A Methodology for Discovering how to Adaptively Personalize to Users using Experimental Comparisons

We explain and provide examples of a formalism that supports the methodology of discovering how to adapt and personalize technology by combining randomized experiments with variables associated with user models. We characterize a formal relationship between the use of technology to conduct A/B experiments and use of technology for adaptive personalization. The MOOClet Formalism [11] captures the equivalence between experimentation and personalization in its conceptualization of modular components of a technology. This motivates a unified software design pattern that enables technology components that can be compared in an experiment to also be adapted based on contextual data, or personalized based on user characteristics. With the aid of a concrete use case, we illustrate the potential of the MOOClet formalism for a methodology that uses randomized experiments of alternative micro-designs to discover how to adapt technology based on user characteristics, and then dynamically implements these personalized improvements in real time

A review on massive e-learning (MOOC) design, delivery and assessment

MOOCs or Massive Online Open Courses based on Open Educational Resources (OER) might be one of the most versatile ways to offer access to quality education, especially for those residing in far or disadvantaged areas. This article analyzes the state of the art on MOOCs, exploring open research questions and setting interesting topics and goals for further research. Finally, it proposes a framework that includes the use of software agents with the aim to improve and personalize management, delivery, efficiency and evaluation of massive online courses on an individual level basis.Peer ReviewedPostprint (author's final draft

Technology-enhanced learning: a new digital divide?

Abstrac

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

Personalization by Partial Evaluation.

The central contribution of this paper is to model personalization by the programmatic notion of partial evaluation.Partial evaluation is a technique used to automatically specialize programs, given incomplete information about their input.The methodology presented here models a collection of information resources as a program (which abstracts the underlying schema of organization and flow of information),partially evaluates the program with respect to user input,and recreates a personalized site from the specialized program.This enables a customizable methodology called PIPE that supports the automatic specialization of resources,without enumerating the interaction sequences beforehand .Issues relating to the scalability of PIPE,information integration,sessioniz-ling scenarios,and case studies are presented

Body Image Perception: Adolescent Boys and Avatar Depiction in Video Games

Research on mass media’s impact on body image has mostly been focused on females thus far. Of the little research that has been done on male body image, most of it has been focused on adult males, and therefore the effect of mass media on adolescent boys’ body image is still a relatively primitive field of knowledge. Through comparing the exposure of adolescent boys to muscular avatars in popular video games, a source of mass media that a majority of adolescent boys are exposed to, and relating it to research done on the effects of frequent ideal image exposure through other forms of mass media on males, the influence of video games on the body image of adolescent boys can be determined. This study consisted of several factors: (1) understanding the impact of constantly viewing ideal images in mass media on males’ perceptions of their own bodies, (2) reviewing the body types of the male avatars in several modern, popular video games played by adolescent boys, (3) relating the exposure of video game avatars on adolescent boys’ views of their own physiques, and (4) examining the implications of negative body image on adolescent boys’ eating and exercise strategies. Although video game avatars tend to have a slightly different body shape than those presented in most types of mass media, their unifying trait of naturally unattainable muscularity resulted a reaction among adolescent boys that was similar to that of adult males with regard to mesomorphic (muscular, V-shaped) body types in mass media. This resulting negative body image can lead to psychological disorders such as depression or such physical disorders as anabolic steroid usage, unnatural dieting, and excessive exercising

Data Cleaning Methods for Client and Proxy Logs

In this paper we present our experiences with the cleaning of Web client and proxy usage logs, based on a long-term browsing study with 25 participants. A detailed clickstream log, recorded using a Web intermediary, was combined with a second log of user interface actions, which was captured by a modified Firefox browser for a subset of the participants. The consolidated data from both records revealed many page requests that were not directly related to user actions. For participants who had no ad-filtering system installed, these artifacts made up one third of all transferred Web pages. Three major reasons could be identified: HTML Frames and iFrames, advertisements, and automatic page reloads. The experiences made during the data cleaning process might help other researchers to choose adequate filtering methods for their data