The emergence of generative artificial intelligence (GenAI) has created unprecedented opportunities to provide individualized learning support in classrooms at scale. However, concerns have been raised that students may engage with these tools in ways that do not support learning, such as asking for direct answers. Moreover, student engagement with GenAI Tutors may vary across instructional contexts, potentially leading to unequal learning experiences. In this study, we utilized de-identified student interaction logs from a large-scale GenAI Tutor and the learning management system in which it is embedded. We systematically examined student engagement (N = 11,406) with the tool across 200 classes in ten post-secondary institutions through a two-stage pipeline: First, we aggregated student-GenAI conversations to sessions and identified four distinct engagement types. In particular, 10.4% of them were ''shallow engagement'' where copy-pasting behavior was prevalent. Then, at the student level, we showed that students transitioned across engagement types over time. However, students who exhibited shallow engagement were more likely to remain in this mode, whereas those who engaged deeply transitioned more flexibly across modes. Finally, at both the session and student levels, we show substantial heterogeneity in student engagement across institution selectivity and course disciplines. In particular, students from less selective institutions were more likely to exhibit shallow engagement. This study advances understanding of real-world GenAI Tutor use by identifying the prevalence, temporal dynamics, and institutional variation of student engagement. It also contributes a scalable two-stage analytic framework for analyzing GenAI interaction data in educational settings and beyond.

Multi-round competitions often double or triple the points awarded in the final round, calling it a bonus, to maximize spectators’ excitement. In a two-player competition with n rounds, we aim to derive the optimal bonus size to maximize the audience’s overall expected surprise (as defined in [7]). We model the audience’s prior belief over the two players’ ability levels as a beta distribution. Using a novel analysis that clarifies and simplifies the computation, we find that the optimal bonus depends greatly upon the prior belief and obtain solutions of various forms for both the case of a finite number of rounds and the asymptotic case. In an interesting special case, we show that the optimal bonus approximately and asymptotically equals to the “expected lead”, the number of points the weaker player will need to come back in expectation. Moreover, we observe that priors with a higher skewness lead to a higher optimal bonus size, and in the symmetric case, priors with a higher uncertainty also lead to a higher optimal bonus size. This matches our intuition since a highly asymmetric prior leads to a high “expected lead”, and a highly uncertain symmetric prior often leads to a lopsided game, which again benefits from a larger bonus.

Information flow measures, over the duration of a game, the audience’s belief of who will win, and thus can reflect the amount of surprise in a game. To quantify the relationship between information flow and audiences' perceived quality, we conduct a case study where subjects watch one of the world’s biggest esports events, LOL S10. In addition to eliciting information flow, we also ask subjects to report their rating for each game. We find that the amount of surprise in the end of the game plays a dominant role in predicting the rating. This suggests the importance of incorporating when the surprise occurs, in addition to the amount of surprise, in perceived quality models. For content providers, it implies that everything else being equal, it is better for twists to be more likely to happen toward the end of a show rather than uniformly throughout.

Massive open online courses (MOOCs) have developed rapidly in recent years, and have attracted millions of online users. However, a central challenge is the extremely high dropout rate — recent reports show that the completion rate in MOOCs is below 5% (Onah, Sinclair, and Boyatt 2014; Kizilcec, Piech, and Schneider 2013; Seaton et al. 2014).What are the major factors that cause the users to drop out?What are the major motivations for the users to study in MOOCs? In this paper, employing a dataset from XuetangX1, one of the largest MOOCs in China, we conduct a systematical study for the dropout problem in MOOCs. We found that the users’ learning behavior can be clustered into several distinct categories. Our statistics also reveal high correlation between dropouts of different courses and strong influence between friends’ dropout behaviors. Based on the gained insights, we propose a Context-aware Feature Interaction Network (CFIN) to model and to predict users’ dropout behavior. CFIN utilizes context-smoothing technique to smooth feature values with different context, and use attention mechanism to combine user and course information into the modeling framework. Experiments on two large datasets show that the proposed method achieves better performance than several state-of-the-art methods. The proposed method model has been deployed on a real system to help improve user retention.

Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei. Different from traditional recommendation where individual preference is mostly static, O2O recommendation should be dynamic to capture variation of users' purposes across time and location. However, precisely inferring users' real-time contexts information, especially those implicit ones, is extremely difficult, and it is a central challenge for O2O recommendation. In this paper, we propose a new approach, called Mixture Attentional Constrained Denoise AutoEncoder (MACDAE), to infer implicit contexts and consequently, to improve the quality of real-time O2O recommendation. In MACDAE, we first leverage the interaction among users, items, and explicit contexts to infer users' implicit contexts, then combine the learned implicit-context representation into an end-to-end model to make the recommendation. MACDAE works quite well in the real system. We conducted both offline and online evaluations of the proposed approach. Experiments on several real-world datasets (Yelp, Dianping, and Koubei) show our approach could achieve significant improvements over state-of-the-arts. Furthermore, online A/B test suggests a 2.9% increase for click-through rate and 5.6% improvement for conversion rate in real-world traffic. Our model has been deployed in the product of "Guess You Like" recommendation in Koubei.

Social influence has been a widely accepted phenomenon in social networks for decades. In this paper, we study influence from the perspective of structure, and focus on the simplest group structure - triad. We analyze two different genres of behavior: Retweeting on Weibo and Paying on CrossFire. We have several intriguing observations from these two networks. First, different internal structures of one's friends exhibit significant heterogeneity in influence patterns. Second, the strength of social relationship plays an important role in influencing one's behavior, and more interestingly, it is not necessarily positively correlated with the strength of social influence. We incorporate the triadic influence patterns into a predictive model to predict user's behavior. Experiment results show that our method can significantly improved the prediction accuracy.

Massive Open Online Courses (MOOCs), which collect complete records of all student interactions in an online learning environment, offer us an unprecedented opportunity to analyze students' learning behavior at a very fine granularity than ever before.