The digital footprint of our students/users gives a better picture of how people are using the systems and the content held in those systems.
Internally for the government department that I work for this means that we are able to see how well content presented in mandatory training is put into practice. Managers can access how long team members have been working through content prior to attempting the tests, which could have impacted on the scores that team members received.
My concern about this is how soon will the length of time someone has spent learning within mandatory course work be used as a measure in performance development meetings by a crackpot manager who does not have the capacity or capability to understand learning styles and the simple point that people learn at many different rates.
Upon saying this I do not feel that learning analytics are bad, but they do need to be used with caution. It would also help if organisations developed strategies around learning analytics to be able to use them in the best to support achieving improved outcomes for students and clients. The Charles Sturt University Learning Analytics Code of Practice is a good example of documents that bound an organisation to how this valuable data set will be used.
Another exceptional use is an example from the Western Australian TAFE sector. Recently I was discussing learning analytics internally with our ICT department, especially the LMS that we supply the WA TAFEs and how users are enrolled into online courses. From this discussion a new building block was created by out ICT team which I was discussing with a client from a TAFE. He had used this new building block (as well as other reporting functionality) to view how staff were using the various tools within the LMS. The client discovered that staff seemed to be enrolling students individually more than by class rolls. There could be a wide number of factors including rolling enrolments where a cohort could have new people added adhoc over the course of the study period. But what this has highlighted for the TAFE team is that they can tailor training for staff better as they are able to watch the watches and support them to become better online trainers.
This does beg the question, who exactly are watching the watchers?
Recently in the agency that I work one of our mandatory courses grade books had been tampered with by a member of HR staff. Corporate Leadership team requested an independent review by a team external to HR who knew how to interrogate the system logs to determine who had access and tampered with the course grade book (as it is a mandatory regulatory course that all staff must complete and pass to maintain employment). I was able to track back through the logs that the HR team were unaware of, locate how the issue came about and reported back to Corporate Executive with recommendations regarding restrictions to the higher level access functions to ensure that this issue did not happen again as well as rolling the course back to the last backup date as no members of staff had been employed in the period that was impacted, which removed the problem. For the future I noted in the system the issue and why the reset had happened so there was a reason to my wiping a month of course logs. As part of my final report I also suggested further training of the HR team was required which has occurred.
In this instance the watchers were completely unaware that they were being watched and monitored until after the fact. I personally feel that this is not the way morally that we should be using this technology. It should be above board and everyone aware that they can be tracked, no matter what.
Simply a Code of Conduct policy around the use learner analytics is so very important for any organisation.
Charles Sturt University (2015). Code of Practice. Retrieved from https://www.csu.edu.au/__data/assets/pdf_file/0007/2160484/2016_CSU_LearningAnalyticsCodePractice.pdf
Welsh S. (2016). INF537, Colloquium 1, Learning Analytics [PowerPoint Slides and Connect recording]. Retrieved from https://connect.csu.edu.au/p65jlka06d6/
Is there too much of a good thing with the amount of content that you can find on the internet? Not only is there an overabundance of good content but it is fast becoming like the proverbial “needle in a haystack” for a user to locate quality information quickly and efficiently amongst the bad, tragic or just mediocre content on offer in our digital smorgasbord.
With the fast approaching world of Web 3.0 and the advent of the Internet of Things (IoT) means that the internet and its horizon is an ever-changing and evolving landscape that can provide personalised information to the user and about the user. You do have to question if this is always a good thing.
The 2016 NMC Technology Outlook – Australian Tertiary Education noted that learner analytics and location intelligence, which information is a form of big data, are areas that will have an impact in the next few years.
Big Data and meta data (data about data) have become a key focus with regards to who is creating, storing, using and most importantly selling data about you and what you look at. Think about the last time you searched for anything and you will have been prompted with possible fee-for-service products that might be similar to what you have been looking at.
Another form that this takes is when you are on social media sites such as Facebook™ you will notice based on your searches, friends and groups that “sponsored” sites appear as suggestions you might like to follow. You will also be aware (if you are using a desktop that there is an advertisement stream that is tailored for you. How does it know what you have been looking at while you are not on Facebook, simply it is from the cache in your computer or smart device and your browser history.
But when you consider the importance of how you can use learner analytics and learner actions within your site to track what they have been reviewing to ensure that the content of the course is meeting their needs then bib data is not seen quite so much in a horrible tracking light – stalking your movements around the internet, but a useful tool to support and help students.
With the increase in the cost of creating print products and the speed that these products become redundant saw the rise of Web 2.0 technologies that enabled user-generated content simply easily and cheaply. This power to the masses revolution of technology has meant that often we do forget that the internet is ‘forever’. Need proof that this is the case, then please feel free to review the Internet Archive Wayback Machine.
It is interesting that with the increase of the individual’s ability to have a voice on the internet has seen many companies fall-by-the-wayside as they have not adapted and changed their business structures to compensate for the new market place. Some may argue that if you do not adapt to the market place to survive then you do not have the right to survive.
So with that thought where does this leave educators? In this brand new world are we expecting teachers to become technology experts to guide students to some mythical promised land of better understanding? I would say that at best we need to encourage our teachers to become the facilitators of tomorrow. This means that we must move away from the “sage on the stage” mentality to perhaps taking up the guide on the side role where technology plays a helpful hand in supporting and augmenting learning for students. Technology can support student outcomes but should never dictate or drive the learning.
So the future is looking bright, but is it looking as bright as it once was or are we seeing it through a binary code induced haze? Time will tell.
De Saulles, M. (2012). New models of information production. In Information 2.0: New models of information production, distribution and consumption (pp. 13-35). London: Facet.
Kellmereit, D. and Obodovski, D. (2013). The Silent Intelligence: The Internet of Things. DnD Ventures 1st edition, California.
Roblyer, M. (2013). Integrating Educational Technology into Teaching. Harlow: Pearson.
The New Media Consortium. (2016). 2016 NMC Technology Outlook Australian Tertiary Education. Retrieved 7 July 2016, from http://www.nmc.org/publication/2016-nmc-technology-outlook-australian-tertiary-education/
As of the 1st January 2014 all students, in the Australian VET sector, have been allocated a Unique Student Identifier. This code follows the student through their life and enables registered training organizations (RTOs) easier access to a student’s VET records and provides a simpler way for students to provide evidence for credit transfer makes it easier for students to transfer between training institutions (Mills, 2013).
But this got me thinking about Big Data and the relationship that this USI could offer in the future for RTOs. On the positive side there is the possibility of responsive training based on the needs of the client making it a system that can contextualize a learning journey through skill sets for the student to eventually achieve a desired goal, but it could also have a darker side the side where direct marketing and disreputable RTOs denying students training based purely on past performances in previous qualifications. Currently the USI does not store informal comments regarding student’s performance currently, but you have to ask yourself does that mean that the system will always stay like this, especially if K-12 students are eventually linked into it.
Education providers, if engaging in digital learning, have the ability to garner information about their clients easily through the technology that we use. Every click a student does within a Learning Management System is recorded in the back end database, which will be archived along with the course in the VET sector for audit purposes. If utilizing social media every mention or micro-blog post can be saved to build a picture of the learners and their capabilities and needs. Now imagine this big picture that one RTO is able to build through, careful and critical analysis of the underlying data, an explicit picture of the student’s choices and make accurate predictions on the same students future study choices. If this data becomes part of public record then one bad grade somewhere in your past could in the future severely impact on what you are able to study in a dystopian world.
In all fairness the USI Registry System has been designed to keep training records and results safe, according to their website (Usi.gov.au, 2015) and goes on in subsequent pages to assure students that their information is safe. But the worry about security when it comes to student academic records is not an isolated concern for Australia but was raised in Education Week (Kamisar, 2014) that discussed issues around security for the organization inBloom which was touted as being the organization that would revolutionize personalized learning and target the needs of individuals based by synthesizing student data. Admittedly there are marked differences between inBloom and the USI Registry System. One stand out difference is that currently the USI is not being managed by a private third party but by a Federal Government agency, however, given recent privatization and the push to consolidate services to reduce Government employment burdens it does beg the question if this will become outsourced in years to come.
Data mining is big business for organisations and more so the art of predictive analytics. Marketing departments in retails stores have been onto this for years as outlined by Duhigg (2012), so why wouldn’t the education industry want to start move into this field especially with a ready made supply of information. This could become a very lucrative market place with the value of this data being almost priceless, and we the consumers may never even realize that our information might have been shared. One must ask the question do students know that the information stored within the registry may be provided to third parties such as regulators, researchers current and former VET RTOs to name a few for a variety of purposes. When a student is enrolling is this ever explained in full to them and all of the ramifications, as in the current system you cannot enrol in a VET qualification without have a USI. I have to say that I could (if I wanted to) create a USI on the website (Usi.gov.au, 2015) and it would have been up to me to have explored all of the sub-pages to dig into what will happen to my results and who has access to my details, but I am not convinced that all of our VET students will do this.
We do not have a perfect VET system, but we are trying to put in place systems that will streamline workloads for organizations. But I do have to wonder who is looking out for the students? This blog post is really the start of my exploration into this very interesting topic and one that could have ramifications in years to come within all sections of the education industry.
BBC News,. (2014). Apple confirms accounts compromised but denies security breach – BBC News. Retrieved 1 May 2015, from http://www.bbc.com/news/technology-29039294
Duhigg, C. (2012). How Companies Learn Your Secrets. The New York Times Magazine. Retrieved from http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?_r=2&
Finance.gov.au,. (2014). Cloud Computing | Department of Finance. Retrieved 4 May 2015, from http://www.finance.gov.au/cloud/
Kamisar, B. (2014). InBloom Sputters Amid Concerns About Privacy of Student Data. Education Week, 33(15), 1-13. Retrieved from http://www.edweek.org/ew/articles/2014/01/08/15inbloom_ep.h33.html
Mills, A. (2013). VET Transparency Agenda – what’s in it for me. Presentation, Training Providers Forum, Perth, Western Australia.
This is a nice easy read, that give a good solid base level introduction to the concept of The Internet of Things. The book itself is not primarily education focused, but you can apply some of the key points to the education space. Well worth a look at.
Kellmereit, D. and Obodovski, D., (2013) The Silent Intelligence: The Internet of Things. DnD Ventures 1st edition, California.
The Silent Intelligence: The Internet of Things by Daniel Kellmereit and Daniel Obodovski (2013) presents an extensively researched analysis about the Internet of Things (IoT) which primarily was written for businesses looking to leverage on practical lessons and guidance from experts and companies in this field. Through interviews and case studies the readers are presented with both authentic examples and future forecast scenarios of use for both industry and individuals. The key objective of the book is for readers’ awareness to be raised about how connecting the physical world around us to the digital world can result in gains. Kellmereit and Obodovski (2013) also shared their own personal views with the reader on how to overcome obstacles with the IoT, which are specifically aimed at business investment and job opportunities.
Kellmereit and Obodovski (2013) pose five significant questions about the IoT: What is the Internet of Things? How is it coming about? What are the key trends? What is the potential? What needs to be done? The authors use these questions as a framework to explore the IoT concept. As part of this exploration three key ideas connecting the chapters: data collection; data transport; data analysis, especially around the issues of the role human interaction will play in the rapid expansion of this technology and the management of data produced by the IoT became apparent. These issues and ideas will form the basis to critique the theories developed in this book.
It becomes apparent thanks, to Kellmereit and Obodovski (2013), that the IoT is not a far-fetched vision of a digital utopia with the IoT incorporating machine–to–machine (M2M), machine–to–person (M2P) and person –to–person (P2) networked technologies. All digital learning technologies currently used in education are part of the IoT, a point many educators may not be aware of yet as the IoT has not been extensively covered in popular media.
In the book there are four identified focal points that the technology industry has created encompassing networked nodes around; connected cities; connected homes; connected health; and connected cars. Connected cities, provide information to town planners for traffic management energy optimization and building automation is collected via sensor and wireless communication data collected. Connected homes are sensing if people are at home and assisting with for energy consumption and making people comfortable and safe. Connected health currently supports people with chronic illnesses and Alzheimer’s by collecting data and location information and providing this to caregivers. Finally connected cars, most cars have ‘drive–by–wire’ implemented. This provides valuable information to service technicians at a service, but also aggregated information can create better fleet management in transport industries. These connected digital technologies are all networked nodes of data collection outlined in this book.
The data is collected via networked nodes, discussed by Kellmereit and Obodovski (2013), and is stored as raw ‘big data’ that can either be used by the original collector or supplied to third parties, provided that the metadata schemas match to will allow simple and effective data mining. The authors believe that this information collection is going to help us all become better citizens and allow us to better manage our facilities, including educational facilities, this is supported by the current project between CISCO and Swinburne University (Johnson, Adams, Estrada, Freeman, 2015).
It is argued in the book that as ‘things’ around us become smarter machines will take over more tasks, the human error rate for activities will fall, for example the research and development Google X project for self-driving cars. However, in education, current technologies are successfully being employed that are filling a niche requirement. Technologies currently being used include radio frequency identification (RFID) tags in an elearning project using RFID tags in a cabinet making workshop (E-standards.flexiblelearning.net.au, 2008) and quick reference (QR) codes for map reading in an outdoor education class (Lai, Chang, Wen-Shiane, Fan & Wu, 2013). These project have had to overcome technology hurdles for successful real-world application.
Technology in the past has been problematic, according to Kellmereit and Obodovski (2013), as the uptake has been prohibited by the expense especially specialist scanning hardware. With the arrival of QR codes to the digital landscape and cheap simple smart technology production using, for example, Rasberry Pi (Raspberrypi.org, 2015) and Arduino (Arduino.cc, 2015) modules means that there is a change in the landscape. This is fast becoming a core topic in many universities computer science courses such as the Open University’s My Digital Life course (Kortuem, Bandara, Smith, Richards & Petre, 2013). The exponential growth of the IoT and M2M arena is being driven by research projects in both industry and educational institutions.
The IoT according to The Silent Intelligence: The Internet of Things (2013) is a rapidly expanding dynamic space and one which Moores Law (Mooreslaw.org, 2015) can be applied to technologies involved. With this rapid expansion of the M2M technology the central challenge, according to the authors, is that people do not understand the reference architecture that is required to grasp the potential of the IoT. Peggy Smedly, from connected Worlds M2M, in the book suggested increasing the profile of IoT in the mass consumer marketplace by packaging technology into something that is easy for both companies and consumers to use, so not only offering a complete solution but from known and trusted brands, which directly contradicts Kellmereit and Obodovski (2013) focus for technology start-ups who will bring the creative edge to the market rather than staid traditional trusted companies. Once again universities adjusting programs in a responsive manner and supporting students in learning about, engaging with and building IoT technology (Callagaghan, 2013; Kellogg, Parks, Gollakota, Smith & Wetherall, 2014; Zhang, Callaghan, Shen, Davies, 2011) they will support the creative edge needed in this new space that Kellmereit and Obodovski (2013) see crucial for success.
A key theme in the book is the phenomenal rate of change that society is experiencing in all aspects of life due to the interconnectivity of smart devices. It is this silent revolution that humanity will not notice due to the pervasiveness of the IoT technology. Everything will become nodes on a network in a world where knowledge is universally accessible (Anderson & Rainie, 2014). Kellmereit and Obodovski (2013) alluded to well–defined old problems that the IoT can solve such as increased track ability, increased productivity, improved risk management, reduction in the guess work for planning projects and better connection to our environment. They do point out that the ‘human element’ is a key risk factor, but stop short of a definitive statement regarding issues resulting from the removal of people from the equation in the input of data in the IoT environment.
Kellmereit and Obodovski (2013) are quick to point out that the ‘human element’ is important and not superfluous to data analysis especially the interpretation of data and the subsequent recommendations for implementation. Data analysis is seen to be a growth area for companies with networked devices feeding systems that can improve tracking and tracing capabilities, such as the DriveCam example (Lytx, 2015) as discussed in the by Mark Wells interview (Kellmereit & Obodovski, p.93, 2013).
Kellmereit and Obodovski (2013) have a positive view of the IoT and handing over data collection, transport and analysis to digital technology. This ideology causes the reader to pause and reflect that our ‘smart devices’ are disrupting and in the very near future, if not already, are going to re-define society’s ways of living, interacting and learning, and consequently, will challenge our belief of what it is to be human (Theinternetofthings.eu, 2015).
Shared business models and industry standards, according to the book, are currently deficient in the IoT sector. Bill Gates said “In an age of interconnectivity, businesses need an architecture that extends outward to partners and customers. The successful companies select a few standards and enforce them strictly” (Gates & Hemingway, 1999). Setting standards and creating shared business models is an important step for industries to make in the IoT and M2M arena, because viable data linking from a variety of sources will allow the true potential to be discovered. Kellmereit and Obodovski (2013) put forward that without industry standards the IoT and M2Ms scale will always be defined by the narrow structures that each individual business use.
With the IoT being a relatively new concept and one which the possible ramifications are not widely understood there are some concerns that Kellmereit and Obodovski (2013) raise. One significant controversy outlined are ethical issues around the big data being captured by M2M, M2P and P2P technologies and the sovereignty of the data, especially as not all data retention and access levels are the same and similarly the treatment of data should not be the same. Sovereignty of data will become a crucial issue in years to come as the collection and use of data from sources versus the rights of individuals who are linked to the data will have ongoing ramifications to all industries, including the education sector.
Kellmereit and Obodovski (2013) mention very little regarding the responsibility of the carriers around data preservation and sharing, simply that there will be regulatory restrictions in other countries (this book was written and published for the American market). They discuss that data acquisition will be the growth market area for industries to capitalize on; this is a concern as there is little regulation regarding how metadata is managed, retained or shared by companies or countries, as seen in current media about metadata retention in Australia (Ag.gov.au, 2015). This lack of regulation has ramifications on data collection, transport, storage and analysis.
Data transportation in the digital realm is a concern for all areas of industry as well as the general community. The need to provide secure transport networks, be it via hardwire/cable, cellular, Wi-Fi and Bluetooth or RFID for short range communication which all have unique security protocols around the network to ensure that the data is safe during transport as well as during storage. This book did not provide any key information around this area other than that a company needed to develop a robust M2M policy to ensure that their data would be protected.
It is outlined in the book that business and industry need to optimise the technology architecture, which can be driven by work and research in the education sectors. This book perceives a disconnect between the high-tech community and industry Kellmereit and Obodovski (2013), however with the education sector making strong inroads into this space there will be a reconnection and synergy between these key market players on the IoT space.
Bill Gates once wrote “Business is going to change more in the next ten years than it has in the last fifty” (Gates & Hemingway, 1999) and this is especially true when looking at the IoT technology and the exponential growth that all industries, including education, will experience thanks to the prevalence of the IoT which will form the starting point for the next digital revolution if Kellmereit and Obodovski (2013) have it right.
Overall Kellmereit and Obodovski (2013) were successful in presenting a book that offered a broad overview of the IoT which then drilled down to specifics related to investment and business strategies. Their discussions around the new changing digital world and the way businesses, including education institutions, must adapt was illustrated by interesting use cases and interviews which met the writers aims set out in their introduction. However, this book only scratches the surface of the IoT and M2M technology and further reading would be necessary for an educator to fully grasp the implications both the IoT and M2M in relation to their current methodologies and technology systems being used. This is a challenging topic and one that Kellmereit and Obodovski (2013) provide a very broad base level introduction to.
Ag.gov.au,. (2015). Data retention | Attorney-General’s Department. Retrieved 18 April 2015, from http://www.ag.gov.au/dataretention
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Callaghan, V. (2013). Buzz-Boarding; practical support for teaching computing based on the internet-of-things. In Higher Education Academy STEM: Annual Learning and Teaching Conference 2013: Where practice and pedagogy meet.. York: The Higher Education Academy. Retrieved from http://journals.heacademy.ac.uk/doi/abs/10.11120/stem.hea.2012.015
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Johnson, L., Adams Becker, S., Estrada, V., Freeman, A. (2015). NMC Horizon Report: 2014 Higher Education Edition. Austin, Texas: The New Media Consortium.
Kellogg, B., Parks, A., Gollakota, S., Smith, J. R., and Wetherall, D. (2014) Wi-Fi Backscatter: Internet Connectivity for RF-Powered Devices, University of Washington, retrieved from: http://iotwifi.cs.washington.edu/files/wifiBackscatter.pdf
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