Analysis of U.S., Kenyan, and Finnish Discourse Patterns in a Cross-Cultural Digital Makerspace Learning Community Through the IBE-UNESCO Global Competences Framework

In 2017, the International Bureau of Education (IBE) at the United Nations Educational, Scientific and Cultural Organization (UNESCO) put forth seven global competences to address accelerating technological progress and increasing levels of complexity and uncertainty affecting many facets of society (Marope, 2017). These competences were used in examining participant discourse in a global, collaborative digital makerspace environment, where students ages 12 to 17 from six countries develop and share STEM-focused media artifacts. The participants communicate synchronously through video conference calls, referred to as online global meet-ups. The meet-ups allow students to present media artifacts they have created, share ideas, exchange information, and provide feedback. In this analysis, epistemic network analysis (ENA), a technique in quantitative ethnography, is used to examine the connections made among the IBE-UNESCO global competences in a meet-up involving participants from Finland, Kenya, and the U.S. ENA network models were created initially for the three sites, then further disaggregated by time segment to analyze how participant discourse patterns may have evolved in each context. Through this approach, the paper explores more broadly the interactive role of media making, cross-cultural engagement, and collaborative learning in the development of global competences in students

Discourse-centric learning analytics: mapping the terrain

There is an increasing interest in developing learning analytic techniques for the analysis, and support of, high quality learning discourse. This paper maps the terrain of discourse-centric learning analytics (DCLA), outlining the distinctive contribution of DCLA and outlining a definition for the field moving forwards. It is our claim that DCLA provide the opportunity to explore the ways in which: discourse of various forms both resources and evidences learning; the ways in which small and large groups, and individuals make and share meaning together through their language use; and the particular types of language – from discipline specific, to argumentative and socio-emotional – associated with positive learning outcomes. DCLA is thus not merely a computational aid to help detect or evidence ‘good’ and ‘bad’ performance (the focus of many kinds of analytic), but a tool to help investigate questions of interest to researchers, practitioners, and ultimately learners. The paper ends with three core issues for DCLA researchers – the challenge of context in relation to DCLA; the various systems required for DCLA to be effective; and the means through which DCLA might be delivered for maximum impact at the micro (e.g. learner), meso (e.g. school), and macro (e.g. governmental) levels

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