In recent years, several frameworks and systems have been proposed that extend Inductive
Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples
must all be explained by a hypothesis together with a given background knowledge. In existing
systems, the background knowledge is the same for all examples; however, examples may be
context-dependent. This means that some examples should be explained in the context of
some information, whereas others should be explained in different contexts. In this paper, we
capture this notion and present a context-dependent extension of the Learning from Ordered
Answer Sets framework. In this extension, contexts can be used to further structure the
background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits
this feature to scale up the existing ILASP2 system to learning tasks with large numbers
of examples. We demonstrate the gain in scalability by applying both algorithms to various
learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i
system can be two orders of magnitude faster and use two orders of magnitude less memory,
whilst preserving the same average accuracy