This article presents the instantiation of the Model of Adaptation of Learning Objects (MALO) developed in previous works, using the competencies to be developed in a given educational context. MALO has been developed for virtual environments based on an extension of the LOM standard. The model specifies modularly and independently two categories of rules, of adaptation and conversion, giving it versatility and flexibility to perform different types of adaptation to the learning objects, incorporating or removing rules in each category. In this work, we instance these rules of MALO using the competencies considered in a given educational context.

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Eco-connectivist communities are groups of individuals with similar characteristics, which emerge in a connectivist learning process within a knowledge ecology. ARMAGAeco-c is a reflexive and autonomic middleware for the management and optimization of eco-connectivist knowledge ecologies using description, prediction and prescription models. Adaptive Learning Objects are autonomic components that seek to personalize Learning Objects according to certain contextual information, such as learning styles of the learner's, technological restrictions, among other aspects. MALO is a system that allows the management of Adaptive Learning Objects. One of the main challenges of the connectivist learning process is the adaptation of the educational context to the student needs. One of them is the learning objects. For this reason, this work has two objectives, specifying a data analytics task to determine the learning style of a student in an eco-connectivist community and, adapting instances of Adaptive Learning Objects using the learning styles of the students in the communities. We use graph theory to identify the referential member of each eco-connectivist community, and a learning paradigm detection algorithm to identify the set of activities, strategies, and tools that Adaptive Learning Objects instances should have, according to the learning style of the referential member. To test our approach, a case study is presented, which demonstrates the validity of our approach.

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