Progressor: Social navigation support through open social student modeling

The increased volumes of online learning content have produced two problems: how to help students to find the most appropriate resources and how to engage them in using these resources. Personalized and social learning have been suggested as potential ways to address these problems. Our work presented in this paper combines the ideas of personalized and social learning in the context of educational hypermedia. We introduce Progressor, an innovative Web-based tool based on the concepts of social navigation and open student modeling that helps students to find the most relevant resources in a large collection of parameterized self-assessment questions on Java programming. We have evaluated Progressor in a semester-long classroom study, the results of which are presented in this paper. The study confirmed the impact of personalized social navigation support provided by the system in the target context. The interface encouraged students to explore more topics attempting more questions and achieving higher success rates in answering them. A deeper analysis of the social navigation support mechanism revealed that the top students successfully led the way to discovering most relevant resources by creating clear pathways for weaker students. © 2013 Taylor and Francis Group, LLC

The big five: Discovering linguistic characteristics that typify distinct personality traits across Yahoo! answers members

Indexación: Scopus.This work was partially supported by the project FONDECYT “Bridging the Gap between Askers and Answers in Community Question Answering Services” (11130094) funded by the Chilean Government.In psychology, it is widely believed that there are five big factors that determine the different personality traits: Extraversion, Agreeableness, Conscientiousness and Neuroticism as well as Openness. In the last years, researchers have started to examine how these factors are manifested across several social networks like Facebook and Twitter. However, to the best of our knowledge, other kinds of social networks such as social/informational question-answering communities (e.g., Yahoo! Answers) have been left unexplored. Therefore, this work explores several predictive models to automatically recognize these factors across Yahoo! Answers members. As a means of devising powerful generalizations, these models were combined with assorted linguistic features. Since we do not have access to ask community members to volunteer for taking the personality test, we built a study corpus by conducting a discourse analysis based on deconstructing the test into 112 adjectives. Our results reveal that it is plausible to lessen the dependency upon answered tests and that effective models across distinct factors are sharply different. Also, sentiment analysis and dependency parsing proven to be fundamental to deal with extraversion, agreeableness and conscientiousness. Furthermore, medium and low levels of neuroticism were found to be related to initial stages of depression and anxiety disorders. © 2018 Lithuanian Institute of Philosophy and Sociology. All rights reserved.https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/275

Controlling Risk of Web Question Answering

Web question answering (QA) has become an indispensable component in modern search systems, which can significantly improve users' search experience by providing a direct answer to users' information need. This could be achieved by applying machine reading comprehension (MRC) models over the retrieved passages to extract answers with respect to the search query. With the development of deep learning techniques, state-of-the-art MRC performances have been achieved by recent deep methods. However, existing studies on MRC seldom address the predictive uncertainty issue, i.e., how likely the prediction of an MRC model is wrong, leading to uncontrollable risks in real-world Web QA applications. In this work, we first conduct an in-depth investigation over the risk of Web QA. We then introduce a novel risk control framework, which consists of a qualify model for uncertainty estimation using the probe idea, and a decision model for selectively output. For evaluation, we introduce risk-related metrics, rather than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA. The empirical results over both the real-world Web QA dataset and the academic MRC benchmark collection demonstrate the effectiveness of our approach.Comment: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieva

PeerWise - The Marmite of Veterinary Student Learning

PeerWise is a free online student-centred collaborative learning tool with which students anonymously author, answer, and evaluate multiple choice questions (MCQs). Features such as commenting on questions, rating questions and comments, and appearing on leaderboards, can encourage healthy competition, engage students in reflection and debate, and enhance their communication skills. PeerWise has been used in diverse subject areas but never previously in Veterinary Medicine. The Veterinary undergraduates at the University of Glasgow are a distinct cohort; academically gifted and often highly strategic in their learning due to time pressures and volume of course material. In 2010-11 we introduced PeerWise into 1st year Veterinary Biomolecular Sciences in the Glasgow Bachelor of Veterinary Medicine and Surgery programme. To scaffold PeerWise use, a short interactive session introduced students to the tool and to the basic principles of good MCQ authorship. Students were asked to author four and answer forty MCQs throughout the academic year. Participation was encouraged by an allocation of up to 5% of the final year mark and inclusion of studentauthored questions in the first summative examination. Our analysis focuses on engagement of the class with the\ud tool and their perceptions of its use. All 141 students in the class engaged with PeerWise and the majority contributed beyond that which was stipulated. Student engagement with PeerWise prior to a summative exam was positively correlated to exam score, yielding a relationship that was highly significant (p<0.001). Student perceptions of PeerWise were predominantly positive with explicit recognition of its value as a learning and revision tool, and more than two thirds of the class in agreement that question authoring and answering reinforced their learning. There was clear polarisation of views, however, and those students who did not like PeerWise were vociferous in their dislike, the biggest criticism being lack of moderation by staff