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In this paper, we theorize the intersectional gendered impacts of COVID‐19 on faculty labor, with a particular focus on how institutions of higher education in the United States evaluate faculty labor amidst the COVID‐19 transition and beyond. The pandemic has disrupted faculty research, teaching, and service in differential ways, having larger impacts on women faculty, faculty of color, and caregiving faculty in ways that further reflect the intersections of these groups. Universities have had to reconsider how evaluation occurs, given the impact of these disruptions on faculty careers. Through a case study of university pandemic responses in the United States, we summarize key components of how colleges and universities shifted evaluations of faculty labor in response to COVID‐19, including suspending teaching evaluations, implementing tenure delays, and allowing for impact statements in faculty reviews. While most institutional responses recenter neoliberal principles of the ideal academic worker that is both gendered and racialized, a few universities have taken more innovative approaches to better attend to equity concerns. We conclude by suggesting a recalibration of the faculty evaluation system – one that maintains systematic faculty reviews and allows for academic freedom, but requires universities to take a more contextualized approach to evaluation in ways that center equity and inclusion for women faculty and faculty of color for the long term. [ABSTRACT FROM AUTHOR]

As universities around the world have begun to use learning management systems (LMSs), more learning data have become available to gain deeper insights into students' learning processes and make data‐driven decisions to improve student learning. With the availability of rich data extracted from the LMS, researchers have turned much of their attention to learning analytics (LA) applications using educational data mining techniques. Numerous LA models have been proposed to predict student achievement in university courses. To design predictive LA models, researchers often follow a data‐driven approach that prioritizes prediction accuracy while sacrificing theoretical links to learning theory and its pedagogical implications. In this study, we argue that instead of complex variables (e.g., event logs, clickstream data, timestamps of learning activities), data extracted from online formative assessments should be the starting point for building predictive LA models. Using the LMS data from multiple offerings of an asynchronous undergraduate course, we analysed the utility of online formative assessments in predicting students' final course performance. Our findings showed that the features extracted from online formative assessments (e.g., completion, timestamps and scores) served as strong and significant predictors of students' final course performance. Scores from online formative assessments were consistently the strongest predictor of student performance across the three sections of the course. The number of clicks in the LMS and the time difference between first access and due dates of formative assessments were also significant predictors. Overall, our findings emphasize the need for online formative assessments to build predictive LA models informed by theory and learning design. Practitioner notesWhat is already known about this topic Higher education institutions often use learning analytics for the early identification of low‐performing students or students at risk of dropping out.Most predictive models in learning analytics rely on immutable student characteristics (e.g., gender, race and socioeconomic status) and complex variables extracted from log data within a learning management system.Prioritizing prediction accuracy without theory orientation often yields "black‐box" models that fail to inform educators on what remedies need to be taken to improve student learning.What this paper adds Predictive models in learning analytics should consider learning theory, pedagogy and learning design to identify key predictors of student learning.Online formative assessments can be a starting point for building predictive models that are not only accurate but also provide educators with actionable insights on how student learning can be improved.Time‐related and score‐related features extracted from online formative assessments are particularly useful for predicting students' course performance.Implications for practice and/or policy This study provides strong evidence for using online formative assessments as the foundation for predictive models in learning analytics.Student data from online formative assessments can help educators provide students with feedback while informing future formative assessment cycles.Higher education institutions should avoid the hype around complex data from learning management systems and instead rely on effective learning tools such as online formative assessments to revolutionize the use of learning analytics. [ABSTRACT FROM AUTHOR]

Student borrowing is a major higher education public policy issue, with students in both England and the United States increasingly relying on loans to finance postsecondary education. Our paper examines prospective higher education students' attitudes towards debt in England and the United States. It exploits a unique dataset which allows us to compare students' responses to similar surveys conducted in both countries during the same time period. Our study is the first of its kind to explore how students' borrowing attitudes differ across the two countries. It confirms widespread loan aversion among prospective higher education students in both countries. But students in the United States are more debt averse than their peers in England. These debt averse attitudes also predict lower intentions to pursue higher education, potentially exacerbating existing inequalities in access. We consider how these attitudes to borrowing are likely shaped by each country's distinctive student loan system. We conclude that the design of loans matters. England's income‐contingent loan repayments, in contrast to North America's mortgage style repayments, make borrowing less risky and reduce the impact of loan aversion on participation decisions, while borrowing is more common, and the system less complicated, in England. Thus, there are lessons for other countries considering introducing student loans or reforming their provision. We contribute to the extant literature on the determinants of, and socioeconomic differences in, higher education participation and the overlooked role of student debt aversion. [ABSTRACT FROM AUTHOR]