ICL09 - iClould Paper 'A fish called Guido'

Conference ICL2009 September 23-25, 2009 Villach, Austria

A Fish Called Guido
e-learning for remote students using mashups, cloud computing,
data blogging, wireless sensor networks and remote laboratories
(cloud based, ubiquitous learning)

James Plummer1, Leo Gaggl2, Samantha Bywaters1, Peter Preece1
1
TAFE SA, Regional Institute, Urrbrae Campus, 2Bright Cookie Educational Technologies

Key words: e-learning, cloud based, ubiquitous learning, cloud computing,
mash-ups, data blogging, wireless sensor networks

Abstract:
Remote learners are now able to study aspects of aquaculture and wetland
management that have been previously denied them because of the lack of locally
based resources. Learners are involved with day to day management of remote
laboratories: aquaculture fish tanks, or wetlands used for biological filtering - located
many hundreds, or thousands of kilometres from where live. The e-learning system
used (cloud based, ubiquitous learning) provides data and learning content to learners
as ‘mashups’ for a range of environmental sensors. The project demonstrates that
mashups, data blogging, wireless sensor networks and cloud hosted based remote
laboratory systems can be effective for higher level forms of remote learning.

1 Background

South Australia is a very large state of Australia, with a sparse population
(approximately 600, 000 people) outside of its capital city Adelaide. The TAFE SA
(Technical and Further Education, South Australia) is a state government body, responsible
for delivering vocationally oriented education and training in nationally accredited courses
and awards. The Regional Institute of TAFE is responsible for delivering vocational training
to all non Adelaide based students.

Unfortuately, many remote students do not have easy access to a number of Institute study
centres (which have video conferencing facilitities), and even if they do, they may not have
access to the physical facilities (such as fish production systems or wetlands) to complete
some of the more advanced system management units of learning. This lack of access to
learning opportunities and management systems can impede some remote students so severely
that they can not complete their courses, or in many cases, entire courses are denied them,
unless they choose to physically move to live closer to study facilities.

For the first time remote students can access learning content that previously would have been
unavailable to them, or would have at least required travelling great distances to be involved
with. Historically, this has left large skills gaps in some learner groups and communities,
which can greatly affect employment prospects. In extreme situations students drop out of

ICL 2009 Proceedings - Page 1053


studies altogether because they cannot see how to complete it in a timely or cost efficient
manner.

The broad objective of this project was to develop and trial an e-learning system that enables
SOME remote learners to study SOME units of courses effectively and successfully.
„Sucessfully“ is defined as meeting the academic requirements of the unit in order to achieve
a „pass“ level. „Effectively“ is defined as having a rich and challenging learning environment
that enables individual and group investigation of problems, social collaboration and team
work and timely and effective feedback similar to a face-to-face class situation.

The words „some remote learners“ and „some units“ are highlighted because this system is
not meant to meet the requirements of all remote learners. Firstly, the learner needs to have
some form of „broadband“ internet access, with at least enough bandwidth to receive medium
quality video streaming. Secondly, the system is designed for those units where there is a
systems management component, such as making decisions on how to manage a tank full of
fish. The system is NOT to teach learners how to carry out basic „hands on“ skills, such as
how to carry out water quality testing where they would need access to an actual fish tank to
learn how to perform these basic tests. For the project, two units of study were chosen to test,
one on fish and water management for aquaculture and one for wetland management for
environmental management purposes – both are management based in focus..

Initial investigations on how to best achive this form of learning with remote students
produced the following narrower objectives:

1. To design, develop and implement a cloud based, e-learning system catering for remote
learners.
2. To provide learning data, and information to the desktops and mobiles of learners, in a
fuss free manner that enables social collaboration, communication and team work.
3. To produce a series of online „how-to“ guides covering the set up of a cloud based, e-
learning system highlighting steps and pitfalls.
4. To conduct a series of learner surveys and small focus groups to indicate the degree of
effectiveness of this system for aquaculture and water management based units of
learning.
5. To present details of the system concept and workings as widely as possible amongst
learning professionals to stimulate debate, more testing of the system and further
development.
6. To more widely introduce the system for other areas of learning, if the trials prove
successful.

Each learner is now able to participate fully in the learning tasks for the chosen units -
virtually - this has not been possible before. The learning not only involves simulated case
studies, but real activities where learners see and hear and are able to interact with ‘real
world’ data and information in near real time, with the support of their facilitators and other
learners. Remote learners participate - in near real time - with the operation, monitoring and
management of aquaculture and water production systems, being an active part of the decision
making requirements for their management. It is important to stress that whilst there are some
simulations used within the learning content, learners are interacting with near real time data
and information. Collaboratively learners must manage either a real fish tank or a real wetland
in real life. The data and information on the operation of the various systems is provided to
learners as it is collected, mostly in real time, but with some manual observations only once
daily.



In fact, this approach may be better than being ‚there’, face-to-face, as learner interactions
may be greater, more widespread and better documented with the on-line systems, resulting in
a true ‘community of practice’ as learning tasks are shared and documented. This approach is
called cloud based, ubitquitous learning.

The aquaculture fish tank contains about two hundred Australian native freshwater fish called
Silver Perch (Bidyanis bidyanus). One of these fish has been named „Guido“ as a piece of
educational „fun“ designed to attract the attention of potential learners and as a way of
engaging learners with the project. The second site is a nearby wetland and the name „Slim“
is used to describe the slime layer that covers part of an associated biological filter.

2 The Learning System
2.1 Introduction

Cloud computing and mobile learning are two e-learning technologies highlighted by 2009
Horizon Report 1 as being key trends affecting the „practice of teaching, learning, research,
and creative expression“. The report suggests that „today’s learners want to be „active
participants in the learning process“ , whilst „needing to control environments and with an
expectation of easy access to the staggering amounts of content and knowledge available at
their fingertips“. The Horizon report lists one of the major benefits of cloud computing as „the
support for group work and collaboration at a distance“ – they suggest the ‘time to adoption’
for cloud computing and mobiles as being one year or less. This appears to make the testing
of e-learning approaches such as those used in this project, both timely and important.

The authors define cloud computing, for this project, as the storing of data somewhere in the
Internet so that it can accessed as needed, anytime, anywhere. This means that the data,
information and learning content are not being stored on local servers, and that many of the
applications learners need are Internet based. The project uses a setup of the educational
version of Google Apps, specifically using Google Sites, Docs, G-mail, Calendar and Talk
applications. Data is stored using Google Apps Engine Datastore running under a Python
environment.

Cloud storage of sensor data was chosen to avoid the need for local storage, which would
require the use of a coorporate firewall that would severly limit external Internet access.
Cloud storage also allows for easy scalability for future expansion, global access to data on
the Internet and potentially less downtime than with the use of a local data server.

The learning management system used to deliver learning content, and in which mashups of
sensor data and learning content occur is „Moodle“. Moodle also contains a number of built-
in communication tools, such as synchronous chat and mail, but importantly mashups using
data widgets also work seamlessly. Currently, mashups are delivered using content data from
the Datastore using Google Apps „widgets“, but soon Flash and Java based widgets will be
trialled.

Smith2 recently defined six broad types of e-learning types commonly used in Australia,
namely:
1. E-training.
2. Distance education.
3. Web in class.



4. The virtual classroom.
5. The digital campus.
6. Blended e-learning.

The project makes use of four of these types, but introduces another based on the relative
„newness“ of cloud based, ubiquitous learning. Ubiquitous learning is viewed, by the authors
as a combination of e-learning and mobile learning methods, such that they seemlessly
integrate the delivery of learning content via computing and communication devices,
especially mobile devices.

The model used for this project is novel, and therefore may become used enough to fit into a
new category or type, that of cloud based, ubiquitous learning (see Table 1). Stated simply,
this means hosting data, information and learning content in a cloud and accessing that by
means of mashups, as needed.

Learners should be given the ability to access this material in a number of different ways, but
preferably in a blended form so that mobile learning is also available. For example, with the
‚A Fish Called Guido’ system we are also catering for learners on campus, in a face-to-face
way, but as they walk around and are involved in monitoring and managing the nearby
wetlands. The learners can access ‚up to date’ data and information about the wetland (e.g.
water level, temperature, pH, dissolved oxygen level, etc.) in near real time, along with the
associated learning content through a range of mobile devices.

Currently, netbook computers, the iPod Touch and iPhone are supported, but soon any
mobile/hand held device with WiFi capabilities may be used. A Bluetooth wireless layer is
also planned to support devices that do not have WiFi access.

2.2 Key Components

The key components of a cloud based, ubiquitous learning system are:

1. sensors – to collect physical data from the field/remote laboratory environment:
usually the sensors are part of a wireless sensor network and data blogging.
2. wireless technology -
a. to link data from sensors to the outside world via the cloud.
b. for data receival on mobile/hand held devices in the field/remote laboratory.
3. Cloud hosting of data.
4. Learning management system – to manage learner access to data and learning content.
5. Mashups – data collect from sensors is pulled from the cloud and ‚mashed’ into either
separate web pages and/or a learning management system.
6. Ubiquitous learning – data, information and learning content is made available to
learners via a range of devices, but also allowing an intergration of all or several of
these. For example, a netbook computer and mobile phone.
7. Social interaction and web based communication tools.



Table 1 – Comparison of the e-learning types used for the project and Cloud Based,
Ubiquitous Learning

Type of e- What is it? Relationship to your project?
learning
Why use this model?

Blended flexible delivery This project makes use of a number of e-learning models in that
learning to enrolled or no one model dominates, although the Distance education and
workplace Virtual classroom are used a little less then the other models.
clients using Flexible delivery using facilitated group learning best summarises
facilitated group the approach used of those listed here. Some learning content will
learning be stored and access managed via a LMS such as Moodle.

Virtual live distance Some aspects of this may be used, especially to support
classroom delivery using synchronous delivery of learning content as though learners are
web standing ‘in the wetland’ or ‘in front of the fish tank’. This is
conferencing NOT the main method of learning to be used.
tools

Digital access to The project makes extensive use of this model, as access to data
campus content and and information and other learning content will be all on-line
information regardless of where the learner is located. Even learners on the
online for all campus will have to access content online, as well as engage in
learners on or team work online. All of the data, information and learning
off campus content will be in a digital form so it makes sense to use a data
repository concept.

Distance mostly The project makes some use of this approach where data and
education asynchronous, information are delivered asynchronously to remote learners.
remote delivery However, learners are expected to respond frequently, but with
to enrolled only small responses – little responses, but often. Some
learners synchronous responses are also expected.

Cloud based, Sensors, Cloud based, virtual, immersive, situated learning will take
virtual, wireless sensor place where learners will ‘feel’ that they are standing in front of a
immersive, network, fish tank or a wetland and whilst receiving data and information
situated wireless about that environment, supplemented by additional videos and
learning network, data small simulations to highlight key learning requirements. For
(Cloud blogging,cloud example, it’s better to simulate sudden, large scale fish death than
based, hosting of data to actually induce it in a fish tank, for obvious economic and
ubiquitous and information, animal welfare issues. Data and information will be linked
learning) msahups of data, (online) to learning content in small, ‘thinking sized’ chunks in a
information and just-in-time fashion. Much of the ‘near real time data and
learning content, information’ required for learning will be stored virtually in the
ubiquitous internet cloud. Learners access the cloud stored data and
learning information via ‘mash ups’ on a web page. Another description
methods might be ‘Cloud based, ubiquitous learning’.



3 System Operation
3.1 Overview

A wireless sensor network on the Urrbrae Campus is used to collect data from a range of
environmental sensors from two main sites. The first site is a fish tank in which grow about
two hundred fish, one of which is „Guido“. The second is a nearby wetland site, which has a
biological filter which houses „Slim“, the slime. An overview of the installed wireless sensor
network is shown in Figure 2. The network includes a number of access points for campus
based learners to download data and learning content, at the project sites. Room has been left
for future expansion of the system.

Sensor data and information from the wetland and Guido’s fish tank is blogged and stored in a
Google Apps Engine Datastore, which is then downloaded and added to a range of learning
systems in the form of a ‘mash up’. Sensor data is blogged to the Internet via an ADSL
modem, and learning content for on-campus learners is downloaded in the same manner. The
wireless network is completely separate to existing campus networks and does not sit behind
the campus coorporate firewall.

Remote learners are able to see and interact with Guido’s tank and the wetland in near real
time, to enable them to actively contribute to its ongoing monitoring and maintenance.
Learners are also required to network and collaborate with their peer group in order to
formulate management plans for these systems (on a daily, weekly and monthly basis) as
appropriate.

For this project, a sensor is defined as a source of data, of which there are two broad
categories: manual and automatic. Sensors can either be single or grouped together (for when
they are based in one location). For example, for Guido’s fish tank readings taken manually
(such as temperature, pH, etc.) and are grouped together as a cluster of sensors because they
are geo-located at the same point – a fish tank with a radius of 3 metres.

Each sensor is given a unique identification automatically by the system and must be set up
with a name, description and geo-location data (latitude and longitude). A sensor cluster is set
up the same as an individual sensor, but has an additional field which stores the identification
of each sensor that is associated with that cluster. Each datum eminating from each sensor is
automatically time stamped and is also given a unique identification as it is sent to the cloud
for storage.

Table 2 – Examples of some sensor types used for the project

Sensor name Single/ Type Location Data
Cluster
Guido_temp Cluster Manual & Aquaculture shed: Guido tank Temperature (oC)
automatic
Guido_pH Cluster Manual & Aquaculture shed: Guido tank pH
automatic
Guido_Con Cluster Manual & Aquaculture shed: Guido tank Conductivity (EC)
automatic
Guido_DO Cluster Manual & Aquaculture shed: Guido tank Dissolved oxygen



automatic (ppm)
Guido_nitrate Cluster Manual Aquaculture shed: Guido tank Nitrate (ppm)
Guido_amm. Cluster Manual Aquaculture shed: Guido tank TAN (ppm)
Guido_morts Cluster Manual Aquaculture shed: Guido tank Fish deaths
Guido_c_hard Cluster Manual Aquaculture shed: Guido tank Carbonate hardness
(ppm)
Guido_nitrite Cluster Manual Aquaculture shed: Guido tank Nitrite (ppm)
Guido_TUA Cluster Manual Aquaculture shed: Guido tank Toxic unused
ammonia (ppm)
Guido_cam1 Cluster Automatic Aquaculture shed: Guido tank Visual via web
camera in tank
Guido_cam2 Cluster Automatic Aquaculture shed: Guido tank Visual via web
camera long shot
Wetland_temp Single Manual Wetland Temperature (oC)
Wetland_pH Single Manual Wetland pH
Wetland_cond Single Manual Wetland Conductivity (EC
units)
Wetland_turb Single Manual Wetland Turbidity (NTU)
Wetland_DO Single Manual Wetland Dissolved oxygen
(ppm)
Wetland_nitrate Single Manual Wetland Nitrate nitrogen
(ppm)
Wetland_visual Single Manual Wetland Visual observation
of water quality

Figure 1. Overview of the wireless sensor network installed for the project.



3.2 How it works

A facilitator sets up the relevant sensors as needed, based on learning requirements and
hardware availability. Data is uploaded (blogged) to the cloud data repository via an Internet
link, usually a wireless sensor network, although it could be manually. Learning content is
prepared for delivery by a learning management system. Learners receive support in the use
of the learning management system, the relevant Google applications (g-mail, talk, etc.) and
how to create the required data widgets. The learning content is prepared in a way that
anticipates potential problems that learners would be expected to deal with as part of their
management of either system (fish tank or wetland).

Each learner accesses a web site daily where their learning content is mashed up and
presented as a logical set of learner’s notes and activities. Text messages can be sent to
learners’ mobile phones alerting them to the fact that a water quality issue needs to be
addressed. Learners are expected to find out the consequences of such an issue and then
respond to the lecturer indicating an appropriate course of action (for example, turning on a
bottle of compressed air for a certain amount of time).

To extend the learning experience, learners then collaborate with each other via a range of
communication tools to share ideas and strategies, as well as conducting their own research.
The lecturer then collates and reviews all learner responses, correcting as necessary and
adding them to the learning management system (Moodle) as reference material for the
benefit of all future students.

Figure 2. Flow diagram of the Guido e-learning system.



4 Assessing Project Effectiveness
Several focus groups have been established to evaluate this project, which at this stage is
ongoing. The project has already met most of its required outcomes by the successful
installation, testing, loading and documenting of the mashup, data blogging and cloud hosted
remote laboratory system, along with a range of feedback and communication channels. A
series of ‘how to’ guides are currently being produced. Delays in installing and configuring
the wireless sensor network have meant that learner testing of the system, and their
subsequent evaluation of it have not yet been completed. This will occur over the next two
months and will be documented on the web site for the project: http://www.icloud.edu.au.

Initial, anecdotal surveys of some learners suggest extremely strong interest in learning this
way, but with some initial concerns with Internet bandwidth requirements for using the web
cameras and the amount of time required for communicating with other learners. The
bandwidth issue for the web cameras has been addressed by setting up an automatic sensor
data logger for the fish tank, which enables the system to raise an alarm and send text
messages to learners when an abnormal sensor level has been detected. This means leaners
then just need to look at the web camera images for the time around when an alarm was
raised. A similar system is being investigated for the wetlands. Leaners are expected to
communicate extensively with their colleagues when studying management orientated units –
if they want to complete their studies they have to make this time available. However,
communication requirements have been made asynchronous (where possible), which should
help with this challange.

5 Conclusion
Setting up an e-learning system for remote students using mashups, cloud computing, data
blogging, wireless sensor networks and remote laboratories has been shown to be feasible.
The effectiveness of learning by this method is still being assessed, but a range of e-learning
systems and methods have used successively for some time and are now standard adult
education approaches. Initial, anecdotal surveys of remote learners suggests that having
access to a wider range of learning using a system like this is both extremely appealing and
less daunting due to the near real time data and information approach used with a cloud based,
ubiquitous learning system.

References:
[1] Johnson, L., Levine, A., & Smith, R.: The 2009 Horizon Report, 2009. Austin, Texas: The New
Media Consortium. (http://wp.nmc.org/horizon2009 )
[2] Smith, C.: Types of e-learning, 2008. The Flexible Learning Framework, Australia.
http://www.flexiblelearning.net.au/designing.
[3] Anon: High Performance Wireless Research and Education Network (HPWREN), 2006,
http://hpwren.ucsd.edu accessed 1 April, 2009
[4] Chris, G.S.; Grebla, H.; Slanca, L.: Mobile Learning Platform for E-Learning, 2009, iJIM – Vol 3,
Issue 3, July 2009.

Author(s):
James Plummer, Lecturer



TAFE SA, Regional Institute, Urrbrae Campus, Centre for Environment, Conservation and
Horticulture
505 Fullarton Road, Netherby, South Australia, 5062
Jim.plummer@tafesa.edu.au

Leo Gaggl, Director and Information Architect
Bright Cookie Educational Technologies
GPO Box 1125, Adelaide, 5001
leo@g3i.com.au

Samantha Bywaters, Lecturer
Horticulture
samantha.bywaters@tafesa.edu.au

Peter Preece, Lecturer
Horticulture
peter.preece@tafesa.edu.au


ICL09 - iClould Paper 'A fish called Guido'

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ICL09 - iClould Paper 'A fish called Guido'