Early intervention is critical in increasing student success in Computer Science (CS) courses, which have attracted a diverse student population. In-class exercises, which are often low-stake and quick assignments, are a popular method for active learning and formative assessment. This study explores the potential of using in-class coding exercises for early intervention in programming courses, particularly before midterm exams. We analyzed historical data from a CS1 course to evaluate whether in-class coding exercises can predict midterm exam performance. Our findings reveal that in-class coding exercises are effective predictors of midterm performance and can serve as valuable tools for early intervention. Specifically, exercise scores and time on task are sufficient indicators of student performance. Although in-class exercises are less powerful predictors than traditional metrics, they offer quicker actionable insights. Additionally, predicting students who are struggling is more feasible than forecasting those who will fail or achieve specific letter grades. This research underscores the potential for designing targeted intervention schemes to support students in CS1 and other programming courses, highlighting the importance of timely and data-driven support mechanisms.

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.

Code4Brownies is a software solution designed to foster active learning, coding, and problem solving in the classroom. Through this active learning style and platform, teachers can instantly provide guided instruction that gradually assists and leads students through various steps of solving a problem before reaching a correct solution. Teachers can even provide individualized instruction that addresses different needs of students with different levels of preparation. Two different delivery modes of guided instruction (teacher controlled and on-demand at student request) support various classroom scenarios and teaching pedagogies. Our experience of using Code4Brownies over a period of several semesters suggests that this tool helped students become more engaged, perform better, and ultimately be more successful.