This past year welcomed widespread Internet of Things (IoT) adoption and hype, big data implementation, and growing concerns around data privacy and cloud deployment. As 2014 draws to a close, we look ahead with much anticipation for what promises to be a signature year for machine learning, predictive analytics, new IoT pioneers and a full overhaul in the distribution of the IoT.
In their discussions over the past year, the EDUCAUSE IT Issues Panel touched several times on the issue of analytics and strategies on how best to implement analytics in higher education. Discussions ranged from implementing analytics to improve core administrative and business functions to how best to implement analytics in a predictive sense to ensure student success in academic programs.
In answering five questions, the following IT Issues panelists provided their thoughts on how analytics can improve higher education and how best to implement analytics:
The rapidly evolving ecosystems associated with personal data is creating an entirely new field of scientific study, say computer scientists. And this requires a much more powerful ethics-based infrastructure.
Learning Analytics, just as with analytics in business, web or healthcare, has the power to empower the people in the training department of your organization with the information they need. Learning managers no longer have to rely on making educated guesses to determine the most useful components of a training module. Incorporating Learning Analytics tools within your enterprise LMS does that far more easily and efficiently.
UK universities are monitoring students’ information to help them to improve their academic performance but are giving insufficient thought to the effectiveness of the technology they use and the rights of those they track.
This is according to Sharon Slade, senior lecturer in the Faculty of Business, Management and Law at The Open University, which is believed to have become the first institution in the UK to produce a publicly available written policy on the ethical use of student data for learning analytics – the practice of collecting and analysing student data with the intention of optimising their educational experience.
Work on a transparent ethical policy at The Open University highlighted concerns about technology and privacy
Learning analytics is the study of student behaviour through patterns in their digital world: how often, when and where they log on; the digital resources that they use; the web sites they visit; the social media platforms that they use. At the present time, this is a nascent field of interest in most colleges and universities. But over the next few years, the analysis of the digital trails that students leave as they move through the digital world will become central to curriculum design, learning support, assessment and quality assurance. Two recent reports by Jisc – British education’s digital solutions provider – set the stage for these changes, and point to some early work that needs to be prioritized if these developments are in the interests of students and enable better education.
That learning analytics are set to take off is hardly surprising. After all, its common cause that many of us allow the new virtual behemoths access to our every keystroke in return for the advantages that this brings us. Amazon uses our search and purchasing patterns to suggest what books we might also like to read on the same subject. Google scans the content of every email to select the advertisements that are most appropriate to our needs. Weather apps use our location to tell us whether the sun will shine on us today. Students use all these services, and many more; they are not surprised if their universities use their personal data in similar ways. Indeed, Jisc’s work shows that students are – at present – relaxed about all this.
Data experts convene to create profile of the “big data specialist” position, which is predicted to be in high demand if data skills remain untaught
Students at both the K-12 and university levels should learn how to handle and interpret big data, but to do this, educators at both levels must be comfortable using and teaching about big data.
Big data is quickly becoming one of the most important fields, and workers who are able to handle, analyze, and interpret data will be in high demand in the workforce. And this need is critical in education, from students who must know how to use data as part of learning, to educators who should be able to interpret student data.
Sensors are cheap and abundant. They’re already in our devices, and soon enough, many of us may elect to carry sensors in and on our bodies, and embed them in our homes, offices, and cities. This terrifies people, Jason Silva says in a new video.
Who hasn’t heard of Big Brother or feared the rise of the surveillance state? But Silva says there’s an upside.
As the world is reduced to “algorithmic cascades of data” he thinks we’ll get what Steven Johnson calls the “long view,” like a microscope or telescope for previously invisible information and datasets.
Billions of sensors measuring location, motion, orientation, pressure, temperature, vital signs and more—each of these will be like a pixel. Seen up close, a modestly flashing primary color. But at a distance, individual pixels dissolve. Discrete points will smooth out into a contiguous image no one could have guessed by looking at looking at each pixel alone.
Understanding how an educational intervention is implemented is essential to evaluating its effectiveness. With the increased use of digital tools in classrooms, however, traditional methods of measuring implementation fall short. Fortunately, there
“ Using data to drive learning outcomes isn’t a new concept, really. For as long as teachers have been giving students assessments, the assessments and results have been used by both students and teachers (even if only loosely) to determine how to move forward. What needs to be reviewed more? What was covered/studied well? Learning analytics takes this concept and kicks it up a notch. Well, more like a thousand notches, especially if you’re considering things like adaptive computer based testing that changes as students use it.”
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