Creating an effective e-learning course: Steps and tips for ROI!
As increasing skills is becoming an emergency, e-learning is increasingly subject to ROI and performance paradigm. Commitment measures are no longer enough; what we want is an effective and measurable transformation of skills. Yes, but how do you do it? Let's sit in the front row to discover how Didask's educational AI makes it possible to make each training session online a stepping stone to mastery and professional excellence.
When designing e-learning programs, it is fundamental to approach the subject through the paradigm of efficiency and return on investment (or ROI). This goes far beyond the simple completion rate or the satisfaction of the participants.. Training is considered effective if and only if it induces a significant improvement and sustainable professional skills and practices. To do this, it is crucial to set goals that are aligned with the concrete results expected.
(See our article on pedagogical effectiveness)
Start with a careful analysis of needs. For example, for project management training, rather than aiming for general knowledge that you want to pass on to your learners, focus on practical skills that will be useful to them, such as the ability to use specific project management tools, to reduce delivery times, or to optimize the budget.
Formulate these goals in SMART terms (Specific, Measurable, Achievable, Relevant, Timely). Instead of “understanding the fundamentals of project management”, aim for “the ability to reduce the duration of project cycles by 15% in the next six months”, a SMART objective to assess the effectiveness of training.
The assessment should not be restricted to end-of-module tests. Schedule behavioral evaluations afterwards. For example, in the context of sales training, this could mean tracking participants' sales figures before and after the training to directly measure the impact.
To go further on this point, we recommend our article Educational effectiveness: measuring it, yes, but how?
Consider an accounting organization setting up training on new tax regulations. An effective objective would be for 90% of participants to successfully apply the new standards correctly in tax simulation exercises, leading to a 20% reduction in tax errors detected in the company in the quarter following the training. The identification of “relevant errors” to be treated would then be a key vector of the impact of future training.
In short, a clear vision of the effectiveness of training, through concrete and measurable objectives, will ensure not only a better allocation of resources, but also a solid path towards achieving substantial results. E-learning then becomes a strategic investment, emphasizing critical skills and contributing directly to organizational performance.
It is still necessary to achieve this famous efficiency. To do this, there is nothing like starting from the human brain and the proven principles of cognitive science. These principles are the foundations for ensuring that information is not only absorbed, but also retained and applicable in real situations.
The core of active learning is interaction and engagement. For example, if an employee practices conflict resolution, educational AI could generate simulated scenarios of disagreements with customers. An analysis of their performance could then provide personalized recommendations to strengthen their capacity to manage conflicts.
The principle of spaced repetition ensures that the revision of materials is not concentrated in a single session, but distributed over time. For example, customer service training could offer periodic training scenarios that simulate different service situations, reinforcing learning at each iteration.
Immediate and constructive feedback allows learners to correct mistakes and refine their skills in real time. In cybersecurity training, after a module on password security, an interactive exercise could test learners' ability to identify weak passwords, providing accurate feedback on mistakes and outlining best practices.
For training to be truly effective, it must facilitate learning transfer - the application of the skills learned to new and different situations. In the health sector, for example, nurses could practice emergency management via a virtual reality environment that simulates different crisis conditions, preparing them as best as possible for the variability of real cases.
Based on these principles proven by cognitive science research, e-learning course designers can create learning experiences that go beyond simple memory to achieve lasting and measurable behavioral changes at work (Read our article) 3 essential keys to move from memorization to application in eLearning training).
Of course, everything is not always so simple: optimizing pedagogical effectiveness, while essential, is accompanied by a degree of complexity that can be significant for trainers. The requirements of educational content that respects the principles of cognitive science, while being personalized and interactive, traditionally require a significant investment in both time and human resources. That's where it comes in Didask's educational AI, an accelerator that will allow you to meet this challenge brilliantly, going as far as multiplying by 10 the speed of designing high-quality interactive training courses.
Thanks to Didask educational AI, tasks that were once manual and time-consuming, such as creating diverse training scenarios or generating adaptive questionnaires, can be automated. For example, a training program targeting data analysis skills could use AI to develop a series of unique use cases, drawn from real data sets, adapting difficulty and complexity based on learner responses, based solely on your raw content such as a guide or PDF.
Educational AI also makes it possible to offer customization at scale, making each learning path unique according to the specific needs of the learner. In an intercultural skills course, AI could identify geographic areas where learners will work and adjust content to prepare for the specific cultural challenges of these regions.
Finally, educational AI can be crucial in the development of dynamic feedback systems. Take the example of training to improve communication in a professional environment: educational AI could analyze the written or verbal responses of participants to simulated communication scenarios, such as team discussions or customer interactions, and propose feedback that is relevant and adapted to everyone. (On the power of feedback in training, see our article Learning by trial and error: unleash the potential of feedback)
Once your courses have been created, don't forget to check that your goals are really achieved! Based on the most frequent mistakes made by your learners, Didask smart analytics will help you to Suggest content changes to improve success rates; you can also use these analytics to anticipate and prevent dropouts at key moments in the training.
By betting on cognitively impacting e-learning, supported by Didask's educational AI, you have all the keys in hand to create effective training courses that generate ROI. So invest in training that really matters and watch the progress in action!
Prenez directement rendez-vous avec nos experts du eLearning pour une démo ou tout simplement davantage d'informations.
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