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Finally. An episode on how search engines use AI to help (or not help) us find answers to questions.

The narrator likened search engines to library systems: They had to gather data, organise them, and find and present answers when needed.

The gathering of data is done by web crawlers — programmes that find and download web pages. The data is then organised by reverse indexes (like those at the back of textbooks).

The indexed web content is associated with numbers. Each time we search with an engine, these numbers are then linked to associated web content.

Example of indexing.

Since there is so much content, it needs to be ranked by accuracy, relevance, recency, etc. We help the AI to this with bounces (returning to the search) to click-throughs (staying with what we were presented).

The narrator also explained how we might be presented with immediate answers and not just links to possibly relevant web resources. AIs use knowledge bases instead of reverse indexes.

Knowledge bases might be built with NELL — Never Ending Language Learner. The video explains this better than I can.

NELL — Never Ending Language Learner.

Fair warning: Search engines still suck at questions that are rarely asked or are nuanced. AI is still limited by what data is available. This means that it is subject to the bias of people who provide data artefacts.

The next episode is about dealing with such bias. Now the series gets really interesting!

The latest round of PISA results for Singapore raise more questions than answers for me.

How about being number 10 in academics?

How about that striving for measures that actually mean something?

How about not playing the game of rankings and comparison?

Larry Cuban shared a collection of comics that provided commentary on kids and technology.

There is some truth in the funny frames, but they mostly rehashed unnuanced tropes. This is probably because everyone, their grandmother, and the occasional comic artist has an opinion about kids and technology.

Such opinion is rarely expert or informed. I cannot blame them if they are not students of edtech because this is a broad, complex, and ever-changing field. But I can point a critical finger at folks who do not bother to ask the kids or keep learning like kids.

A layperson’s view of edtech is not just inadequate, it is irresponsible particularly if that person is a teacher who internalises popular culture. This is why I promote professional development that addresses mindsets first. If we do not change attitudes and beliefs, we will not change behaviours.

I object to end-of-course student evaluations, particularly if the course is, say, only two sessions deep. Heck, they can happen at the end of a half semester (after about six sessions) or a full semester (about double the number of sessions) and I would still object.

This not because I got poor results when I was a teaching faculty member. Quite the opposite. I had flattering scores that were often just shy of perfect tens in a variety of courses I used to facilitate.

No, I object to such evaluations because they rarely are valid instruments. While they might seem to be about the effectiveness of the course, they are not. These evaluations are administrative and ranking tools for deciding which courses and faculty to keep.

Course evaluations are also not free from bias. Even if the questions are objective, the participants of the questionnaire are not. One of the biggest problems with end-of-course evaluations are that they can be biased against women instructors [1] [2] [3].

I would rather focus on student learning processes and evidence of learning. Such insights are not clearly and completely observable from what are essentially perception surveys.

If administrators took a leaf from research methodology, they might also include classroom observations, interviews, discourse analysis (e.g, of interactions), and artefact analysis (e.g., of lesson plans, resources, assignments, and projects).

But these are too much trouble to conduct, so administrators settle for shortcuts. Make no mistake, such questionnaires can be reliable when repeated over time, but they are not valid for what they purport to measure.

Some might say that end-of-course evaluations are a necessary evil. If so, they could be improved to focus on processes and products of learning. This article by Faculty Focus has these suggestions.

Article by Faculty Focus has these suggestions for questionnaires that focus on processes and products of learning.

Are there any takers?

Yes, let’s all acknowledge this dig at Singapore civil servants not getting as large a year-end bonus as they might have expected.

But let’s also take a step back and practice some nuanced thought. Those in the lower income brackets are getting more help than the higher earners [source]. And so they should.

The same source reported that overall bonus is “the lowest since 2009”. That might be the root of the tweet joke.

Now consider those who work without guaranteed bonuses. Our civil servants (teachers in particular) are afforded so much more than some workers elsewhere. The lower-bonus-than-expected is not just a first-world problem, it exists in an even more selective bubble.

Perhaps we should learn to look outside that thought bubble and reward ourselves with a bonus of gratitude — some of us fell on the right side of the divide and are enjoying the dividends. Perhaps those that did will offer the rest of us a handout.


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The message from Hank Green was about nuanced thought. His comical examples illustrate how “common” sense or superficial thinking are antithetical to fact, logic, and nuance.

It does not take that much dive deeper to make meaningful sense of what is going on. When challenged to skip the effort, we might consider what George Couros reflected on:

What is life, if not the sum of a hundred thousand daily battles and tiny decisions to either gut it out or give it up?

The small things matter because they add up. No “butts” about it.


When I say “basic literacy” now, I am not referring to being able to read. I am thinking about the ability to create.

Creating short content, e.g., in the form of tweets, requires both traditional literacy and the basic literacy of now. Take the tweet below for example. The creator of the tweet received numerous reminders from commenters how to copy and paste.

Once an item is in a device’s clipboard, there is no need to copy it again. Copying the same thing again is an unnecessary step and a boomer mistake.

But the next tweet was more on point.

It indirectly pointed out the need for better visual design. The specific concept to apply was contrast.

There is no point teaching kids content and skills they will not use. It is just as harmful to not teach them content and skills they will need.

There is no need to look into the crystal ball for what content and skills might be relevant in ten years. One just needs examples of what is important now.

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