Category: 20. Learning sciences

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meaning-making, losing it?

kueh attended a meeting and heard his boss huat shared on his latest beloved AI tool called LnbkM, and how he used it to summarise the so many readings and articles on the internet. boss huat encouraged all at the meeting to do what he does.
as kueh listened, he scratched his head and felt confused. slipping his hand into his left pocket, he took out his recipe and flipped to some notes he jotted down just last week, when attending a talk by a professor How PL who shared the following (research-based) ideas abt human learning:

  • learning is about meaning-making. and reading is a means through which meaning-making takes place.
  • neuroscience (brain-based) research also suggested that cognitive functions may be lost if they are unused or used less — comprehension, analysis, synthesis to just name a few

what boss huat suggested was to let AI be the one to “read”, and humans only receiving the summary of “reading” by the AI. if so, who is doing the meaning-making? kueh wondered what would be the future of humans be like if they begin to lose the brain functions of meaning-making? if so, what does it mean to be humans anymore?

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only good and good-er

realised i have not posted to elaborate on the philosophy of ‘good-er’ although i have used the term in a few posts in the past. so shall do it now.

yes, it’s non-existent in English and a grammatically wrong word.
but guess that made the word stands out even more, cos in the ‘good-er’ worldview, there’s no right or wrong; only good, and good-er exist. the philosophy can be interpreted as an extension of the core of the 12 principles of knowledge building (Scardamalia, 2002; Scardamalia and Bereiter, 2006), i.e., improvable ideas.

often, in the ‘right vs. wrong’ world, it’s not uncommon that we hear: ‘this idea is stupid, it is wrong, it’s unacceptable’… and pple began to dismiss their own and others’ ideas and not to mention the emotional side of things, bad, sad, angry …
contrastingly, in the ‘only good and good-er’ world: ‘all ideas are good ideas!”. marrying it with kb’s principle of improvable ideas, all ideas become improvable. end result: all good ideas can and will only become good-er ideas over time.

the good-er philosophy was first instituted as part of my design/model for blended learning for CL teachers’ professional development. it was my research project between 2015-2016 when i was with the SCCL. it’s the first ‘rule’ (among three) that my community of teachers adopt: “只有好，和更好”。 so if u ever need to cite a source for the good-er theory/philosophy, you could either use this blog post (Tan, Y. H. (2024, November 9). Only good and good-er. Edublog.net. https://yhfamily.com/2024/11/09/only-good-and-good-er/ ), or my handbook if you prefer an earlier source:

Tan, Y. H., Tan, Y. N., & Chow, F. Y. (2019). Blended learning for in-service teachers’ professional development: Handbook for new instructors. Singapore: NTU-SCCL Press.
OR
陈育焕、陈雁妮、周凤儿 (2019). 混成式在职教师培训：新手指导员手册 [Blended learning for in-service teachers’ professional development: Handbook for new instructors]。新加坡：南大-新加坡华文教研中心出版社。

Nov 7, 2024, Co-generative dialogue on educational research @RGS

towards only good and gooder!

Tan, Y. H. (2024, November 9). Only good and good-er. Edublog.net. https://yhfamily.com/2024/11/09/only-good-and-good-er/

p.s. scan QR code for a draft of the handbook — Tan, Tan, & Chow (2019) — that documented the 2015-2016 research
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on PLC for organisational KM and knowledge creation perspective

Individuals come, individuals leave (retire, switch career, 躺平, whatever). How does an organisation ‘retains’ as much tacit institutional knowledge as possible? Documentations, guides, playbooks are some ways, but these have their limitations. Why? Becos meanings are not hard cold words, graphics, and videos, print, online, or otherwise. Humans are social beings, and meanings are socially negotiated in respective social contexts.

Communities (or societies?) are where knowledge ‘resides’, some pple would say. By forming professional learning communities (PLC; or otherwise commonly known as CoP, although the term ‘CoP’ includes various conceptions due to different interpretations of what “communities” meant), organisations create an additional avenue for retaining tacit institutional knowledge. But the value goes beyond knowledge retention (if knowledge can be ‘retained’; also note that documents, guides, playbooks stop here). Members of a PLC, through regular interactions, create new knowledge. Thus, the body of organisational knowledge continually grows and renews.

People in PLC don’t necessarily work together every day, but they are bounded by their respective activity systems at work, which may not be conducive for knowledge creation. In the PLC activity system, rules that encourage learning and creating knowledge together can be negotiated and practised by the community.

references: Engeström (1987);Wenger, McDermott, & Synder (2002)

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LLMs, chatgpt & ubi 4.0

with the blazing speed that LLMs are (or commonly known as “chatgpt”) developing, some people may be worried abt job security.

it looks more like a case of “the rich gets richer, the poor gets poorer” 贫者愈贫，富者愈富, not in the monetary sense, but the knowledge creation sense. more precisely, LLMs/chatgpt are going to make the expert-layman’s speed/efficiency gap ever bigger. layman can only (blindly, unknowingly, ‘trustingly’) copy-n-paste without understanding (cos they do not have enough prior knowledge to assess the output), while the expert can build on what LLMs/chatgpt throw out and idea-improve repeatedly with the system with further prompting and/or data input.

based on the above theory, to ensure everyone’s livelihood and well-being tmr (not limited to those who are worried abt job security), the idea of universal basic income (UBI) would probably need to upgrade to at least a 4.0:

UBI – food, water housing
UBI 2.0 – food, water, housing, power/electricity
UBI 3.0 – food, water, housing, power/electricity, wifi
UBI 4.0 – food, water, housing, power/electricity, wifi, negotiate/prompt LLMs

so what would UBI 4.0 mean for a k-12 school teacher? oh btw, openai just released its Code Interpreter within chatgpt4 few days ago. another game changer?

and thanks ziwei for the early brain-waking convo (:

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how we learn – a neuroscientific perspective

have been dealing with how we/people learn from the (social) constructivist and cognitive perspective for a long while. recently reading Dehaene (2021)’s How We Learn.

and chapter 1 captures the seven types of learning; learning is:

  1. adjusting the parameters of a mental model
  2. exploiting a combinatorial explosion
  3. minimising errors
  4. exploring the space of possibilities
  5. optimising a reward function
  6. restricting search space
  7. projecting a priori hypothesis

“our brain too is molded with assumptions of all kinds. Shortly, we will see that, at birth, babies ‘ brains are already organised and knowledgeable…… Darwinian selection, is in effect, a learning algorithm — an incredibly powerful program that has been running for hundreds of millions of years.” (p.76)

and parallels to the development of AI is drawn and described in chapter 1. more later (:

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