IADIS INTERNATIONAL JOURNAL ON WWW/INTERNET

16-33

Authors: Konstantina Vemou and Maria Karyda

Built-in privacy is important for promoting users’ privacy and trust in Social Networking Services (SNS). Up to now, privacy research has its focus on the development and employment of Privacy-Enhancing Technologies as add-on applications and on investigating users’ privacy preferences. This paper draws on the principles of privacy-by-design and extends previous literature by identifying privacy requirements for the development of privacy-friendly SNS platforms. The paper also evaluates currently embedded privacy practices in four popular SNS platforms (Facebook, Google+, Twitter and Pinterest) to assess the level of built-in privacy and proposes a list of guidelines and tools SNS platform designers can employ.

51-67

Authors: María Dolores Castrillo, Elena Martín-Monje and Elena Bárcena

This article analyzes the adequacy of mobile chatting via WhatsApp for the enhancement of a type of spontaneous and colloquial written interaction that has a strong connection with oral discourse. This is part of a research project undertaken with Spanish students of German as a foreign language with a beginner’s or quasi-beginner’s level. The article describes the context and parameters of this learning experience and the methodology followed. Then, an analysis of the results is undertaken in terms of student response, attitude and participation, and also in terms of the meaning negotiation strategies and language used by the participants. Finally, conclusions are drawn regarding the adequacy of the WhatsApp technology for the practice and improvement of interactive skills for foreign language students at the initial learning levels.

85-101

Authors: Chientzu Candace Chou, Lanise Block and Renee Jesness

This study examines the impact of iPad integration on teaching and learning activities in a large school district in Midwest United States. The iPad cart initiative was conceived to provide equitable digital learning access among schools in the district and innovate learning activities. Forty social studies classrooms received iPad carts to engage students in learning. Teachers received professional development opportunities in the forms of workshops, conferences, one-to-one coaching, and online communities. The findings showed positive impact on student learning in the areas of digital literacy, engagement, collaboration, productivity, and creativity. Facing shifting pedagogy to learner-centered learning, teachers have also found increased opportunities for collaboration and creativity. Technology anxiety and distraction remain the main challenges for teachers and students. School districts need to promote collaboration in learning circles, one-to-one mentoring, and transformative modeling as the best practice for iPad integration.

115-130

Authors: Andreas Both, Viet Nguyen, Mandy Keck, Martin Herrmann, Dietrich Kammer, Rainer Groh and Dana Henkens

A novel generation of (e.g. touch-driven) applications leads to a new universe of interaction paradigms and a growing need for simple, inspiring and smart interfaces while the size of searchable data sets is increasing permanently (big data). A system intended for non-experts should only present information the user needs to solve his task, instead of confronting him with the large and complex underlying data structure. In this paper, we focus on users wanting to perform a product search driven by a vague information need. We call this kind of search motive-based search, which is often initiated by unconscious motives and expectations that are difficult to transform into a specific search query at the beginning of the search process. Hence, the user needs guidance to fulfill his search task. A search approach will be developed in this paper, which allows a step-by-step reduction of the result set by selecting (visualized) concepts such as "beach", "relaxing" and "culture". Concepts are often organized as multiple faceted hierarchies (polyhierarchies) to represent different views on things. Hierarchies can also be used as navigation paradigm, named faceted search. We will present the significant flaws of this approach concerning larger knowledge bases. Alternatively, we propose a selection-based recommendation-driven search, based on the principle of divide and conquer. An experiment compares both approaches proving that the proposed approach allows to solve the given search tasks in shorter time and with less effort.

149-167

Authors: Edson B. Santos Junior, Marcelo Garcia Manzato and Rudinei Goularte

One of the major challenges in Recommender Systems is how to predict users’ preferences in a group context. There are situations in which a user could be recommended an item appropriated for one of their groups, but the same item may not be suitable when interacting with another group. There are situations in which a user could be recommended an item appropriated for one of their groups (e.g. poetry friends), but the same item may not be suitable when interacting with another group (e.g. soccer team). We note that recommender systems should try to satisfy the group’s demands, but it should also respect the user’s individuality. The demographic data are an effective way to consider users’ characteristics, enabling analysis about group of users and their contextual constraints. In our past work we have proposed a multifaceted hybrid recommender model, which integrates a set of different user’s inputs into a unified and generic latent factor model, achieving better results than the other state-of-the-art approaches. The recommender exploits users’ demographics, such as age, gender and occupation, along with implicit feedback and items’ metadata. Depending on the personal information from users, the recommender selects content whose subject is semantically related to their interests. In this paper we evaluate our model, aiming to analyze the impact of demographic data on hybrid recommenders, in order to understand how these systems are beavering in terms of the recommendation accuracy, precision and recall, computational cost and improvements of technical results.