groepen slechts één van de visualisaties voor alle deeltaken en werden dus alleen ondersteund in het uitvoeren van één van de deeltaken. In de match conditie ontvingen de groepen alle visualisaties op een gefaseerde wijze; voor iedere deeltaak een deeltaakcongruente visualisatie. De resultaten tonen aan dat groepen in de match conditie meer deeltaakspecifieke interactie (i.e., concepten, oplossingen en relaties) hadden en meer succesvol waren in het oplossen van het gestelde probleem

Visualization of argumentation as shared activity

The use of argumentation maps in CSCL does not always provide students with the intended support for their collaboration. In this chapter we compare two argumentation maps from two research projects, both meant to support the collaborative writing of argumentative essays based on external sources. In the COSAR-project, the Diagram-tool with which students could specify positions, proarguments, con-arguments, supports, refutations and conclusions in a free graphical format to write a social studies essay, was highly appreciated by students and teachers, but did not result in better essays. In the CRoCiCL-project, the Debate-tool with which students could specify positions, proarguments, con-arguments, supports and refutations in a structured graphical format, meant to visualize the argumentative strength of the positions, resulted in better history essays. The difference in representational guidance between both tools might explain these differences in effects, with the Debate-tool stimulating students to attend to the justification of positions and their strengths

Cognitive effects of argument visualization tools

External representations play a crucial role in learning. At the same time, cognitive load theory suggests that the possibility of learning depends on limited resources of the working memory and on cognitive load imposed by instructional design and representation tools. Both these observations motivate a critical look at Computer-Supported Argument Visualization (CSAV) tools that are supposed to facilitate learning. This paper uses cognitive load theory to compare the cognitive efficacy of RationaleTM 2 and AGORA

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

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

Design and effects of representational scripting on group performance

This study investigated the effects of representational scripting on non-expert student learning while collaboratively carrying out complex learning-tasks. The premise underlying this research is that effective cognitive activities would be evoked when complex learning-tasks are structured into phase-related part-tasks and are supported by providing students with part-task-congruent external representations for each phase; representational scripting. It was hypothesized that this approach would lead to increased individual learning and better complex learning-task performance. In groups, 96 secondary education students worked on a complex business-economics problem in four experimental conditions, namely one condition in which the groups received representations that were part-task-congruent for all three phases and three conditions in which the groups received one of these representations for all three phases (i.e., part-task-incongruent for two of the three phases). The results indicate that groups receiving part-task-congruent representations in a phased order performed better on the complex learning-task, though this did not result in increased individual learning

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

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