Another dot in the blogosphere?


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Kyle Hill of Because Science was asked (at the 21min 40 sec mark): What is the thing you are most glad you learnt (or learned in US English)?

I was so struck by that question that I paused the video and drafted this reflection before I continued watching the video.

Hill had his answer and I have mine. I am glad that I learnt to question and to ask questions.

Schooling taught me not to question, or if I had to, to ask only safe questions. It was when I pursued postgraduate degrees that I learnt most to question what I had been taught and to ask good questions.

I was not taught to question nor was I taught strategies for questioning. Instead I observed more seasoned others in the form of professors and peers. It probably helped that I was in a different country where questioning and questions were the norm.

My regret is that I have spent only about half of my 30 years as a teacher and educator encouraging my learners to question. Questioning is one of the best ways to learn and I endeavour to work on my pedagogy of questions.

If you seek to indoctrinate, provide the answers. If you seek to educate, provide questions.


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This week’s episode focused on one example of supervised learning — how AI recognises human handwriting. This is a problem that was tackled quite a while ago (during the rise of tablet PCs) and is a safe bet as an example.

The boiled down basic AI ingredients are:

  1. Labelled datasets of handwriting
  2. Neural network programmed with initial rule sets
  3. Training, testing, and tweaking the rules

The oversimplified process might be: Convert handwritten letters to scanned pixels, allow the neural network to process the pixels, make the neural network learn by comparing its outputs with the labelled inputs, and reiterating until it reaches acceptable accuracy.

The real test is whether the neural network can read and interpret a previously unseen dataset. The narrator demonstrated how he imported and tweaked such data so that it was suitable for the neural network.

My takeaway was not the details because that is not my area of expertise nor my focus. It is the observation that the choice of datasets and how they are processed is key.

If there is not enough data or if there is only partial representation of a larger set, then we cannot blame AI entirely for mistakes. We make the data choices and their labels, so the fault is ours.

Ha, ha. We get the in-jokes in this tweet.

As you move from being schooled to being educated to being an educator, you experience harsh realities. But the realities are harsher.

An adjunct educator is not likely to get benefits like leave, insurance, or the equivalent of Medisave and CPF.

An adjunct is likely to be paid below market rate and not benefit from annual or other logical periodic increments.

An adjunct is likely to do some work effectively for free, e.g., attend meetings, provide consultations, grade papers.

An adjunct is likely to be highly qualified, skilled, and/or experienced, but none of those factor into renumeration spreadsheets.

The reality is less funny and not represented in a tweet. The reality are claims and observations that should be examined and addressed. The reality is that very little will happen.

The first Friday of September is Teachers’ Day in Singapore.

Schools celebrated the day yesterday with half days and staff dinners. Today is a school holiday and an early start to a one-week break.

Teachers’ Day is great for businesses that take advantage of it. But quite a few teachers still return to school during the break to get work done.

Whether teachers get to enjoy a break or not, they might be thankful that they were not subject to the rules of the past.


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We do not need a time machine to travel back to the past in order to reflect on how much (or how little) has changed, and to appreciate what we have now.

We do not need to share exactly the same contexts (US or Singapore) to appreciate how difficult it is to teach or how much more difficult it is to educate.

As if I needed more reasons to dislike how LMS are implemented…
 

 
The new semester started three weeks ago and I am already grading student work. This would be standard fare for me if not for two things that happened this year.

I discovered that I could no longer log in to the institutional LMS. This was the first time since 2015 that I had been locked out. Why? Someone in IT decided to remove all adjunct accounts without telling those most affected by the move.

So I started the first week of semester without access to the LMS. Fortunately, I do not rely much on the clunky layout and closed nature of the LMS. Instead I maintain almost all content and activities on an alternative platform.

Reminder number 1: Always have an alternative.

Fast forward to Week 3 when the first assignment was due for grading. Despite getting having my login reinstated, I discovered that all my customised Turnitin Feedback Studio (TFS) comments were gone. This meant I lost about eight semesters worth of cumulative work in one fell swoop.

I had taken the precaution of manually copying and pasting my comments to a private Google Doc. At last count six months ago, my collection of comments was nine page long. I am slowly repopulating my comments in TFS as I grade assignments.

Reminder number 2: Backup, backup, backup.

IT and LMS can be a good thing from an administrative and control point of view. But it is also disempowering and frustrating.


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This video provides some insights into why we seem to have a negative bias when it comes to news.

We are wired to pay more attention to bad news. Our brains process such information more thoroughly than good news. This might explain why we might focus on one criticism even though we also receive nine plaudits.

The surprise finding might be how social media might counter our Debbie downer tendency. The narrator highlighted studies that found how we might share and spread more positive content. Why?

We consume news as outside observers, but we use social media as active participants.

So actively sharing positive content might a coping and counter mechanism to how we are biologically wired.

But how we are wired keeps us vigilant. The point is not to shield ourselves or hide from bad news. That same news keeps us informed so that we can take action.

Recently I had the opportunity to provide my perspective on a policy and administrative design (PAD) of university courses. I call it a PAD because the pedagogy and learning design seemed secondary.

Consider two institutes of higher learning (IHLs). IHL A is more typical in that it offers 24 hours of class contact time over 12 weeks (i.e., 2-hour classes); IHL B offers 24 hours of class time over 6 weeks (i.e., a truncated semester with 4-hour classes).

Despite the stress place on learners in IHL B, its leaders rationalise that the number of teaching or contact hours from the truncated semester is effectively the same as a typical semester.

How? If you factor in public holidays, semester breaks, exams weeks, and other calendar interruptions, you might get similar numbers of contact time. If you design a curriculum on a spreadsheet, you might buy in to that argument.
 

 
Now consider some nuance by focusing on learning. Learning is like baking cookies. You might need to leave them in the oven for 15 minutes at 180°C. If you play the numbers game, you argue that you can bake the cookies in 7.5 minutes at 360°C.

However, you cannot bake cookies faster by simply increasing the temperature. You will burn them because rushing the physics affects the chemistry of baking.

Learning also takes time. Teaching might enable learning as does time allocated for learning. Teaching ability and time during and between classes are within the control of designers of courses. If we rely more on PAD and not on what research and practice tell us, we risk burning out our learners.

Students will still learn, but they will feel the heat of being rushed and overloaded. Assignments and assessments become even more dreaded deadlines (emphasis on dead). Left unchecked, the learning that happens, if any at all, becomes strategic and superficial instead of reflective and deep.

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