Ontologies & Learning Experience (Draft)

Preamble

The buzz surrounding generative artificial intelligence and language models may be clouding the practical issues of individual and collective learning experience. When these issues are considered, the focus should be put on a two-pronged integration of:

Knowledge contents and teaching modalities
Personal and collective learning

That can be best achieved through ontologies supporting built-in knowledge modalities; on that account (cf Knowledge management booklet), Learning management systems (LMS) would be organised along three dimensions:

Managed categories (information), describing the shared, continuous, and consistent representation of actual courses, modules, sessions, profiles, …
Concepts (knowledge), for the the definition of objectives, methods, practices, roadmaps, …
Facts (data), for managed resources: documents and references, datasets, case studies, student records, …

The implementation of these principles can be realised with OWL/Protégé:

Facts are identified instances in environments that may or may not be represented by managed surrogates in systems
Categories describe symbolic representations in systems that may or may not have actual counterparts in environments
Concepts are pure symbolic entities that may or may not be matched with actual realisations in environments or with managed representations in systems

*An preparatory view of a learning environment*

Information: Learning Categories

The aim is to define the categories used to manage learning constituents independently of their domain-specific nature.

Courses

Courses are organised with regard to objectives and audiences independently of teaching modalities.

Learning Units

Learning units are the courses building blocks; they are meant to be taught as stand-alone consistent entities meant to be executed in sessions and learning processes.

Learning Sessions

Sessions represent the actual teaching of learning units; no prior assumption should be made about the way sessions are carried out: individual or collective, physical or virtual, or assisted learning.

Students

Students achievements are recorded at session and course levels, possibly with regard to teaching modalities.

Knowledge: Teaching Modalities

Compared to information whose purpose is to manage learning contents, knowledge is about intents, goals, and methods.

Use Cases

Use cases describe how LMSs can be used; e.g.:

Teaching Processes

Teaching processes are defined for states and rely on assessment and assistance functions.

Contextual (local) assistance functions operate on sessions, helping students with references, corrections, additional elements, explanations, or practice.

Compared to contextual ones, transverse (editing) assistance functions are not defined for learning units but are supported by platforms and operate on contents according to their nature: images, numbers, logic, written or verbal.

Goals & Scripts

Learning paths are by nature multiple and thus should be adaptable not only to students abilities and experience, but also to concrete learning sessions.

Resources

Resources are identified documents or dataset which can be recorded (events) or reproduced (sources) but are not meant to be managed as surrogates.

As far as teaching resources are concerned the primary benefit of knowledge modalities is the management of virtual contexts for case studies, simulations, multi-players sessions, etc.

*Negotiation game with actual and assessed observations*

Environments

Knowledge-driven learning platform four ontological (aka knowledge) modalities, cognitive model, language model, organisation.

Ontological Modalities

Ontological approaches to learning rely on built-in knowledge modalities:

Conceptual/Intensional modalities deal with the status of knowledge: abstract, concrete, nominal, virtual, fictional, etc.
Empiric/Extensional modalities deal with the status of facts: observed, asserted, assessed, deduced, managed, etc.
Logical/Symbolic modalities deal with representations: identification, instanciation, behavior, life cycle, etc.

Cognitive Model

Learning processes are best understood in terms of long established cognitive operations: observation (senses), reasoning (logic), judgement (decision), and experience (practice).

Given the role of Artificial technologies this built-in distinction is a key for the transparency and traceability of learning processes.

Language Model

The spreading of generative language models is ironing out the distinction between language and knowledge, arguably un unfortunate outcome for learning. Hence the need to clarify key language functions:

Nominals: lexicon of words or groups of words meant to be attached to facts
Modeling & Programming languages: syntax and semantics meant to be executed by computers
Natural language: syntax, semantics, and pragmatics as used in human communication

And then to circumscribe the possible employ of generative languages in LMS:

Scientific (empiric) : Modeling & Programming languages meant to be aligned with observed facts (e.g. Simula)
Formal (logic): Programming languages meant to preserve truth (e.g. Prolog)
Generative (pragmatic): Inverse semantic parsers meant to produce natural language from nominals (e.g. ChatGPT)

Organisation

Last but not least, learning must be embodied in organisations, which entails the integration of implicit to explicit knowledge, individual as well as collective:

Between people and organizations, it’s typically done through a mix of experience and collaboration (a)
Between systems and representations, it’s the nuts and bolts of Machine learning and Knowledge graphs technologies (b)
Between people and systems, learning relies on the experience feedback achieved through the integration of ML into the OODA loop (c)
Between organization and systems, learning relies on the functional distinction between judgment, to be carried out at the organizational level, and observation and reasoning, supported by systems (d)

Ontologies & Learning Experience (Draft)

Preamble