Experts and likely to be closed discussions in question and answer communities: An analytical overview

Procaci, Thiago Baesso; Siqueira, Sean W. M.; Nunes, Bernardo Pereira; Nurmikko-Fuller, Terhi

doi:10.1016/j.chb.2018.06.004

Cited by 16 publications

(12 citation statements)

References 23 publications

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“…However, within this factor we found that a question's relevance to experts' expertise or experience was the characteristic they appreciated the most. This result is in agreement with preceding studies (Baltadzhieva and Chrupała, 2015) and explains to a great extent the on-going research for accurate expert-question matching on CQA platforms (Procaci, et al, 2019;Yuan, et al, 2020). This characteristic was also related to the question's clarity (Asaduzzaman, et al, 2013;Hao, Shu and Irawan, 2014;Baltadzhieva and Chrupała, 2015) and challenging nature (Oh, 2012;Procaci, et al, 2019).…”

Section: Discussionsupporting

confidence: 91%

“…This result is in agreement with preceding studies (Baltadzhieva and Chrupała, 2015) and explains to a great extent the on-going research for accurate expert-question matching on CQA platforms (Procaci, et al, 2019;Yuan, et al, 2020). This characteristic was also related to the question's clarity (Asaduzzaman, et al, 2013;Hao, Shu and Irawan, 2014;Baltadzhieva and Chrupała, 2015) and challenging nature (Oh, 2012;Procaci, et al, 2019). At the same time, question feasibility and learning potential issues did not concern experts to a significant extent (Hao, Shu and Irawan, 2014;Liu and Jansen, 2018).…”

Section: Discussionsupporting

confidence: 90%

“…To make a CQA platform an effective source of information for learners, it is crucial that the question response rate is high. Yet, regardless of experts' degree of participation on CQA platforms, many questions remain unanswered (Asaduzzaman, et al, 2013;Liu and Jansen, 2018;Procaci, et al, 2019;Yuan, et al, 2020). Although information technology literature has extensively explored the factors that influence experts' answer-sharing on CQA platforms, there is remarkably little known about their participation on educational CQA platforms, wherein they address specific learning communities such as school classes and training programs with specific instructional goals (Le, Shah and Choi, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Why Answer this Question? Experts’ Behaviors on Educational Community Question-Answering Platforms

Stokhof

Meli

Lavidas

et al. 2022

EJEL

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This study explores the factors that influence experts’ regular contribution to educational community question-answering (CQA) platforms. Providing answers is essential for sharing knowledge on CQA platforms, but it also affects learners’ progressive inquiry. Therefore, the purpose of this study is to develop and test a theoretical model that aims to explain which factors influence whether experts answer questions on educational CQA platforms and how these factors correlate with each other to form a “map” of experts’ respective behavior. We examined experts’ perceptions of three dimensions: the CQA platform’s usability, the quality of questions asked, and the added value of answering these questions. We examined the factors involved in these dimensions from the perspective of the Theory of Planned Behavior to connect them with the experts’ perceptions, intentions, and actions on a CQA platform. As our case study, we took the 100mentors web and mobile app, a small-scale platform that addresses learning communities around the world, and we conducted a survey for their registered experts (N=126). The factorial structure indicated that experts first perceived the question quality mostly based on its relevance to their expertise or experience (question quality); secondly, that their intention to answer was mainly set by their motives to make a difference for the learner and partially by the user-friendliness of the platform (added value of answering and CQA platform’s usability); and finally, that their actions were connected to the regular use of the platform for answer-sharing (CQA platform’s usability). A future research challenge is to test the factorial structure in large-scale educational CQA platforms. The further confirmation of the expert behavior pattern can have a practical implication for the platforms to guide their expert community more efficiently and for the learners to pursue their learning through progressive inquiry.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Why Answer this Question? Experts’ Behaviors on Educational Community Question-Answering Platforms

Stokhof

Meli

Lavidas

et al. 2022

EJEL

View full text Add to dashboard Cite

show abstract

“…This new insight is interesting and important because, as Matuk and Linn (2018) noted, students who enhance existing preliminary ideas with higher levels of support (evidence and facts) construct more coherent explanations than those who generate many preliminary ideas with lower levels of support. With respect to the literature on collaborative filtering recommender systems, this finding supports Procaci et al (2019) who found that 74% of ratings in a community accurately identified message quality in AODs. Moreover, it enriches prior research, which has shown that recommendations (i.e., rating-based information) allow students to summarize ideas George and Lal (2019a) and encourage them to ask questions (Reynolds & Wang, 2014).…”

Section: Effects Of Recommendations On the Interaction Analysis Modelsupporting

confidence: 79%

“…Emphasizing gender, Rizvi et al (2019) found that female students are more inclined to post help seeking messages. Finally, Procaci et al (2019) integrated a rating mechanism into an AOD to allow community members to collaboratively identify message quality. Drawing on expert analysis, this same study reported that 74% of the ratings were consistent with the experts' decisions.…”

Section: Message Quality and Learning Analyticsmentioning

confidence: 99%

Effects of recommendations on message quality and community formation in online conversations

et al. 2020

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As asynchronous online discussions gain a broader usage due to the COVID-19 epidemic, the need for understanding of students' ideas from unstructured textual data becomes more pressing. In this experimental study, we examine the effects of recommendations on message quality and community formation from voluminous online discussions. Drawing on literature from group cognition, knowledge building discourse, and learning analytics, we calculate message quasi-quality index (QQI) scores based on message lexical complexity and topic-related keyword usage by participants in explaining their ideas. Furthermore, we examine the empirical evidence on the relationship between QQI scores and participants' interactions. Finally, we visualize network structures via sociograms and hierarchically cluster participants to identify subgroups. Our analysis of 281 messages finds that recommendations helped participants to write more messages that compared alternative viewpoints and refined preliminary ideas with higher QQI scores. Results also show that recommendations cultivated a sense of collective agency to increase the opportunity for creativity and reduce the likelihood that peripheral participants will be dissatisfied and fail to identify with a community. Theoretical contributions and practical implications are discussed.

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Intern retrieval from community question answering websites: A new variation of expert finding problem

Rostami

Neshati

2021

Expert Systems with Applications

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Experts and likely to be closed discussions in question and answer communities: An analytical overview

Cited by 16 publications

References 23 publications

Why Answer this Question? Experts’ Behaviors on Educational Community Question-Answering Platforms

Why Answer this Question? Experts’ Behaviors on Educational Community Question-Answering Platforms

Effects of recommendations on message quality and community formation in online conversations

Intern retrieval from community question answering websites: A new variation of expert finding problem

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