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 Plummer

1, Leo Gaggl

2, Samantha Bywaters

1, Peter Preece

1

1TAFE 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

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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.

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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-

learning

What is it? Relationship to your project?

Why use this model?

Blended

learning

flexible delivery to enrolled or workplace clients using facilitated group learning

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

Virtual

classroom

live distance delivery using web conferencing tools

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

Digital

campus

access to content and information online for all learners on or off campus

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

Distance

education

mostly asynchronous, remote delivery to enrolled learners

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

Cloud based,

virtual,

immersive,

situated

learning

(Cloud

based,

ubiquitous

learning)

Sensors, wireless sensor network, wireless network, data blogging,cloud hosting of data and information, msahups of data, information and learning content, ubiquitous learning methods

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

Cluster

Type Location Data

Guido_temp Cluster Manual & automatic

Aquaculture shed: Guido tank Temperature (oC)

Guido_pH Cluster Manual & automatic

Aquaculture shed: Guido tank pH

Guido_Con Cluster Manual & automatic

Aquaculture shed: Guido tank Conductivity (EC)

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 [email protected] Leo Gaggl, Director and Information Architect Bright Cookie Educational Technologies GPO Box 1125, Adelaide, 5001 [email protected] Samantha Bywaters, Lecturer TAFE SA, Regional Institute, Urrbrae Campus, Centre for Environment, Conservation and Horticulture 505 Fullarton Road, Netherby, South Australia, 5062 [email protected] Peter Preece, Lecturer TAFE SA, Regional Institute, Urrbrae Campus, Centre for Environment, Conservation and Horticulture 505 Fullarton Road, Netherby, South Australia, 5062 [email protected]

ICL 2009 Proceedings - 62

icl09 - iclould paper 'a fish called guido

Education

learning data

remote learners

learning content

learning opportunities

elearning system catering

remote laboratories

guido elearning

time remote students