Talk with Me: Student Pronoun Use as an Indicator of Discourse Health

Epp, Carrie Demmans; Phirangee, Krystle; Hewitt, Jim

doi:10.18608/jla.2017.43.4

Cited by 13 publications

(4 citation statements)

References 38 publications

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“…Studies related to behavioral engagement analyzed student course engagement and resource-seeking behavior, often utilizing hundreds of thousands of interactions from up to tens of thousands of students. For example, Epp et al (2017) examined communication behavior in online discussions, with a particular emphasis on student pronoun use. They found that students in instructor-facilitated courses demonstrated higher levels of interaction and used more personal pronouns, whereas students in peer-facilitated courses exhibited lower levels of engagement and used fewer personal pronouns.…”

Section: Detecting Behavioral Engagementmentioning

confidence: 99%

Mining Big Data in Education: Affordances and Challenges

Fischer

Pardos

Baker

et al. 2020

Review of Research in Education

274

138

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The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education.

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Section: Detecting Behavioral Engagementmentioning

confidence: 99%

Mining Big Data in Education: Affordances and Challenges

Fischer

Pardos

Baker

et al. 2020

Review of Research in Education

274

138

View full text Add to dashboard Cite

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“…Previous research in several research fields (eg, Atabek & Yildiz, 2010; Yazdi‐Amirkhiz et al, 2014) has focused on the use of personal pronouns, one of the most commonly used function words, in conversations. Pronoun metrics can, for example, be a way to assess progress in online collaborative work (Demmans Epp et al, 2017). More specifically, Thompson et al (2013) propose that changes in the frequency of the first‐person plural pronoun indicate changes in focus from individual to group concerns.…”

Section: Learning Analytics For Cpsmentioning

confidence: 99%

“…More specifically, Thompson et al (2013) propose that changes in the frequency of the first‐person plural pronoun indicate changes in focus from individual to group concerns. Differences in patterns of pronoun use may also suggest differences in collaboration quality (Demmans Epp et al, 2017). Inclusive personal pronouns (ie, “we”) can reduce social and psychological distance between participants, contributing to a sense of group membership and cohesion.…”

Section: Learning Analytics For Cpsmentioning

confidence: 99%

Assessing verbal interaction of adult learners in computer‐supported collaborative problem solving

Buseyne,

Rajagopal,

Danquigny

et al. 2023

Brit J Educational Tech

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The objective of this study is to explore new ways of assessing collaborative problem solving (CPS) processes based on different modalities of audio data and their combination. The data collection took place in an educational lab setting during an experiment with adult teams from professional contexts who collaboratively solved multiple problems as part of a CPS training. From audio data, both verbal (ie, speech) and non‐verbal (ie, pitch) aspects were extracted. Four analysis methods were used, including (a) content analysis; (b) linguistic inquiry and word count; (c) verbal entrainment analysis; and (d) acoustic–prosodic entrainment based on pitch data. Insights are given into the CPS processes of the participating groups using these measures and relevant relationships between some of these measures are further investigated. Based on content analysis, it was found that most of the interactions during the CPS process are task oriented, whereas team‐oriented interactions are less present. Second, three measures of proportion of contribution in CPS were investigated and clear differences in participation patterns between and within teams were found. We suggest that a combination of utterance count and words per sentence could provide valuable insights for quantity and equality of participation. Third, the study explored pronoun use and found that the most frequently used personal pronouns were first‐person singular. Next, the results indicated a relationship between pronoun use and the relative frequency of interactions. Fourth, a rather weak relationship between lexical entrainment measures and the acoustic–prosodic measures were found, suggesting that these measures are indicative of separate communicative aspects in CPS. This study contributes to a better understanding of which type of audio‐based data is most informative to teachers and students as a feedback or assessment tool. This study complements previous research as it focuses on spoken human‐to‐human communication collected in an authentic context. Practitioner notesWhat is already known about this topic Support and guidance systems for learning coaches, teachers and learners are needed to foster the educational quality of collaborative problem solving (CPS) activities. CPS is a complex process and measuring the quality of CPS processes remains challenging. Multimodal learning analytics, focusing on verbal and non‐verbal data sources and using content analysis, linguistic inquiry and word count and verbal and acoustic entrainment measures could be valuable to measure the quality of CPS. What this paper adds The majority of interactions during CPS processes are task oriented or cognitive of nature, whereas team‐oriented interactions are less present. Utterance count and words per sentence should be used in combination, as they are indicative of different aspects. Pronoun use in learners' discourse is related to the types of CPS interactions. Lexical entrainment measures and acoustic–prosodic are indicative of distinctive communicative aspects in CPS. Implications for practice and/or policy Quality indicators of CPS processes should include both verbal and non‐verbal measures of students' interactions. Educational researchers and the (Edtech) industry should further leverage their forces to foster the development of (semi‐)automated systems for measuring the quality of CPS processes. It should be further investigated how quality indicators of CPS processes can be most meaningful to trainers, teachers and learners, for example, through the use of dashboards.

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“…It may be possible to satisfy this desire by adding text summarization or content from student self-reports and reflections, as suggested by Ji et al (2013). Summarization approaches similar to those that are now being used in other educational settings could also be of benefit (Luo et al, 2016) as could measures of student language use that include the adoption or use of vocabulary (Demmans Epp, Phirangee, & Hewitt, 2017b;Rahimi et al, 2017), cohesiveness of student posts (Cade et al, 2014), topic dispersion among students, and other qualities of how or what students are discussing.…”

Section: More Qualitative and Expressive Metrics Are Needed To Enable A Holistic Understanding Of Student Learning Experiencementioning

confidence: 99%