English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Accounts of the nature and role of productive dialogue in fostering educational outcomes are now well established in the learning sciences and are underpinned by bodies of strong empirical research and theorising. Allied to this there has been longstanding interest in fostering computer-supported collaborative learning (CSCL) in support of such dialogue. Learning analytic environments such as massive open online courses (moocs) and online learning environments (such as virtual learning environments, VLEs and learning management systems, LMSs) provide ripe potential spaces for learning dialogue. In prior research, preliminary steps have been taken to detect occurrences of productive dialogue automatically through the use of automated analysis techniques. Such advances have the potential to foster effective dialogue through the use of learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting, such dialogue. However, the translation of learning science research to the online context is complex, requiring the operationalization of constructs theorized in different contexts (often face to face), and based on different data-sets and structures (often spoken dialogue).. In this paper we explore what could constitute the effective analysis of this kind of productive dialogue, 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. We begin by outlining what we mean by ‘productive educational dialogue’, before going on to discuss prior work that has been undertaken to date on its manual and automated analysis. We then highlight ongoing challenges for the development of computational analytic approaches to such data, discussing the representation of features, segments, and temporality in computational modelling. The paper thus foregrounds, to both learning-science-oriented and computationally-oriented researchers, key considerations in respect of the analysis dialogue data in emerging learning analytics environments. The 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.

Dialogue as Data in Learning Analytics for Productive Educational Dialogue