  Sequencing items in adaptive learning systems typically relies on a large
pool of interactive assessment items (questions) that are analyzed into a
hierarchy of skills or Knowledge Components (KCs). Educational data mining
techniques can be used to analyze students performance data in order to
optimize the mapping of items to KCs. Standard methods that map items into KCs
using item-similarity measures make the implicit assumption that students
performance on items that depend on the same skill should be similar. This
assumption holds if the latent trait (mastery of the underlying skill) is
relatively fixed during students activity, as in the context of testing, which
is the primary context in which these measures were developed and applied.
However, in adaptive learning systems that aim for learning, and address
subject matters such as K6 Math that consist of multiple sub-skills, this
assumption does not hold. In this paper we propose a new item-similarity
measure, termed Kappa Learning (KL), which aims to address this gap. KL
identifies similarity between items under the assumption of learning, namely,
that learners mastery of the underlying skills changes as they progress through
the items. We evaluate Kappa Learning on data from a computerized tutor that
teaches Fractions for 4th grade, with experts tagging as ground truth, and on
simulated data. Our results show that clustering that is based on Kappa
Learning outperforms clustering that is based on commonly used similarity
measures (Cohen Kappa, Yule, and Pearson).
