Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learningrelated constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established 740335R ERXXX10.

We examined the coherence of trauma memories in a trauma-exposed community sample of 30 adults with and 30 without PTSD. The groups had similar categories of traumas and were matched on multiple factors that could affect the coherence of memories. We compared the transcribed oral trauma memories of participants with their most important and most positive memories. A comprehensive set of 28 measures of coherence including 3 ratings by the participants, 7 ratings by outside raters, and 18 computer-scored measures, provided a variety of approaches to defining and measuring coherence. A MANOVA indicated differences in coherence among the trauma, important, and positive memories, but not between the diagnostic groups or their interaction with these memory types. Most differences were small in magnitude; in some cases, the trauma memories were more, rather than less, coherent than the control memories. Where differences existed, the results agreed with the existing literature, suggesting that factors other than the incoherence of trauma memories are most likely to be central to the maintenance of PTSD and thus its treatment.

Roles are one of the most important concepts in understanding human sociocognitive behavior. During group interactions, members take on different roles within the discussion. Roles have distinct patterns of behavioral engagement (i.e., active or passive, leading or following), contribution characteristics (i.e., providing new information or echoing given material), and social orientation (i.e., individual or group). Different combinations of roles can produce characteristically different group outcomes, and thus can be either less or more productive with regard to collective goals. In online collaborative-learning environments, this can lead to better or worse learning outcomes for the individual participants. In this study, we propose and validate a novel approach for detecting emergent roles from participants' contributions and patterns of interaction. Specifically, we developed a group communication analysis (GCA) by combining automated computational linguistic techniques with analyses of the sequential interactions of online group communication. GCA was applied to three large collaborative interaction datasets (participant N = 2,429, group N = 3,598). Cluster analyses and linear mixed-effects modeling were used to assess the validity of the GCA approach and the influence of learner roles on student and group performance. The results indicated that participants' patterns of linguistic coordination and cohesion are representative of the roles that individuals play in collaborative discussions. More broadly, GCA provides a framework for researchers to explore the micro intra- and interpersonal patterns associated with participants' roles and the sociocognitive processes related to successful collaboration.

Collaborative problem-solving (CPS) has become an essential component of today’s knowledge-based, innovation- centred economy and society. As such, communication and CPS are now considered critical 21st century skills and incorporated into educational practice, policy, and research. Despite general agreement that these are important skills, there is less agreement on how to capture sociocognitive processes automatically during team interactions to gain a better understanding of their relationship with CPS outcomes. The availability of naturally occurring educational discourse data within online CPS platforms presents a golden opportunity to advance understanding about online learner sociocognitive roles and ecologies. In this paper, we explore the relationship between emergent sociocognitive roles, collaborative problem-solving skills, and outcomes. Group Communication Analysis (GCA) — a computational linguistic framework for analyzing the sequential interactions of online team communication — was applied to a large CPS dataset in the domain of science (participant N = 967; team N = 480). The ETS Collaborative Science Assessment Prototype (ECSAP) was used to measure learners’ CPS skills, and CPS outcomes. Cluster analyses and linear mixed-effects modelling were used to detect learner roles, and assess the relationship between those roles on CPS skills and outcomes. Implications for future research and practice are discussed regarding sociocognitive roles and collaborative problem-solving skills.

ABSTRACT:The goal of this article is to preserve and distribute the information presented at the LASI (2014) workshop on Coh-Metrix, a theoretically grounded, computational linguistics facility that analyzes texts on multiple levels of language and discourse. The workshop focused on the utility of Coh-Metrix in discourse theory and educational practice. We discuss some of the motivating factors that led to the development of Coh-Metrix, situated within the context of multilevel theoretical frameworks of discourse comprehension and learning. A review of published studies will highlight the applications of Coh-Metrix, ranging from the scaling and selection of educational material to learning environments at scale. The examples illustrate the relationship between discourse and cognitive, affective, and social processes. We walk through the methodological guidelines that should be followed when analyzing texts using Coh-Metrix. Finally, we conclude the paper with a general discussion of the future directions for Coh-Metrix including methodological and practical implications for the learning analytics (LA) and educational data mining (EDM) communities.

The aim of this study was to examine the potential independent and joint impact of 2 specific therapist nonverbal behaviors—eye contact and trunk lean—on perceptions of therapist empathy, the relationship between client and therapist, and the credibility of the treatment. Four different psychotherapists were filmed in 4 combinations of eye contact and trunk lean. Participants rated these therapists after viewing a randomized order of the therapy session videos. Findings indicate that high eye contact and forward trunk lean enhanced perceived therapist empathy, therapeutic alliance, and treatment credibility. These results suggest that therapists could improve their practice by using specific nonverbal behaviors.

This special issue brings together a rich collection of papers in collaboration analytics. With topics including theory building, data collection, modelling, designing frameworks, and building machine learning models, this issue represents some of the most active areas of research in the field. In this editorial, we summarize the papers; discuss the nature of collaboration analytics based on this body of work; describe the associated opportunities, challenges, and risks; and depict potential futures for the field. We conclude by discussing the implications of this special issue for collaboration analytics.

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