leadership is about people, not production. it doesn’t matter how big of an expert you are in your field, mastery isn’t the same as influence.
The problem is that the people on your team aren’t you. Not only are their strengths and talents different — their output is different.
So, you wind up putting controls in place.
controls are temporary in their results. They don’t create loyalty or a following.
Not only that, but the controls work against you because by their very nature, they make people feel as if they aren’t trusted. And that lack of trust is a huge limiter on your influence.
Trust has to be given before it is received, and there is no influence without trust.
why do so many organizations rely on control to produce output? In short, because control is far easier to achieve than influence. control like sugar. It’s easy to get. It’s addictive. It’s tasty. It feels good and feeds our ego. It provides instant rewards — so we often ignore that it’s not a long-term strategy. Besides, control is a quick fix when it comes to output.If control is sugar, then influence is more like protein. It’s full of the building blocks for muscle. It takes time and consistency to build. But it also has more strength and staying power.
Importantly, today’s educators in the digital-era have a range of new teaching methods, activities and resources they can consider when choosing their learning designs. Although the traditional face-to-face lecture is not dead, delivering a monologue for an hour to a passive audience of learners is hardly the gold standard of good teaching in the 21st Century–irrespective of delivery mode. This point should not be overlooked in the rush to replace conventional teaching with live online sessions using platforms like Zoom.
Most importantly, what we want to avoid is using old 19th Century teaching methods on new 21st Century technologies to merely dump large volumes of undigested information down large digital diameter pipes to relatively inactive and passive learners.
ICDE has a series of forthcoming webinars you can join and you will find around a dozen different types of online course offerings available right now for educators on our NIDL Resource Bank.
The error I see many beginning to make is forgetting about the diverse needs of our younger students or, worse, pushing tools intended for older students on younger ones. When considering immersive technology resources for our early elementary students, I’ve shared some important, practical areas to keep in mind.
Algorithmic test proctoring’s settings have discriminatory consequences across multiple identities and serious privacy implications.
While racist technology calibrated for white skin isn’t new (everything from photography to soap dispensers do this), we see it deployed through face detection and facial recognition used by algorithmic proctoring systems.
While some test proctoring companies develop their own facial recognition software, most purchase software developed by other companies, but these technologies generally function similarly and have shown a consistent inability to identify people with darker skin or even tell the difference between Chinese people. Facial recognition literally encodes the invisibility of Black people and the racist stereotype that all Asian people look the same.
As Os Keyes has demonstrated, facial recognition has a terrible history with gender. This means that a software asking students to verify their identity is compromising for students who identify as trans, non-binary, or express their gender in ways counter to cis/heteronormativity.
These features and settings create a system of asymmetric surveillance and lack of accountability, things which have always created a risk for abuse and sexual harassment. Technologies like these have a long history of being abused, largely by heterosexual men at the expense of women’s bodies, privacy, and dignity.
my note: I am repeating this for years
Sean Michael Morris and Jesse Stommel’s ongoing critique of Turnitin, a plagiarism detection software, outlines exactly how this logic operates in ed-tech and higher education: 1) don’t trust students, 2) surveil them, 3) ignore the complexity of writing and citation, and 4) monetize the data.
Technological Solutionism
Cheating is not a technological problem, but a social and pedagogical problem.
Our habit of believing that technology will solve pedagogical problems is endemic to narratives produced by the ed-tech community and, as Audrey Watters writes, is tied to the Silicon Valley culture that often funds it. Scholars have been dismantling the narrative of technological solutionism and neutrality for some time now. In her book “Algorithms of Oppression,” Safiya Umoja Noble demonstrates how the algorithms that are responsible for Google Search amplify and “reinforce oppressive social relationships and enact new modes of racial profiling.”
Anna Lauren Hoffmann, who coined the term “data violence” to describe the impact harmful technological systems have on people and how these systems retain the appearance of objectivity despite the disproportionate harm they inflict on marginalized communities.
This system of measuring bodies and behaviors, associating certain bodies and behaviors with desirability and others with inferiority, engages in what Lennard J. Davis calls the Eugenic Gaze.
Higher education is deeply complicit in the eugenics movement. Nazism borrowed many of its ideas about racial purity from the American school of eugenics, and universities were instrumental in supporting eugenics research by publishing copious literature on it, establishing endowed professorships, institutes, and scholarly societies that spearheaded eugenic research and propaganda.
Moving instruction online can enable the flexibility of teaching and learning anywhere, anytime, but the speed with which this move to online instruction is expected to happen is unprecedented and staggering.
“Online learning” will become a politicized term that can take on any number of meanings depending on the argument someone wants to advance.
Online learning carries a stigma of being lower quality than face-to-face learning, despite research showing otherwise. These hurried moves online by so many institutions at once could seal the perception of online learning as a weak option
Researchers in educational technology, specifically in the subdiscipline of online and distance learning, have carefully defined terms over the years to distinguish between the highly variable design solutions that have been developed and implemented: distance learning, distributed learning, blended learning, online learning, mobile learning, and others. Yet an understanding of the important differences has mostly not diffused beyond the insular world of educational technology and instructional design researchers and professionals.
Typical planning, preparation, and development time for a fully online university course is six to nine months before the course is delivered. Faculty are usually more comfortable teaching online by the second or third iteration of their online courses.
In contrast to experiences that are planned from the beginning and designed to be online, emergency remote teaching (ERT) is a temporary shift of instructional delivery to an alternate delivery mode due to crisis circumstances. It involves the use of fully remote teaching solutions for instruction or education that would otherwise be delivered face-to-face or as blended or hybrid courses and that will return to that format once the crisis or emergency has abated.
A full-course development project can take months when done properly. The need to “just get it online” is in direct contradiction to the time and effort normally dedicated to developing a quality course. Online courses created in this way should not be mistaken for long-term solutions but accepted as a temporary solution to an immediate problem.
