Tallahassee Community College boosts affordability, access in math courses using open educational resources
In 2015 there were roughly 20 million students in the United States. Why is this significant? According to this release from Lumen Learning, which is similar to others I've seen, the use of open educational resources - specifically, open textbooks - saved 4,825 students some $535,000 in that same year. That's more than $1,000 per student, which means that this program alone, if applied nationally in the U.S. would save students $20 billion - with a B. Now this number is probably high - the average student spends only $900 on textbooks, and the entire market size is only $14 billion, so we're not going to hit $20 billion. But no matter what, the numbers are staggering. Which begs the question: why is this not happening?
At a certain point of overuse a word loses its meaning. "Transform" is one such word, according to Larry Cuban. "When it comes to school reform, as the quotes above indicate, the word 'transform' hits the jackpot of overhyped words in reformers’ vocabulary.... Yes, I have gotten allergic to the word 'transform' when it is applied to schooling. That allergy has prompted me to ask any policymaker, researcher, practitioner, high-tech entrepreneur, venture capitalist, or parent using the word, certain questions about what he or she means." What follows is a lost of questions that should be asked of people promoting transformation. What does it mean? What problems are being solved? What exactly is transformed? What does it become? How fast? Why is it better? But, of course, these questions could be asked against any of our buzzwords today - analytics, reform, open, online, whatever. And they should be asked. Slogans aren't plans.
MediaSmarts (which once upon a time had a much better name, the Media Awareness Network) has released the final installment of Use, Understand & Create: A Digital Literacy Framework for Canadian Schools. The framework "includes over 50 lessons and interactive games, organized by grade level from kindergarten to grade 12, that are aligned with seven aspects of digital literacy: finding and verifying; ethics and empathy; privacy and security; digital health; consumer awareness; community engagement; and making and remixing." View the framework here. For the theoretical background, see this "mapping of the features and focal points of digital literacy and digital citizenship from across the country": 75 page PDF.
This is an interesting and useful post. Bodong Chen first distinguishes between learning analytics and academic analytics (the former being directly concerned with teaching and learning) and educational data mining (the latter being more focused on the exploration of data from academic settings). He then outlines some areas of interest: first, the emphasis on big data in learning analytics, and second the need for it to consider the more nuanced aspects of learning itself. This leads to a discssion of the 'tensions': first, divisions based on different accounts of learning; second, the tension between learning and algorithms; third, agency and control; and fourth, the ethics of learning analytics. It's also worth viewing his Learning Analytics course.
Best takedown of the day: "The state of knowledge among people who have actually run large online communities is so far advanced beyond the research community that most research in this area is more amusing than helpful." The line comes from Michael Caulfield and he cites Clay Shirky on a pre-2011 truism about online community: "You have to find some way to protect your own users from scale... you can’t try to make the system large by taking individual conversations and blowing them up like a balloon." This of course is what we were also trying to do when we shifted our MOOC users away from the centralized Moodle platform and into their own blogs and communities. And as Caulfield says, "This is discovered repeatedly throughout history, and you could write a taxonomy of different techniques we use to protect users from scale." Image: Waltman and van Eck, community detection (worth a read in its own right).
I have a story I often tell. Suppose, I say, two people want to travel from Edmonton to Calgary. What's the best way to do this? Should they each get a separate car and race? Should they bid against each other for the one remaining car, with only the winner traveling? No, competition won't get these drivers to Calgary faster nor more efficiently. The rational thing to do is to share a ride. John Warner writes, "Competition works really well when the goal is to determine who is a winner and who is a loser and the winners benefit, receiving their tributes and rewards. When the rewards are outsized, or the punishment severe, truly terrible behaviors can result." He's right.