Can Computer Supported Collaborative Learning (CSCL) Promote Counter-stereotypical Gender Communication Styles in Male and Female University Students?

Tomai, Manuela; Mebane, Minou; Rosa, Veronica; Benedetti, Maura

doi:10.1016/j.sbspro.2014.01.952

Cited by 13 publications

(12 citation statements)

References 35 publications

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“…In addition to the features used in off-task behavior prediction, we examined two additional features, students' gender and their ecosystems pre-test score. These features were chosen since previous studies suggested that gender and prior knowledge level can influence students' negative behavior patterns in CSCL environments [16,26,46]. Lastly, because disruptive talk is judged by the annotators considering a series of chat messages from the group, rather than a single message, we pre-process the data so that each data point retains the contextual information containing the history of previous messages from the group communication.…”

Section: Automatic Disruptive Talk Detectionmentioning

confidence: 99%

Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments

Park

Sohn

Mott

et al. 2021

LAK21: 11th International Learning Analytics and Knowledge Conference

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Collaborative game-based learning environments offer significant promise for creating engaging group learning experiences. Online chat plays a pivotal role in these environments by providing students with a means to freely communicate during problem solving. These chat-based discussions and negotiations support the coordination of students' in-game learning activities. However, this freedom of expression comes with the possibility that some students might engage in undesirable communicative behavior. A key challenge posed by collaborative game-based learning environments is how to reliably detect disruptive talk that purposefully disrupt team dynamics and problem-solving interactions. Detecting disruptive talk during collaborative game-based learning is particularly important because if it is allowed to persist, it can generate frustration and significantly impede the learning process for students. This paper analyzes disruptive talk in a collaborative game-based learning environment for middle school science education to investigate how such behaviors influence students' learning outcomes and varies across gender and students' prior knowledge. We present a disruptive talk detection framework that automatically detects disruptive talk in chat-based group conversations. We further investigate both classic machine learning and deep learning models for the framework utilizing a range of dialogue representations as well as supplementary information such as student gender. Findings show that long short-term memory network (LSTM)-based disruptive talk detection models outperform competitive baseline models, indicating that the LSTM-based disruptive talk detection framework

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Section: Automatic Disruptive Talk Detectionmentioning

confidence: 99%

Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments

Park

Sohn

Mott

et al. 2021

LAK21: 11th International Learning Analytics and Knowledge Conference

View full text Add to dashboard Cite

show abstract

“…However, it remains an open question if and to what extent gender-related differences in online communication behavior have changed as a result of the increasing popularity of social media. For example, Tomai, Mebane, Rosa, and Benedetti (2014) suggested that using social media might diminish gender differences in communication behavior.…”

Section: Gender-specific Aspectsmentioning

confidence: 99%

Communication and Self‐Presentation Behavior on Academic Social Networking Sites: An Exploratory Case Study on Profiles and Discussion Threads on ResearchGate

Ostermaier-Grabow

Linek

2019

Asso for Info Science & Tech

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Several Academic Social Networking Sites (ASNSs) have been launched in the last few years and their number of members is growing. Researchers using ASNSs come from very divergent scientific backgrounds and academic levels, prompting one to ask the question, What kind of communication and self‐presentation behaviors occur within these structures? The qualitative study presented in this article analyzed the communication behavior of a selected sample on ResearchGate (RG). It investigates how researchers present themselves on their personal‐profile sites and how they interact with other researchers. Overall, the results show that mostly young male academics without previous connections to each other (for example, faculty colleges) use RG for their scholarly exchanges. In general, communication behavior is characterized by an objective, professional, unemotional choice of words and seldom the use of polite salutations or words of farewell. However, there seems to be a correlation between long discussion and an increased use of colloquial and emotional language. Based on our findings, we derived preliminary practical recommendations for communications on ASNSs to improve the relationships in online academic interactions, to foster inclusiveness of gender and culture, and to reduce insecurity in matters of communication, presentation, and the exchange of scientific data.

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“…However, it is an open question whether gender-specific communication styles (still) exist in academic online communication or whether females have adapted to the communication style of the majority of their male colleagues. In addition, findings on counter-stereotypical behavior in online media [21] suggest that gender-specific communication behavior might disappear with the ongoing use of social media, including Twitter.…”

Section: Gender Gap In Science and Gender-specific Media Usagementioning

confidence: 99%

Women Prefer Reciprocity: Gender-Related Differences in Academic Networking on Twitter

Linek¹,

Jäschke²,

Hoffmann³

2018

ICERI2018 Proceedings

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Can Computer Supported Collaborative Learning (CSCL) Promote Counter-stereotypical Gender Communication Styles in Male and Female University Students?

Cited by 13 publications

References 35 publications

Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments

Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments

Communication and Self‐Presentation Behavior on Academic Social Networking Sites: An Exploratory Case Study on Profiles and Discussion Threads on ResearchGate

Women Prefer Reciprocity: Gender-Related Differences in Academic Networking on Twitter

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