Discovering Social Network to Improve Recommender System for Group Learning Support

Wan, Xin; Jamaliding, Qimanguli; Okamoto, Toshio

doi:10.1109/cise.2009.5363890

Cited by 3 publications

(7 citation statements)

References 19 publications

(17 reference statements)

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“…Song et al, 2011;I. Song et al, 2011;Sun et al, 2015;Szabó et al, 2012;Szymański & Rzeniewicz, 2016;Takács et al, 2008;Takáes et al, 2009;Tsapatsoulis et al, 2015;Tsuji et al, 2014;Verma et al, 2016;Vialardi et al, 2011;Wan et al, 2009;Wang et al, 2012;Wei et al, 2011;Xin et al, 2014;Yan et al, 2013;Yap et al, 2005 Unsupervised learning 46 24 (Bar et al, 2013;Bjelica, 2010;Bouneffouf et al, 2012;Buettner, 2016;Degemmis et al, 2007;Devi & Venkatesh, 2013;Elmongui et al, 2015;Z. Fan et al, 2016;Ghazarian & Nematbakhsh, 2015;Halder et al, 2014;Hassan et al, 2010; T.-J.…”

Section: Big Data Technologiesmentioning

confidence: 99%

“…Zhang et al, 2009) Social 14 (Aouay et al, 2014;Cai et al, 2010Cai et al, , 2012Diaby et al, 2013;Elmongui et al, 2015;Hassan et al, 2010;Islam et al, 2015;Krzywicki et al, 2015;Y. Song et al, 2011;Verma et al, 2016;Wan et al, 2009;Yan et al, 2013;Yuan et al, 2014;K. Zhao & Pan, 2015) Academic 12 (Das et al, 2013;Hernández del Olmo et al, 2009;Huang & Nikulin, 2014;Krohn-Grimberghe et al, 2011;Luong, Huynh, Gauch, Do, & Hoang, 2012;Middleton et al, 2004;Montañés et al, 2009;Oyama et al, 2012;Pecli et al, 2015;Vialardi et al, 2011;Xin et al, 2014) News 11 (Bellogín et al, 2011;Brouard & Pomot, 2016;Buabin, 2012;Das et al, 2013;Z.…”

Section: Performance Metricsunclassified

“…Liu et al, 2011;Lops et al, 2009;Luong, Huynh, Gauch, Do, & Hoang, 2012;McLaughlin & Herlocker, 2004;Murfi & Obermayer, 2009;Musto et al, 2010;Nguyen et al, 2016;Nie et al, 2013;Pantraki & Kotropoulos, 2015;Pecli et al, 2015;Y. Song et al, 2011;Szymański & Rzeniewicz, 2016;Verma et al, 2016;Wan et al, 2009;Yap et al, 2005;Yuan et al, 2014;K. Zhao & Pan, 2015) Recall 58 32 (Aouay et al, 2014;Baldominos et al, 2015;Banerjee et al, 2012;Cai et al, 2010Cai et al, , 2012Caraballo et al, 2014;Costa et al, 2012Costa et al, , 2013De Gemmis et al, 2008;Devi & Venkatesh, 2013;Elmongui et al, 2015;T.-K.…”

Section: Accepted Manuscriptunclassified

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The use of machine learning algorithms in recommender systems: A systematic review

Portugal

Alencar

Cowan

2018

Expert Systems with Applications

517

220

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Section: Big Data Technologiesmentioning

confidence: 99%

Section: Performance Metricsunclassified

Section: Accepted Manuscriptunclassified

See 1 more Smart Citation

The use of machine learning algorithms in recommender systems: A systematic review

Portugal

Alencar

Cowan

2018

Expert Systems with Applications

517

220

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“…Social science research has revealed that people build social relationships with each other, and these relationships may help them locate either information or services more effectively. Wan et al (26) found that a collaborative group-learning environment in which students could express their thoughts, voice their opinions, and share their experiences had a positive outcome.…”

Section: Group Collaborationmentioning

confidence: 99%

“…In addition, new technology and web-based education has changed old learning paradigms into a new opportunity to learn "anywhere and anytime". During their study Wan et al (26) established a new student user profile. This profile included abilities, knowledge, and learning preferences.…”

Section: Group Collaborationmentioning

confidence: 99%

A Systematic Review of Technological Advancements to Enhance Learning

Cudney¹,

Ezzell²

2015 ASEE Annual Conference and Exposition Proceedings

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Assessing student learning styles and incorporating thought-provoking activities has been a focus of research for years. Virtual technology and social media are transforming traditional classrooms into training spaces that can be tailored for individual learning patterns and personalized for different skill levels. These technological tools are not only revolutionizing the conventional lecture-based classroom but also beginning to incorporate options such as flipped and blended classrooms. Students in these nontraditional settings are given additional hands-on experience that allows them to become immersed in a variety of subjects. Flipped classrooms in particular use class time effectively by challenging students to prepare prior to class. In return the allotted time provides a place for students to work through problems and encourage cooperative learning. Furthermore, social media is being used to increase subject interest and boost class attendance by improving instructor and student interactions. These techniques challenge students enough to maintain focus while remaining within their capabilities to preserve student curiosity. Learning enhancement using these new teaching styles was assessed through surveys provided at the beginning and end of each experiment. The studies sampled students from a variety of backgrounds and skill sets including military, medical, and college students. Alternative and cost effective approaches are revolutionizing learning to help improve each student's motivation, concentration, and confidence.

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Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

Champaign

Cohen

2010

Intelligent Tutoring Systems

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In this thesis, we present an artificial intelligence approach for tutoring students in environments where there is a large repository of possible learning objects (e.g. texts, videos). In particular, we advocate that students learn on the basis of past experiences of peers. This aligns with McCalla's proposed ecological approach for intelligent tutoring, where a learning object's history-of-use is retained and leveraged to instruct future students. We offer three distinct models that serve to deliver the required intelligent tutoring: (i) a curriculum sequencing algorithm selecting which learning objects to present to students based on benefits to knowledge obtained by similar peers (ii) a framework for peers to provide commentary on the learning objects they've experienced (annotations) together with an algorithm for reasoning about which annotations to present to students that incorporates modeling trust in annotators (i.e. their reputation) and ratings provided by students (votes for and against) for the annotations they have been shown (iii) an opportunity for peers to guide the growth of the corpus by proposing divisions of current objects, together with an algorithm for reasoning about which of these new objects should be offered to students in order to enhance their learning. All three components are validated as beneficial in improving the learning of students. This is first of all achieved through a novel approach of simulated student learning, designed to enable the tracking of the experiences of a very large number of peers with an extensive repository of objects, through the effective modeling of knowledge gains. This is also coupled with a preliminary study with human participants that confirms the value of our framework. In all, we offer a rich and varied role for peers in guiding the learning of students in intelligent tutoring environments, made possible by careful modeling of the students who are being taught and of the potential benefits to learning that would be derived with the selection of appropriate tutorial content.iii Acknowledgements First and foremost I would like to thank my supervisor, Professor Robin Cohen, for guiding me through the completion of this dissertation and my PhD studies. Robin overwhelmed me with the amount of time and effort she put in to helping me complete my doctoral work. My committee members, Professor Edward Lank and Professor Peter van Beek, have provided detailed and helpful feedback throughout the process, which I greatly appreciate. Thanks as well to Professor Gord McCalla for his helpful advice. My appreciation goes out to the anonymous reviewers who considered earlier versions of this work and provided feedback during conference and journal submissions over the last 4 years.I would like to acknowledge Ahmed Ataullah, Simina Brânzei, John Doucette and Lachlan Dufton for being wonderful officemates: always willing to talk through issues I was working on, go for coffee and share a laugh. I greatly enjoyed working in the AI Group at the University of ...

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Discovering Social Network to Improve Recommender System for Group Learning Support

Cited by 3 publications

References 19 publications

The use of machine learning algorithms in recommender systems: A systematic review

The use of machine learning algorithms in recommender systems: A systematic review

A Systematic Review of Technological Advancements to Enhance Learning

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

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