Supporting group collaboration with interpersonal awareness devices

1999 ◽  
Vol 3 (1-2) ◽  
pp. 13-21 ◽  
Author(s):  
Lars Erik Holmquist ◽  
Jennica Falk ◽  
Joakim Wigstr�m
Keyword(s):  
Group Collaboration ◽  
Interpersonal Awareness

A Group Collaboratable Proof of Retrievability Scheme for Cloud Data Storage

2014 ◽  
Vol 13 (7) ◽  
pp. 4625-4632
Author(s):  
Jyh-Shyan Lin ◽  
Kuo-Hsiung Liao ◽  
Chao-Hsing Hsu
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Cloud Service ◽  
Third Party ◽  
Group Collaboration ◽  
Cloud Data ◽  
Cloud Data Storage ◽  
Important Trend ◽  
Data Update ◽  
Proof Of Retrievability

Cloud computing and cloud data storage have become important applications on the Internet. An important trend in cloud computing and cloud data storage is group collaboration since it is a great inducement for an entity to use a cloud service, especially for an international enterprise. In this paper we propose a cloud data storage scheme with some protocols to support group collaboration. A group of users can operate on a set of data collaboratively with dynamic data update supported. Every member of the group can access, update and verify the data independently. The verification can also be authorized to a third-party auditor for convenience.

scholarly journals Attitudinal Factors Affecting Wiki Group Collaboration for English Writing

2015 ◽  
Vol 18 (2) ◽  
pp. 22-35 ◽  
Author(s):  
Chwen Jen Chen ◽  
Kee Man Chuah ◽  
Jimmy Tho ◽  
Chee Siong Teh
Keyword(s):  
Language Learning ◽  
Foreign Language Learning ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Perceived Ease Of Use ◽  
Factors Affecting ◽  
Group Collaboration ◽  
Construction Joint ◽  
Attitudinal Factors ◽  
And Performance

Abstract Wikis, being one of the popular Web 2.0 tools, have impacted students’ engagement and performance particularly in the aspects of second and foreign language learning. While an increasing number of studies have focused on the effectiveness of wiki in improving students’ writing skills, this study was conducted to examine the attitudinal factors that influence English as a Second Language (ESL) students’ group collaboration in using wikis for a writing task that was divided into three phases: pre-writing, individual-construction, joint-constructions. Data collected from these students after completing this task was analyzed based on three attitudinal aspects: motivation, perceived usefulness, and perceived ease of use. The findings reveal high mean scores for all aspects. Further multiple regression analysis reveals that motivation is the most important factor associated with group collaboration, indicating the need to boost students’ motivation to encourage effective collaboration in completing wiki writing tasks.

scholarly journals Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral Cues

2021 ◽  
Vol 3 ◽  
Author(s):  
Anirudh Som ◽  
Sujeong Kim ◽  
Bladimir Lopez-Prado ◽  
Svati Dhamija ◽  
Nonye Alozie ◽  
...  
Keyword(s):  
Recommendation System ◽  
Feedback System ◽  
Individual Student ◽  
Teaching Staff ◽  
Student Group ◽  
Student Groups ◽  
Group Collaboration ◽  
Individual Level ◽  
Constructive Feedback ◽  
Spatio Temporal

Early development of specific skills can help students succeed in fields like Science, Technology, Engineering and Mathematics. Different education standards consider “Collaboration” as a required and necessary skill that can help students excel in these fields. Instruction-based methods is the most common approach, adopted by teachers to instill collaborative skills. However, it is difficult for a single teacher to observe multiple student groups and provide constructive feedback to each student. With growing student population and limited teaching staff, this problem seems unlikely to go away. Development of machine-learning-based automated systems for student group collaboration assessment and feedback can help address this problem. Building upon our previous work, in this paper, we propose simple CNN deep-learning models that take in spatio-temporal representations of individual student roles and behavior annotations as input for group collaboration assessment. The trained classification models are further used to develop an automated recommendation system to provide individual-level or group-level feedback. The recommendation system suggests different roles each student in the group could have assumed that would facilitate better overall group collaboration. To the best of our knowledge, we are the first to develop such a feedback system. We also list the different challenges faced when working with the annotation data and describe the approaches we used to address those challenges.

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