Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education
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Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education
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Rescooped by Kim Flintoff from Digital Learning - beyond eLearning and Blended Learning in Higher Education
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Curtin Teaching and Learning - eLearning: eLearning advisors

Curtin Teaching and Learning - eLearning: eLearning advisors | Learning Analytics, Educational Data Mining, Adaptive Learning 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.

 

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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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Progress for the U-Pace Online Instructional Approach

U-Pace instructors leverage learning management system analytics about student engagement and performance to provide proactive, personalized support. Evidence shows that the model increases success for all students, at risk or not.
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Analytics for the Classroom Teacher

Analytics for the Classroom Teacher | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
Do you want to be more reflective in your teaching practice and wonder if there are technologies that can help? Are you curious about how data-driven, evidence-based teaching practices can improve your students’ learning? This is the course for you!

Analytics for the Classroom Teacher is an introduction to the emerging field of teaching and learning analytics from the perspective of a classroom teacher.

Experts from all over the world will provide an overview of the current state-of-the-art in teaching and learning analytics. You’ll learn how teachers, curriculum developers and policy makers are collecting and analysing data from the classroom to help guide decisions at all levels.

The course will then focus on the school teacher, and how data analytics can help you to make improvements in your classroom.

You’ll learn to use analytics to improve your lesson plans and your delivery of those plans, and discover more about your students' learning.

No previous knowledge in data-driven instruction, teaching and learning analytics is needed. Join us and a large community of innovative teachers from around the globe and become a pioneer of teaching and learning analytics in your school.
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Understanding Learning and Learning Design in MOOCs: A Measurement-Based Interpretation | Milligan | Journal of Learning Analytics

Understanding Learning and Learning Design in MOOCs: A Measurement-Based Interpretation
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The Challenge of Understanding MOOC Data -- Campus Technology

The Challenge of Understanding MOOC Data -- Campus Technology | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
Four years after the launch of edX, the data generated by massive open online courses still mystifies many institutions. Could inter-university collaboration unlock the secrets to better course delivery?
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Learning analytics don't just measure students' progress – they can shape it

Learning analytics don't just measure students' progress – they can shape it | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
From online forum debates to predictive essay writing software, data showing how students learn can help universities adapt their teaching
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José Luis M. León's curator insight, September 15, 1:43 AM
El Aprendizaje de Análisis No Solo Medir El Progreso de los Estudiantes - Que pueden Darle forma
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Big data's deluge in higher ed

As college students click, swipe and tap through their daily lives – both in the classroom and outside of it – they're creating a digital footprint of how they think, learn and behave that boggles the mind.
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Big Data Analysis in Higher Education: Promises and Pitfalls

Big Data Analysis in Higher Education: Promises and Pitfalls | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it

In short, we want educational predictions to be wrong. If our predictive model can tell that a student is going to fail, we want that to be true only in the absence of intervention. If the student does in fact fail, that should be seen as a failure of the system. A predictive model should be part of a prediction-and-response system that (1) makes predictions that would be accurate in the absence of a response and (2) enables a response that renders the prediction incorrect (e.g., to accurately predict that, given a specific intervention, the student will succeed). In a good prediction-and-response system, all predictions would ultimately be negatively biased. The best way to empirically demonstrate this is to exploit random variation in the assignment of the system—for example, random assignment of the prediction-and-response system to some students but not all. This approach is rarely used in residential higher education but is newly enabled by digital data.The grand challenge in data-intensive research and analysis in higher education is to find the means to extract knowledge from the extremely rich data sets being generated today and to distill this into usable information for students, instructors, and the public.

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In the Clutches of Algorithms - Hybrid Pedagogy

In the Clutches of Algorithms - Hybrid Pedagogy | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
We sacrifice control in the name of convenience. As we become like cyborgs, we should expect more control over our technology. Tech has long aimed to provide additional conveniences for modern living, with the idea that a gadget would take care of something for us. The premise is that our lives are made easier when we worry less about the small stuff, stepping aside to allow technology do the grunt work. But the more we step aside, the less involved we are, and the less we control our environment, our information, our lives. We are giving algorithms control over increasingly complex aspects of our lives.

The idea of using an algorithm to care for humans has received popular attention recently with the case of a driver who died when his Tesla Model S drove underneath a semi that was crossing his lane. The car was in autopilot mode, with assistive radar and cameras activated; the driver died when the top of his car was sheared off by the underside of the semi trailer. Now begins the blame-aversion game that will become increasingly common as automation takes over automobiles: The car maker says autopilot is an assist feature and that the fault lies with the driver. Consumer Reports says the name “autopilot” suggests autonomy and that the fault lies with the software system. The driver — the one person directly affected by the incident — cannot share his take on things.
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Predictive Analytics: Nudging, Shoving, and Smacking Behaviors in Higher Education

Predictive Analytics: Nudging, Shoving, and Smacking Behaviors in Higher Education | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
Although nudging in small doses makes a difference, nudging is no panacea for all of the complex problems found in higher education. There are few studies that evaluate the overall effectiveness of nudging in changing behaviors and sustaining impact.6 Some studies even note the adverse effects of nudging.7 Like anything else in life, knowing when to use nudging — and when enough is enough — can be a challenge.

The answer is not simple. Perhaps the deepest concern lies in the definition of the problem and in who decides the direction of nudges. Nudging can easily become shoving or smacking. Obviously, the intentions behind most higher education practices are pure, but with new technologies, we need to know more about the intentions and remain vigilant so that the resulting practices don’t become abusive. The unintended consequences of automating, depersonalizing, and behavioral exploitation are real. We must think critically about what is most important: the means or the end.

With the transformative nature of new capabilities, we should explore both the opportunities and the threats associated with nudging in higher education. This is especially true at a time when academic credentials beyond the high school diploma are needed to acquire entry-level jobs, when colleges and universities are experiencing retention challenges, and when funding for higher education is decreasing. Nudging, used wisely, offers a promising opportunity to redirect students’ decisions and to contribute to the success of those students facing the steepest barriers.
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Moving the Red Queen Forward: Maturing Analytics Capabilities in Higher Education

Moving the Red Queen Forward: Maturing Analytics Capabilities in Higher Education | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
This article is drawn from the recent research by the EDUCAUSE Center for Analysis and Research (ECAR) and Gartner researchers on the state of analytics in higher education. This research explores the analytics trends as well as future predictions for the deployment of analytics technologies. Publications include The Analytics Landscape in Higher Education, 2015; Institutional Analytics in Higher Education; and Learning Analytics in Higher Education. More information about the analytics maturity index and deployment index can be found in the EDUCAUSE Core Data Service (participating) and the EDUCAUSE Benchmarking Service.
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Moving the Red Queen Forward: Maturing Analytics Capabilities in Higher Education

Moving the Red Queen Forward: Maturing Analytics Capabilities in Higher Education | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
Analytics progress in higher education is moving slowly, at best. How can colleges and universities mature their analytics capabilities without working twice as hard?
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Predictive Analytics: Nudging, Shoving, and Smacking Behaviors in Higher Education

Predictive Analytics: Nudging, Shoving, and Smacking Behaviors in Higher Education | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
With predictive analytics, colleges and universities are able to “nudge” individuals toward making better decisions and exercising rational behavior to enhance their probabilities of success.
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Getting past the broken teachers vs. tech debate

Getting past the broken teachers vs. tech debate | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it

The truth is, in the era of artificial intelligence, the most valued and secure jobs will be those that require complex social skills—such as teaching. Good teachers do more than just convey information. They coach and mentor their students to make learning relevant and meaningful, and they foster students’ interests in tackling complex, real-world problems. And while technology can replicate teachers’ expertise in dispensing information and assessing students’ knowledge of rote facts and skills, it is far from replacing the teacher’s role in providing expert feedback on critical thinking, communication, and leadership.


It’s important to remember, however, that substituting technologies for certain teaching tasks is a critical innovation for advancing the education system’s ability to serve all students. In the current factory-based educational model, teachers simply can’t give all their students the individualized feedback and coaching they need. But the more we utilize the best recorded lectures, documentary films, and instructional technologies to replace live lectures, the more we can free up teachers to spend their time working closely with their students to foster deeper learning. When students are able to get foundational knowledge and skills through technology-based instruction, teachers can evolve their purpose in the classroom and focus their time on providing expert feedback on higher-order skills and tackling complex, real-world problems with their students.

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Moving the Red Queen Forward: Maturing Analytics Capabilities in Higher Education

Moving the Red Queen Forward: Maturing Analytics Capabilities in Higher Education | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
Analytics progress in higher education is moving slowly, at best. How can colleges and universities mature their analytics capabilities without working twice as hard?
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UTA to design new models for networked group learning and online work settings for the third generation of internet, or Web 3.0 - UTA News Center

UTA to design new models for networked group learning and online work settings for the third generation of internet, or Web 3.0 - UTA News Center | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it

New research at The University of Texas at Arlington will analyze massive scale data traces from online work and learning communities to create new designs for networked learning and next generation knowledge building on the internet. 


The internet is today characterized by the convergence of ubiquitous connectivity, networked computing, and more intelligence through machine learning and artificial intelligence.  The data sets used include social networking sites, medical devices, telescopes and satellites to emails, streaming data, financial and commercial transactions.

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Gaps in education data: there are many questions for which we don't have accurate answers

Gaps in education data: there are many questions for which we don't have accurate answers | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
Education policy in Australia is being held back by a lack of data.
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Video: Gartner's Glenda Morgan on Learning Analytics

Video: Gartner's Glenda Morgan on Learning Analytics | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
Glenda Morgan talks about the current and future state of learning analytics.
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Leading the Way in Learning Analytics by @jessiebrown224

Leading the Way in Learning Analytics by @jessiebrown224 | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
Earlier this week my Ithaka S+R colleagues and I published “Student Data in the Digital Era: An Overview of Current Practices,” in which we review how institutions of higher education are currently using student data, and some of the practical and ethical challenges they face in doing so. As we conducted research for this report, part of our Responsible Use of Student Data in Higher Education project with Stanford University, we heard recurring concerns about the growing role of for-profit vendors in learning analytics. These third-party vendors, the argument goes, operate without the ethical obligations to students that institutions have, and design their products at a remove from the spaces where learning happens.
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What Clicks From 70,000 Courses Reveal About Student Learning

What Clicks From 70,000 Courses Reveal About Student Learning | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
Students who frequently check their grades throughout the semester tend to get better marks than do those who look less often.

That’s one of the findings from a new study by Blackboard, a company that sells course-management software to hundreds of colleges. It’s probably one of the deepest data dives ever done on student clicks on college web systems, analyzing aggregate data from 70,000 courses at 927 colleges and universities in North America during the spring 2016 semester.
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How Can Learning Analytics Improve the Student Experience? (EdSurge News)

How Can Learning Analytics Improve the Student Experience? (EdSurge News) | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it

Colleges and universities are doubling down on learning analytics. They’re trying to figure out how to better use the rich data they’re increasingly capturing about their students and how to improve our collective understanding of the impact of analytics on teaching and learning. At the Universit
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PAR at Hobsons: Joining Research and Predictive Models with Real Time Data -- Campus Technology

PAR at Hobsons: Joining Research and Predictive Models with Real Time Data -- Campus Technology | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
CT covered the Hobsons announcement this past January, 2016, of its acquisition of the PAR (Predictive Analytics Reporting) Framework. Here, we talk with Ellen Wagner, Chief Research Officer for the PAR Framework and VP for Research at Hobsons to get a brief update on the current work of PAR after its first eight months with Hobsons.
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Big Data Analysis in Higher Education: Promises and Pitfalls

Big Data Analysis in Higher Education: Promises and Pitfalls | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
Digital assessments have long been an effective means for freeing up instructors' time, particularly in blended learning settings, as well as for providing immediate formative feedback.17 Building on this work is the move to authentic assessment, to approaches in which humans and machines work in concert to quickly and accurately assess and provide feedback on student problems, where data is integrated from very diverse sources, and where data is collected longitudinally.18

With this shift we have, for the first time, data about virtually all aspects of students' skills, including the complex abilities that higher education attempts to foster—abilities that, in the modern economy, are more important than simple factual knowledge.19 We have the potential to assess postsecondary learners in ways that can improve depth, frequency, and response time, possibly expanding the scope with which students and instructors can monitor learning, including assessment of higher-level skills, and proving personalized feedback based on those assessments. However, the tools for understanding this data (e.g., edX ORA, Insights, EASE, and Discern) are still in their infancy. The grand challenge in data-intensive research and analysis in higher education is to find the means to extract such knowledge from the extremely rich data sets being generated today and to integrate these understandings into a coherent picture of our students, campuses, instructors, and curricular designs.
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Big Data Analysis in Higher Education: Promises and Pitfalls

Big Data Analysis in Higher Education: Promises and Pitfalls | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it
The grand challenge in data-intensive research and analysis in higher education is to find the means to extract knowledge from the extremely rich data sets being generated today and to distill this into usable information for students, instructors, and the public.
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Reflecting on Learning Analytics

Technological leaders must draw on the strengths of both the proponents and the skeptics in our communities to ensure that institutional mechanisms are in place to examine the overall efficacy of learning analytics systems.
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The Unpredictability of Predictive Analytics 2.0

The Unpredictability of Predictive Analytics 2.0 | Learning Analytics, Educational Data Mining, Adaptive Learning in Higher Education | Scoop.it

If I were into scrying (the art of predicting the future by gazing into a crystal ball), I would prophesy that EDUCAUSE Review readers will have two equal and opposite reactions on seeing an issue devoted to predictive analytics. The first reaction might be: "Are we still talking about how to use predictive analytics?" And the second reaction might be: "I wonder what predictive analytics we are using on our campus." We are all accustomed to tracking technologies that are emerging or that may seem to be more hype than substance, but what do we make of technologies like analytics? Here is a combination of tools and practices whose fundamental value is rarely questioned but that have not achieved the traction we might have expected by now. This issue of EDUCAUSE Review is a timely consideration of the state of predictive (and other) analytics across higher education: How are these tools and practices being used, how can they be better used, and how can institutions understand their own progress? How are the tools and practices of predictive analytics being used, how can they be better used, and how can institutions understand their own progress with analytics?

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