In this study, we develop and test three measures for conceptualizing the potential impact of co-enrollment in different courses on students' changing risk for academic difficulty in a focal course. Two of these measures, concurrent enrollment in at least one difficult course and academic difficulty in the prior week in courses other than the focal course, significantly increase students' odds of academic difficulty in the focal course in our models. Our results have implications for the designs of Early Warning Systems and suggest that academic planners consider the relationship between course co-enrollment and students' academic success.

Recent research using learning analytics data to explore student performance over the course of a term suggests that a substantial percentage of students who are classified as academically struggling manage to recover. In this study, we report the result of a hazard analysis based on students' behavioral engagement with different digital instructional technologies over the course of a semester. We observe substantially different adoption and use behavior between students who did and did not experience academic difficulty in the course. Students who experienced moderate academic difficulty benefited the most from using tools that helped them plan their study behaviors. Students who experienced more severe academic difficulty benefited from tools that helped them prepare for exams. We observed that students adopted most tools and system features before they experienced academic difficulty, and students who adopted early were more likely to recover.

Prior research suggests that some social media practices can play a role in shaping students' adjustment to college; however, the specific mechanisms by which social media can support the identity work associated with successful transitions to college are not well understood. This paper investigates how social media experiences and interactions can support college-focused identity construction for low-income, first-generation college students. Drawing on in-depth, semi-structured interviews with 31 emergent adults from disadvantaged backgrounds in the United States, we identify social media affordances that support the process of identity work during a critical transition period. Findings indicate that social media platforms support provisional identity work, but disadvantaged college students lack access to mentor-like figures that could be accessible through social media. We also find barriers to sharing information online that may extend to other life transitions in multiple life contexts and review the design implications of our findings.

In this paper we discuss the results of a study of students' academic performance in first year general education courses. Using data from 566 students who received intensive academic advising as part of their enrollment in the institution's pre-major/general education program, we investigate individual student, organizational, and disciplinary factors that might predict a students' potential classification in an Early Warning System as well as factors that predict improvement and decline in their academic performance. Disciplinary course type (based on Biglan's [7] typology) was significantly related to a student's likelihood to enter below average performance classifications. Students were the most likely to enter a classification in fields like the natural science, mathematics, and engineering in comparison to humanities courses. We attribute these disparities in academic performance to disciplinary norms around teaching and assessment. In particular, the timing of assessments played a major role in students' ability to exit a classification. Implications for the design of Early Warning analytics systems as well as academic course planning in higher education are offered.

Little is known about the processes institutions use when discerning their readiness to implement learning analytics. This study aims to address this gap in the literature by using survey data from the beta version of the Learning Analytics Readiness Instrument (LARI) [1]. Twenty-four institutions were surveyed and 560 respondents participated. Five distinct factors were identified from a factor analysis of the results: Culture; Data Management Expertise; Data Analysis Expertise; Communication and Policy Application; and, Training. Data were analyzed using both the role of those completing the survey and the Carnegie classification of the institutions as lenses. Generally, information technology professionals and institutions classified as Research Universities--Very High research activity had significantly different scores on the identified factors. Working within a framework of organizational learning, this paper details the concept of readiness as a reflective process, as well as how the implementation and application of analytics should be done so with ethical considerations in mind. Limitations of the study, as well as next steps for research in this area, are also discussed.