*Dynamic and Adaptive Playout of Competency-Based Learning Games Based on Data… DOI: http://dx.doi.org/10.5772/intechopen.105513*

configuration (DAC). The DAC contains the reference to the DST identifier, a title, a Uniform Resource Identifier (URI)—the implementation is reachable with—and a Universally Unique Identifier (UUID). The UUID is used to link the external system with the DAC. An extension of the KM-EP is implemented to provide the possibility to add and edit DACs, allowing the simultaneous existence of different DAs for one DST.

The system suggests to the user that data rank the registered DAs out of the related DACs. For the recommendation, the system uses several classification parameters to describe learners and the DA. For the DA, the DAC holds the parameters. The parameters are different devices the media type is introduced, which offers a generic categorization. The DAC contains the flags for accessibility ("readout available," "audio required," and "plain language"), information about the possible screen resolutions, an extended reality flag, and a flag to describe the need for user interaction (input device) and the level of interactivity. The concept for the level of interactivity is derived from [51]. It describes six stages of possible levels.

Learners are described by multiple parameters, which describe the learners' preferences, limitations, and devices. The visual aural read kinesthetic (VARK) profile [52] is used to adjust the preferences. Fleming and Mills introduced the model in 1992 [52]. The model categorizes the learners into four types: visual, aural, read, and kinesthetic. Via a test, learners can identify four dimensions points in four dimensions and push them into a radar diagram. The radar shows afterward the preference meaning a higher swing to one of the four dimensions. The limitations of learners are reflected by three flags, which are "visual impairment," "hearing impairment," and "plain language requested." The device and its attributes are automatically derived from the browsers' information and contain the resolution, XR device, and interaction possible (input device) [52].

In the DA recommendation, these two classifications are confronted with each other. This is done via a transposition of the DA classification as a point into a twodimensional graph. This graph contains the centric point of the VARK profile. The distance between both points defines the rating for a recommendation. If the points are close, the learners' preferences coincide with the DA. The learners' limitations overrule the recommendation process to offer the learners only the DAs they can consume. For example, the DA recommendation offers blind persons ("visual impairment") only DAs with the flag "readout available" true. After rating the different DAs, they are listed in a UI in descending order.

DA recommender and learner adaptive flow enrich this system with additional features for tracking the learners' progress and a CQ-based learning path for every individual learner.

According to the learners' presets according to their VARK profile and their impairments, the registered DAs, and the media type, the so-called didactical application recommender (DAR) calculates the best-fitting DAs. This calculation shows all eligible DAs in descending ranking order in a table so that the learners can choose on their own the DA they want to continue with learning. For more information on how the ranking is calculated, see [22, 53].

As shown in **Figure 1** on the right side, the architecture of Moodle consists of six components [15]. The core, the subsystems, the plugins, the plugin types, the sub-plugins, and the dependencies. In **Figure 1**, the three components, namely core, plugin, and subsystems, are represented [50]. The Moodle concept is explained in more detail in [50]. The PAGEL learning resources are part of the core of Moodle. The core contains all the basic functions of Moodle [15].

The course authoring tool (CAT) on the right side offers a user-friendly CAT to create Moodle courses without much previous knowledge [17].

On the left side in **Figure 1** are placed all components within the KM-EP, which are used in the context of this paper and described in further subsections.
