Acquisition of Higher Order Knowledge by a Dynamic Modeling Environment Based on the Educational Concept of Self-Regulated Learning

  • Stefanie A. Hillen
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 17)


I aim to show that learning with this modeling based Educational Learning System (ELS) can accomplish the target of achieving higher order knowledge. The ELS is a system consisting of internal and external elements. The external prerequisites consist of technical and physical elements and the internal ones are shaped by the students pre-knowledge and the instructors teaching competencies including his/her social, emotional, and disciplinary knowledge necessary for teaching. The ELS is based on a theoretical framework of different theories and models such as concept mapping, elaboration of mental models, cognitive tool-approach, and self-regulated learning (SRL). Different features for visualization and modeling of the subject matter to be learned can be chosen by the students as well as the frequencies using the simulation feature to receive feedback to the model constructed. This enables the students to work self-regulated because of the feedback of the system, by providing the simulation results in desired graphical or analytical representation formats. The notation of the ELS, the symbols themselves are considered as an intuitive language because the symbols are connected to real world phenomena. It is assumed that the expression of knowledge is co-determined by the applied language. It is concluded that a less differentiated language does not hinder thinking but does not support thinking as a ‘cognitive tool’. Hence the hypothesis is: there are significant differences in the complexity of the expressed knowledge of the students using the notation in comparison to a control group using verbal protocols to express the knowledge acquired.


Mental Model Cognitive Conflict High Order Thinking Advance Organizer Cognitive Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.University of AgderKristiansandNorway

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