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., May-2012, In : Instructional Science. 40, 3, p. 623-643 21 p.

Research output: Contribution to journalArticleAcademicpeer-review

  • B. Slof
  • G. Erkens
  • P. A. Kirschner
  • J. Janssen
  • J. G. M. Jaspers

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.

Original languageEnglish
Pages (from-to)623-643
Number of pages21
JournalInstructional Science
Issue number3
Publication statusPublished - May-2012


  • Complex learning-tasks, Computer-supported collaborative learning, Qualitative and quantitative representations, Representational scripting, Learner interaction, REPRESENTATIONS, VISUALIZATION, CONSTRUCTION, DISCOVERY, WORK, INSTRUCTION, KNOWLEDGE, FRAMEWORK, COGNITION, GUIDANCE

ID: 2247023