Bridging the Gap: 21st Century Media Meets Theoretical Pedagogical Literacy Practices

In this chapter, the researchers used an ethnographic stance to demonstrate how conversation evolved within a social media platform. They investigated the online discussions and face-to-face dialogues between teacher educators and pre-service teachers. They compared the participants’ reciprocal conversations within this case study to analyze patterns in the language used in each forum in order to identify the affordances and constraints of perceived understanding. Through this discourse analysis the authors sought to identify indicators of each participant’s metacognitive development while engaging in an online book discussion through a social media platform. Data analysis indicated that there was metacognitive growth when comparing the initial reciprocal conversations with the final conversations

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers

Text-based Computer-mediated Discourse Analysis: What Causes an Online Group to Become a Virtual Community?

The present study seeks to explore how computer-mediated discourse analysis can be useful for the study of online interaction, in particular, text-based multiparty interaction. The data are from the text-based chat group of high school English language teachers in China, which consists of more than one thousand members. Because of the fluid membership, reduced social accountability, and lack of shared geographical space, it seems that not every online group automatically becomes a “community”. Addressing this concern, and informed by the computer-mediated discourse analysis (CMDA) proposed by Herring, the researcher seeks to investigate the properties of virtual communities and to assess the extent to which they are realized by specific online groups

Voices' inter-animation detection with ReaderBench. Modelling and assessing polyphony in CSCL chats as voice synergy

International audienceStarting from dialogism in which every act is perceived as a dialogue, we shift the perspective towards multi-participant chat conversations from Computer Supported Collaborative Learning in which ideas, points of view or more generally put voices interact, inter-animate and generate the context of a conversation. Within this perspective of discourse analysis, we introduce an implemented framework, ReaderBench, for modeling and automatically evaluating polyphony that emerges as an overlap or synergy of voices. Moreover, multiple evaluation factors were analyzed for quantifying the importance of a voice and various functions were experimented to best reflect the synergic effect of co- occurring voices for modeling the underlying discourse structure

Voices' inter-animation detection with ReaderBench. Modelling and assessing polyphony in CSCL chats as voice synergy

International audienceStarting from dialogism in which every act is perceived as a dialogue, we shift the perspective towards multi-participant chat conversations from Computer Supported Collaborative Learning in which ideas, points of view or more generally put voices interact, inter-animate and generate the context of a conversation. Within this perspective of discourse analysis, we introduce an implemented framework, ReaderBench, for modeling and automatically evaluating polyphony that emerges as an overlap or synergy of voices. Moreover, multiple evaluation factors were analyzed for quantifying the importance of a voice and various functions were experimented to best reflect the synergic effect of co- occurring voices for modeling the underlying discourse structure

Scraping news sites and social networks for prejudice term analysis

Computer-Mediated Communication (CMC) has paved the way for new patterns of linguistic aggravation. Hidden behind the screen, anyone can comment on any other person's opinion using an offensive or injurious tone. Besides, types of prejudice such as homophobia, sexism, racism, xenophobia, anticlericalism, body/addiction shaming, among others, are easily found nowadays in social networks and other forms of interactive Web sites potentiated by Web 2.0. This increasing violence deserves further investigation from different academic perspectives, among which Sociolinguistics stands out. This paper is concerned with the design and development of a set of computer-based tools to collect articles and posts with the respective comment threads that can be used as sources to extract prejudice terms and allow different analyses to be conducted. These prejudice terms were devised using a sociolinguistic variable stratificat ion approach. We will focus on the filters used to extract the relevant fields from the Web pages collected, and on the converters used to adapt formats to obtain a common format for information representation. We will also introduce the statistical analysis processor that explores the extracted data, in that format, to output a set of indicators.This work has been supported by FCT -Fundação para a Ciência e Tecnologia within the Project Scope: PTDC/LLT-LIN/29304/2017.We are also indebted to our students, André Salgueiro, Bruno Carvalho andFábio Araújo, for their valuable help in developing the statistical analysis and visualization components.We alsowishto thank Daria Bębeniecfor her carefulreading of the previous versions of the paper and her helpful comments and suggestion

“You’re trolling because…” – A Corpus-based Study of Perceived Trolling and Motive Attribution in the Comment Threads of Three British Political Blogs

This paper investigates the linguistically marked motives that participants attribute to those they call trolls in 991 comment threads of three British political blogs. The study is concerned with how these motives affect the discursive construction of trolling and trolls. Another goal of the paper is to examine whether the mainly emotional motives ascribed to trolls in the academic literature correspond with those that the participants attribute to the alleged trolls in the analysed threads. The paper identifies five broad motives ascribed to trolls: emotional/mental health-related/social reasons, financial gain, political beliefs, being employed by a political body, and unspecified political affiliation. It also points out that depending on these motives, trolling and trolls are constructed in various ways. Finally, the study argues that participants attribute motives to trolls not only to explain their behaviour but also to insult them