blog by George Siemens reflecting on his first week of a dual-layer MOOC, October 28, 2014.
"I'm biased toward learners owning their own content and owning the spaces where they learn. My reason is simple: knowledge institutions mirror the architecture of knowledge in the era in which they exist. Today, knowledge is diverse, messy, partial, complex, and rapidly changing. What learners need today is not instructivism but rather a process of personal sensemaking and wayfinding where they learn to identify what is important, what matters, and what can be ignored. Most courses assume that the instructor and designer should sensemake for learners. The instructor chooses the important pieces, sets it in a structured path, and feeds content to learners. Essentially, in this model, we take away the sweet spot of learning. Making sense of topic areas through social and exploratory processes is the heart of learning needs in complex knowledge environments. "
Though I am biased toward learner-in-control, I do recognize the value of formal instruction, particularly when the topic area is new to a learner. Even then, I would like to see rapid transitions from content provision to having learners create artifacts that reflect their understanding. These artifacts can be images, audio, video, simulations, blog posts, or any other resource that can be created and shared with other learners. Learning transparently is an act of teaching.
Digest of ideas by Gwen Teatro, You Are Not the Boss of Me, reprinted 9/7/14, originally written in 2010.
Very interesting look at the Fifty Discipline by Peter Senge.
"There was a time when everyone was jumping onto The Learning Organization bandwagon. This usually happened when times were good, when organizations felt a little more ebullient...Budgets were cut....wisdom and decisions would only come from the few and learning for the many was a luxury no one could afford."
Learning Organization components
1. Vision--shared--may start with one person, it must be embraced and shared by all. Can be simple, i.e., Zappo's Delivering Happiness
2. Team learning--in an age where shared leadership is or will become critical, the need to understand the dynamics and functional operation of teams is pretty great--how team members communicate with each other, how they manage conflict, and how they examine their successes...and their failures
3. Personal Mastery--taking the time to study and understand our reality and our purpose
4. Mental models--dangers of clinging to and operating from narrow perspectives--assumptions and biases in our thinking
5. Systems thinking--paying attention to the connections between and among a variety of elements that make up the whole.
Algorithms have become one of the most powerful arbiters in our lives. They make decisions about the news we read, the jobs we get, the people we meet, the schools we attend and the ads we see. Yet there is growing evidence that algorithms and other types of software can discriminate.
The people who write them incorporate their biases, and algorithms often learn from human behavior, so they reflect the biases we hold.
Fairness, Accountability and Transparency in Machine Learning workshop, which considers the role that machines play in consequential decisions in areas like employment, health care and policing.
The tech world is notoriously resistant to regulation, but do you believe it might be necessary to ensure fairness in algorithms?
Yes, just as regulation currently plays a role in certain contexts, such as advertising jobs and extending credit.
Should computer science education include lessons on how to be aware of these issues and the various approaches to addressing them?
Absolutely!
Upstart has over the last 15 months lent $135 million to people with mostly negligible credit ratings. Typically, they are recent graduates without mortgages, car payments or credit card settlements.
ZestFinance, is a former Google executive whose company writes loans to subprime borrowers through nonstandard data signals.
someone has ever given up a prepaid wireless phone number. Where housing is often uncertain, those numbers are a more reliable way to find you than addresses; giving one up may indicate you are willing (or have been forced) to disappear from family or potential employers. That is a bad sign.
Character (though it is usually called something more neutral-sounding) is now judged by many other algorithms. Workday, a company offering cloud-based personnel software, has released a product that looks at 45 employee performance factors, including how long a person has held a position and how well the person has done. It predicts whether a person is likely to quit and suggests appropriate things, like a new job or a transfer, that could make this kind of person stay.
characterize managers as “rainmakers” or “terminators,”
“Algorithms aren’t subjective,” he said. “Bias comes from people.”
Algorithms are written by human beings. Even if the facts aren’t biased, design can be, and we could end up with a flawed belief that math is always truth.
blog post by Quentin Hardy, NYT, on how new companies developing algorithms are using them to loan money to people who are better risks than their financial circumstances might suggest, track high performers in sales jobs to find the indicators of their success for export and use by other employees, etc. July 26, 2015
Biases cause people to focus too much on success, take action too quickly, try too hard to fit in, and depend too much on experts.
Challenge #2: A fixed mindset.
The psychologist Carol Dweck identified two basic mindsets with which people approach their lives: “fixed” and “growth.” People who have a fixed mindset believe that intelligence and talents are largely a matter of genetics; you either have them or you don’t. They aim to appear smart at all costs and see failure as something to be avoided, fearing it will make them seem incompetent.
people who have a growth mindset seek challenges and learning opportunities.
A partner at the firm, Karena Strella, and her team believed the answer was individuals’ potential for improvement. After a two-year project that drew on academic research and interviews, they identified four elements that make up potential: curiosity, insight, engagement, and determination.
great HBR article by Gino and Staat on what organizational leaders need to do to learn and help their employees learn with reflection after doing among other actions. November 2015