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The Teacher’s SMART Guide

To Choosing and Using Data Loggers

ave time and money making your datalogging purchases

ake light work of covering the syllabus

lleviate your anxiety about teaching practical lessons

eceive praise from parents, students and colleagues on your teaching ability

ransform your students from apathetic to enthusiastic

BY PHIL JONES
The Logical Interace

Copyright statement
You may print copies of this book, or send it as an electronic file to friends and colleagues provided no part of this
book is modified, altered or abbreviated and that such copies are not sold or used in any way for a commercial
purpose. Should you wish to use the material for commercial purposes you must seek permission from the copyright
holder. Queries should be forwarded to philjones@logint.com.au or by traditional mail to Phil Jones, The Logical
Interface, 96 Fowler Road, Illawong, NSW 2234, Australia.

Liability Disclaimer
The material contained in this book is general in nature and is not intended as specific advice on any particular
matter. The Logical Interface and the author expressly disclaim any and all liability to any persons whatsoever in
respect of anything done by any such person in reliance, whether in whole or in part, on this book.
Table of Contents

Table of contents .................................................................................... 2

About the author..................................................................................... 3

What is a data logger and how does it work? .............................................. 4

What are the different types of data loggers? ............................................. 4

What are sensors and what types of sensors are there? ............................... 5

How do you record from a data logger? ..................................................... 5

What are sample rates and sample numbers? ............................................. 5

How do I select the right data logger? ....................................................... 6

How much should I pay? .......................................................................... 8

Using data loggers to improve class participation ........................................ 9

Data logging activities to get you started. .................................................10

The SMART buyer’s guide ........................................................................18

The formula for a SMART decision ............................................................19

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About the Author

Phil Jones is passionate about science and science teaching.

At the age of twelve, he cleaned out his school banking account

to buy the parts to make a telescope. His parents were furious,
not only about the money, but also because they preferred him
to follow more “practical” pursuits.

This did not deter him. Phil went on to build his working telescope (which he later
donated to one of the schools he taught in) and pursued his interest in science.

He achieved a Bachelor of Science with Honours from Sydney University, and a Master
of Science with Honours from Macquarie University.

For four years, Phil was a researcher in bio-physics at Sydney University but kept
being drawn back to his main interest, teaching others to share his love of science.

He took a Diploma of Education at the end of his first science degree and worked in
schools in Australia, UK and Europe. Later, he moved on to teaching adults and to
teaching teachers how to transfer their knowledge and interest in science to their
students.

This led Phil to opening The Logical Interface (TLI) in 1986, a business dedicated to
supporting maths and science teachers in making their classes more relevant and
engaging. It sells interactive technology for use with science and maths classes, with
much of the software and devices being designed by the TLI design team, specifically
for the classroom.

Phil is a sought-after speaker at Science Teachers Association meetings around

Australia. He also runs technology workshops for teachers at universities, TAFE
colleges and schools.

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What is a data logger and how does it work?

Data loggers collect scientific data. For example, data loggers can record temperature,
pH, dissolved oxygen levels, position, force, heart rate, EKG, EEG, light intensity,
sound etc. They record changes in these values with time. Some data loggers can
measure only one quantity, others can be used to record many different types of data.

What are the different types of data loggers?

Data loggers can stand alone or work in conjunction with a computer.

Stand alone data loggers store data in their own internal memory and require
batteries for power. There are two kinds of stand alone data loggers:
o Those that have their own LCD to display data in a table, or graphical form.
Some have touch screens. Examples include Fourier Multilog Pro, Nova 5000,
and Pasco Spark
o Those that store data and then have to be attached to a computer to analyse it.

Other data loggers only work when they are connected directly to a computer through
the USB or serial port. These data loggers use the computer’s memory and screen to
collect and display data. e.g. TLI Ezilog USB. The introduction of mini notebooks and
ultra mobile PCs has made the USB logger very attractive.

Each type of logger has advantages and disadvantages which you will discover as you
read on.

Dedicated logger – does not require a computer. USB logger – requires a computer.

Mini computers make USB logging portable.

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What are sensors and what types of sensors are there?
Data loggers need sensors to collect data.

Sensors connect to data loggers through a cable. Some sensors, for example pH and
DO2, use electrodes for measurement.

Each sensor measures a particular quantity. There are specific sensors for
temperature, pH, heart rate, sound, etc. In more versatile data loggers you can use
two or more sensors at a time. Most educational data loggers have this ability. This
means you have flexibility in the experiments you can conduct with them. You will see
some experiments at the end of this guide which ask you to use two or more sensors.

How do you record from a data logger?

In the laboratory the most effective way to use a data logger is to connect it directly
to a computer and control it with software that comes with the logger.

In the field, it is more convenient to use a stand alone data logger to record data. The
amount of data you can collect is only limited by the memory of the data logger. For
more flexibility, you can take along a notebook computer, Ultra Mobile PC, or a PDA.
When the data has been recorded, it can be uploaded to the computer for further
analysis while you are still in the field.
This is fantastic for day-long field trips.

What are sample rates and sample numbers?

A data logger records its data against time. It must sample this data at regular
intervals. It can, for example, record data once per second (s -1), once per minute,
once per hour, or even 50,000 times s-1.

On the specifications of the logger, the “maximum sample rate” quoted is

always the number of samples recorded per second.

The number of samples recorded and the sample rate determine the duration of the
experiment. You could, for example, conduct an experiment at 10 samples s -1 and
record 1000 samples. Your experiment would then take 10,000 seconds.

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How do I select the right data logger?
To select a suitable data logger, ask yourself the following questions:

1. What do I want to measure?

The quantity you wish to measure will determine the sensor(s) you will need
to use with your data logger.
2. How many sensors do I need for an experiment?
Data loggers can have a number of inputs to allow more than one sensor to
be used. You should have at least two inputs for your data logger so two
sensors can be used together. See experiment examples at the end of the
book.
3. What sample rate will I need?
Many biology experiments only need to sample once every second, or even
once every 10 minutes. Many physics experiments require sampling rates of
20,000 samples s-1. If you require a data logger to work across all subject
areas, you will need to buy a logger with a sampling rate of at least 20,000
samples s-1.
4. How long does it take to collect data?
The sample rate and sample number you select determine the duration of the
experiment. The number of samples a data logger can collect depends on its
memory. Data loggers that connect directly to a computer such as the Ezilog
USB can collect large amounts of data at high sampling rates making them
ideal for physics experiments.
5. Will I have access to a computer while I conduct an experiment?
Direct connect data loggers that only work with a computer are generally
cheaper and more robust. If you plan to use your data logger only with a
computer then this type of data logger is most likely the best choice.
6. How sturdy do our data loggers need to be?
Most educational data loggers are not waterproof and will break if mistreated.
Stand alone data loggers are less sturdy than those that connect to a
computer. The most likely points of failure are LCDs, battery packs and
keypads.
Industrial loggers like Australian-made TPS loggers are better for heavy duty
field experiments. These are a good alternative to the educational loggers.
7. Which Data Logger is best for Field Work.
The decision on which data logger to buy is often based upon their suitability
for field work. Biology teachers look for portability and robustness so that they
can take the logger into the field.

Data loggers are designed to record data against time and are often used by
researchers and industry for many applications including remote monitoring of
environmental conditions, however electronic meters are ideal when you only
wish to take individual readings and in most cases provide the perfect solution
for field work. Electronic meters are accurate, very portable, easy to maintain
and calibrate and consequently provide a more suitable alternative to data
loggers for conducting measurements in the field.

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 See the SMART buyer’s guide at the end of this book to help you
compare the data loggers you are looking at and make your decision.

Some purchasing traps

Trap 1: Sensors will be your biggest expense and some brands of data loggers will
only allow you to use their own brand of sensors.

However, you will find other brands that allow you to use a number of brands
of sensors. These brands provide greater flexibility in that you are not locked
into one brand of sensor and can mix and match your data loggers and
sensors if you want to.

For cost-effectiveness and long-term flexibility, avoid data loggers

that will only receive data from their own brand of sensors.

Trap 2: Data loggers are often described as being 8 bit (binary digit), 10 bit, 12 bit
and so on. This tells you the resolution of the data logger. For example, an 8
bit data logger can resolve the measured quantity to 28 = 256 parts, a 10 bit
to 210 = 1024 parts and so on. The greater the number of bits the greater the
resolution of the data logger. Some sensors (for example the TLI Mass-
Sensor balance) require at least a 12 bit resolution to satisfy their accuracy.
That is the balance is 0-300 g with a 0.1 g resolution – it can resolve mass to
3000 parts.

For greater flexibility you need to look for data loggers with at least a
12 bit resolution.

The experiments at the end of this document refer to a number of suitable data
loggers. This will be of assistance in selecting a data logger to suit your needs.

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How much should I pay?
Price doesn’t always indicate a better solution. Frequently you will find that the most
expensive data loggers are complicated and have features you may not use in a
classroom. (This is much the same as the differences between an expensive
professional digital camera and the simple point-and-shoot variety most people who
are not professionals prefer.)
If you have not used data loggers before, you will find simple data loggers that do not
have memory, or LCD, are less expensive, more robust, and provide an ideal solution
if you have ready access to computers.

The biggest expense will be the sensors, so always check the sensor prices as well as
the logger prices.

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Using data loggers to improve class participation.
A generation ago, 70 per cent of students were what researchers Dunn and Price
called “structured auditory learners”, meaning they learnt by listening to the teacher.
Nowadays less than 30 per cent learn this way.

Students today have been shaped by technology almost from birth. They were playing
computer games before they went to school. They carry mobile phones (with
cameras) and MP3 players wherever they go. They spend hours chatting on the
internet and playing virtual reality games.
They are multi-modal communicators, living in a highly visual hyperlinked world
where they are 20 seconds away from any information they seek. They absorb
information from multiple sources, and for them facts come and go. In their world,
virtual reality has replaced scientific method, and text has replaced language as it
used to be.
What this means - and you know better than most – is students today are cynical and
easily bored.
So how do you ensure they don’t “dip out” before they even “dip in” to your class?
The good news about this generation is they are experiential and participative, and
endlessly curious about “stuff”. They love doing things and working together, and
have a sixth sense about using technology.
So you won’t need to worry about teaching them how to use data loggers or
encouraging them to accept the technology. If you are comfortable about
relinquishing some control, they’ll probably be able to teach you what to do with
them! And if you set them a problem and give them the tools, they will find the
answers in ways you might never have thought about.
“So tell me if the temperature around the room is constant and where’s the best place
to sit?” will have them out of their seats and shaking their booties. They’ll be so
involved they won’t even hear the end-of-class bell.
Don’t be surprised if you hear them tell their friends the class was “a bomb”.

Unconvinced? Not comfortable with the technology?

Some TAFE Colleges run workshops for teachers or you can email Phil at
philjones@logint.com.au to enquire about running a workshop at your
school.

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Data logging activities to get you started.
The following activities are designed to help you kick start your data logging. They are
deliberately abbreviated with just the essential points to illustrate how a data logger
can be used to acquire data for a range of activities. Detailed experiments for
chemistry, biology and physics are available from The Logical Interface. See
www.logint.com.au .

Exercise 1:
Using one or two sensors to examine DC and AC voltage from a school
power pack (transformer), hand crank generator and two coil
transformer.

Note: The data logger used for this exercise must have at least a sample rate of 20,000 s -1 It
can be either stand alone, or a computer based logger, however the experiment is best done
with the logger connected to a computer. It must have at least two in-puts:
e.g.TLI Ezilog USB, Nova or Fourier Multilog Pro

Part 1: Output from a power supply

1. Connect a suitable voltage sensor to the AC voltage of a school 0-12 V power
supply.
2. Set your data logger to 25 samples s-1 and describe the appearance of the data
on your graph.
3. Set the sampling rate to 20,000 samples s-1 and repeat the experiment. Using
an appropriate method measure the period of the data obtained.
4. Repeat this experiment for the DC output.

Part 2: Output from a generator

1. Connect a suitable voltage sensor to a hand-crank generator.
2. Set the sampling rate to 20,000 samples s-1.
3. Start sampling and crank your generator at varying speeds.
4. Examine the effect of speed on amplitude and frequency of the AC generated.

Part 3: Output from a transformer – two sensors are required for this
activity
1. Connect a suitable voltage sensor (+/- 10 V) to the primary coil of a two coil
transformer connected to a power supply.
2. Connect a second voltage sensor to the secondary coil of the transformer
connected to a power pack.
3. Set the sampling rate to 20,000 samples s-1.
4. Start sampling and note period, phase and amplitude of the primary and
secondary voltage.

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Exercise 2:
Using a single sensor to measure heart rate.

Note: The data logger used for this exercise must have at least a sample rate of 50 s -1 It can
be either stand alone, or a computer based logger, however the experiment is best done with
the logger connected to a computer.Suitable loggers include: Ezilog USB, Nova or Fourier
Multilog Pro.

Connect a heart rate sensor to your data logger. If the sensor supplied uses a
piezoelectric crystal to monitor your heart rate, place this tightly on your thumb as
shown. If it is of the clip type, clip it to your ear lobe, or finger.

Set your data logger to 25, or 50 samples s-1 and observe the appearance of the data
on your graph. Determine your heart rate by:

1. measuring the period between pulses and

2. counting the number of pulses within a given time.

If your data logger has a high sampling rate set

the sampling rate to 20,000 samples s-1 and
repeat the experiment. Observe and describe the
features of the graph.

Output from a piezoelectric heart

rate sensor at 20,000 samples s-1

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Exercise 3:
Using a single sensor to examine pH changes in Coca-Cola™ caused by
an antacid.

Note: The data logger used for this exercise must have at least a sample rate of 50 s -1 It can
be either stand alone, or a computer based logger. However, the experiment is best done with
the logger connected to a computer. Suitable loggers include: Ezilog USB, Fourier Nova or
Multilog Pro

1. Place a small amount of Coca-Cola™ in a beaker (250 ml).

2. Connect a pH electrode to a data logger and connect the data logger to a
computer.
3. Place the pH electrode into the Coca-Cola™.
4. Collect data at a sampling rate of around 10 samples s-1 for around 90 s.
5. Start your data logger collecting data.
6. Place an Alka-Seltzer™ tablet into the Coca-Cola™.
7. Observe the changes on the computer screen.
8. Estimate the change in pH and the time taken to reach the new pH level.
9. From the values obtained in 8 estimate the rate of change in pH.

Repeat this example with another brand of antacid. Experiment with crushed tablets
and uncrushed to see if this makes a difference.

Exercise 4:
Using a single sensor to examine sound waves from a speaker.

Note: The data logger used for this exercise must have at least a sample rate of 20,000 s -1 It
can be either stand alone, or a computer based logger. However, the experiment is best done
with the logger connected to a computer.Suitable loggers include: Ezilog USB, Fourier Nova or
Fourier Multilog Pro.

1. Use a dual channel signal generator to generate beats.

2. Set one channel to approximately 450 Hz and the second
to 440 Hz.
3. Place a microphone near the speakers to record the
sound.
4. Set your sampling rate and number of samples to
appropriate settings (20,000 sample s-1 and 100,000
samples).
5. Record the data and determine the beat frequency from your graph.

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Exercise 5:
Using two temperature sensors to record heat loss.

Note: The data logger used for this exercise must have at least a sample rate of 10 s -1 It can
be either stand alone, or a computer based logger. However, the experiment is best done
with the logger connected to a computer. Suitable loggers include: Ezilog USB, Fourier Nova
or Fourier Multilog Pro.

1. Place warmed water (150 ml) into a glass beaker and the same quantity into a
polystyrene cup.
2. Place a temperature sensor into each container and connect the sensors to a
data logger and connect the logger to a computer.
3. Start logging at around 1 to 10 samples s-1 for ten minutes.
4. Determine the rate of change of temperature for both the beaker and
polystyrene cup.
5. Comment on the difference in rates. Was the difference as great as you
thought? What is the main method of heat loss for the beaker and polystyrene
cup?
6. Repeat steps 1 to 5 using a polystyrene cup with a lid in place of the beaker.
The lid must be a typical coffee cup lid, with a hole through which we can place
the temperature sensor.

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Exercise 6: Titration.
Using two sensors to record volume (mass) and pH changes during a
titration.

Note: The data logger used for this exercise must have at least a sample rate of 10 s -1 It can
be either stand alone, or a computer based logger. However the experiment is best done with
the logger connected to a computer. Suitable loggers include: Ezilog USB, Fourier Nova or
Fourier Multilog Pro.

1. Connect a Mass Sensor Balance and a pH

sensor to your data logger.
2. Set your data logger to an appropriate
sampling rate (10 samples s-1) and number
of samples.
3. Using appropriate equipment, record both
volume and pH changes during a titration
(see below for comments on equipment
setup).
4. Allow the solution to flow at a steady rate.
5. Start the logger.
pH and mass for a titration using
6. Monitor the change in pH and volume.
Ezilog USB
7. Plot a graph of pH vs volume.

Note: This experiment should be performed with the beaker into which you are
titrating placed on a magnetic stirrer, and the solution should be titrated from a
container on the Mass-Sensor Balance. This allows the solution to mix and the pH to
equilibrate more quickly. Failure to do this will mean that the true pH will fall behind
the volume change.

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Exercise 7: Ohm’s Law.
Using two sensors to verify Ohm’s Law.

In this activity a voltage and current sensor are used to collect single readings
(snapshots). There are many experiments where you will want to plot one variable
against another. This activity is an example. The ultimate aim is to verify Ohm’s Law
so we wish to plot current against voltage. Note that neither variable is to be plotted
against time.

Note: The data logger used for this exercise must be capable of taking single readings, or
snapshots. Suitable loggers include: Ezilog USB, Fourier Nova or Fourier Multilog Pro.

1. Connect a 5 Ohm resistor in a series circuit to a rheotstat as a potential divider

across a true DC source (for example, a battery).
2. Connect a voltage sensor in parallel with the resistor and a current sensor in
series.
3. Connect the sensors to your data logger.
4. Adjust the rheostat to give 1 volt and take a single (snapshot) reading of the
current and voltage.
5. Repeat for a number of voltage settings.
6. From the table of data collected create a plot of current vs voltage.

Exercise 8: Fermentation of sucrose.

Using one sensor to measure mass change during fermentation.

Note: The data logger used for this exercise must have at least a sample rate of 10 s -1 It
can be either stand alone, or a computer based logger. Suitable loggers include: Ezilog USB,
Fourier Nova or Fourier Multilog Pro.

1. Set up a data logger, with a mass sensor attached, in a warm place such as a
sunny window sill or near a heater (fermentation works best if the temperature
is above 30°C).
2. Adjust the data logger so that it stores a mass reading every 10 minutes for at
least 2 days.
3. Place 20.0 g of glucose powder into a clean, dry 500 mL flask.
4. Add 3.0 grams of dried yeast to the flask.
5. Add 150 mls of water to the flask and stir it to dissolve the glucose.
6. Place the flask on the mass sensor connected to the data logger.
7. Record the mass of the flask and its contents.
8. Start the logger.
9. Allow the flask to stand for the period over which the data collection is to occur.

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Exercise 9: Position vs time graphs, and velocity vs time
graphs. (A data logger based “ticker timer”.)
Using one sensor to produce position and velocity vs time graphs.

Note: The data logger used for this exercise must have at least a sample rate of 20,000 s -1 It
can be either stand alone, or a computer based logger. However, the experiment is best done
with the logger connected to a computer. Suitable loggers include: Ezilog USB or Fourier
Multilog Pro.

1. Connect a photo gate to your data logger.

2. Clip a calibration strip (picket fence) to an airtrack glider, or trolley.
3. Place the photo gate over the track so the picket fence will break the beam.
4. Start the logger – using a high sampling rate.
5. Place the glider/trolley onto the track and release.
6. Examine the pattern created by the picket fence on your graph. (You may have
to zoom in.)
7. Measure and record the time interval from the first band to the next.
8. Repeat this process for successive bands.
9. Create a table of position vs time and graph this data.
10. Compare this with the graph created by your software.

a) Graph produced from dropping a b) Position vs time and velocity vs time

picket fence through a photo gate graphs generated from graph a using
using the Ezilog USB. the Ezilog USB.

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Exercise 10: Transpiration.
Using two sensors to measure transpiration rate during changes in light
intensity.

Note: The data logger used for this exercise must have at least a sample rate of 10 s -1 It can
be either stand alone, or a computer based logger. Suitable loggers include: Ezilog USB,
Fourier Nova or Fourier Multilog Pro

1. Connect a mass sensor and a light sensor to your data logger to record both
volume and light changes during a transpiration.
2. Place a suitable stem into a conical flask and place the conical flask on a
Mass-Sensor balance. Support the stem with a retort stand.
3. Set your data logger to an appropriate sampling rate and number of samples
(1 per sec).
4. Start the logger.
5. Allow the experiment to proceed over extended periods of dark and light.
6. Monitor your graph.
7. After a suitable time has elapsed stop recording.
8. Is there a relationship between light intensity and transpiration rate?

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The SMART buyer’s guide

Data Loggers
Questions Criteria
Matching Criteria
Can we connect to a e.g. Computer connected
computer when using the
data logger?

What do we want to e.g. pH

measure? (What sensors
do we need?)

What sample rate do we e.g. 20,000 per second

need?

What resolution will we e.g. 12 bit

require to use our sensors?

How many sensors will we e.g. two

use simultaneously? (How
many inputs will the data
logger need?)

How sturdy does the data e.g. Strongest educational

logger need to be? quality

Find out how to compare the data loggers that meet all your criteria on the next page.

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The formula for a SMART decision

Check what brands meet all your criteria, list them, and answer the questions.

Brand and Model Price Is supply Uses Support Easy to

reliable? various service use?
brands of available
sensors? from
supplier?

yes or no yes or no yes or no yes or no

Just add up the number of “Yes” answers and compare the prices to decide.

The data logger best for us is ___________________________

Number of loggers required ________

(Note: One logger for every 2 students is recommended as a class set)

Sensors and quantity to be purchased ______________

______________
______________
______________
______________
______________
______________
______________

Budget Estimate

Data loggers $___________

Sensors $___________

Freight $___________

Total __ $___________

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