Modelling for Systems Thinking
An important application of DynaLearn is learning systems thinking, which is the ability to quickly gain an understanding of dynamic systems. DynaLearn supports such education via a progression through the learning spaces, which are six complete modelling and simulation environments in which incrementally more model ingredients are available. These model ingredients correspond to terms in the conceptual vocabulary that are necessary to understand dynamic systems. The videos below explain why DynaLearn is appropriate for systems thinking, what new systems thinking vocabulary each learning space introduces, and how this new vocabulary can be used to learn about new aspects of dynamic systems. The models shown in the videos are in the domain of climate change (particularly the carbon cycle). A number of exercises are provided with the videos.
This video illustrates the necessity of systems thinking through a famous exercise about carbon emissions, and shows how conceptual modelling and simulation can aid in learning such thinking.
This video explains the how the learning spaces (LSs) aid the learning of systems thinking.
Learning space 1
This video explains how conceptual modelling (in LS1) aids in learning to identify the components that play a role in a system.
Learning space 2
This video explains how modelling in LS2 aids in distinguishing between the structural and behavioural components that play a role in the a system.
This video explains how modelling in LS2 aids in identifying the causal relations that govern a system.
Learning space 3
This video explains how modelling in LS3 allow the changes within a system to be predicted.
This video explains how models can be improved to prevent unintended simulation results.
Learning space 4
This video explains how modelling in LS4 aids in identifying the different types of causal relations that govern systems.
This video explains how modelling in LS4 aids in determine the relative strengths of processes in systems.
This video shows how modelling in LS4 helps to gain an understanding of processes in systems that seek an equilibrium.
This video shows how modelling in LS4 helps in choosing boundaries between systems and their environment.
This video provides a number of exercises to learn the meaning of the different types of causal relations in DynaLearn.
This video provides the answers to the exercises in the previous video.
This video explains how modelling in LS4 helps to gain an understanding of positive and negative feedback loops.
Modelling in the Learning Spaces
The videos below show how the systems thinking knowledge that is step-wise introduced above, can be applied to do full-fledged modelling exercises in each of the learning spaces. Particularly, the videos below show how ‘a liquid in a container’ can be modelled in the learning spaces in DynaLearn. In each learning space new aspects of the system are modelled.
This video shows the modelling of the container containing liquid, the quantities volume, diameter, height and reaction force of the container, the quantities amount, height, pressure and width of the liquid, and the causal relations between these quantities. The some quantities are given value assignments so that the trends of the quantities can be simulated.
This video shows the addition of quantity spaces for some quantities. As a consequence of these quantity spaces (and assigning values to them), the simulation can now predict how the quantities will change value over time (shown as a state graph and a value history).
This video shows the modelling of a flow of liquid from a tap into a container. The model focuses on fewer quantities (which now all have quantity spaces). The key feature at this learning space is the differentiation of processes (modelled as influences I+/I-), which are causes of change, and propagation of change (modelled as proportionalities P+/P-).
This video shows the modelling a liquid overflowing as a result of a container becoming full because of an open tap. This becomes possible in LS5 as a result of modelling sets of ingredients that are only active under certain conditions (called conditional knowledge).
This video shows how different initial situations and parts of domain theories can be modelled. This becomes possible because LS6 distinguishes between specific knowledge about systems (scenarios) and general knowledge (re-usable model fragments). Each model fragment indicates under which conditions it applies. In this model, the notions of a contained liquid, flow from a tap, and flow out of an outlet are modelled separately. In the scenario with a single container, each of the fragments applies once. However, in the scenario with three containers, the fragments apply three times (this shows the idea that knowledge can be applicable to multiple situations).
Modelling support functionality in DynaLearn
DynaLearn investigated different kinds of modelling support. Below the most distinguished versions of these types are shown. Note that some of them appear only as ‘proof of concept’ within DynaLearn.
The ‘What is‘ functionality provides explanations about model ingredients in terms of both the meaning of these ingredients as part of the modelled domain and their meaning as part of the systems thinking vocabulary.
The ‘How to‘ functionality provides explanations about how to perform particular tasks in the DynaLearn software, such as adding, altering and removing model ingredients and running simulations.
The ‘Why‘ functionality explains why certain simulations results are the way they are.
The ‘Diagnosis‘ functionality helps learners to troubleshoot their models by indicating how the model has to be changed in order to get the desired simulation results.
The ‘grounding‘ functionality helps learners in using the correct terminology by aligning the terms they use in their model to terms in Wikipedia.
Special Interaction Modes
DynaLearn investigated special modes of interaction. Below two of these are shown.
In the ‘Teachable Agent‘ interaction, the learner teaches a virtual character about a particular domain by building a model (which represents the characters knowledge). The learner can ask the character questions, and eventually send the character to the quiz master. The character answers questions based on the developed model. The character is graded based on the number of correctly answered questions.
Equivalent to the previous video, but with a larger model.
In the ‘Quiz‘ interaction, the learner answers multiple choice questions about a particular topic (based on a model developed by a teacher).
Basic software functionality
The videos below show basic operation functionality of DynaLearn.
This video shows how a learner can start a new model in a particular learning space in DynaLearn.
This video shows how a learner can save, load and close models in DynaLearn.
This section contains example materials that are used by DynaLearn’s educational partners to introduce their students to qualitative reasoning and to DynaLearn.
- Nuclear Power Plant (introduction to DynaLearn and QR) (Andreas Zitek, BOKU)
This document introduces students to the six learning spaces of DynaLearn and to the basic modeling ingredients of qualitative reasoning.In a short scenario, the impact of an accident in a nuclear power plant on the environment is sketched out. Then the six learning spaces and the basic modelling ingredients available are explained.Students are asked to identify the learning space and ingredients necessary to model the situation of the sketched out scenario.An accompanying presentation shows modelling approaches to the scenario in learning spaces 1-5.
- Introduction to QR (Richard Noble, UH)
Two sets of slides that present the basic ingredients and principles of QR modelling.The first set focuses on the approach and the ingredients of conceptual modelling. The second set describes the different types of relationships between quantities and how these can be used to describe and generate behaviour in simulations.
You can find the login screen to the repository here.
Glossary: Look at the glossary for an explanation of specific DynaLearn and Qualitative Reasoning terminology.
FAQ: In the FAQ you can find an overview of frequently asked questions.