Computer science's increased recognition as a prominent field of study has attracted students with diverse academic backgrounds. This has significantly increased the already high failure rates in introductory courses. To address this challenge, it is essential to identify struggling students early on. Incorporating in-class coding exercises in these courses not only offers additional practice opportunities to students but may also reveal their abilities and help teachers identify those in need of assistance. In this work, we seek to determine the extent to which the practice of using in-class coding exercises enhances the ability to predict student performance, especially early in the semester. Based on data obtained in a CS1 course taught at a mid-size American university, we found that in-class exercises could improve the prediction of students' eventual performance. In particular, we found relatively accurately predictions as early as academic weeks 3 through 5, making it possible to devise early intervention strategies. This work can benefit future studies on the impact of in-class exercises as well as intervention strategies throughout the semester.

One way to introduce active learning in large introductory computer science courses is for students to solve coding exercises in class. Although it is commonly understood that re-solving a problem after receiving feedback can deepen understanding and improve performance, students often do not have opportunities to make multiple attempts on in-class exercises due to practical classroom constraints in time and logistics. In this experience report, we share the results from our experience with multiple attempts in our CS1 course of 114 undergraduate students. In each of 2 lectures on arrays, students were given two in-class coding problems. The first was a practice problem, where they had either one attempt or two attempts to solve the problem, and the second was a test problem where all students had only one attempt. We measured how having one attempt or two attempts on the practice problem impacted student performance on the test problem. We observed that students who used a second attempt to try re-solving missed practice problems were more likely to succeed on the test problem, even if they missed both tries on the practice problem. This work suggests that, given the right context and tool, multiple attempts on in-class exercises in CS1 might improve student performance.

In large programming courses, it can be difficult for instructors to identify students who need help. Often the earliest indication of trouble is when a student fails an exam, which unfortunately can be too late. Using data from 7 sections of CS1 over multiple semesters, we show that performance on lab and in-class coding exercises can be used to accurately predict which students will fail or struggle on upcoming weekly lab assignments. We found that recent relevant in-class coding exercises were the best features for building accurate models. This approach has potential in helping CS1 instructors identify students who need help, determine which topics need additional attention, and formulate intervention plans, all on a weekly basis before each lab meeting.

A medical sensor network can wirelessly monitor vital signs of humans, making it useful for long-term health care without sacrificing patient comfort and mobility. For such a network to be viable, its design must protect data privacy and authenticity given that medical data are highly sensitive. We identify the unique security challenges facing such a sensor network and propose a set of resource-efficient mechanisms to address these challenges. Our solution includes (1) a novel two-tier scheme for verifying the authenticity of patient data; (2) an ECC-based secure key exchange protocol to set up shared keys between sensor nodes and base stations; and (3) symmetric encryption/decryption for protecting data confidentiality and integrity. We have implemented the proposed mechanisms on a wireless mote platform and our results confirm their feasibility.