The challenge of student retention in introductory-level computing classes is a key contributor to the lack of diversity in computing fields. While teaching assistants (TAs) play a critical role in these classes, their role in supporting student retention is yet to be explored. We employed a special TA, a retention TA (RTA), who focuses exclusively on supporting students who are at risk of failing the course. RTAs target students who are retaking the class or underperforming in exams or assignments. This contrasts with regular TAs, who focus on grading and other standard duties. In Spring 2024, four RTAs were assigned to four different introductory-level computing CS1 and CS2 classes. At the end of the semester, we analyzed student attendance records and compared the performance of students who sought help from RTAs to the overall class performance. In two of the four classes, students who engaged with RTAs performed better than the class average. We share experiences from one of those classes, including specific RTA strategies that may have contributed to student success, such as personalized communication, reviewing course content, and helping students get started on assignments.

How predictable is success in complex social systems? In spite of a recent profusion of prediction studies that exploit online social and information network data, this question remains unanswered, in part because it has not been adequately specified. In this paper we attempt to clarify the question by presenting a simple stylized model of success that attributes prediction error to one of two generic sources: insufficiency of available data and/or models on the one hand; and inherent unpredictability of complex social systems on the other. We then use this model to motivate an illustrative empirical study of information cascade size prediction on Twitter. Despite an unprecedented volume of information about users, content, and past performance, our best performing models can explain less than half of the variance in cascade sizes. In turn, this result suggests that even with unlimited data predictive performance would be bounded well below deterministic accuracy. Finally, we explore this potential bound theoretically using simulations of a diffusion process on a random scale free network similar to Twitter. We show that although higher predictive power is possible in theory, such performance requires a homogeneous system and perfect ex-ante knowledge of it: even a small degree of uncertainty in estimating product quality or slight variation in quality across products leads to substantially more restrictive bounds on predictability. We conclude that realistic bounds on predictive accuracy are not dissimilar from those we have obtained empirically, and that such bounds for other complex social systems for which data is more difficult to obtain are likely even lower.

Transmission of disease, spread of information and rumors, adoption of new products, and many other network phenomena can be fruitfully modeled as cascading processes, where actions chosen by nodes influence the subsequent behavior of neighbors in the network graph. Current literature on cascades tends to assume nodes choose myopically based on the state of choices already taken by other nodes. We examine the possibility of strategic choice, where agents representing nodes anticipate the choices of others who have not yet decided, and take into account their own influence on such choices. Our study employs the framework of Chierichetti et al. [2012], who (under assumption of myopic node behavior) investigate the scheduling of node decisions to promote cascades of product adoptions preferred by the scheduler. We show that when nodes behave strategically, outcomes can be extremely different. We exhibit cases where in the strategic setting 100% of agents adopt, but in the myopic setting only an arbitrarily small ε do. Conversely, we present cases where in the strategic setting 0% of agents adopt, but in the myopic setting (100-ε)% do, for any constant ε > 0. Additionally, we prove some properties of cascade processes with strategic agents, both in general and for particular classes of graphs.