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Learning analytics uses large amounts of data about learner interactions in digital learning environments to understand and enhance learning. Although measurement is a central dimension of learning analytics, there has thus far been little research that examines links between learning analytics and assessment. This special issue of Computers in Human Behavior highlights 11 studies that explore how links between learning analytics and assessment can be strengthened. The contributions of these studies can be broadly grouped into three categories: analytics for assessment (learning analytic approaches as forms of assessment); analytics of assessment (applications of learning analytics to answer questions about assessment practices); and validity of measurement (conceptualization of and practical approaches to assuring validity in measurement in learning analytics). The findings of these studies highlight pressing scientific and practical challenges and opportunities in the connections between learning analytics and assessment that will require interdisciplinary teams to address: task design, analysis of learning progressions, trustworthiness, and fairness – to unlock the full potential of the links between learning analytics and assessment. 

The present conceptual literature review analyzes 50 studies that systematically examined the effects of authentic learning settings on cognitive or motivational learning outcomes. The analysis focuses on describing the context of the studies, the design elements of authentic learning settings, and the pursued intentions of authenticity. The review further describes the effects of authentically designed learning settings on cognitive outcomes, motivational outcomes, and learners’ perceived authenticity revealed by previous research. Building on these findings, we conducted Epistemic Network Analysis (ENA) of contrasting cases to identify design elements and intentions of authenticity characterizing studies that show high effectiveness for cognitive and motivational outcomes versus those with low effectiveness. The ENA results suggest, for instance, that providing authentic materials (as a design element of authentic learning settings) to resemble real-life experiences (as an intention of authenticity) could be a double-edged sword, as they feature both authentically designed learning settings with low effects on cognitive outcomes and settings with high effects on motivational outcomes. Overall, the results of the present literature review point to critical limitations of previous research, such as a lack of clear definitions and operationalizations of authentic learning. Consequently, we draw specific conclusions about how future research could improve our understanding of how to create and implement powerful methods of authentic learning. 

In quantitative ethnography (QE) studies which often involve large da-tasets that cannot be entirely hand-coded by human raters, researchers have used supervised machine learning approaches to develop automated classi-fiers. However, QE researchers are rightly concerned with the amount of human coding that may be required to develop classifiers that achieve the high levels of accuracy that QE studies typically require. In this study, we compare a neural network, a powerful traditional supervised learning ap-proach, with nCoder, an active learning technique commonly used in QE studies, to determine which technique requires the least human coding to produce a sufficiently accurate classifier. To do this, we constructed multi-ple training sets from a large dataset used in prior QE studies and designed a Monte Carlo simulation to test the performance of the two techniques sys-tematically. Our results show that nCoder can achieve high predictive accu-racy with significantly less human-coded data than a neural network. 

This paper explores methodological questions in the study of identity through an examination and discussion of the empirical papers in this special issue. Particular attention is paid to the ways identity is operationalized in the study of how learning environments foster changes in students’ sense of self. The paper concludes that identity is a difficult construct to study in the context of learning environments because it is simultaneously performative and subjective, and these dual aspects of identity may be best operationalized in an interactional view, in which identity is conceptualized as a set of relations between aspects of identity rather than as a state that can be coded directly in data on learning. 

In this paper, we describe iPlan, a web-based software platform for constructing localized, reduced-form models of land-use impacts, enabling students, civic representatives, and others without specialized knowledge of land-use planning practices to explore and evaluate possible solutions to complex, multi-objective land-use problems in their own local contexts.