insights

CUSTOMISING LEARNING EXPERIENCES USING THE ADAPT MODEL!

Customising learning experiences is a process that cannot be improvised, it takes time, thought and a certain amount of preparation. That is why it is important to use theoretical and practical reference systems such as the ADAPT model that can guide you throughout the process. Let’s take a closer look… As a designer, do you ever think that the training path you put forward may not be appropriate to the improvement goals of the target audience? As a user, have you ever had the unpleasant feeling of finding yourself in an environment that does not fit your needs? In the first phase of eLearning – companies created training assets by focussing on learning objects. Nowadays, with well-established training assets, companies pay more attention to the person needing to learn and to their specific needs – with the aim of creating tailor-made training experiences Adaptive learning methods and technologies are designed for this purpose, so that everyone has an automatic personalised solution. The multiplicity of languages ??and micro-learning, i.e. the breakdown of teaching resources into short, granular and self-consistent units, make it easier to personalise because they allow multiple forms of combinations that are tailored to each individual. LEARNING ANALYTICS Imagine being able to interview the people taking part in an online course one at a time… What would you ask them? What would you like to know in order to improve their learning experience? Most LMS systems provide only three types of data for each student: &bull the completion of the course &bull how much time was spent on the course &bull the percentage of correct answers in the final test. To avoid wasting information, we use Learning Analytics, a collection, measurement, analysis and data communication process, the aim of which is to optimise the learning experience. The advantage of learning analytics is twofold: users receive continuous feedback throughout the learning process and managers have access to a variety of dashboards and views. RECOMMENDATION SYSTEM Recommendation Systems are among the most commonly used mechanisms to make online learning adaptive. They are artificial intelligence tools capable of creating and analysing a student’s profile, i.e. the collection of user data taken from the platform: personal information, existing knowledge, needs and didactic preferences. Using a recommendation algorithm, the system predicts which content best suits the user. The system provides targeted SUGGESTIONS on didactic materials based on the recommendation algorithm and the variables of interest involved. It makes the student feel recognised and supported by the system. ADAPTIVE LEARNING AND TRAINING ON THE JOB Adaptive learning can also be an important tool for enhancing training on the job. Indeed, an adaptive system can provide solutions targeted to the work environment. In particular, in apprenticeship processes, it can provide specific training modules for the practices that an apprentice has to learn at each stage of the production process. Adaptive training on the job that provides the training object required by the specific need requires granular, self-consistent training products that can be used on-demand. THE ADAPT MODEL Do you want to start designing CUSTOMISED LEARNING ENVIRONMENTS, PATHS AND EXPERIENCES for your organisation? USE THE ADAPT MODEL Ask (ask yourself the right questions, choose the kind of customisation you need and which individual differences to keep in mind) Data (collect data) Analytics (identify techniques for data processing) Personalise (provide a customised learning experience) Tuning (always improve the process) Chiara Moroni

Scritto da: Chiara Moroni

Compila il form e contattaci,
sei solo a un passo dall’iniziare.

I the undersigned state to having read the Information Notice and give specific consent

contattaci

Potrebbe interessarti anche

© Copyright 2020 Amicucci Formazione | P.IVA 01405830439 | Cap. Soc.: Euro 100.000,00 (i.v.) | C.C.I.A.A. (Macerata) | R.E.A. (149815) | Privacy policy | Cookie policy