منابع مشابه
Secure Communication in I-MINDS – a Computer Supported Collaborating System
I-MINDS is a computer-supported collaborative learning environment which allows an instructor to establish a virtual online classroom where the students can login an collaborate with each other. This collaboration allows the students to learn from both the instructor and the other students. During this classroom session, I-MINDS works as a communication platform for the students and the instruc...
متن کاملCollaborating with Focused and Unfocused Users under Imperfect Communication
A totally focused user always finishes the current task or subtask before moving on to another. Typical users, however, sometimes shift back and forth between incomplete tasks and do not always communicate before doing so. This behavior poses a problem for a software agent that uses plan recognition to support its collaboration with users. Our solution is a discourse interpretation algorithm wh...
متن کاملon the relationship between iranian learners personality type and communication strategies in speaking.
چکیده شخصیت به مجموعه عوامل روانی، عقلی، احساسی، و فیزیکی تشکیل دهنده یک فرد اطلاق میشود، خصوصا فرد ازدیدگاه دیگران ( مرجع). تدابیر یاد گیری سعی در اگاهی از اقدامات زبان اموزان موفق زبان دوم یا یک زبان بیگانه را دارد که توسط خود زبان اموزان گزارش میشود،یا حین یادگیری زبان دوم یا زبان بیگانه از انها قابل مشاهده است( روبین و وندن 1987). همچنین تدابیر ارتباطی به زبان اموزان کمک می کند تا بر مشکلا...
15 صفحه اولexamining the relationship between fear of negative evaluation and communication strategies by iranian efl learners
the purpose of the present study was to investigate the relationship between fear of negative evaluation (fne) and communication strategies (css) among iranian efl learners. it was aimed to examine the differences in the use of communication strategies between speakers with high or low degree of fear of negative evaluation. the current study was a case study consisting of 10 english learners at...
Adaptive Collaborating Filtering
In this paper, we study collaborative filters that adapt future recommendations based on feedback (like/dislike) received for past recommendations. We consider a mathematical model where users and items are clustered, and ratings are noisy. We apply sequential decision techniques to study structure of collaborative filters that maximize the time average of the expected ratings. While our previo...
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ژورنال
عنوان ژورنال: Lab Animal
سال: 2013
ISSN: 0093-7355,1548-4475
DOI: 10.1038/laban.258