Table 3 Types of modeling and simulation challenges and scaffolds

Challenges with identifying relevant entities and their interactions

Difficulty identifying the agents, their properties, and their behaviors; which properties a behavior depends on and which properties a behavior affects; and how different agents interact with each other

Modeling work done and energy consumed instead of speed of the rollercoaster; difficulty understanding relation between steepness and speed

Difficulty identifying types of environmental components (in this cases, gases) that are needed to model procedures like “breathe” and “eat”

Interviewer points out the aspects of the phenomena that need to be modeled; interviewer prompts students to think about the agents to be modeled, their properties and behaviors, and the interactions between agents and agents and their environment

Challenges with choosing correct preconditions

Difficulty in identifying and setting appropriate initial conditions and preconditions for different processes and actions

Difficulty understanding that modeling acceleration requires specifying an initial velocity

Difficulty understanding that a fish needs to be hungry and needs to have duckweed present to be able to eat

Prompt students to think about the preconditions necessary for certain functions/behaviors; encourage students to vary initial conditions and test outcomes

Systematicity challenges

Difficulty in methodical exploration; guessing and modifying the code arbitrarily instead of using the output behaviors to inform changes

Non-systematic exploration and testing of different turn angles to generate a triangle or circle

Lack of confidence about model being built; changing model arbitrarily in an attempt to correct errors

Encourage students to think about their goals, the starting points, and their plans of action

Challenges with specifying model parameters and component behaviors

Difficulty determining parameters for the visual primitive blocks in the C-World to produce measurable and observable outcomes and understanding individual effects of different components of a code segment on the behavior of the entire code segment

Difficulty choosing optimal input parameters to generate clearly visible outputs; confusion understanding effects of turn angle, speed up factor, and number of repeats on figure dimensions

Inability to specify outcomes when a condition is true and when it is not, for example, a fish dies when there is no oxygen

Prompt students to make changes in parameter values to produce clearly visible outputs; encourage testing outcomes by varying parameter values

Model verification challenges

Difficulty verifying and validating the model by comparing its behavior with that of the given expert model and identifying differences between the models

Difficulty comparing user and expert rollercoaster models; difficulty correlating model with simulation

Difficulty comparing user and expert fish tank models; difficulty correlating changes in the model and changes in user model output

Ask students to slow down the simulation to make agent actions more visible; point out the differences between the user and expert models