A User-Focused Reference Model for Wireless Systems Beyond 3G

This whitepaper describes a proposal from Working Group 1, the Human Perspective of the Wireless World, for a user-focused reference model for systems beyond 3G. The general structure of the proposed model involves two "planes": the Value Plane and the Capability Plane. The characteristics of these planes are discussed in detail and an example application of the model to a specific scenario for the wireless world is provided

Considering temporal aspects in recommender systems: a survey

Under embargo until: 2023-07-04The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.acceptedVersio

Intelligent Mobile Learning Interaction System (IMLIS): A Personalized Learning System for People with Mental Disabilities

The domain of learning context for people with special needs is a big challenge for digi- tal media in education. This thesis describes the main ideas and the architecture of a system called Intelligent Mobile Learning Interaction System (IMLIS) that provides a mobile learning environment for people with mental disabilities. The design of IMLIS aims to enhance personalization aspects by using a decision engine, which makes deci- sions based on the user s abilities, learning history and reactions to processes. It allows for adaptation, adjustment and personalization of content, learning activities, and the user interface on different levels in a context where learners and teachers are targeting autonomous learning by personalized lessons and feedback. Due to IMLIS dynamic structure and flexible patterns, it is able to meet the specific needs of individuals and to engage them in learning activities with new learning motivations. In addition to support- ing learning material and educational aspects, mobile learning fosters learning across context and provides more social communication and collaboration for its users. The suggested methodology defines a comprehensive learning process for the mentally disabled to support them in formal and informal learning. We apply knowledge from the field of research and practice to people with mental disabilities, as well as discuss the pedagogical and didactical aspects of the design

Dynamic Key-Value Memory Networks for Knowledge Tracing

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence to help students learn knowledge concepts efficiently. However, existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing either model knowledge state for each predefined concept separately or fail to pinpoint exactly which concepts a student is good at or unfamiliar with. To solve these problems, this work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept. Unlike standard memory-augmented neural networks that facilitate a single memory matrix or two static memory matrices, our model has one static matrix called key, which stores the knowledge concepts and the other dynamic matrix called value, which stores and updates the mastery levels of corresponding concepts. Experiments show that our model consistently outperforms the state-of-the-art model in a range of KT datasets. Moreover, the DKVMN model can automatically discover underlying concepts of exercises typically performed by human annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW), 201