In 2016, a curricular information service was launched on the main campus of the University of Michigan. Through course guide entry links, students could view five-year historical summaries of various course properties. In subsequent years, the service expanded to share more elements of the academic landscape, including recent information on instructors and degrees. Rebranded as Atlas and positioned as an aid to improving academic decisions, this local campus innovation has scaled to be used by 99% of undergraduate students, with more than 54,000 unique users in calendar year 2022. A schedule builder feature and the ability to create and share collections have driven user growth and offer unique research windows into student academic preferences and decisions. As Atlas pivots from sharing passive historical views to more actively guiding students' academic pathways, the faculty and staff team behind the service continue to experiment with community building, translational research, and maintaining innovation at scale. Here, we review the growth and evolution of the service and share the experience of innovating at scale at a major American research university.

It is difficult for instructors, and even students themselves, to become aware in real-time of inequitable behaviors occurring on student teams. Here, we explored a potential measure for inequitable teamwork drawing on data from a digital pedagogical tool designed to surface and disrupt such team behaviors. Students in a large, undergraduate business course completed seven surveys about team health (called team checks) at regular intervals throughout the term, providing information about team dynamics, contributions, and processes. The ways in which changes in students’ scores from team check to team check compared to the median changes for their team were used to identify the proportions of teams with outlier student scores. The results show that for every team size and team check item, the proportion of teams with outliers at the end of the term was smaller than at the beginning of the semester, indicating stabilization in how teammates evaluated their team experiences. In all but two cases, outlying students were not disproportionately likely to identify with historically marginalized groups based on gender or race/ethnicity. Thus, we did not broadly identify teamwork inequities in this specific context, but the method provides a basis for future studies about inequitable team behavior.

For nearly a century, pre-college standardized test scores and undergraduate letter grades have been de facto industry standard measures of achievement in US higher education. We examine a sample of millions of grades and a half million pre-college test scores earned by undergraduates between 2006 and 2019 at a large public research university that became increasingly selective, in terms of test scores of matriculated students, over that time. A persistent, moderate correlation between test score and grades within the period motivates us to employ a simple importance sampling model to address the question, “How much is increased selectivity driving up campus grades?”. Of the overall 0.213 rise in mean undergraduate grade points over the thirteen-year period, we find that nearly half, 0.098 ± 0.004, can be ascribed to increased selectivity. The fraction is higher, nearly 70%, in engineering, business and natural science subjects. Removing selectivity’s influence to surface curricular-related grade inflation within academic domains, we find a factor four range, from a low of ∼ 0.05 in business and engineering to a high of 0.18 in the humanities, over the thirteen year period.

Gameful pedagogy is a novel approach to teaching that has emerged in the past decade that emphasizes intentionally designing curricula to support student motivation. In this study we investigate how five gameful courses at a large R1 university have impacted students when analyzed with an eye towards equity: are men vs women, underrepresented minorities vs majority students, and first-generation students vs traditional students able to achieve similar amounts of success in gameful courses? Results show that for both men and minority students, there is evidence to suggest they are underachieving in the courses studied as compared to women and majority students, but when we control for prior academic performance these trends disappear. For first-generation students, we see conflicting evidence, with cases of both under and over-achievement present. We again see these discrepancies disappear when we control for prior performance.

Large courses act as gateways for college students and often have poor outcomes, particularly in STEM fields where the pace of improvement has been glacial. Students encounter barriers to persistence like low grades, competitive cultures, and a lack of motivation and belonging. Tailored technology systems offer one promising path forward. In this observational study, we report on the use of one such system, called ECoach, that provides students resources based on their psychosocial profile, performance metrics, and pattern of ECoach usage. We investigated ECoach efficacy in five courses enrolling 3,599 students using a clustering method to group users by engagement level and subsequent regression analyses. We present results showing significant positive relationships with small effect sizes between ECoach engagement and final course grade as well as grade anomaly, a performance measure that takes into account prior course grades. The courses with the strongest relationship between ECoach engagement and performance offered nominal extra credit incentives yet show improved grades well above this “investment” from instructors. Such small incentives may act as a catalyst that spurs deeper engagement with the platform. The impact of specific ECoach features and areas for future study are discussed.

MOOCs have developed into multiple learning design models with a wide range of objectives. Teach-Outs are one such example, aiming to drive meaningful discussions around topics of pressing social urgency without the use of formal assessments. Given this approach, it is crucial to evaluate learners' engagement in the discussion forum to understand their experiences. This paper presents a pilot study that applied unsupervised natural language processing techniques to understand what and how students engage in dialogue in a Teach-Out. We used topic modeling to discover the emerging topics in the discussion forums and evaluated the on-topicness of the discussions (i.e. the degree to which discussions were relevant to the Teach-Out content). We also applied content analysis to investigate the sentiments associated with the discussions. We have taken a step toward extracting structure from students' discussions to understand learning behaviors happen in the discussion forum. This is the first study to analyze discussion forums in a Teach-Out.

Gameful course designs require a significant shift in approach for both students and instructors. Transforming a standard course into a good game involves fundamentally altering how the course functions, most notably by giving students greater control over their work. We have developed an application, GradeCraft, to support this shift in pedagogy. A key feature of the application is the Grade Predictor, where students can explore coursework options and plan pathways to success. We observed students in two gameful courses with differing designs using the Grade Predictor in similar ways: they spent similar amounts of time per session, increased usage when assignments were due and before making significant course decisions, predicted different types of assignments at different rates, and made more predictions in preparation for the end of semester. This study describes how students plan their coursework using the GradeCraft Grade Predictor tool.

The "gamification" of courses (i.e., designing courses that leverage motivational mechanisms found in videogames) is a movement that is gaining traction in educational research communities and universities. Two game-inspired courses were developed at a high-enrollment public university in an effort to increase student engagement, and to provide students with more personalized learning experiences. We designed a learning management system, GradeCraft, to foreground the affordances of these grading systems, and to enhance the "game-like" experience for students. Along with serving as a translation layer for the grading systems of these courses, GradeCraft is also designed with an eye towards learning analytics, and captures information that can be described as student "process" data. Currently this data includes what types of assignments students choose to complete; how students assign percentage weights to their chosen assignments; how often and how accurately students check or model their course grades; and how successfully assignments are completed by students individually and the class as a whole across a structured grading rubric. We hope GradeCraft will give instructors new insight into student engagement, and provide data-driven ideas about how to tailor courses to student needs.