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.
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
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:
McGraw Hill Plus, a new tool, Focusing first on math and then expanding to ELA and science, its objective is to make personalized learning scalable.
Smith: The modern classroom sits at the intersection of blended learning, competency-based learning and personalized learning.
reimagine instructional time and use technology to scale personalized learning.
First, pulling data into one place is the key fundamental driver that will change the teacher workflow. Second, we need to manipulate that data into some advanced data visualization tools, so it’s easy for teachers to understand and use. Third, we need to be able to visualize student performance and take action on it.
Using these data analytics, we can drive personalized learning based on student performance. And the last thing is the automation of teacher workflow.
eachers get data visualization from different sources, such as an adaptive software solution like our ALEKS program, our Redbird Mathematics, or our recently acquired Achieve3000 Literacy.
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.
MOOCs
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.
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.
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.”