@article {245,
	title = {Enabling automatic just-in-time evaluation of in-class discussions in on-line collaborative learning practices},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2010},
	pages = {322 - 329},
	abstract = {Learning by discussion when applied to on-line collaborative learning settings can provide significant benefits for students in education in general. Indeed, the discussion process plays an important social task in collaborative learning practices. Participants can discuss about the activity being performed, collaborate with each other through the exchange of ideas that may arise, propose new resolution mechanisms, justify and refine their own contributions, and as a result, acquire new knowledge. Considering these benefits, current educational organizations incorporate on-line discussions into web-based courses as part of the very rationale of their pedagogical models. However, in-class collaborative assignments are usually greatly participated and contributed, which makes the monitoring and assessment tasks by tutors and moderators time-consuming, tedious and error-prone. Specially hard if not impossible by human tutors is to manually deal with the sequences of hundreds of contributions making up the discussion threads and the relations between these contributions. Consequently, tutoring tasks during on-line discussions usually restrict to offer evaluation results of the contributing effort and quality after the collaborative learning activity takes place and thus neglect the essential issue of constantly considering the process of knowledge building while it is still being performed. In this paper, we propose a multidimensional model based on data analysis from online collaborative discussion interactions that provides a first step towards an automatic evaluation in just-in-time fashion. The context of this study is a real on-line discussion experience that took place at the Open University of Catalonia.},
	keywords = {Automatic evaluation, Collaborative learning, Groupware, Knowledge discovery, Machine learning},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960216812\&partnerID=40\&md5=0f38528ca7e6fa26bdcaf9c0b81206c9},
	author = {Caball{\'e}, S. and Lapedriza, {\`A}. and Masip, D. and Xhafa, F. and Abraham, A.}
}
@article {194,
	title = {Enabling automatic just-in-time evaluation of in-class discussions in on-line collaborative learning practices},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Cited By (since 1996):3Export Date: 10 July 2014},
	month = {2009},
	pages = {289 - 296},
	abstract = {Learning by discussion when applied to on-line collaborative learning settings can provide significant benefits for students in education in general. Indeed, the discussion process plays an important social task in collaborative learning practices. Participants can discuss about the activity being performed, collaborate with each other through the exchange of ideas that may arise, propose new resolution mechanisms, justify and refine their own contributions, and as a result, acquire new knowledge. Considering these benefits, current educational organizations incorporate on-line discussions into web-based courses as part of the very rationale of their pedagogical models. However, in-class collaborative assignments are usually greatly participated and contributed, which makes the monitoring and assessment tasks by tutors and moderators time-consuming, tedious and error-prone. Specially hard if not impossible by human tutors is to manually deal with the sequences of hundreds of contributions making up the discussion threads and the relations between these contributions. Consequently, tutoring tasks during on-line discussions usually restrict to offer evaluation results of the contributing effort and quality after the collaborative learning activity takes place and thus neglect the essential issue of constantly considering the process of knowledge building while it is still being performed. In this paper, we propose a multidimensional model based on data analysis from online collaborative discussion interactions that provides a first step towards an automatic evaluation in just-in-time fashion. The context of this study is a real on-line discussion experience that took place at the Open University of Catalonia.},
	keywords = {Automatic evaluation, Collaborative learning, Groupware, Knowledge discovery, Machine learning},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77953238102\&partnerID=40\&md5=6dce6350dd861170e2133b9b2f597627},
	author = {Caball{\'e}, S. and Lapedriza, {\`A}. and Masip, D. and Xhafa, F. and Abraham, A.}
}
