Training Time Optimization through Adaptive Learning Strategy
Alessandro Pagano, Agostino Marengo
Abstract
Digital Learning is rapidly evolving and adapting to new learning needs. In every field of daily life, training is a fundamental asset to achieve any goals. Modern e-learning systems aim to make learning quick and effective. The training courses are often delivered sequentially, and there is a high waste of time since learners must attend lessons on topics they already master. This research aims to demonstrate that an Adaptive Learning Strategy can optimize training by drastically reducing the throughput time of the learning path, avoiding time-wasting, and maintaining a high level of learner engagement. Those goals will be reached using a learning management system platform and an adaptive learning algorithm on a modular course to build up and deliver personalized learning paths, recognizing the prior knowledge of each user. Adaptive Learning Strategy allows the learner to optimize his/her training achieving the learning goals in a shorter time. He/she will not have to attend topics he already demonstrates to have a complete knowledge level.