Models in adaptive learning environments

All of the above categories of adaptation in learning environments are based on a rather well-established set of models and processes. The rest of this section presents brief accounts of some of the models that one typically encounters in ALEs.

􀂃 The domain model: Since most current ALEs are focused on adaptive course delivery, the domain-, or application- model is usually a representation of the course being offered. However, in those cases where more general learning activities are supported, the domain model may additionally contain information about workflows, participants, roles, etc. The most important aspect of adaptive-course models is that they are usually based on the identification of relationships between course elements, which are subsequently used to decide upon adaptations (Brusilovsky, 2003).

􀂃 The learner model: The term learner model is used to refer to special cases of user models, tailored for the domain of learning. The specific approach to modeling may vary between adaptive learning environments. Nevertheless, there is at least one characteristic shared by practically all existing systems: the model can be updated at interaction time, to incorporate elements or traces of the user’s interaction history. In other words, the learner model in ALEs, not only encapsulates general information about the user (e.g., demographics, previous achievements, etc.), but also maintains a “live” account of the user’s actions within the system.

􀂃 Group models: Similarly to user / learner models, group models seek to capture the characteristics of groups of users / learners. The main differentiating factors between the two are: (a) group models are typically assembled dynamically, rather that “filled in” dynamically, and (b) group models are based on the identification of groups of learners that share common characteristics, behaviour, etc. As such, groups model are used to determine and “describe” what makes learners “similar” or not, as well as whether any two learners can belong to the same group. This dynamic approach to identifying groups and user participation in them is already used widely in collaborative filtering and product recommenders, and bears great promise in the context of e-Learning.

􀂃 The adaptation model: This model incorporates the adaptive theory of an ALE, at different levels of abstraction. Specifically, the (possibly implicit) adaptation model defines what can be adapted, as well as when and how it is to be adapted. The levels of abstraction at which adaptation may be defined, range from specific programmatic rules that govern run-time bahaviour, all the way to general specifications of logical relationships between ALE entities, that get enforced automatically at run-time. The most widely known ALEs today (e.g., NetCoach (Weber, and Brusilovsky, 2001), AHA! (De Bra et al., 2002b), InterBook (Brusilovsky et al.,1998), etc.) use adaptation models that generically specify system behaviour on the basis of properties of the content model (such as relationships between content entities).

Although there would be probably little contention as to the enumeration of the models encountered in ALEs, the related literature reports a proliferation of approaches in their representation and utilization within different systems (Brusilovsky, 2003). It is argued that this is one of the major stumbling blocks that stand between adaptation and the e-Learning mainstream today. Awareness of this problem has given rise to several research efforts, aimed at standardizing as much of the adaptation modelling process as possible, on the basis of existing standards (see, e.g., the “Workshop on Adaptive E-Learning and Metadata” carried out under the auspices of the WM2003 conference).

The “reuse” of existing e-Learning standards and their “retargeting” for use in the context of adaptation, which is also a premise of this paper, is intended to: (a) facilitate the smooth and gradual transition from existing non-adaptive learning environments and courses to their adaptive counterparts, and (b) enable the graceful downgrading of adaptive content and activities when delivered over, or supported by, a “traditional” learning environment.