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Teaching and Learning: A Synthesis

Terry Critchley

Learning and subject retention is a joint exercise between instructor and learner and how this might be achieved in an online, distance learning environment is outlined in this blog. Download the full paper here.

Without this synthesis, learning would be like doing the tango alone; everybody knows "it takes two to tango."

Traditional Teaching

We all have a mental picture of traditional teaching; the lecturer or school teacher holding forth on some subject to a passive audience who cannot or do not exchange ideas on that subject with their neighbors during class.

The collateral is often quite old and any new ideas need to be scribbled down, if there is time, and assimilated later along with reading that collateral. Asking questions of the instructor can elongate the class time, display one's ignorance or the answer confuses other students. This ability to ask questions at the time the particular sub-topic is covered is the main advantage this method has over online learning; the social interaction aspect is also pertinent.

Face to face tutorials and similar encounters are also useful in this environment but when it comes to scaling up this method, there are obvious flaws, especially in volatile subjects like information technology (IT).

Covid-19 Impact

The onrushing Covid-19 pandemic has made online learning, in whatever form can be achieved in a short time, mandatory across nearly all academic institutions. Much of this will persist although "seat of the pants" online training will need development to mimic face-to-face teaching as far as possible.

There is, today, a worldwide skills shortage in cybersecurity of about 3.5 m. positions and that topic is by no means the only skill needed in modern workplace IT, despite being flavor of the month, along with AI.

In short, and in the IT ecosphere, volume and volatility are the enemies of traditional teaching; this has given rise to more online learning.

Computer Aided Learning

Online learning is not new, and computer based training has been around for decades but relatively neglected.

Learning online and at a distance from the educational source has the disadvantage that it generates "The Loneliness of the Long Distance Learner" syndrome. The absence of interaction with others or learning diversions can be detrimental to absorbing and retaining knowledge.

In theory, there is no instructor involved, except in spirit in the material presented, and no interaction with one's peers. That presence needs to be woven into the online material somehow, as discussed in the next section.

The Online Learning Needs

The need then is to simulate the face-to-face environment as closely as possible in the online/distance arena. These are the areas I feel need bolstering to lift this form of learning above the "read page after page"then tackle some review questions technique. Apart from the obvious quality and relevance of the material needed, a modern online course should:

■ Have standard user interface (UI) at least in the same organization or course vendor. Some global guidelines may be more acceptable than a compulsory UI in the first instance

■ Have a flow reflecting IT as a whole and not be just a series of topics without any obvious synergy. Many course are just that.

■ Be modular with the ability to stop at any point and restart there. In addition, it should allow students to skip sections which are patently not in their list of needs.

■ Emulate the campus feeling and the world of FAQs, it should be possible to use the course as a forum where peers can exchange ideas, memory tips, give pointers to other material and so on. In short, create a Zoom/Skype sub-environment.

■ Have a self-test facility with guided support in a Q & A session for the student, perhaps generating a question for peers if the topic refuses to stick in the student's mind.

■ Give the feeling to the student that this course is not the end of learning, but emphasizes that, like breathing, it is a lifetime occupation.

■ This emphasis might point the student to journals or sites where up to date articles and other supplementary information can be acquired.

■ Have optional course exit/re-entry points, taking the student to an external medium, such as an internet article, YouTube video etc. to broaden the learner's perspective.

■ If multiple course developers are involved, their styles should conform to some standard, otherwise student confusion can arise.

■ Have the material QA (quality assured) by experienced IT people for consistency, accuracy and adequate topic coverage.

■ Eventually the course will need to be overhauled but that is common problem.

Learner Notes

I have learned several things during my long sojourn in IT, including some gems from other people:

1. IT careers (as opposed to a single job) needs a lifelong commitment to learning your trade and passing what you know on to others.

2. Learning and retaining knowledge is best achieved by studying on the (my) LOVE principle; little, often varied and extensive. I also call this "osmotic learning," osmosis being the gradual seeping of moisture into a substance; the LOVE form of learning mimics this. Perhaps with a joke or revelation to bring them back to attentiveness.

3. Organize your study; don't just go through all the material in haphazard fashion as you will lose the ethos of your overall subject.

4. Remember that the half-life of an IT job or position is about 24 months after which it will mutate, change radically or, in some cases, disappear. You should train for a career, not a job.

Summary

IT training is changing, due to the complexity of IT, its volatility and the changing computing requirements in the workplace. Volatile material and audiences of thousands mandates online training.

"It's what you learn after you know it all that counts"
– US baseball coach

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Teaching and Learning: A Synthesis

Terry Critchley

Learning and subject retention is a joint exercise between instructor and learner and how this might be achieved in an online, distance learning environment is outlined in this blog. Download the full paper here.

Without this synthesis, learning would be like doing the tango alone; everybody knows "it takes two to tango."

Traditional Teaching

We all have a mental picture of traditional teaching; the lecturer or school teacher holding forth on some subject to a passive audience who cannot or do not exchange ideas on that subject with their neighbors during class.

The collateral is often quite old and any new ideas need to be scribbled down, if there is time, and assimilated later along with reading that collateral. Asking questions of the instructor can elongate the class time, display one's ignorance or the answer confuses other students. This ability to ask questions at the time the particular sub-topic is covered is the main advantage this method has over online learning; the social interaction aspect is also pertinent.

Face to face tutorials and similar encounters are also useful in this environment but when it comes to scaling up this method, there are obvious flaws, especially in volatile subjects like information technology (IT).

Covid-19 Impact

The onrushing Covid-19 pandemic has made online learning, in whatever form can be achieved in a short time, mandatory across nearly all academic institutions. Much of this will persist although "seat of the pants" online training will need development to mimic face-to-face teaching as far as possible.

There is, today, a worldwide skills shortage in cybersecurity of about 3.5 m. positions and that topic is by no means the only skill needed in modern workplace IT, despite being flavor of the month, along with AI.

In short, and in the IT ecosphere, volume and volatility are the enemies of traditional teaching; this has given rise to more online learning.

Computer Aided Learning

Online learning is not new, and computer based training has been around for decades but relatively neglected.

Learning online and at a distance from the educational source has the disadvantage that it generates "The Loneliness of the Long Distance Learner" syndrome. The absence of interaction with others or learning diversions can be detrimental to absorbing and retaining knowledge.

In theory, there is no instructor involved, except in spirit in the material presented, and no interaction with one's peers. That presence needs to be woven into the online material somehow, as discussed in the next section.

The Online Learning Needs

The need then is to simulate the face-to-face environment as closely as possible in the online/distance arena. These are the areas I feel need bolstering to lift this form of learning above the "read page after page"then tackle some review questions technique. Apart from the obvious quality and relevance of the material needed, a modern online course should:

■ Have standard user interface (UI) at least in the same organization or course vendor. Some global guidelines may be more acceptable than a compulsory UI in the first instance

■ Have a flow reflecting IT as a whole and not be just a series of topics without any obvious synergy. Many course are just that.

■ Be modular with the ability to stop at any point and restart there. In addition, it should allow students to skip sections which are patently not in their list of needs.

■ Emulate the campus feeling and the world of FAQs, it should be possible to use the course as a forum where peers can exchange ideas, memory tips, give pointers to other material and so on. In short, create a Zoom/Skype sub-environment.

■ Have a self-test facility with guided support in a Q & A session for the student, perhaps generating a question for peers if the topic refuses to stick in the student's mind.

■ Give the feeling to the student that this course is not the end of learning, but emphasizes that, like breathing, it is a lifetime occupation.

■ This emphasis might point the student to journals or sites where up to date articles and other supplementary information can be acquired.

■ Have optional course exit/re-entry points, taking the student to an external medium, such as an internet article, YouTube video etc. to broaden the learner's perspective.

■ If multiple course developers are involved, their styles should conform to some standard, otherwise student confusion can arise.

■ Have the material QA (quality assured) by experienced IT people for consistency, accuracy and adequate topic coverage.

■ Eventually the course will need to be overhauled but that is common problem.

Learner Notes

I have learned several things during my long sojourn in IT, including some gems from other people:

1. IT careers (as opposed to a single job) needs a lifelong commitment to learning your trade and passing what you know on to others.

2. Learning and retaining knowledge is best achieved by studying on the (my) LOVE principle; little, often varied and extensive. I also call this "osmotic learning," osmosis being the gradual seeping of moisture into a substance; the LOVE form of learning mimics this. Perhaps with a joke or revelation to bring them back to attentiveness.

3. Organize your study; don't just go through all the material in haphazard fashion as you will lose the ethos of your overall subject.

4. Remember that the half-life of an IT job or position is about 24 months after which it will mutate, change radically or, in some cases, disappear. You should train for a career, not a job.

Summary

IT training is changing, due to the complexity of IT, its volatility and the changing computing requirements in the workplace. Volatile material and audiences of thousands mandates online training.

"It's what you learn after you know it all that counts"
– US baseball coach

Hot Topics

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...