Training Evaluation

Introduction
Training is a very important part of change as you are teaching new skills to handle the new situation.
Key criteria for assessing the efficacy of training
i) participants reactions
ii) learning behaviour change
iii) achieve tangible results like return on investment (ROI)
iv) practical application of ideas generated so that transferable
However,
"...Reality often tells a different story, with some estimates suggesting a mere 10% is the transfer rate of training to actionable skills and behaviour..."
Amanda Julian, 2024
Possible reasons for this are
- forgetfulness (new information is rapidly forgotten
"...nearly half of new information can be lost within 20 minutes of acquisition..."
Ebbinghuas as quoted by
Amanda Julian, 2024
Reminders are a powerful way of combating forgetfulness. However, timing is crucial for maximum impact, ie
"...They need to be provided immediately before they are meant to be acted upon, in order to be effective..."
Amanda Julian, 2024
- inertia (this can be linked with complacency and preference for status quo, ie keep things as they are; subtle modifications in the environment can 'nudge' individuals towards the desired behaviour, eg transforming opt-in programs to opt-out ones, automatically scheduling one-on-ones with direct reports offering feedback and support
Summary
"... AI and ML are adapt at analysing extensive datasets to pinpoint optimal training, mediums, and contexts for deploying reminders and nudges. These technologies ensure interventions are meticulously tailored to the individual learner's needs and preferences. By evaluating real-time data, AI and ML continuously refine nudges, enhancing their personalisation and contextual relevance, and bolstering their influence on......behavioural adaptations..."
Amanda Julian, 2024
Unlike humans, who are constrained by the limits of the cognitive capacity, AI and ML has the advantage of accessing and processing a vast expansion of information instantaneously.
What humans can offer valuable insights drawn from personal experience and knowledge they lacked the research capabilities of AI and ML.
Also, humans are not immune to cognitive biases. However, AI has the potential to perpetuate and even amplified using biases that could lead to skewed outcomes; both highlighting the need for innovation, diversity and inclusion to avoid algorithmic biases.
Both AI and ML will continue to be integrated into the operations of organisation at all levels.