Providing adaptivity based on learning styles can support learners and make learning easier for them. However, for providing proper adaptivity, the learning styles of learners need to be known first. While most systems, which consider learning styles, use questionnaires in order to identify learning styles, we propose an automatic student modelling approach, which analyses the actual behaviour ...

We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a posterior distribution on the agent’s preferences, policy and optionally, the obtained reward sequence, from observations. We examine the relati...

In order to provide information and communication services without disrupting human activity, information services must implicitly conform to the current context of human activity. However, the variability of human environments and human preferences make it impossible to preprogram the appropriate behaviors for a context aware service. One approach to overcoming this obstacle is to have service...

Our goal is to facilitate better human-robot collaboration by enabling robots to learn our preferences. To learn preferences, robots need to interact with users. We propose using comparison based learning, which learns preferences by asking a user to compare several alternatives. To minimize user burden, we use active learning. A challenge of comparison based learning is that it can be difficul...

The purpose of this study was to investigate whether a relationship existed between student learning styles and their preferences toward the various activities associated with the Problem-Based Learning (PBL) approach in the first-year pharmacy curriculum at the University of British Columbia. These PBL activities comprise group discussions, independent research, in-class critical-thinking and ...

There exists increasing demand for intelligent learning environments which are adaptive to learner’s preferences and tasks. This paper illustrates a study of the intelligent learning environment where the learner’s preferences are diagnosed using learner behavior patterns on user interfaces. In this research, a learning-style model by Felder & Silverman has been adopted as the most appropriate ...

A range of work in applied machine learning, psychology, and social science involves inferring a person’s preferences and beliefs from their choices or decisions. This includes work in economics on Structural Estimation, which has been used to infer beliefs about the rewards of education from observed work and education choices [1] and preferences for health outcomes from smoking behavior [2]. ...

Children prefer learning from, and affiliating with, their racial in-group but those preferences may vary for biracial children. Monoracial (White, Black, Asian) and biracial (Black/White, Asian/White) children (N = 246, 3-8 years) had their racial identity primed. In a learning preferences task, participants determined the function of a novel object after watching adults (White, Black, and Asi...

There exists an increasing demand for intelligent learning environments that are adaptive to learner’s preferences and tasks. This paper presents a study of an intelligent learning environment where the learner’s preferences are diagnosed using learner behavior patterns on user interfaces. In this research, a learning-style model by Felder & Silverman has been adopted as an appropriate basis fo...

We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a posterior distribution on the agent’s preferences, policy and optionally, the obtained reward sequence, from observations. We examine the relati...