Tacit Knowledge, Organisational Memory, and Learning Systems

In this Article

• What happens when expertise leaves before it is written down
• How tacit knowledge differs from explicit knowledge
• How organisational memory works in higher education
• Why learning systems need more than repositories
• How to design knowledge transfer without flattening expertise
• Where these concepts reach their limits

What Happens When Expertise Leaves Before It Is Written Down?

A senior lecturer leaves at the end of term, the handover folder looks complete, and the programme team still spends the next semester asking questions no document answers.

The folder contains assessment rubrics, moderation notes, committee minutes, platform guides, and archived email templates. Those are useful. They do not explain why one borderline dissertation case needed a second reader, why a blended seminar works better when the case discussion is delayed by ten minutes, or why a particular learning platform issue appears only after a late enrolment batch is processed.

This is the practical tension at the centre of knowledge management. Institutions can see manuals, repositories, and standard operating procedures. They struggle to see judgement, timing, informal routines, and contextual interpretation until the person who carries them is no longer available.

In many institutional settings, handover protocols are commonly cited as running for 14 to 21 days. That is enough time to transfer files. It is rarely enough time to expose how an experienced project manager reads risk in a curriculum approval meeting, or how a systems architect distinguishes a configuration fault from a user-behaviour pattern. Capturing undocumented contextual interpretation requires watching real work as it happens, not relying only on an exit interview.

Summary: Knowledge retention is not file storage with a longer name. It is the disciplined preservation of decisions, routines, relationships, and interpretive habits that allow work to continue intelligently.

Tacit Knowledge: The Know-How That Resists Simple Capture

Tacit knowledge is personal, experience-based knowledge that is difficult to codify because it sits inside practice, judgement, relationships, and context. It is not mystical. It is the know-how people use before they have time to explain what they are doing.

Michael Polanyi’s foundational observation that people can know more than they can explicitly tell remains useful here. It should not be treated as a complete theory of organisational learning, but it gives precise language for a common institutional problem: some expertise appears only in action.

Academic examples where tacit knowledge shows up

• Supervising a research student: An experienced supervisor senses when a student needs methodological narrowing rather than motivational encouragement.
• Diagnosing a learning platform issue: A technician reads server logs while also recognising unwritten user behaviour patterns, such as how part-time students upload work after office hours.
• Moderating a blended seminar: A lecturer knows when online silence means confusion, preparation, disagreement, or simple bandwidth fatigue.
• Judging a policy exception: A programme leader distinguishes compassionate flexibility from a precedent that could weaken academic standards.

The timeframe for a junior lecturer to acquire the tacit judgement needed for independent research supervision is often estimated at 24 to 36 months of active co-supervision. That range matters because tacit knowledge is learned through repeated exposure to cases, not through one briefing document.

Quick Tip: When documenting expert work, ask for the cue that triggered the decision. The cue is often more valuable than the final answer.

Tacit vs Explicit Knowledge: A Working Comparison

Explicit knowledge can be written, structured, stored, searched, and transmitted. It appears in documents, databases, checklists, rubrics, templates, standard operating procedures, and policy manuals.

Explicit knowledge supports consistency. Tacit knowledge supports interpretation and adaptive action. A postgraduate programme needs both.

A practical contrast

• Explicit knowledge: The assessment rubric states the criteria for originality, argumentation, evidence, and presentation.
• Tacit knowledge: The marker recognises when a technically awkward argument contains a genuinely original research move.
• Explicit knowledge: The help-desk procedure lists steps for troubleshooting platform synchronisation.
• Tacit knowledge: The support lead knows when the issue is not technical at all, but caused by how students interpret a submission reminder.

Developing a detailed standard operating procedure for learning platform troubleshooting can require an initial drafting phase, based on reported figures, of 45 to 60 days. The draft then needs refinement against actual help-desk resolution patterns, because a procedure that looks clear to its author may not match the terminology used by frontline academic staff.

A detailed knowledge repository that remains unaccessed because the search taxonomy does not align with the actual diagnostic terminology used by frontline academic staff.

Nonaka’s work on organisational knowledge creation is influential because it examines movement between tacit and explicit knowledge. His article, A Dynamic Theory of Organizational Knowledge Creation, remains a useful reference point for postgraduate learners studying how organisations articulate, share, and recombine knowledge.

Organisational Memory: How Institutions Remember

Organisational memory is the retained knowledge, routines, records, relationships, and interpretive habits that allow an institution to act based on prior experience.

Walsh and Ungson’s research gives this concept a practical structure. They locate organisational memory not only in individuals, but also in culture, transformations, structures, ecology, and external archives. That matters in higher education because institutions remember through many channels at once.

Where memory lives in a university setting

• Curriculum review notes that explain why a module changed, not only that it changed
• Quality assurance decisions that record the rationale behind assessment design
• Alumni feedback that influences professional relevance in programmes such as an MSc in E-Commerce
• Learning analytics governance notes that define acceptable use, escalation, and privacy boundaries
• Programme committee practice that shapes how borderline cases are discussed
• Informal teaching norms that lecturers pass to one another between formal reviews

Curriculum review cycles are often recorded around 36 to 60 months. During those periods, informal teaching norms may diverge from the originally documented syllabus before the next formal quality assurance audit catches the change. The formal record is still necessary, but it is not the whole memory of the programme.

In a Hong Kong context, this becomes visible when professional programmes must respond to changing workplace expectations while maintaining academic standards. HKCyberU, as an educational institution, sits inside that kind of regional knowledge environment. The same logic applies when a School of Nursing team revises clinical teaching practice, or when a technology programme compares what was approved on paper with what working learners can actually use.

Learning Systems Are Not Just Repositories

A learning system is the socio-technical arrangement that helps people learn from experience. It may include a learning management system, a knowledge base, mentoring routines, after-action reviews, communities of practice, programme review cycles, and the norms that govern how people ask for help.

A learning management system alone does not produce organisational learning. It can store files, sequence activities, collect submissions, and display analytics. It does not, by itself, make staff reflect on what happened, retrieve the right prior case, or adapt future practice.

What the system must support

1. Reflection: Staff need a structured way to ask what happened and why it happened.
2. Feedback: Learners, teachers, and support teams need channels that do not disappear after a semester ends.
3. Retrieval: Knowledge must be searchable using the language people actually use in work.
4. Adaptation: Programme teams need authority to change routines when evidence warrants it.

Implementing peer review and mentoring in a blended postgraduate programme can involve an observation period estimated at 8 to 12 weeks per academic term. After-action reviews need to capture both technical system performance and pedagogical outcomes. A platform outage log is incomplete if it does not record how the outage changed teaching decisions, assessment timing, or learner confidence.

For working professionals in Hong Kong, flexibility is not a decorative feature. It is part of the learning design. A system that preserves institutional knowledge while allowing context-sensitive learning will serve a part-time learner in an MSc/PgD in Software Technology better than a rigid archive of old documents.

Designing Knowledge Transfer Without Flattening Expertise

The best knowledge transfer process I use starts with the decision, not the document. Final answers are easy to capture; the reasoning that produced them is the part worth preserving.

A practical sequence

1. Identify critical knowledge domains: Focus on work where errors are costly, delays are common, or only one person knows the routine.
2. Locate knowledge holders: Include formal experts and quiet operators who understand exceptions.
3. Observe real work: Watch meetings, troubleshooting sessions, moderation discussions, and handover conversations.
4. Capture decision rationales: Record why one option was chosen over another.
5. Build reusable artefacts: Create case notes, checklists, scenario libraries, and annotated examples.
6. Create feedback loops: Let users correct, extend, and retire artefacts as practice changes.

A structured shadowing programme designed to capture trade-offs in systems architecture decisions may require, according to available data, 15 to 20 contact hours spread across a single academic semester. That window gives the observer enough exposure to see variation: a routine support issue, a borderline case, a time-sensitive escalation, and a decision made under incomplete information.

Useful methods include shadowing, structured interviews, teaching case notes, reflective logs, handover conversations, scenario-based training, and peer observation. The effectiveness of shadowing programs varies significantly depending on whether the expertise involves highly visible technical troubleshooting or internal, unvocalized pedagogical judgments.

Note: Structured interviews and reflective logs produce better material when the expert can explain their own decision-making. Some experts act skilfully but struggle to describe the cues they use.

High-value capture should focus on decisions and trade-offs. For example, documenting a policy exception should include the contextual trigger, the alternatives considered, the risk accepted, and the boundary that prevents the exception from becoming a new informal rule.

Scope and Limitations: What These Concepts Cannot Promise

Tacit knowledge, organisational memory, and learning systems are analytical concepts. They are not guaranteed solutions to staff turnover, teaching quality, or institutional innovation.

This limitation matters because the field carries strong authority signals: Polanyi’s theory of tacit knowing, Nonaka’s knowledge creation framework, Walsh and Ungson’s organisational memory model, and ISO 30401:2018 for knowledge management systems. Those references help structure the work, but postgraduate education changes faster than many institutional memory routines can stabilise.

The integration of new knowledge management frameworks into existing workflows typically demands an adaptation phase recorded around 6 to 9 months. Measurable shifts in organisational memory occur only after multiple cycles of onboarding and departure. A single repository launch, however polished, will not carry that load.

There are also ethical and managerial risks. Over-documentation can slow expert work. Surveillance-style capture can make staff defensive. Treating human expertise as a database object strips away judgement, responsibility, and professional trust.

Hong Kong I-Education Limited, as copyright holder in relevant institutional materials, and bodies such as The Hong Kong Polytechnic University or Hong Kong Polytechnic University in broader higher education practice, illustrate a wider point: knowledge assets need governance, provenance, and context. Names and archives are not enough. The institution still has to decide what should be retained, who may interpret it, and when old memory should be challenged.