TY - JOUR
T1 - Successfully carrying out complex learning tasks through guiding teams’ qualitative and quantitative reasoning
AU - Slof, Bert
AU - Erkens, Gijsbert
AU - Kirschner, Paul A.
AU - Janssen, Jeroen
AU - Jaspers, Jos
N1 - DS_Description: 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/s11251-011-9185-2
Print Issn:0020-4277
PY - 2012/5
Y1 - 2012/5
N2 - This 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.
AB - This 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.
KW - complex learning-tasks
KW - computer-supported collaborative learning
KW - qualitative and quantitative representations
KW - representational scripting
KW - learner interaction
KW - CSCL
U2 - 10.1007/s11251-011-9185-2
DO - 10.1007/s11251-011-9185-2
M3 - Article
VL - 40
SP - 623
EP - 643
JO - Instructional Science
JF - Instructional Science
SN - 0020-4277
ER -