The idea of learning objects and its working standardization for the Learning Object Metadata as recommended by IEEE 1484 significantly revolutionized the latest educational technology -- especially in many existing e-learning systems where course or quiz materials can be conveniently anchored on a network of well-defined learning objects. In many real-world applications, such networks/repositories may consist of hundreds or thousands of learning objects spanning different levels of detail. Some learning objects are independent concepts (or nodes) while others are engaged in some complex relations.

To allow the designers of those e-learning systems to restructure the network of learning objects for greater accessibility or faster navigation, and more importantly facilitating both educators and learners to quickly identify some important relation(s) among the involved learning objects (or concepts), this is clearly where adaptive information visualization techniques may help. Through adaptive visualization, users can focus on various subsets of learning objects with different properties for careful analysis. Unfortunately, there is no existing information visualization technique customizable for dynamic viewing of different subsets of learning objects.

In this paper, we consider adapting the well-known force scan algorithm (FSA) as flexible variants integrated with effective heuristics to produce appropriate diagrams of various scales or shapes for different analysis or screen sizes. For diagrams solely involving independent learning objects, our aim is to enhance the visibility by evenly spreading out the learning objects with adjustable angular or pixel displacement while avoiding node overlapping on the different levels. However, for network diagrams of related learning objects, the main challenge is to avoid both node and edge overlapping while spreading out the concerned learning objects on pre-assigned levels. In both cases, our adapted FSA works for its best interest to preserve the mental map of the initial diagrams of learning objects. To demonstrate the possible advantages of our proposal, we implemented two variants of our adapted FSA for adaptive visualization of networks of learning objects, and ran them on both random and real test cases. The preliminary experimental results demonstrated the strength of our proposal from which existing e-learning systems can benefit. More importantly, it shed light on several interesting directions for further investigation.