Opening up the Interpretation Process in an Open Learner Model
17 (3): "Special Issue (Part 2) "Open Learner Models: Future Research Directions" "
Opening a model of the learner is a potentially complex operation. There are many aspects of the
learner that can be modelled, and many of these aspects may need to be opened in different ways. In addition,
there may be complicated interactions between these aspects which raise questions both about the accuracy of
the underlying model and the methods for representing a holistic view of the model. There can also be complex
processes involved in inferring the learner's state, and opening up views onto these processes – which leads to
the issues that are the main focus of this paper: namely, how can we open up the process of interpreting the
learner's behaviour in such a manner that the learner can both understand the process and challenge the
interpretation in a meaningful manner. The paper provides a description of the design and implementation of an
open learner model (termed the xOLM) which features an approach to breaking free from the limitations of
"black box" interpretation. This approach is based on a Toulmin-like argumentation structure together with a
form of data fusion based on an adaptation of Dempster-Shafer. However, the approach is not without its
problems. The paper ends with a discussion of the possible ways in which open learner models might open up
the interpretation process even more effectively.