B—Modelling and simulation

B.1 The basic model (8 hours)
Students are expected to use a range of standard spreadsheet software in a variety of ways to create models.
There is no need to purchase additional specialist software.

Assessment statement Obj Teacher's notes
B.1.1 Define the term computer modelling. 1
B.1.2 Identify a system that can be modelled. 2 Simple examples would involve
financial planning, population
growth, climate change, building
design, engineering design, etc.
Other situations could be modelling
a game such as checkers or Mancala.
B.1.3 Identify the variables required to
model a given system.
2 In an examination students will not
be expected to identify more than
four variables in a given system.
AIM 4 Applying thinking skills to
identify variables and resolve a
specified problem.
B.1.4 Describe the limitations of computer
(mathematical) models.
2 In many situations it is not possible
to know all of the variables involved.
AIM 9 An appreciation of
the limitations of computer
(mathematical) models.
MYP Mathematics: forms of
numbers, algebra—patterns and
sequences, logic, algorithms.
B.1.5 Outline sensible grouping for
collections of data items, including
sample data.
2 For example, if age, height and
weight are recorded for each person,
group these as individual cells in a
row in a table, or as items in parallel
lists.
MYP Mathematics: forms of
numbers, algebra—patterns and
sequences, logic, algorithms.
B.1.6 Design test-cases to evaluate a model. 3 MYP Design cycle.

Computer science guide


Syllabus content

Assessment statement Obj Teacher's notes
B.1.7 Discuss the effectiveness of a test-case
in a specified situation.
3
B.1.8 Discuss the correctness of a model
by comparing generated results with
data that were observed in the original
problem.
3 AIM 6 Develop logical and critical
thinking to discuss the correctness
of a model.

B.2 Simulations (14 hours)
Assessment statement Obj Teacher's notes
B.2.1 Define the term simulation. 1
B.2.2 Explain the difference between a
model and a simulation.
3
B.2.3 Describe rules that process data
appropriately and that produce results.
2 Rules may be presented as
mathematical formulae, pseudocode
algorithms, tables of input and
output values (conversions), or in
any other clear, understandable and
specific format (eg a detailed English
description).
MYP Mathematics: forms of
numbers, algebra—patterns and
sequences, logic, algorithms.
B.2.4 Discuss rules and data representations
and organization.
3 Students are expected to make
critical comments, for example
whether data has been sensibly
organized and whether rules have
been described correctly.
MYP Mathematics: forms of
numbers, algebra—patterns and
sequences, logic, algorithms.
B.2.5 Construct simple models that use
different forms of data representation
and organization.
3 Students are expected to be familiar
with spreadsheets in the SL/HL
paper 2.
Students are expected to have
experience of other modelling
software.
MYP Mathematics: forms of
numbers, algebra—patterns and
sequences, logic, algorithms.

Computer science guide


Syllabus content

Assessment statement Obj Teacher's notes
B.2.6 Design test-cases to evaluate a
simulation program.
3 MYP Design cycle.
LINK Connecting computational
thinking and program.
B.2.7 Outline the software and hardware
required for a simulation.
2 MYP Technology.
B.2.8 Describe changes in rules, formulae
and algorithms that would improve the
correspondence between results and
observed data.
2 MYP Mathematics: forms of
numbers, algebra—patterns and
sequences, logic, algorithms.
MYP Design cycle.
LINK Connecting computational
thinking and program.
B.2.9 Construct examples of simulations that
involve changes in rules, formulae and
algorithms.
3 LINK Connecting computational
thinking and program.
B.2.10 Describe changes in data collection
that could improve the model or
simulation.
2 These changes could include ideas
such as collecting different more
data, or organizing and representing
data differently.
MYP Design cycle.
B.2.11 Discuss the reliability of a simulation
by comparing generated results with
data that were observed in the original
problem.
3 MYP Design cycle.
B.2.12 Outline the advantages and
disadvantages of simulation in a given
situation rather than simply observing
a real-life situation.
2
B.2.13 Discuss advantages and disadvantages
of using a simulation for making
predictions.
3 Discuss the social consequences
and ethical issues of the use of
simulations.
AIM 8 An awareness of the social
impacts and ethical considerations
of using computer systems.
AIM 9 An appreciation of the
increased accuracy of simulations as
computer systems develop.

B.3 Visualization (8 hours)
Assessment statement Obj Teacher's notes
B.3.1 Define the term visualization. 1
B.3.2 Identify a two-dimensional use of
visualization.
2
B.3.3 Outline the memory needs of 2D
visualization
2
B.3.4 Identify a three-dimensional use of
visualization.
2
B.3.5 Outline the relationship between
the images in memory and the 3D
visualization.
2 Students do not need to know all
the mathematical details but should
understand the concept of rendering
and be familiar with the terms wire-
framing, ray tracing, lighting, key
frame, mapping, texture.
B.3.6 Discuss the time and memory
considerations of 3D animation in a
given scenario.
3 In an examination the situation
would be clearly described.
AIM 9 An appreciation of the
implications on system resources
as 3D animation packages become
more complex.

HL Extension

B.4 Communication modelling and simulation (15 hours)
Assessment statement Obj Teacher's notes
B.4.1 Outline the use of genetic algorithms. 2 Genetic algorithms develop the
solution to a problem by starting
with a random set of solutions and
repeatedly using a subset of these to
generate a better one.
Students need to be familiar with
the term fitness function.
Examples are those where the
suitability of the solutions can be
measured, for example the travelling
salesman problem.

B.4.2 Outline the structure of neural
networks.
2 Students need to understand that
neural networks are based on the
knowledge of biological networks,
but they will not be examined on the
relationship. A simple block diagram
showing inputs, hidden units and
outputs and a brief is sufficient
detail.
LINK Connecting computational
thinking and program.
B.4.3 Compare applications that use neural
network modelling.
3 Examples include speech
recognition, optical character
recognition, natural language
processing.
B.4.4 Compare different ways in which
neural networks can be used to
recognize patterns.
3 The concepts of supervised and
unsupervised learning should be
applied to the examples above and
to any clearly explained example
presented in the examination, as
should the importance of genetic
algorithms.
MYP Mathematics: forms of
numbers, algebra—patterns and
sequences, logic, algorithms.
LINK Connecting computational
thinking and program.
B.4.5 Identify the key structures of natural
language.
2 The terms noun, verb, syntax and
semantics should be understood.
B.4.6 Discuss the differences between
human and machine learning when
related to language.
3 Using knowledge, such as the
syntax of a language, leads to an
appreciation of the difficulties
involved in machine language
learning. Students should be familiar
with the concept of cognitive
learning and the use of heuristics
and probabilities in machine
learning.
TOK How can a machine know how
it learns?
B.4.7 Outline the evolution of modern
machine translators.
2
B.4.8 Describe the role of chatbots to
simulate conversation.
2 Students should be encouraged to
use and analyse such standards as
Eliza, Alice and Jabberwacky (as well
as any more recent acknowledged
ones) to compare conversations.
AIM 9 An appreciation of the
possibilities associated with
continued developments in
computer systems.
MYP Technology: software such as
Alice.
B.4.9 Discuss the latest advances in natural
language processing.
3 AIM 9 An appreciation of the
possibilities associated with
continued developments in
computer systems.