Open learner models (OLM) are learner models which are accessible to the learner, allowing them to view, and sometimes modify, their model. This openness may raise questions of learner trust in their learner model: if users do not agree with, or trust the information they can see about themselves, their trust in the interaction will likely be reduced. Using a Wizard-of-Oz approach, we consider ...

The advent of humanoid robots has enabled a new approach to investigating the acquisition of language, and we report on the development of robots able to acquire rudimentary linguistic skills. Our work focuses on early stages analogous to some characteristics of a human child of about 6 to 14 months, the transition from babbling to first word forms. We investigate one mechanism among many that ...

Major objective of the web-based learning is to design customized courseware taking into account of the input knowledge level and the specific objective of each individual learner. In order to improve the design of adaptive dynamic courseware to meet the requirement of individual learner we have already presented concept of Learner Quanta (LQ) & Learner Quanta Cloud (LQC) [9]. In this paper, co...

Using an eye-tracker, this paper investigates the information that learners visually attend to in their open learner model, and the degree to which this is related to the method of displaying the model to the learner. Participants were fourteen final year undergraduate students using six views of their learner model data. Results suggest some views of the learner model information may be more l...

Open learner models (OLM) enable users to access their learner model to view information about their understanding. Opening the learner model to the learner may increase their perceptions of how a system evaluates their knowledge and updates the model. This raises questions of trust relating to whether the learner believes the evaluations are correct, and whether they trust the system as a whol...

Recent meta-learning models often learn priors from observed tasks using a network optimized via stochastic gradient descent (SGD), which usually takes more training steps to convergence. In this paper, we propose an accelerated Bayesian structure with inference (ABML-SIN). The proposed model aims solve the procedure of improve speed and efficiency. Current approaches hardly converge within few...

In order for interactive learning environments to enrich and individualize the communication with learners, they use learner models, which can be seen as explicit representations in the system of some characteristics of that particular learner. These explicit learner models are created during the interaction with the learner and based on the actions performed by him or her. The created content ...

In supervised learning variable selection is used to find a subset of the available inputs that accurately predict the output. This paper shows that some of the variables that variable selection discards can beneficially be used as extra outputs for inductive transfer. Using discarded input variables as extra outputs forces the model to learn mappings from the variables that were selected as in...