Confusion Detection Dataset of Mouse and Eye Movements
Abstract. Affective computing promises improvement of computer-human interaction by adaptive interventions based on the machines' understanding of the human emotions. However, since emotion detection mostly employs supervised machine learning, big labeled datasets are needed to train accurate detectors. Currently, there is a lack of the open datasets, especially in the domain of confusion detection on the web. In this paper, we introduce a confusion detection dataset comprising of two modalities - the mouse movements and the eye movements of the users. The dataset was gathered during a quantitative controlled user study with 60 participants. We chose a travel agency web application for the study, where we carefully designed six tasks reflecting the common behavior and the problems of the day-to-day users. In the paper, we also discuss the issue of labeling emotional data during the study and provide exploratory analysis of the dataset and insights into the confused users' behavior.
Towards Personalisation for Learner Motivation in Healthcare: A Study on Using Learner Characteristics to Personalise Nudges in an e-Learning Context
Abstract. Lifelong learning is a key requirement for anyone working in healthcare, but many healthcare professionals find it challenging to undertake learning activities during their daily tasks. Digital solutions such as e-learning platforms have been proposed to encourage and support the self-management of learning activities. In order to enhance the effectiveness of e-learning provision, personalised interventions in the form of prompts or nudges can be used, but first we need to ascertain (i) what kind of nudges are effective in an e-learning scenario, and (ii) which learner characteristics will be useful for personalisation of such nudges. In this paper we report the results of a study among medical and healthcare students which looks at the relationships between users’ interests, demographics and psychological traits, and the perceived effectiveness of five choice architecture techniques implemented as textual nudges on an e-learning platform in the healthcare domain. We found that even without personalisation different nudges vary in effectiveness in this context, and that interest (and to lesser extent other user characteristics) influence the perceived effectiveness of nudges. We finish with a set of recommendations for nudge design in this domain.
Simulation environment for guiding the design of contextual personalization systems in the context of hearing aids
Abstract. Adjusting the settings of hearing aids in a clinic is challenging as the measured thresholds of audibility do not reflect many aspects of cognitive perception or the resulting differences in auditory preferences across different contexts. Online personalization systems have a potential to solve this problem, yet the lack of contextual user preference data constitutes a major obstacle in designing and implementing them. To address this challenge, we propose a simulation-based framework to inform and accelerate the development process of online contextual personalization systems in the context of hearing aids. We discuss how to model hearing aid users and context, and propose how to generate plausible preference models using Gaussian Processes incorporating assumptions about the environment in a controlled way. Finally, on a simple example we demonstrate how an agent can learn in the proposed framework.
DANOS: A Human-Centered Decentralized Simulator in SIOT
Abstract. The added value of Social Internet of Things (SIoT) is constantly highlighted during the recent years. The idea is to exploit the social relationships among real-world smart heterogeneous objects to the benefit of their owners, through e.g., search and finding, dedicated services, tasks augmentation. In this paper we discuss a decentralized human-centered simulator, DANOS, that enhances objects' profiles and their interaction behavior with intelligence, based on specific human aspects, i.e., personality traits. Preliminary results show that when objects travel with intelligence in the virtual space, they are able to locate faster similar objects, establishing stronger and more qualitative relationships, while at the same time minimizing the network complexity and load. Such results, increase the probability of discovering faster the information based on given intents and providing best-fit recommendations with fewer costs.
Towards Open Learner Models including the Flow state
Abstract. MOOCs are a learning environment that can assure Lifelong Learning. Learner’s personalization is an essential concept in Lifelong Learning. Studies have shown the importance of modeling the learner for a more personal and tailored learning experience in MOOC. Furthermore, Open Learner Models have proven their added value in facilitating learner's follow-up and course content personalization. However, while modeling the learner's knowledge is a common practice, modeling the learner's psychological state is a relegated concern within the community. This is despite the myriad of scientific evidence backing up the importance and repercussion of the learner's psychological state during and on the learning process. Flow is a psychological state characterized by total immersion in a task and a state of optimal performance. Programmers often refer to it as “being in the zone”. It reliably correlates favorable learning metrics, such as motivation and engagement, among others. The aim of this paper is to propose a functional and technical architecture (comprising a Domain Model, a Flow Model and an Open Learner Model for MOOC in a Lifelong Learning context) accounting for the learner's Flow state. This work is dedicated to MOOC designers/ providers, pedagogical engineers, psychology and education researchers who meet difficulties to incorporate and account for the Flow psychological state in a MOOC.