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Predicting students' performance early in programming courses is crucial because it allows instructors to intervene early, improving learning outcomes. Currently, no existing platforms can effectively forecast student performance in programming activities based on students' developed code. Forecasting student scores based on their programming activities is challenging because the accuracy of different predictive models often varies throughout these activities. To address this challenge, we introduce a novel framework utilizing Mixture of Experts (MoE). The MoE method combines insights from various neural networks and dynamically picks the most accurate predictions. This system significantly enhances the reliability of forecasting each student's performance within the first 15 minutes of a 30-minute programming session. By enabling early predictions, the MoE provides instructors with a powerful mechanism to understand and support the student learning process in real-time. 

Strain engineering in two-dimensional (2D) materials is a powerful but difficult to control approach to tailor material properties. Across applications, there is a need for device-compatible techniques to design strain within 2D materials. This work explores how process-induced strain engineering, commonly used by the semiconductor industry to enhance transistor performance, can be used to pattern complex strain profiles in monolayer MoS2 and 2D heterostructures. A traction–separation model is identified to predict strain profiles and extract the interfacial traction coefficient of 1.3 ± 0.7 MPa/μm and the damage initiation threshold of 16 ± 5 nm. This work demonstrates the utility to (1) spatially pattern the optical band gap with a tuning rate of 91 ± 1 meV/% strain and (2) induce interlayer heterostrain in MoS2–WSe2 heterobilayers. These results provide a CMOS-compatible approach to design complex strain patterns in 2D materials with important applications in 2D heterogeneous integration into CMOS technologies, moiré engineering, and confining quantum systems. 

Data privacy, a critical human right, is gaining importance as new technologies are developed, and the old ones evolve. In mobile platforms such as Android, data privacy regulations require developers to communicate data access requests using privacy policy statements (PPS). This case study cross-examines the PPS in popular social media (SM) apps---Facebook and Twitter---for features of language ambiguity, sensitive data requests, and whether the statements tally with the data requests made in the Manifest file. Subsequently, we conduct a comparative analysis between the PPS of these two apps to examine trends that may constitute a threat to user data privacy.