Bloom's Ontology - Comments Required

[Ali Raza]

Dear all,

I hope you are fine.

Last time I requested help from learned group members regarding creating ontology of Bloom's taxonomy (as part of my research). I got suggestions and help from esteemed members that really helped me proceed. I am really thankful.

I am sharing a visualization of the Bloom ontology. Kindly have a look and don't hesitate in giving your opinion.

Kind regards,

Ali

[Alex Shkotin]

Hi Ali,

It is always exciting to see the birth of a formal ontology. And it seems to me that you do not use any known methodology for creating ontologies.

My advice:

- remove Keyword, Taxonomy from the ontology.

- Rationale: Do not put knowledge of the subject area and knowledge of the knowledge of this subject area into one formalization.

- also remove the Domain class.

- Rationale: There is no Domain term in this subject area. There are terms of several words: "cognitive domain" etc.

- add the Educational Objective class. Moreover, it should be the root for other classes.

- Rationale: the author himself wrote this.

7 days ago I asked you where the theory that you formalize is presented? So what book are you using? Apparently, this is the 2001 version?

Have a look at formalization of the following portion of knowledge. True, it is taken not from the original source but from Wikipedia:

"The taxonomy divides learning objectives into three broad domains: cognitive (knowledge-based), affective (emotion-based), and psychomotor (action-based), each with a hierarchy of skills and abilities."

We derive this unit of knowledge

eng: The learning objective must be of one and only one particular kind: cognitive, affective, psychomotor.

And formalize it

owl2: 

Declaration(Class(:learning_objective)) Declaration(Class(:cognitive_learning_objective)) Declaration(Class(:affective_learning_objective)) Declaration(Class(:psychomotor_learning_objective))

DisjointUnion(:learning_objective :cognitive_learning_objective :affective_learning_objective :psychomotor_learning_objective)

It is an interesting methodological question if we can reduce full terms like "cognitive learning objective" just to "cognitive" if the word "cognitive" is only used inside the term "cognitive learning objective". But I don't think we have this case here.

From the portion of knowledge we extract the knowledge unit and then formalize it.

Per aspera ad astra🏋️

Alex

вт, 1 окт. 2024 г. в 06:34, Ali Raza <aliraz...@gmail.com>:

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[Ali Raza]

Hi Alex,

Hope you are fine.

Thanks for well explained suggestions.

I went through the methodology that you mentioned and it's logical and it seems that I missed step 3. (Perhaps because of the very common terms we use as alternatives such as Cognitive Domain for Cognitive Learning Objectives.)

Moreover, your advice that, "Do not put knowledge of the subject area and knowledge of the knowledge of this subject area into one formalization." is very logical but here my question is that each Cognitive_Learning_Objective_Level has certain keywords (verbs) associated with them. In this case, how to describe them? as instances of the respective six levels?

Kind regards,

Ali

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[Alex Shkotin]

Hi Ali,

You still haven't sent us which version of BT you're working with. But between the first in 50s and the second in 90s versions, an important change occurred: they stopped classifying objectives and moved on to classifying mental abilities themselves. I'll take a look tomorrow, because the field of knowledge itself: education, the learning process, and so on is of course very interesting as a branch of Psychology.

Alex

ср, 2 окт. 2024 г. в 12:26, Ali Raza <aliraz...@gmail.com>:

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[John F Sowa]

Alex,

As I have said many times, in email notes, slides, and publications, a precise formal ontology of everything cannot be done until all the unsolved problems in the foundations of every science have been solved.  That includes psychology (of humans and all other living things on any planet in the universe).  

But I do believe that it's possible and highly desirable to develop formal ontologies of things that are implemented on digital computers.  The reason why that's possible is that anything implemented  in strings of bits is discrete and finite.  Therefore the implementation itself is a formal definition of what the program does. And a formal description in a more concise and readable form is possible and valuable..

I am happy to see that you are considering "the field of knowledge itself: education, the learning process, and so on is of course very interesting as a branch of Psychology."   

Nothing in any of those topics can be formalized precisely because every one of them has an enormous number of unknown issues for which the best known studies are incomplete.  I have a high respect for what has been done in those fields.  But every research issue they solve opens up many, many more unsolved problems.

Attempts at formalization can be useful in order to show the vast realm of unknown and unknowable issues that make any formal theories of everything hopeless.  

Re:  " they stopped classifying objectives and moved on to classifying mental abilities themselves."   

That seems to be a step toward  recognizing the immense scope of the problem.    But it's essential to make the distinction between the formal representation of what is computable and the unknown and poorly understood  continuum of the mind and the world.   The discrete can be formalized, but it's impossible to formalize the continuum in any finite notation with a discrete set of symbols.

Engineers have an excellent way of summarizing these issues:  "All theories are wrong, but some are useful."

John

 

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From: "Alex Shkotin" <alex.s...@gmail.com>

[Alex Shkotin]

John, 

Any verbal knowledge can be formalized, at least for the English language🦉 How precisely this knowledge is a topic for scientists and practitioners working in a particular area of reality. We simply formalize knowledge to use the power of a computer. But you are right, we need a reason for formalization as it's hard. In some cases, formalization can reveal some unclear areas in informal knowledge. And sometimes, in very rare cases, formalization can find errors in a mathematical text. There is a report of this kind from the Isabelle research group.

Just to make it clear: even wrong, inaccurate, vague knowledge may be formalized. If we need to.

And after that we can run the verification algorithm and it will say that this knowledge is incorrect, inaccurate, or vague.

The first person to put forward this idea as a project was G. Leibniz, who was 25 years old. He hoped to obtain a formal language in 2-3 years🏋️

Alex

ср, 2 окт. 2024 г. в 21:43, John F Sowa <so...@bestweb.net>:

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[Alex Shkotin]

Ali,

I have to stop until it is found out where the taxonomy you are formalizing is published.

In the meantime, ask Google like this "formal ontology for education domain".

You might be interested in the answer.

Alex

ср, 2 окт. 2024 г. в 12:26, Ali Raza <aliraz...@gmail.com>:

Hi Alex,

To view this discussion on the web visit https://groups.google.com/d/msgid/ontolog-forum/CAEWU1hn10%3DgFFz5P1ShpCES-Z8L%3DX7M4waHXbDnt_4snVKXFqg%40mail.gmail.com.

[Ali Raza]

Hi Alex,

The taxonomy that I referred to, is Bloom's Revised taxonomy, published in 2001, titled A taxonomy for Teaching, Learning, and Assessment, by LW Anderson.

Kind regards,

Ali

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[Ali Raza]

Hi,

Mary Forehand from the University of Georgia provided a guide to the revised version of Bloom's Taxonomy giving a brief summary of the revised taxonomy and a helpful table of the six cognitive processes and four types of knowledge. I have attached the file.

Ali

[Alex Shkotin]

Hi,

Guide is fine but access to the source we formalize is mandatory. I'll try to get it.

Meantime following Kingsley way I have created notebookLM "formal ontology for education domain" and let me cite one note generated by nbLM: ==>(rus2eng by Google trans)[1]

What do you think?

Alex

[1]

"Formal Ontologies in Education: Overview and Availability

There are a number of formal ontologies in the literature that have been developed for the educational sector. They have been created to address a variety of issues, such as curriculum management, e-learning systems development, and semantic search of educational resources.

Some of the ontologies mentioned are:

1. EduOnto: [1, 2]

Purpose: Representation of learning objects and their metadata to ensure interoperability and reuse.
Description: Analysis of the types of components and terms used in learning objects. Creation of a matrix to display the relationships between learning objects, knowledge, and technology.
Availability: The availability of the EduOnto ontology is not indicated in the literature.

2. OntoEdu: [1, 3, 4]

Purpose: Creation of an ontology for an educational grid system for e-learning.
Description: The ontology is based on the principles of grid technologies and is designed to support e-learning.
Availability: The sources do not indicate the availability of the OntoEdu ontology.
3. Ontoportal: [5]

Purpose: Creating a universal platform for building educational web portals.
Description: An ontological hypertext platform that allows creating metadata based on ontologies and generating educational portals.
Availability: The sources do not indicate the availability of Ontoportal.
4. Education Standards Ontology: [5]

Purpose: Improving literacy and numeracy skills of students, as well as creating a basis for the semantic web in the field of education.
Description: The ontology contains information about educational standards and methods for their automatic generation.
Availability: The sources do not indicate the availability of the educational standards ontology.
5. CURONTO: [5]

Purpose: Curriculum management in higher education institutions.
Description: The ontology is designed for the general management of the entire curriculum, as well as to facilitate the process of checking and evaluating the program.
Availability: No availability listed for CURONTO.
6. Gescur: [5]

Purpose: Support for curriculum development and management.
Description: A platform that supports curriculum development and facilitates the management of curriculum.
Availability: No availability listed for Gescur.
7. Ontology for Learner Profile Management: [5]

Purpose: Learner profile management and learning adaptation using the VARK model.
Description: An ontology that is used to manage a learner profile so that the learner can be guided to the correct learning path.
Availability: No availability listed for the ontology for learner profile management.
8. Ontology for e-Learning (OeLe): [6]

Purpose: Automatic assessment of learner responses to conceptual questions.
Description: A system that automatically evaluates students' open-ended responses to conceptual questions and provides feedback and performance assessment to both individual students and teachers.
Availability: No availability information available for OeLe.
9. EIO (Educational Institutional Ontology): [2, 4-33]

Purpose: Representing knowledge about organizations and institutions related to education.
Description: A multifaceted ontology developed using faceted analysis principles that covers various aspects of educational institutions.
Availability: The ontology is available in the WebProtégé library: https://webprotege.stanford.edu/#projects/3249bf90-bb06-4e3e-b9b7-ecf6b7bed0ab/e dit/Classes [19]

It is important to note that the availability of ontologies may change over time. It is recommended to check the relevance of references and contact the authors or organizations responsible for developing the ontologies for more information."

чт, 3 окт. 2024 г. в 14:48, Ali Raza <aliraz...@gmail.com>:

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[Ali Raza]

Hi Alex,

Thanks for sharing notebookLM "formal ontology for education domain", I have joined and i will go through it.
A few ontologies that you shared, among them my research is close to "Ontology for e-Learning (OeLe)" with an additional task that I need to classify questions using machine learning augmented by ontology. I have searched for any available ontology close to my research but could not find it.

Kind regards,

Ali

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[Chuck Woolery]

Alex.  Well stated! 

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[John F Sowa]

Alex and Chuck,

That claim is FALSE in general, and determining the error bounds is essential.

It is true that you can write a formal statement that seems to state something similar to what is stated in English or other natural languages.  But that does not imply that the two statements are equivalent.

A vague statement may express a continuous range of possibilities, but the translation to a statement in logic is limited to a very precise and very limited range of possibilities.  Sometimes that is an advantage, but sometimes it can be horribly false or misleading or disastrous.

Engineers know this point very well.   I quoted their motto in my previous note:  "All theories are false, but some are useful."    This point is absolutely TRUE.  And I would apply it to your claim about formalization.

The critical issue is to determine what range of values in a translation is acceptable or useful.   Unless you emphasize that range of options, your formalization is an invitation to DISASTER.

Re Leibniz:  He had many good ideas, but he oversimplified issues about precision.  He did not emphasize the importance of vagueness and the dangers of ignoring the error bounds.

John

 

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From: "Chuck Woolery" <ch...@igc.org>

[Chuck Woolery]

John,
Thank you for your honest, and I’m sure accurate feedback.

But my work in the regular world working to end humanity’s insanity will require more of an engineering approach.

A logical end run around religion, politics, economics, or poetry where words can mean nearly anything anyone wants. 

I’m confident that there are “self-evident truths", fundamental principles, or first principles...whatever you prefer to call them -- that are all basically the same throughout the universe...

ChatGPT agreed with me... so I know its accurate 😉  and yes. It makes colossal mistakes. 

But I also know that the word phrase “the Laws of Nature and of Nature’s God” in the first paragraph of the Declaration of Independence (not the second paragraph ChatGPT suggested) resonates with most people...regarding humanities NEED to ‘take care of nature’...and abide by ‘the golden rule’. 

As a biologist I’m clear about the need to take care of nature.  But having lost a teaching job for explaining the evolution of Christianity attempting to demonstrate the fundamental principles of the theory of evolution...’genetic diversity is strength.  And all life forms either adapt or perish.’ I finally discovered that nearly every major religion is based on the same ‘fundamental principle’...the golden rule.    I can make the case that it is in our DNA...and the DNA of most other advanced species...or they wouldn’t have survived.

 

I saw a great T-shirt in a bar last week.  “Oh wait!  Let me overthink that.” 

The greatest flaw of the human mind (I believe) is its capacity to believe ANYTHING! 😉

 

And as long as 95%+/- of humanity can be convinced that we need to put the protection of human rights and nature, above the protection of ‘National Sovereignty’ and corporations, we have a chance (it may be too late) to stop the accelerating chaos we are now witnessing.  And with AI contributing to the evolution of weaponry and war, and nature’s evolution of pathogens faster than our minds understand, and far faster than our governments can respond effectively to prevent or even react, our time is running out to discover and apply ‘self-evident truths’.  Like a child should not die before their parent(s).

 

That’s my mental story in relatively unambiguous words... and I’m sticking to it. 😉

 

And I truly appreciate you’ll allowing this hillbilly to be involved in your discussions.

 

cw

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[Alex Shkotin]

John,

Let's split a formalization in two steps.

I) structural representation of knowledge. Here, instead of a sequence of words, we get a structure (aka syntactic). It can even be just nonsense like

"Гло́кая ку́здра ште́ко будлану́ла бо́кра и курдя́чит бокрёнка" see

Proposal for structural representation of English sentences see, for formal languages here.

II) structural knowledge processing. What kind of "logic" i.e. a rule of knowledge processing we use in this or that science, engineering or everyday life?

We should ask these particular scientists, engineers or citizens.

How to formalize their rules of knowledge processing is our task here. These rules are far from Modus Ponens.

Some rules we use to solve simple tasks about ugraphs pointed out here.

It should be also mentioned that there is an initial step usually not included in formalization: formal, mathematical representation of physical bodies and processes.

We usually call them computer models.  3D-twins are the most famous.

We apply our formalized knowledge to 3D twins using a computer to gain useful insights into real things and processes.

It's a good idea to separate language and logic. In many cases, we know the language of our opponent, but we don't know her rules for processing knowledge.

So we have a first-order LANGUAGE (actually a family of languages, but let's take one) and a set of first-order logics.

We need to formalize our scientific theories to use computers to their full potential.

Alex

чт, 3 окт. 2024 г. в 23:15, John F Sowa <so...@bestweb.net>:

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[John F Sowa]

Alex:  "We need to formalize our scientific theories to use computers to their full potential."   

I agree, but the formalization is ALWAYS context dependent.  The engineering motto is fundamental:

ALL THEORIES ARE WRONG, BUT SOME ARE USEFUL.

That is true about formalization.  It's only precise for subjects that can be expressed  in finite bit strings.  For 99.9% of all the information we get every second of our lives, vagueness is inescapable.  We must deal with it by informal methods of approximations.  Any formal statement is FALSE in general, but it may be useful when the limitations are made explicit.

 

In your note below, you mention computer models.  But any model for a digital computer has already assumed a mapping to bit strings. But an engineering model must recognize the complexity and CONTINUITY of the world.

Natural languages are very flexible and much more expressive than any model for a digital computer.  If you ignore that flexibility, you destroy their power and your formalization is guaranteed to be FALSE .

A translation of a natural language to a formal language may SOMETIMES be necessary.  But different applications will require different ways of translating the same NLs,   As the engineers will agree, any formal specification can only be made in the context of and with the knowledge about the specific application.

John

---

From: "Alex Shkotin" <alex.s...@gmail.com>

[Alex Shkotin]

Hi Ali,

Let me summarize our interesting discussion.

We have outlined three formal ontologies:

- taxonomic (in it individuals are terms),

- subject area - where terms are defined logically, i.e. as components of theoretical knowledge, in the form in which they are applied to the entities of the subject area. And here it is necessary to fix what these entities are: mental abilities are one of the most important here, and what else?

- applied - in the simplest case, this is the part of the subject ontology that is needed for your task.

And since you use ML in the information system you are creating, I will note that our entire last Summit was devoted to ISs that have both ontologies and AI components.

It will be very interesting to learn about the architecture of your system and the expected interaction or simply the place of ontology and ML in it.

Do you have a technical specification for the system and how much of it are you willing to share?

Best regards,

Alex

чт, 3 окт. 2024 г. в 17:53, Ali Raza <aliraz...@gmail.com>:

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