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Learning analytics adoption in Higher Education

SoLAR Webinar “Learning analytics adoption in Higher Education: Reviewing six years of experience at Open University UK”

presented by Prof. Bart Rienties from the Open University, The United Kingdom.

To register, go to

Time and date: Thursday, Jun 11, 2020, 5:00 PM – 6:00 PM Central European time

(11:00 AM–12:00 PM Eastern US time, 8:00 AM–9:00 AM Pacific US time, 4:00 PM–5:00 PM London, UK Time)

Location: Zoom (meeting URL provided in the registration email)

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Learning analytics, student satisfaction, and student performance

Learning analytics, student satisfaction, and student performance at the UK Open University
Rienties and his team linked 151 modules (courses) and 111,256 students with students’ behaviour, satisfaction and performance at the Open University UK, using multiple regression models.

There is little correlation between student course evaluations and student performance

The design of the course matters

Student feedback on the quality of a course is really important but it is more useful as a conversation between students and instructors/designers than as a quantitative ranking of the quality of a course.  In fact using learner satisfaction as a way to rank teaching is highly misleading. Learner satisfaction encompasses a very wide range of factors as well as the teaching of a particular course. 

this research provides quantitative evidence of the importance of learning design in online and distance teaching. Good design leads to better learning outcomes. We need a shift in the power balance between university and college subject experts and learning designers resulting in the latter being treated as at least equals in the teaching process.

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learning analytics

ACRL e-Learning webcast series: Learning Analytics – Strategies for Optimizing Student Data on Your Campus

Webcast One: Learning Analytics and the Academic Library: The State of the Art and the Art of Connecting the Library with Campus Initiatives
March 29, 2016

Webcast Two: Privacy and the Online Classroom: Learning Analytics, Ethical Considerations, and Responsible Practice
April 13, 2016

Webcast Three: Moving Beyond Counts and Check Marks: Bringing the Library into Campus-Wide Learning Analytics Programs
May 11, 2016

mobile technology, badges, flipped classrooms, and learning analytics according to Bryan Alexander

Very short video of Bryan Alexander, senior fellow at the National Institute for Technology in Liberal Education, discussing the issues and opportunities facing mobile technology, badges, flipped classrooms, and learning analytics:

learning systems in 2020

The Biggest Education Technology Trends for 2020 [Update]

#1: Big Data and Analytics

#2: Gamification

#3: Adaptive Learning

#4: MicroLearning

#5: Content Curation

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influential tools for online learning

Online Learning’s ‘Greatest Hits’

Robert Ubell (Columnist)     Feb 20, 2019

dean of web-based distance learning

Learning Management Systems

Neck and neck for the top spot in the LMS academic vendor race are Blackboard—the early entry and once-dominant player—and coming-up quickly from behind, the relatively new contender, Canvas, each serving about 6.5 million students . The LMS market today is valued at $9.2 billion.

Digital Authoring Systems

Faced with increasingly complex communication technologies—voice, video, multimedia, animation—university faculty, expert in their own disciplines, find themselves technically perplexed, largely unprepared to build digital courses.

instructional designers, long employed by industry, joined online academic teams, working closely with faculty to upload and integrate interactive and engaging content.

nstructional designers, as part of their skillset, turned to digital authoring systems, software introduced to stimulate engagement, encouraging virtual students to interface actively with digital materials, often by tapping at a keyboard or touching the screen as in a video game. Most authoring software also integrates assessment tools, testing learning outcomes.

With authoring software, instructional designers can steer online students through a mixtape of digital content—videos, graphs, weblinks, PDFs, drag-and-drop activities, PowerPoint slides, quizzes, survey tools and so on. Some of the systems also offer video editing, recording and screen downloading options

Adaptive Learning

As with a pinwheel set in motion, insights from many disciplines—artificial intelligence, cognitive science, linguistics, educational psychology and data analytics—have come together to form a relatively new field known as learning science, propelling advances in a new personalized practice—adaptive learning.


Of the top providers, Coursera, the Wall Street-financed company that grew out of the Stanford breakthrough, is the champion with 37 million learners, followed by edX, an MIT-Harvard joint venture, with 18 million. Launched in 2013, XuetangX, the Chinese platform in third place, claims 18 million.

Former Yale President Rick Levin, who served as Coursera’s CEO for a few years, speaking by phone last week, was optimistic about the role MOOCs will play in the digital economy. “The biggest surprise,” Levin argued, “is how strongly MOOCs have been accepted in the corporate world to up-skill employees, especially as the workforce is being transformed by job displacement. It’s the right time for MOOCs to play a major role.”

In virtual education, pedagogy, not technology, drives the metamorphosis from absence to presence, illusion into reality. Skilled online instruction that introduces peer-to-peer learning, virtual teamwork and other pedagogical innovations stimulate active learning. Online learning is not just another edtech product, but an innovative teaching practice. It’s a mistake to think of digital education merely as a device you switch on and off like a garage door.

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learning experience design

Building a Learning Innovation Network

new interdisciplinary field of learning innovation emerging.

Learning innovation, as conceptualized as an interdisciplinary field, attempts to claim a space at the intersection of design, technology, learning science and analytics — all in the unique context of higher education.

professional associations, such as POD, ELI, UPCEA, ( OLC (, ASU GSV ( and SXSW Edu ( — among many other conferences and events put on by professional associations.

A professional community of practice differs from that of an interdisciplinary academic network. Professional communities of practice are connected through shared professional goals. Where best practices and shared experiences form the basis of membership in professional associations, academic networks are situated within marketplaces for ideas. Academic networks run on the generation of new ideas and scholarly exchange. These two network models are different.


“Learning Experience Design™ is a synthesis of Instructional Design, educational pedagogy, neuroscience, social sciences, design thinking, and User Experience Design.”

The Process: ADDIE Vs. Design Thinking

The Process: ADDIE Vs. Design Thinking

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ELI 2018 Key Issues Teaching Learning

Key Issues in Teaching and Learning

A roster of results since 2011 is here.

ELI 2018 key issues

1. Academic Transformation

2. Accessibility and UDL

3. Faculty Development

4. Privacy and Security

5. Digital and Information Literacies
Three Models of Digital Literacy: Universal, Creative, Literacy Across Disciplines

United States digital literacy frameworks tend to focus on educational policy details and personal empowerment, the latter encouraging learners to become more effective students, better creators, smarter information consumers, and more influential members of their community.

National policies are vitally important in European digital literacy work, unsurprising for a continent well populated with nation-states and struggling to redefine itself, while still trying to grow economies in the wake of the 2008 financial crisis and subsequent financial pressures

African digital literacy is more business-oriented.

Middle Eastern nations offer yet another variation, with a strong focus on media literacy. As with other regions, this can be a response to countries with strong state influence or control over local media. It can also represent a drive to produce more locally-sourced content, as opposed to consuming material from abroad, which may elicit criticism of neocolonialism or religious challenges.

p. 14 Digital literacy for Humanities: What does it mean to be digitally literate in history, literature, or philosophy? Creativity in these disciplines often involves textuality, given the large role writing plays in them, as, for example, in the Folger Shakespeare Library’s instructor’s guide. In the digital realm, this can include web-based writing through social media, along with the creation of multimedia projects through posters, presentations, and video. Information literacy remains a key part of digital literacy in the humanities. The digital humanities movement has not seen much connection with digital literacy, unfortunately, but their alignment seems likely, given the turn toward using digital technologies to explore humanities questions. That development could then foster a spread of other technologies and approaches to the rest of the humanities, including mapping, data visualization, text mining, web-based digital archives, and “distant reading” (working with very large bodies of texts). The digital humanities’ emphasis on making projects may also increase

Digital Literacy for Business: Digital literacy in this world is focused on manipulation of data, from spreadsheets to more advanced modeling software, leading up to degrees in management information systems. Management classes unsurprisingly focus on how to organize people working on and with digital tools.

Digital Literacy for Computer Science: Naturally, coding appears as a central competency within this discipline. Other aspects of the digital world feature prominently, including hardware and network architecture. Some courses housed within the computer science discipline offer a deeper examination of the impact of computing on society and politics, along with how to use digital tools. Media production plays a minor role here, beyond publications (posters, videos), as many institutions assign multimedia to other departments. Looking forward to a future when automation has become both more widespread and powerful, developing artificial intelligence projects will potentially play a role in computer science literacy.

6. Integrated Planning and Advising Systems for Student Success (iPASS)

7. Instructional Design

8. Online and Blended Learning

In traditional instruction, students’ first contact with new ideas happens in class, usually through direct instruction from the professor; after exposure to the basics, students are turned out of the classroom to tackle the most difficult tasks in learning — those that involve application, analysis, synthesis, and creativity — in their individual spaces. Flipped learning reverses this, by moving first contact with new concepts to the individual space and using the newly-expanded time in class for students to pursue difficult, higher-level tasks together, with the instructor as a guide.

Let’s take a look at some of the myths about flipped learning and try to find the facts.

Myth: Flipped learning is predicated on recording videos for students to watch before class.

Fact: Flipped learning does not require video. Although many real-life implementations of flipped learning use video, there’s nothing that says video must be used. In fact, one of the earliest instances of flipped learning — Eric Mazur’s peer instruction concept, used in Harvard physics classes — uses no video but rather an online text outfitted with social annotation software. And one of the most successful public instances of flipped learning, an edX course on numerical methods designed by Lorena Barba of George Washington University, uses precisely one video. Video is simply not necessary for flipped learning, and many alternatives to video can lead to effective flipped learning environments [].

Myth: Flipped learning replaces face-to-face teaching.

Fact: Flipped learning optimizes face-to-face teaching. Flipped learning may (but does not always) replace lectures in class, but this is not to say that it replaces teaching. Teaching and “telling” are not the same thing.

Myth: Flipped learning has no evidence to back up its effectiveness.

Fact: Flipped learning research is growing at an exponential pace and has been since at least 2014. That research — 131 peer-reviewed articles in the first half of 2017 alone — includes results from primary, secondary, and postsecondary education in nearly every discipline, most showing significant improvements in student learning, motivation, and critical thinking skills.

Myth: Flipped learning is a fad.

Fact: Flipped learning has been with us in the form defined here for nearly 20 years.

Myth: People have been doing flipped learning for centuries.

Fact: Flipped learning is not just a rebranding of old techniques. The basic concept of students doing individually active work to encounter new ideas that are then built upon in class is almost as old as the university itself. So flipped learning is, in a real sense, a modern means of returning higher education to its roots. Even so, flipped learning is different from these time-honored techniques.

Myth: Students and professors prefer lecture over flipped learning.

Fact: Students and professors embrace flipped learning once they understand the benefits. It’s true that professors often enjoy their lectures, and students often enjoy being lectured to. But the question is not who “enjoys” what, but rather what helps students learn the best.They know what the research says about the effectiveness of active learning

Assertion: Flipped learning provides a platform for implementing active learning in a way that works powerfully for students.

9. Evaluating Technology-based Instructional Innovations

Transitioning to an ROI lens requires three fundamental shifts
What is the total cost of my innovation, including both new spending and the use of existing resources?

What’s the unit I should measure that connects cost with a change in performance?

How might the expected change in student performance also support a more sustainable financial model?

The Exposure Approach: we don’t provide a way for participants to determine if they learned anything new or now have the confidence or competence to apply what they learned.

The Exemplar Approach: from ‘show and tell’ for adults to show, tell, do and learn.

The Tutorial Approach: Getting a group that can meet at the same time and place can be challenging. That is why many faculty report a preference for self-paced professional in simple self-assessment checks. We can add prompts that invite people to engage in some sort of follow up activity with a colleague. We can also add an elective option for faculty in a tutorial to actually create or do something with what they learned and then submit it for direct or narrative feedback.

The Course Approach: a non-credit format, these have the benefits of a more structured and lengthy learning experience, even if they are just three to five-week short courses that meet online or in-person once every week or two.involve badges, portfolios, peer assessment, self-assessment, or one-on-one feedback from a facilitator

The Academy Approach: like the course approach, is one that tends to be a deeper and more extended experience. People might gather in a cohort over a year or longer.Assessment through coaching and mentoring, the use of portfolios, peer feedback and much more can be easily incorporated to add a rich assessment element to such longer-term professional development programs.

The Mentoring Approach: The mentors often don’t set specific learning goals with the mentee. Instead, it is often a set of structured meetings, but also someone to whom mentees can turn with questions and tips along the way.

The Coaching Approach: A mentor tends to be a broader type of relationship with a person.A coaching relationship tends to be more focused upon specific goals, tasks or outcomes.

The Peer Approach:This can be done on a 1:1 basis or in small groups, where those who are teaching the same courses are able to compare notes on curricula and teaching models. They might give each other feedback on how to teach certain concepts, how to write syllabi, how to handle certain teaching and learning challenges, and much more. Faculty might sit in on each other’s courses, observe, and give feedback afterward.

The Self-Directed Approach:a self-assessment strategy such as setting goals and creating simple checklists and rubrics to monitor our progress. Or, we invite feedback from colleagues, often in a narrative and/or informal format. We might also create a portfolio of our work, or engage in some sort of learning journal that documents our thoughts, experiments, experiences, and learning along the way.

The Buffet Approach:

10. Open Education

Figure 1. A Model for Networked Education (Credit: Image by Catherine Cronin, building on
Interpretations of
Balancing Privacy and Openness (Credit: Image by Catherine Cronin. CC BY-SA)

11. Learning Analytics

12. Adaptive Teaching and Learning

13. Working with Emerging Technology

In 2014, administrators at Central Piedmont Community College (CPCC) in Charlotte, North Carolina, began talks with members of the North Carolina State Board of Community Colleges and North Carolina Community College System (NCCCS) leadership about starting a CBE program.

Building on an existing project at CPCC for identifying the elements of a digital learning environment (DLE), which was itself influenced by the EDUCAUSE publication The Next Generation Digital Learning Environment: A Report on Research,1 the committee reached consensus on a DLE concept and a shared lexicon: the “Digital Learning Environment Operational Definitions,

Figure 1. NC-CBE Digital Learning Environment

Academic libraries teaching and learning outcomes

Chad, K., & Anderson, H. (2017). The new role of the library in teaching and learning outcomes (p. ). Higher Education Library Technology.
p. 4 “Modern university libraries require remote access for large numbers of concurrent users, with fewer authentication steps and more flexible digital rights management (DRM) to satisfy student demand”. They found the most frequent problem was that core reading list titles were not available to libraries as e-books.
p. 5 Overcoming the “textbook taboo”
In the US, academic software firm bepress notes that, in response to increased student textbook costs: “Educators, institutions, and even state legislators are turning their attention toward Open Educational Resources (OER)” in order to save students money while increasing engagement and retention. As a result bepress has developed its infrastructure to host and share OER within and across institutions.21 The UMass Library Open Education Initiative estimates it has saved the institution over $1.3 million since its inception in 2011. 22 Other textbook initiatives include SUNY Open Textbooks, developed by the State University of New York Libraries, which has already published 18 textbooks, and OpenStax, developed by Rice University.
p.5. sceptics about OER rapid progress still see potential in working with publishers.
Knowledge Unlatched 23 is an example of this kind of collaboration: “We believe that by working together libraries and publishers can create a sustainable route to Open Access for scholarly books.” Groups of libraries contribute to fund publication though a crowdfunding platform. The consortium pays a fixed upfront fee for the publisher to publish the book online under a Creative Commons license.
p.6.Technology: from library systems to educational technology.The rise of the library centric reading list system
big increase in the number of universities in the UK, Australia and New Zealand deploying library reading lists solutions.The online reading list can be seen as a sort of course catalogue that gives the user a (sometimes week-by-week) course/module view on core resources and provides a link to print holdings information or the electronic full text. It differs significantly from the integrated library system (ILS) ‘course reserve’ module, notably by providing access to materials beyond the items in the library catalogue. Titles can be characterised, for example as ‘recommended’ or ‘essential’ reading and citations annotated.
Reading list software brings librarians and academics together into a system where they must cooperate to be effective. Indeed some librarians claim that the reading list system is a key library tool for transforming student learning.
Higher education institutions, particularly those in Australia, New Zealand and some other parts of Europe (including the UK) are more likely to operate a reading list model, supplying students with a (sometimes long) list of recommended titles.
p.8. E-book platforms (discusses only UK)
p.9. Data: library management information to learning analytics
p.10. Leadership
“Strong digital leadership is a key feature of effective educational organisations and its absence can be a significant barrier to progress. The digital agenda is therefore a leadership issue”. 48 (Rebooting learning for the digital age: What next for technology-enhanced higher education? Sarah Davies, Joel Mullan, Paul Feldman. Higher Education Policy Institute (HEPI) Report 93. February 2017. )
A merging of LibTech and EdTech
The LITA discussion is under RE: [lita-l] Anyone Running Multiple Discovery Layers?
from Ken Varnum:

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personalized learning

Personalized Learning: What It Really Is and Why It Really Matters


Personalized Learning: What It Really Is and Why It Really Matters

The following is a re-post from a 2016 EDUCAUSE Review article of ours with minor updates.

(1) the circumstances under which personalized learning can help students and

(2) the best way to evaluate the real educational value for products that are marketed under the personalized learning banner.

The most descriptive label we could come up with for the practices that the two of us have observed in our school visits might be undepersonalized teaching.

The most stereotypical depersonalized teaching experience is the large lecture class, but there are many other situations in which teachers do not connect with individual students and/or meet the students’ specific needs. For example, even a small class might contain students with a wide-enough range of skills, aptitudes, and needs that the teacher cannot possibly serve them all equally well. Or a student may have needs (or aptitudes) that the teacher simply doesn’t get an opportunity to see within the amount of contact time that the class allows. The truth is that students fall through the cracks all the time, even in the best classes taught by the best teachers. Failing a course is the most visible evidence, but more often students drift through the class and earn a passing grade—maybe even a good grade—without getting any lasting educational benefit.

personalized learning as a practice rather than a product

Technology then becomes an enabler for increasing meaningful personal contact. In our observations, we have seen three main technology-enabled strategies for lowering classroom barriers to one-on-one teacher/student (and student/student) interactions:

  1. Moving content broadcast out of the classroom: Even in relatively small classes, a lot of class time can be taken up with content broadcast such as lectures and announcements. Personalized learning strategies often try to move as much broadcast out of class time as possible in order to make room for more conversation. This strategy is sometimes called “flipping” because it is commonly accomplished by having the teacher record the lectures they would normally give in class and assign the lecture videos as homework,
  2. Turning homework time into contact time: In a traditional class, much of the work that the students do is invisible to the teacher. For some aspects, such as homework problems, teachers can observe the results but are often severely limited by time constraints.Personalized learning approaches often allow the teacher to observe the students’ work in digital products, so that there is more opportunity to coach students.
  3. Providing tutoring: Sometimes students get stuck in problem areas that don’t require help from a skilled human instructor. Although software isn’t good at teaching everything, it can be good at teaching some things. Personalized learning approaches can offload the tutoring for those topics to adaptive learning software that gives students interactive feedback while also turning the students’ work into contact time by making it observable to the teacher at a glance through analytics.


personalized learning

In the business world, an analogous initiative might be called “business process redesign.” Emphasis is on process. The primary question being asked is, “What is the most effective way to accomplish the goal?” The redesigned process may well need software, but it is the process itself that matters. In personalized learning, the process we are redesigning is that of teaching individual students what they need to learn from a class as effectively as possible (though we can easily imagine applying the same kind of exercise to improving advising, course registration, or any other important function).

Self-Regulated Learning

Students in the course spend part of their class time in a computer lab, working at their own pace through an adaptive learning math program. Students who already know much of the content can move through it quickly, giving them more time to master the concepts that they have yet to learn. Students who have more to learn can take their time and get tutoring and reinforcement from the software. Teachers, now freed from the task of lecturing, roam the room and give individual attention to those students who need it. They can also see how students are doing, individually and as a class, through the software’s analytics. But the course has another critical component that takes place outside the computer lab, separate from the technology. Every week, the teachers meet with the students to discuss learning goals and strategies. Students review the goals they set the previous week, discuss their progress toward those goals, evaluate whether the strategies they used helped them, and develop new goals for the next week.

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