Rogers Kaliisa

Quantitative Ethnography (QE) is an emerging methodological approach that combines ethnographic and statistical tools to analyze both Big Data and smaller data to study human behavior and interactions. This paper presents a methodological scoping review of 60 studies employing QE approaches with an intention to characterize and establish where the boundaries of QE might and should be in order to establish the identity of the field. The key finding is that QE researchers have enough commonality in their approach to the analysis of human behavior with a strong focus on grounded analysis, the validity of codes and consistency between quantitative models and qualitative analysis. Nonetheless, in order to reach a larger audience, the QE community should attend to a number of conceptual and methodological issues (e.g. interpretability). We believe that the strength of work from individual researchers reported in this review and initiatives such as the recently established International Society for Quantitative Ethnography (ISQE) can present a powerful force to shape the identity of the QE community

While there has been much growth in the use of microblogging platforms (e.g., Twitter) to share information on a range of topics, researchers struggle to analyze the large volumes of data produced on such platforms. Established methods such as Sentiment Analysis (SA) have been criticized over their inaccuracy and limited analytical depth. In this exploratory methodological paper, we propose a combination of SA with Epistemic Network Analysis (ENA) as an alternative approach for providing richer qualitative and quantitative insights into Twitter discourse. We illustrate the application and potential use of these approaches by visualizing the differences between tweets directed or discussing Democrats and Republicans after the COVID-19 Stimulus Package announcement in the US. SA was integrated into ENA models in two ways: as a part of the blocking variable and as a set of codes. Our results suggest that incorporating SA into ENA allowed for a better understanding of how groups viewed the components of the stimulus issue by splitting them by sentiment and enabled a meaningful inclusion of data with singular subject focus into the ENA models.

This paper suggests a technology-supported teacher-led approach that includes leveraging learning analytics (LA) to support data-informed learning design (LD) decisions in blended learning environments. The context of this study is three to five blended Bachelors courses using the Canvas learning management system (LMS) at two public universities in Norway. This is a design-based research study, employing quantitative ethnography approaches. Data will be collected from multiple sources, i.e. course analytics, discussion forums, interviews, teachers LD representations, and in-class observations. The analysis will be conducted using social and epistemic network analysis, automated discourse analysis, inferential statistics and inductive thematic analysis. This PhD project is anticipated to contribute towards an empirically based theoretical discussion about the potential affordances of LA to transform LD from a craft into a sounder and more evidence-based field of research and practice. (4) (PDF) Unpacking the Bi-directional relationship between learning analytics and learning design in blended learning environments. Available from: https://www.researchgate.net/publication/340133140_Unpacking_the_Bi-directional_relationship_between_learning_analytics_and_learning_design_in_blended_learning_environments [accessed Mar 30 2020].