Opening up the interpretation process in an open learner model

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

Exploring participative learner modelling and its effects on learner behaviour

scholarship 64999/111091The educational benefits of involving learners as active players in the learner modelling process have been an important motivation for research on this form of learner modelling, henceforth referred to as participative learner modelling. Such benefits, conceived as the promotion of learners' reflection on and awareness of their own knowledge, have in most cases been asserted on the grounds of system design and supported only by anecdotal evidence. This dissertation explores the issue of whether participative learner modelling actually promotes learners' reflection and awareness. It does so by firstly interpreting 'reflection' and 'awareness' in light of "classical" theories of human cognitive architecture, skill acquisition and meta-cognition, in order to infer changes in learner abilities (and therefore behaviour) amenable to empirical corroboration. The occurrence of such changes is then tested for an implementation of a paradigmatic form of participative learner modelling: allowing learners to inspect and modify their learner models. The domain of application centres on the sensorimotor skill of controlling a pole on a cart and represents a novel type of domain for participative learner modelling. Special attention is paid to evaluating the method developed for constructing learner models and the form of presenting them to learners: the former is based on a method known as behavioural cloning for acquiring expert knowledge by means of machine learning; the latter deals with the modularity of the learner models and the modality and interactivity of their presentation. The outcome of this research suggests that participative learner modelling may increase the abilities of learners to report accurately their problem-solving knowledge and to carry out novel tasks in the same domain—the sort of behavioural changes expected from increased learners' awareness and reflection. More importantly perhaps, the research suggests a viable methodology for examining the educational benefits of participative learner modelling. It also exemplifies the difficulties that such endeavours will face