Titlebook: User Modeling, Adaptation and Personalization; 22nd International C Vania Dimitrova,Tsvi Kuflik,Geert-Jan Houben Conference proceedings 201

Dendritic-Cells

四指套

Adaptive Support versus Alternating Worked Examples and Tutored Problems: Which Leads to Better Learh assistance the student received in the previous problem. The results show that students in the adaptive condition learnt significantly more than their peers who were presented with a fixed sequence of worked examples and problems.

法律

Hangar

0302-9743 n, held in Aalborg, Denmark, in July 2014. The 23 long and 19 short papers of the research paper track were carefully reviewed and selected from 146 submissions. The papers cover the following topics: large scale personalization, adaptation and recommendation; Personalization for individuals, groups

玉米

Modelling Long Term Goalstions for people’s long term goals. This paper describes our theoretically-grounded design of user models for long term goals; notably, the theory points to the critical role of the user interface to this Goal Model, to enable people to set, monitor and refine their models over the long term. We rep

LIMN

A Personalization Method Based on Human Factors for Improving Usability of User Authentication Taskser proposes a two-step personalization approach of user authentication tasks based on individual differences in cognitive processing as follows: i) recommend a textual or graphical user authentication mechanism based on the users’ cognitive styles of processing textual and graphical information, and

Delectable

colony

Toward Fully Automated Person-Independent Detection of Mind Wanderingas negative effects on performance, hence, intelligent interfaces that detect mind wandering can intervene to restore attention to the current task. We investigated the use of eye gaze and contextual cues to automatically detect mind wandering during reading with a computer interface. Participants w

商店街

Decline

Recommendation Based on Contextual Opinionsques mainly aim at exploiting item-level contextual information for modeling users’ preferences, while few works attempt to detect more fine-grained aspect-level contextual preferences. Therefore, in this article, we propose a contextual recommendation algorithm based on user-generated reviews, from