Learning Analytics in Higher Education
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Scaling Learning Analytics across Institutions of Higher Education | EDUCAUSE.edu

Scaling Learning Analytics across Institutions of Higher Education | EDUCAUSE.edu | Learning Analytics in Higher Education | Scoop.it

Abstract

The Open Academic Analytics Initiative (OAAI) seeks to increase college student retention by performing early detection of academic risk using predictive analytics. OAAI examined the degree to which a model built using data from Marist College would compare to the original model built at Purdue University, and we found the models to be statistically similar. For the research project reported in this paper, the OAAI sought to improve understanding of how learning analytics can best be scaled across institutions of higher education. During the spring 2012 semester, the OAAI successfully deployed an open-source learning analytics solution at two community colleges (Cerritos College and College of the Redwoods) and one historically black university (Savannah State University) as a means to further research in this emerging field.

The Seeking Evidence of Impact (SEI) program is intended to bring the teaching and learning community into a discussion about ways of gathering evidence of the impact of our innovations and current practices. The goal of the SEI case studies is to provide examples of successful projects evaluating the impact of innovation, technology, and best practices in teaching and learning.

In addition to the SEI case studies, you may find other ELI resources useful in addressing teaching, learning, and technology issues at your institution. To learn more, please visit the ELI Resources page.

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Curtin Teaching and Learning - eLearning: eLearning advisors

Curtin Teaching and Learning - eLearning: eLearning advisors | Learning Analytics in Higher Education | Scoop.it
The diverse team of eLearning advisors provide elearning workshops, send out periodic newsletter, provide customised consultation, support the eScholar program and more.

 

Use the 'Filter' pull-down menu above to search for topics by keywords.


Via Kim Flintoff
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Craig Patterson's comment, June 13, 2013 1:52 AM
Is this link working?
Kim Flintoff's comment, June 13, 2013 2:12 AM
The website was redesigned and we disappeared ... This scoop is simply a flag about who's curating... We didn't expect anyone wold ever want to visit us.....
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Why Colleges Should Take the Lead in Learning Analytics Development (EdSurge News)

Why Colleges Should Take the Lead in Learning Analytics Development (EdSurge News) | Learning Analytics in Higher Education | Scoop.it

The college classroom has been impenetrable for those seeking to understand how students learn. This was more a function of the traditional methods of teaching and learning than a result of any intentional barriers. Student behaviors were fairly opaque—some note taking, possibly classroom discuss
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Humanizing Education's Algorithms (EdSurge News)

Humanizing Education's Algorithms (EdSurge News) | Learning Analytics in Higher Education | Scoop.it
No tutoring algorithm should be based purely on interaction data. The nuts and bolts of students’ learning experiences – from the lesson they are given through to the choice of knowledge representation – should be based on proven pedagogical principles. It is vital that these principles are baked into tutoring algorithms from the start, and that engineers work closely alongside pedagogical experts throughout the creation process.

Tutoring algorithms have a natural counterpart in real-time progress reports. It is the reports that are fed to parents and educators, who are ideally positioned to uncover the story behind each student’s data. They must be empowered to do exactly that.

An algorithmic approach is not sufficient to serve our students. Joshua has met with success because his teachers are active agents in his learning journey. His progress data may act as a guide, but it is Joshua’s teachers who can interpret his data within his unique context and take the relevant actions. For instance, the reports may highlight sporadic usage patterns (and give precise meaning to terms like ‘sporadic’) but the additional support that is needed for Joshua is a judgement best left to his school.

Algorithms and data need not be the mechanical vices of data scientists – with the right intentions, they can uplift educators and amplify their efforts to meet every the needs of every student. It is the combined potential of algorithms and human insight that will win the day.
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Gartners Hype Cycle, Dashboards en recent technology news

Gartners Hype Cycle, Dashboards en recent technology news | Learning Analytics in Higher Education | Scoop.it

In this episode we discuss successively:

Gartner Hype Cycle : What is it? What can you do? Provides reliable information? All this in response to the question of edublogger Don South Chesterman to a critical analysis of the latest Hype Cycle for Education. 


Dashboards in Education: The message center for digital learning? What is it? Why ask teachers, school administrators and directors so? How dashboards support differentiation and customization? And how can you use the Hype Cycle in this issue? 


Recent technology introductions, such as the Apple TV, which we tried in the meantime, the question that has to do an iPad Pro with a Tesla, the dirt-cheap laptop from Lenovo and ditto Lumia smartphone from Microsoft.

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What is Learning Analytics? – Infographic

What is Learning Analytics? – Infographic | Learning Analytics in Higher Education | Scoop.it

"Learning Analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs."


Via Beth Dichter, Gordon Dahlby, Pauline Farrell
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Jan MacWatters's curator insight, July 20, 2014 1:51 PM

This is definitely something that has piqued my interest to read more. about this topic..

Kiruthika Ragupathi's curator insight, July 20, 2014 7:47 PM

a simple but useful infographic!

John Lemos Forman's curator insight, July 20, 2014 10:55 PM

Muita expectativa mas ainda poucos resultados concretos ... De qualquer modo, esta se formando uma percepção de que o modelo educacional vai ser fortemente impactado nos próximos anos

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All Stakeholders Must Engage in Learning Analytics Debate -- Campus Technology

All Stakeholders Must Engage in Learning Analytics Debate -- Campus Technology | Learning Analytics in Higher Education | Scoop.it
Whether they like it or not, members of the educational technology community and faculty members should engage in debates about learning analytics algorithms, transparency and data access. That was the message delivered by George Siemens, executive director of the Learning Innovation and Networked Knowledge Research Lab (LINK) at the University of Texas at Arlington, during a recent Future Trends Forum discussion with futurist Bryan Alexander.

"You can hate something and still recognize that it is an important factor to think about," Siemens stressed. "You can think analytics are crap, but you can't deny they are influential. If we want a future that embodies values that are important to us, then we have to be active participants in the sociotechnical and economic spaces driving that change."
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WATCH: Google lets you speak in natural language & get analytics reports

WATCH: Google lets you speak in natural language & get analytics reports | Learning Analytics in Higher Education | Scoop.it
Think of it as Google Now, Siri or Alexa for Google Analytics. Speak the stat you want, get the answer, courtesy of machine learning.
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Ask and it shall be given...
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The barbell effect of machine learning

The barbell effect of machine learning | Learning Analytics in Higher Education | Scoop.it
Machine learning will have a barbell effect on the technology landscape. On one hand, it will democratize basic intelligence through the commoditization and..
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... diffusion of services such as image recognition and translation into software broadly. On the other, it will concentrate higher-order intelligence in the hands of a relatively small number of incumbents that control the lion’s share of their industry’s data.
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A Visual Introduction to Machine Learning

A Visual Introduction to Machine Learning | Learning Analytics in Higher Education | Scoop.it
What is machine learning? See how it works with our animated data visualization.
davidgibson's insight:
What if crowd-sourced knowledge is distilled into AI trees and then served up to decision-makers in the classroom?
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LLZ Uni Halle's curator insight, June 9, 5:59 AM
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Why You Should Use the Tin Can (xAPI) to Track Learner Activity

Why You Should Use the Tin Can (xAPI) to Track Learner Activity | Learning Analytics in Higher Education | Scoop.it
So why should you consider moving to the xAPI?
In addition to the items I've already mentioned, there are a number of advantages to using the xAPI. I've listed a few below:

You can provide detailed test results and the report results for multiple test scores
It will work outside of a browser or an LMS
You have more control over your content
You can easily transition from computers to mobile devices and you can use mobile apps for learning detailed results can be tracked from more in-depth games and simulations
You can track real-world performance. Say that you're running separate security tests that relate to training you are providing to your employees. You can have the results of those tests linked to their course; then adapt course content for those specific learners.
You can track the results form interactive learning objects and adaptive learning modules that are delivered based on a learners specific learning path.
The xAPI also makes it possible to learn over long periods of time as well as support team-based learning activities 
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All Stakeholders Must Engage in Learning Analytics Debate -- Campus Technology

All Stakeholders Must Engage in Learning Analytics Debate -- Campus Technology | Learning Analytics in Higher Education | Scoop.it
Higher education needs to actively participate in discussions around learning analytics algorithms, transparency and data access, says UT Arlington's George Siemens.
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Key Tensions in the Field of Learning Analytics

Key Tensions in the Field of Learning Analytics | Learning Analytics in Higher Education | Scoop.it
Learning analytics is an emerging, highly interdisciplinary field where many disciplines--such as education, computer science, and engineering--intersect. Since its first significant scholarly gathering in 2011, learning analytics has been increasingly mentioned in news, technical reports, academic publications, and grant solicitations. The surge of this nascent field rests on a promise--and also a premise--that digital traces of learning could be turned into actionable knowledge to promote learning and teaching.
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An Introduction to the xAPI | #LearnxAPI | MOOC

An Introduction to the xAPI | #LearnxAPI | MOOC | Learning Analytics in Higher Education | Scoop.it
Interest and use of the xAPI has really gathered a pace since the turn of the year, so we’re updating our very popular Learn xAPI MOOC with new content and conversations.
Join this MOOC to explore both the technical realities and the strategic possibilities of the xAPI.
If you want to write your first xAPI statement and understand the difference between an Activity Type and a Context Extension, this is the place to be.
Equally, if neither of these things mean a darn thing, we are the community that will help you make sense out of your data strategy, and your roadmap for the medium term.
This MOOC will be open to contribution and allow you to explore the content and conversations that best fit your needs.
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As Big Data Comes to College, Officials Wrestle to Set New Ethical Norms

As Big Data Comes to College, Officials Wrestle to Set New Ethical Norms | Learning Analytics in Higher Education | Scoop.it
But as more colleges experiment, they're facing complex questions about what to do with the findings the data-crunching reveals.

What, if anything, should students be told about the judgments institutions are making about them from the data footprints they’re leaving behind? Should companies be able to profit from that data? And should students have the right to opt out of being monitored?

Just as a new medical finding can create standards by which doctors provide care to their patients, does having such information establish a new standard of care for colleges?

"We are entering a new era of data and data responsibility," says Mitchell Stevens, an associate professor in Stanford University’s Graduate School of Education who has long pushed for ethical standards around educational data that go beyond legal issues of privacy or security. In an era of ubiquitous data, he says, colleges need to decide: "Are we acting responsibly as educators? What values are we trying to pursue and preserve?"

Those were also some of the questions Mr. Stevens put front and center this month at a private convening of several dozen academics and a smattering of ed-tech company and foundation leaders.
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Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement [Final Report 2016]

Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement [Final Report 2016] | Learning Analytics in Higher Education | Scoop.it
Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement1Executive SummaryThe analysis of data from user interactions with technologies is changing how organisations function, prioritise and compete in an international market. All industries have been influenced or impacted by the so-called digital revolution and the associated analysis of user data. In the higher education (HE) sector this wave of data analytics has flowed through to the concept of learning analytics (LA). This field of research has been touted as a game changer for education whereby the outcomes of LA implementations will address core education challenges. These include concerns regarding student retention and academic performance, demonstration of learning and teaching quality, and developing models of personalised and adaptive learning. While there is broad consensus across the sector as to the importance for LA there remain challenges in how such endeavours are effectively and efficiently rolled out across an organisation. The lack of institutional exemplars and resources that can guide implementation and build institutional capacity represents a significant barrier for systemic adoption. 

This report seeks to unpack these challenges to institutional adoption and provide new insights that can aid future implementations of LA and help advance the sophistication of such deployments. The study does so by interrogating the assumptions underpinning the adoption of LA in the Australian University sector and contrasting this with the perspectives of an international panel of LA experts. The findings and recommendations highlight the need for a greater understanding of the field of LA including the diversity of LA research and learning and teaching applications, alongside the promotion of capacity building initiatives and collaborations amongst universities, government bodies and industry.
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How Experience API (xAPI) - Tin Can API Tracks Informal Learning

How Experience API (xAPI) - Tin Can API Tracks Informal Learning | Learning Analytics in Higher Education | Scoop.it
Organizations can break free of the restrictions that learning management systems dictate and release learners to experience learning beyond the classroom. Learning doesn’t need to take place solely in front of a computer screen and keyboard. A system that tracks learning can take informal, social learning, or experiences in the real world and include those events alongside formal ones. When organizations can extend learning beyond a LMS and browser, employers can drive their employees’ learning opportunities and harness untapped potential.
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Scottish Exchange of Data (ScotXed)

Scottish Exchange of Data (ScotXed) | Learning Analytics in Higher Education | Scoop.it

The ScotXed Unit is part of Education Analytical Services Division within the Learning and Justice Directorate of the Scottish Government.

We support and develop a significant number of secure, efficient and effective electronic data exchanges between partners in the Scottish Government and wider service communities. ScotXed initially collected data solely for the Learning and Justice Directorate but is now being used more widely within the Scottish Government and its agencies. Data collections include the pupil and staff censuses, Looked After Children, Mental Health Benchmarking and Drug Treatment and Testing Orders to name a few. To view our extensive range of surveys, please see the links within the Data Collection Topics section below or the topics menu on the left hand side of this page.

We provide data to our analytical colleagues within the Scottish Government for national and international statistical publications. Analytical products contribute to the evidence base for policy development and making. We adhere to the principles of the National Code of Statistics and the Data Protection Act (1998) when collecting data for research and statistical purposes.

Kim Flintoff's insight:
System wide use of student data and learning analytics offers the potential for more coordination of support and resources, assisting students with progress and transitions, and other very positive social outcomes.... with all the usual caveats around ethical use and data security.
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Deep Learning and Neuromorphic Chips

Deep Learning and Neuromorphic Chips | Learning Analytics in Higher Education | Scoop.it
#DeepLearning and Neuromorphic Chips: https://t.co/LsqksBwFhF #BigData #DataScience #Analytics #MachineLearning
davidgibson's insight:
Will we someday have foundation courses for higher education on chips? Yep.
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Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?

Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? | Learning Analytics in Higher Education | Scoop.it
RT @SamiGhazali: What software you used for #Analytics, #DataMining, #DataScience, #MachineLearning projects? https://t.co/2bLGMbKTzZ via @…
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It's always helpful to see what others are doing...
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The future of machine learning: 5 trends to watch around algorithms, cloud, IoT, and big data

The future of machine learning: 5 trends to watch around algorithms, cloud, IoT, and big data | Learning Analytics in Higher Education | Scoop.it
Algorithms. Cloud. Internet of Things. Data. No one can predict the future of technology with 100 percent accuracy. But these four pillars are certainly at
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The trouble with big data? It’s called the ‘recency bias’

The trouble with big data? It’s called the ‘recency bias’ | Learning Analytics in Higher Education | Scoop.it
There’s a counter-intuitive downside to collecting vast amounts of information about economics and human behaviour: it can mean we ignore the lessons of history.
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Addressing Information Security and Privacy in Postsecondary Education Data Systems

Key Takeaways
Any national postsecondary education data system will consist of a large collection of data designed to provide useful and reliable information about postsecondary student success and outcomes.
Using this data to improve student outcomes must acknowledge the necessity of maintaining the privacy of students and their families and properly securing any data sets containing personally identifiable data.
With thoughtful planning, comprehensive information security and privacy practices can be implemented within the national postsecondary education data infrastructure in a way that reduces risk, safeguards data, and ensures transparency, accountability, and trust.
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Drive education towards technology: Pearson boss

Drive education towards technology: Pearson boss | Learning Analytics in Higher Education | Scoop.it

The chief executive of the Australian branch of multinational private education firm Pearson will warn an audience of educators that they risk becoming irrelevant in the hot-topic education debate.


In his speech to next week’s EduTECH conference in Brisbane, David Barnett, chief executive of Pearson Australia, will say those who work in education will have their voices drowned out in the economic and political debate about their own industry.


“Right now, some Australian educators are in danger of descending into irrelevance, by allowing economists to lead the debate about the future skills and jobs of Australians,” Barnett’s speech reads. “This should be the domain of educators.”


An advance copy of the speech was provided by Pearson Australia to Campus Review.


And where does Barnett want educators to focus the debate? The answer is technology. More specifically, education “should wrap our arms around technology just as tight as business does”, and acknowledge that automation will take away jobs. This is as inevitable as the implications of the car were for the horse and carriage.”


This line is part of a framework for the major part of Barnett’s promotion of artificial intelligence-assisted learning, the new business push for Pearson. The company released a report earlier this year, Intelligence Unleashed: an argument for AI in Education – in collaboration with experts from the UK’s University College London – promoting the case for unleashing AI in education.
AI has great potential, Barnett argues, from learning analytics in higher education, to online education games in schools. Barnett believes that by embracing technology, teachers can remove “distractors” to education.


“One day soon, we’ll look back on a past limited by what John Hattie [Australian Institute for Teaching and School Leadership chair and University of Melbourne education academic] calls ‘distractors’, like student-teacher ratios, and marvel not at how much has changed, but at how long we clung to the past,” Barnett’s speech reads.

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Why Colleges Should Take the Lead in Learning Analytics Development (EdSurge News)

Why Colleges Should Take the Lead in Learning Analytics Development (EdSurge News) | Learning Analytics in Higher Education | Scoop.it
The college classroom has been impenetrable for those seeking to understand how students learn. This was more a function of the traditional methods of teaching and learning than a result of any intentional barriers. Student behaviors were fairly opaque—some note taking, possibly classroom discuss
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Strayer U Pilot Shows Student Success with Predictive Analytics -- Campus Technology

Strayer U Pilot Shows Student Success with Predictive Analytics -- Campus Technology | Learning Analytics in Higher Education | Scoop.it
A university that has been testing out the use of an analytics infrastructure to improve student outcomes and retention has reported positive results, including improvements in attendance, course drop rates and course success.

Strayer University, a for-profit institution with about 43,000 on-campus and online students in its fall 2015 enrollment, has been working with analytics software from Civitas Learning, including Student Insights Platform and Inspire for Faculty. The first is an online program that integrates and analyzes data from multiple sources and applies predictive analytics to provide users with information about student behavior and engagement. The second helps instructors pinpoint those students who are doing well and those who are struggling in order to target outreach and interactions.
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