Adaptivity & Personalization

Personalizing persuasive technologies can increase their efficacy at motivating the desired behavior change. Building on the success of the 2016 workshop which witnessed 16 paper presentations, 2 keynote presentations, and 43 participants from over 10 different countries, this year's workshop aims to advance the research area even further by addressing outstanding challenges and opportunities identified during the previous workshop. The workshop aims to connect a diverse group of researchers and practitioners interested in personalizing and tailoring persuasive technologies to share their experiences, ideas, discuss key challenges facing the area, and how to move the field forward. The workshop will cover broad areas of personalization and tailoring, including but not limited to personalization models, design and evaluation methods, and personalized persuasive technologies. We welcome submissions and ideas from any domain of persuasive technology and HCI including, but not limited to health, sustainability, games, safety and security, marketing, eCommerce, entertainment, and education. Workshop papers and ideas will be archived online to be accessible to the general public. For more information see: https://personalizedpersuasion.wordpress.com/

There are ongoing efforts by researchers to find effective ways to personalize persuasive applications for maximum impact on behavioral change. Recently, research has shown that personality traits can predict the responsiveness of individuals to persuasive strategies [1], [4]. While there have been few studies on the influence of the Big Five on Cialdini’s six principles of persuasion (e.g., [1] among the Turkish population), studies among individualist cultures are scarce. To bridge this gap, we conducted a study of 216 Canadians to uncover how the Big Five impacts Cialdini’s persuasion principles and compare our findings with those of Alkış and Temizel [1]. The prior study by these authors has some limitations, such as the use of convenience sample. Our study addresses this limitation by recruiting subjects beyond the four walls of the university.

In this article we present a review of selected literature of context-aware pervasive computing while integrating theory and practice from various disciplines in order to construct a theoretical grounding and a technical follow-up path for our future research. This paper is not meant to provide an extensive review of the literature, but rather to integrate and extend fundamental and promising theoretical and technical aspects found in the literature. Our purpose is to use the constructed theory and practice in order to enable anywhere and anytime adaptive e-learning environments. We particularly elaborate on context, adaptivity, context-aware systems, ontologies and software development issues. Furthermore, we represent our view point for context-aware pervasive application development particularly based on higher abstraction where ontologies and semantic web activities, also web itself, are of crucial.

This paper introduces a novel approach for intelligent content personalisation in internet TV using the new MPEG-21 standard in the framework of an ongoing project, which aims at the delivery of interactive TV (iTV) services to disabled children. The approach includes the development of an authoring tool for a diversity of multimedia resources supporting a metadata model according to MPEG-21. The authoring tool works in conjunction with a simulation platform, acting as an interaction interface between our internet TV architecture and the prospective viewer. Based on that, interactivity in IP Television accessibility services is faced through metadata and adaptation.

In previous studies, it was revealed that the importance of learning styles, motivation, and knowledge ability factors are facilitated diverse learners in e-learning. However, the overall of factors are not yet fully accommodated in e-learning process. Meanwhile, the results of preliminary study of this research indicated that the existence of these factors as the inherent structure that reflect the relationship among learning styles and motivation to the knowledge ability (Triple-Factor) in elearning process. In order to accommodate the existence of inherent structure, this study explores the learning types based on triple-factor in e-learning process. The approach can be used as the basis for the learning recommendation and personalization in e-learning process. The approach consists of three steps: (1) identifying activity and evaluation of learning outcome, (2) forming Triple-Factor, and (3) detecting learning types based on Triple-Factor. The approach use 36 types of learning which consists of: 18 learning types that has the ability of knowledge good or very good, irrespective of the learning styles and motivation; and 18 learning types that has the ability of knowledge fail or sufficient, irrespective of the learning styles and motivation. Furthermore, the experiment for testing steps was carried by dividing two stages: the first stage did not implement the approach, second stage using learning recommendation and personalization. Result of the testing of these two stages are: step of " identifying activity and evaluation of learning outcome " at the second stage, showed that there was a significant increase on the activity of learning (0.007<0.05), and discussion forums (0.006<0.05), meanwhile the evaluation of learning outcomes (0.227>0.05) did not increase significantly. Step of "forming Triple-Factor", and " detecting learning type base on Triple-Factor" at the second stage showed: learning styles and motivation with the knowledge ability of good and very good increased from 57 to 69 students. In contrast learning styles and motivation with the knowledge ability of fail and sufficient decreased from 61 to 49. The results show that the approach used in the study successfully improve the learning process and its outcomes through learning recommendation and personalization.