#HighSchool #education in #SouthAfrica is completely warped by #exams. My daughter’s school in particular has gone into an almost suspensive state for a month while ‘end-of-year’ exams are going on. It’s nowhere close to the end of the year, it’s madness!
There must be better ways.
I hang out with #educators who talk about how they are #data driven. They seem to misunderstand the importance of accurate interpretation of data.
While claiming to be "data-driven," educators are incredibly sloppy with their collection, analysis, management, and reporting of data. Their sloppiness derives from blind acceptance that tests measure what they proportion (and other unchallengeable assumptions), their reliance on single measures, and their lack of sophistication in analysis. Statisticians have created tools to help us understand large data sets. Let’s insist they start using those tools.
Two Types of Problems
Most jobs require workers to solve problems. Usually, we are taught to solve problems with this process: Set a goal, develop and implement a plan to solve it, and then decide if the goal was accomplished. That leads to new goals and the process is repeated.
1) One begins with goals-- we decide what we want to accomplish and how we will accomplish it.
2) Next, one decides what actions will result in the goal being accomplished. In designing these actions, planners assume that they know what will happen if they take the actions they developed. All that needs to happen is that the actions be taken or implemented.
3) After the actions are taken, one checks to see if the goals were accomplished. If not, then the steps are taken to redesign the actions or to improve the implementation of the actions.
This model is built upon the assumption that one can accurately predict the effects of actions. This is sometimes described as cause and effects. Everything we observe has a single cause (or a small number of causes) and one can know those clearly. The objective-based problem solving works best for problems that are:
- definable-- We can clearly identify the cause of the problem
- understandable-- We know what will happen if it is solved.
- consensual-- Most everyone agrees that it should be solved.
Someone who has a stroke has a definable (there is a blood clot preventing blood from flowing to a part of the brain), understandable (that part of the brain without blood will die), and consensual (the patient and his family and the medical team all want to clear to blood clot). Scholars refer to such problems as tame. Tame does not mean that it is not complex or important, only that the course of action is known.
Another collection of problems, especially those in the social sciences and those that deal with social issues and problems, are very difficult to define and understand, and not everyone agrees what to do or even that the problem should be solved.
These problems are referred to as wicked problems, and because cause and effect are difficult to establish with these problems, solving them is difficult or even impossible.
Making people “smart” is a good example of a wicked problem. No one really knows what it means to be smart (and smart depends on the situation-- knowing about computers is not much help when you need a plumber!), we don’t know how to make people smart (what helps me learn may not help you) and we don’t even agree that we should make people smart.
The importance of goals and objectives in the process is a relatively recent idea to be added to planners’ and managers’ work; before the 1960’s most workers focused on making their work more efficient or “better” and not “what should my system do?” In this figure, we see a version of “outcomes-based action” that has been common in recent decades:
What we mean by "they learned it:"
In addition to having strong foundational knowledge (what we traditionally understand learning to be), we want those who have “learned it” to be able to use it critically; they should be able to judge the quality of their knowledge and the degree to which it will suffice in the current situation. We want those who have “learned it” to be able to use it creatively; they should be able to use what they have learned to craft something that did not exist previously. We want those who have “learned it” to be able to use it pragmatically; when faced with real-world situations and challenges, an educated person should be able to use it to solve the problem. We want those who have “learned it” to value it and value the world differently because of it.
I'm interest in #teaching #learning #communitycollege #highered #onlinelearning #science #math #education #computers
Richard Feynmann served on the commission that investigated the Challenger disaster in 1986, and he observed that much empirical evidence had been ignored and that decisions that led to the disaster had been made for political reasons. Feynman concluded his observations, which appeared in an appendix rather than in the full report, with the statement “For a successful technology, reality must take precedence over public relations, for Nature cannot be fooled.” Progressive discourse requires the group to regard reality as Nature intends, and observation is necessary to expand the factual basis of progressive discourse.
Looking to find everyone who is still fighting to reduce the transmission of COVID-19.
We need to work together to lobby for governments to mandate masks, protect children in schools, and protect the vulnerable.
Thinking about facts...
Research depends on “facts.” In the vernacular, fact typically means information that is true and accurate; implicit also is the assumption that the fact is objectively defined so that every observer will agree on the both reality of the fact and the meaning of the fact. A more sophisticated view of facts recognizes the role that one’s perspective exerts on how one senses and interprets facts. In science, a fact is any idea that can be tested; and some are refuted by tests while others are supported by tests. Those facts refuted by observation are probably inaccurate, and those supported by observation are more likely to be true and accurate.
I've been rediscovering Wolfram Demonstrations after not using them for several years.
https://demonstrations.wolfram.com/
I sure appreciate the folks who develop and share them.
@garyackerman yeah, we focus on how faculty often have no training in #teaching, but an equally big issue is there's no training on #management & #leadership. HBR articles on toxic bosses were useful to me https://hbr.org/2018/09/what-to-do-when-you-have-a-bad-boss & here's a video on different types of toxic bosses https://youtu.be/m28OBk1o4d4
One of my “professional hobbies” has been observing leaders… how they act and react to circumstances, plan and implement initiatives, and interact with others. On occasion, I have decided I must distance myself from certain leaders.
I find reasons to avoid meetings at first, but eventually I am clear about my active dissociation from them.
One of the projects that has captured my attention lately is getting a test server with WeBWork up and running for the math faculty at my college.
https://openwebwork.org/
This is an open source project designed to provide a platform for "online math homework" (think MyMathLab).
We have the test up and running so the faculty can explore it, and will be moving to development in the spring, with a target of production for the fall semester.
I'd be glad to hear advice/ recommendations from folks who have rolled this out.
Platforms like #Twitter, #Facebook, #LinkedIn etc are all business that NEED to grow since we, the user are the products.
Revenue should not be a goal of a social platform, there are enough websites out there already who squeeze you for every penny in some form.
The internet wasn't created for profit alone but it seems many have forgotten..
Let's bring that back! ![]()
I keep seeing people say Mastodon is nothing like Twitter. And they’re right.
Mastodon is an echo of the old internet, it’s decentralised, chaotic. What you get depends on your sysadmin. You can’t search, everything has to be shared to you by a human. Networks split apart and rejoin. What you see is your unique connection to it.
Is this good? Maybe. But for me that’s the internet I grew up with. No algorithms, no targeted adverts, just human interaction, and it was glorious.
Dr. Gary Ackerman here. I’m a retired #science #math #computerscience #teacher and #technology coordinator. Now serving as the director of the teaching and learning innovation center at a #communitycollege in northeast USA.
My interests include online teaching and learning, educational technology, sound pedagogy, andragogy and heutagogy.
Director of Teaching and Learning Innovation at a community college in New England
Retired k-12 science/ math/ technology teacher/ technology integration specialist/ coordinator