Presentations are effective ways of communicating information, especially in the field of education, but they might not be equally or fully beneficial and persuasive to all users. Each member of the audience might be interested in a particular topic, come from a different background and profession, and have his or her own personality traits. In this conceptual paper, we first describe our persuasive personalization model; the Individualization Pyramid based on Yale Attitude Change Approach. The model consists of the following main sections: selecting contents by applying segmentation, adjusting comprehensibility of the text, tailoring the language of the text to fit with user's personality and recommending content that is associated with user's personal history within the related subjects. We then propose an enhanced version of our previously published presentation builder, which uses users' digital traces such as those on social media to personalize presentation content. Finally, we highlight the available tools and algorithms to assist us with developing the system.

No abstract

Providing content and experiences to users in order to influence their thoughts and opinions is essential in many fields such as marketing, education, and politics. With advances and the growing availability of the Internet and Web technologies, posting documents online has become an effective means of communicating to large audiences. However, given individuals' different respective interests, characteristics, and abilities, shared documents will not likely be equally persuasive to all users. To communicate persuasively, the categorization of users according to factors like age, marital status, education, and occupation is becoming increasingly prevalent, as is providing users with content specific to their categories. This approach assumes individuals within a category are similar, which is not necessarily true. Persuasive communication should recognize differences at the individual level and personalize-rather than categorize-the content.However, a systematic software solution that tailors and prepares separate persuasive content for individuals does not yet exist. Given the increasing popularity and usage of social media, socialnetwork extractable data can potentially provide a tremendous source of insight and background about individuals. Inspired by the Yale Attitude Change approach, this thesis proposes a multilayer model called Pyramid of Individualization and the related software framework to generate persuasive content based on initial author input and audience social-media data. Preliminary results show the proposed system can create personalized information that (a) matches reader interests (attention), (b) is tailored to reader ability to understand the information (comprehension), and (c) is supported by a trustable source (acceptance).ii ACKNOWLEDGMENT First and foremost, I would like to express my sincere appreciation to my supervisors Dr.Ali Arya and Dr. Michael Hine without whom this study would not have been possible. Their guidance, patience, and encouragements made all the difference.

For most people, decision-making involves collecting opinion and advice from others who can be trusted. Personalizing a presentation's content with trustworthy opinions can be very effective towards persuasiveness of the content. While the persuasiveness of presentation is an important factor in face-to-face scenarios, it becomes even more important in an online course or other educational material when the "presenter" cannot interact with audience and attract and influence them. As the final layer of our personalization model, the Pyramid of Individualization, in this paper we present a conceptual model for collecting opinionative information as trustworthy support for the presentation content. We explore selecting a credible publisher (expert) for the supporting opinion as well as the right opinion that is aligned with the intended personalized content.