Simulation-Based Inquiry Learning and Computer Modeling: Pitfalls and Potentials. (June 2015)

• Record Type: Journal Article
• Authors: Mulder, Yvonne G.; Lazonder, Ard W.; de Jong, Ton
• Other Names: Alessi Stephen guest-editor.; Kopainsky Birgit guest-editor.
• Is Part Of: Simulation & gaming. Volume 46:Number 3/4(2015:Jun./Aug.)
• Page Start: 322
• Page End: 347
• Publication Date: 2015-06
• Subjects: inquiry learning -- learning environments -- model progression -- model-based learning -- problem solving -- scaffolding -- science education -- simulation-based learning -- system dynamics -- worked-out examples; Social sciences -- Study and teaching -- Periodicals; Simulation methods -- Periodicals; Game theory -- Periodicals; Sciences sociales -- Étude et enseignement -- Périodiques; Simulation, Méthodes de -- Périodiques; Jeu de rôle -- Périodiques; Éducation; Jeu d'entreprise; Jeu de simulation; Méthode de simulation; Sciences sociales; Théorie des jeux; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 003.3
• DOI: 10.1177/1046878115577159
• Languages: English
• ISSNs: 1046-8781

Abstract

Background. Inquiry learning environments increasingly incorporate simulation and modeling facilities. Students acquire knowledge through systematic experimentation with the simulations and express that knowledge in runnable computer models. Aim. Asinquiry andmodeling activities are new and demanding for students, support for learning is needed. This article reports three experimental studies that examine how students' inquiry and modeling activities can be supported. Need for support. Study 1 wasan empirical assessment of students' support needs. It compared a group of domain novices to two more knowledgeable reference groups in order to determine the novices' support needs. Model progression and worked examples. In Studies 2 and 3, the need for support was addressed bymodel progression (gradually increasing task complexity) andworked-out examples, examining the effect of those interventions on students' performance and learning. Results suggest positive effects due to both increasing model complexity and providing worked examples that show what the activities in each model progression phase entail and how they should be performed. Implications. The pattern of results across the three studies are discussed with regard to students' use of available resources, influence of prior knowledge, and the relationship between performance and learning.