. (2013) Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-forprofit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher's statement:This is the pre-peer reviewed version of the following article: Stepanyan, Karen, Mather, Richard and Dalrymple, Roger. (2013) Culture, role and group work : a social network analysis perspective on an online collaborative course. British Journal of Educational Technology, which has been published in final form at: http://dx.doi.org/10.1111/bjet.12076. A note on versions:The version presented here is a working paper or pre-print that may be later published elsewhere. If a published version is known of, the above WRAP url will contain details on finding it. Harcourt Hill Campus, Oxford, OX2 9AT, United Kingdom. Tel: +44 (0) 18 6548 8600. Email: rdalrymple@brookes.ac.uk Abstract This paper discusses the patterns of network dynamics within a multi-cultural online collaborative learning environment. It analyses the interaction of participants (both students and facilitators) within a discussion board that was established as part of a three-month online collaborative course. The study employs longitudinal probabilistic social network analysis (SNA) to identify the patterns and trends within the network. It conjectures and tests a set of hypotheses concerning the tendencies towards homophily/heterophily and preferential attachment. The paper presents identified interaction network patterns in relation to cultural differences. It also evaluates network dynamics by considering participant roles and group work in the course under study. Results of social network analyses are reported along with measures of statistical confidence in findings. The potential for extending exploratory SNA methods and visualisation techniques in educational research are discussed here.2
Abstract. This paper presents a quantitative study on the use of Topolor -a prototype that introduces Web 2.0 tools and Facebook-like appearance into an adaptive educational hypermedia system. We present the system design and its evaluation using system usability scale questionnaire and learning behavior data analysis. The results indicate high level of student satisfaction with the learning experience and the diversity of learning activities.
Abstract. The paper summarizes participatory action research that explores student attitudes towards a peer assessment exercise and further reveals a distinctive pattern in student responses. A formative and reciprocal peer assessment exercise was studied to identify possible reasons for low levels of student participation. The target group included students in an undergraduate course in computing. A follow-up questionnaire, undertaken by 36 students, was analyzed and compared against assignment marks. Finally, the access statistics of the virtual learning environment (VLE) were examined. The major results indicate the following: [i] an expectation of more explanatory and supportive tutor intervention; [ii] a student preference towards anonymity; [iii] student interest in accessing peer work; and [iv] that the allocation of marks and in-class activities factors are important in encouraging student involvement.
This paper addresses the problem of determining the best answer in Community-based Question Answering websites by focussing on the content. Previous research on this topic relies on the exploitation of community feedback on the answers, which involves rating of either users (e.g., reputation) or answers (e.g. scores manually assigned to answers). We propose a new technique that leverages the content/textual features of answers in a novel way. Our approach delivers better results than related linguistics-based solutions and manages to match rating-based approaches. More specifically, the gain in performance is achieved by rendering the values of these features into a discretised form. We also show how our technique manages to deliver equally good results in real-time settings, as opposed to having to rely on information not always readily available, such as user ratings and answer scores. We ran an evaluation on 21 StackExchange websites covering around 4 million questions and more than 8 million answers. We obtain 84% average precision and 70% recall, which shows that our technique is robust, effective, and widely applicable.
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