With the number of jobs in computer occupations on the rise, there is a greater need for computer science (CS) graduates than ever. At the same time, most CS departments across the country are only seeing 25–30% of women students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University–New Brunswick, a large public R1 research university, using three data sets that span thousands of students across six academic years. Specifically, we combine these data sets to study the gender gaps in four core CS courses and explore the correlation of several factors with retention and the impact of these factors on changes to the gender gap as students proceed through the CS courses toward completing the CS major. For example, we find that a significant percentage of women students taking the introductory CS1 course for majors do not intend to major in CS, which may be a contributing factor to a large increase in the gender gap immediately after CS1. This finding implies that part of the retention task is attracting these women students to further explore the major. Results from our study include both novel findings and findings that are consistent with known challenges for increasing gender diversity in CS. In both cases, we provide extensive quantitative data in support of the findings.

Autograding systems are being increasingly deployed to meet the challenges of teaching programming at scale. Studies show that formative feedback can greatly help novices learn programming. This work extends an autograder, enabling it to provide formative feedback on programming assignment submissions. Our methodology starts with the design of a knowledge map, which is the set of concepts and skills that are necessary to complete an assignment, followed by the design of the assignment and that of a comprehensive test suite for identifying logical errors in the submitted code. Test cases are used to test the student submissions and learn classes of common errors. For each assignment, we train a classifier that automatically categorizes errors in a submission based on the outcome of the test suite. The instructor maps the errors to corresponding concepts and skills and writes hints to help students find their misconceptions and mistakes. We apply this methodology to two assignments in our Introduction to Computer Science course and find that the automatic error categorization has a 90% average accuracy. We report and compare data from two semesters, one semester when hints are given for the two assignments and one when hints are not given. Results show that the percentage of students who successfully complete the assignments after an initial erroneous submission is three times greater when hints are given compared to when hints are not given. However, on average, even when hints are provided, almost half of the students fail to correct their code so that it passes all the test cases. The initial implementation of the framework focuses on the functional correctness of the programs as reflected by the outcome of the test cases. In our future work, we will explore other kinds of feedback and approaches to automatically generate feedback to better serve the educational needs of the students.

There are many factors that contribute to the success of students learning to code. For students in introductory programming classes, one source of complexity is the availability of a wide variety of information sources. In this paper, we report observations of students seeking information when working on programming homework assignments. Our data was collected from a think-aloud protocol embedded into semi-structured, individual interviews with students enrolled in a CS1 course. We analyze our data through the lens of information seeking behavior. We observed students using multiple sources of information, including referring back to course materials and searching for information online, and discussing how they sought help from friends, classmates, and family members. Herein, we discuss implications for teaching and future research based on our initial observations. For example, instructors could consider designing early homework assignments that would prompt students to seek information and follow up this assignment with an in-class discussion about homework strategies. Future research could investigate the mechanisms by which students progress from haphazard to more strategic information seeking behaviors.

Computer science universities and colleges around the nation are experiencing large growth in enrollments. To maintain in-person interaction with students, large courses typically include multiple recitations, each led by a Teaching Assistant (TA). Students in each group struggle with various course content, and these weaknesses are best evaluated by the TAs working closely with the students. TAs, however, have only a superficial understanding of education theory, and instructors must closely monitor and evaluate the content of recitations. Existing instruction management systems can be used to organize course-wide content, but, to our knowledge, none of them operate at the granularity of recitations. This poster presents Dynamic Recitation, an open-source platform through which TAs and instructors can create and share practice problems and lesson plans tagged with the learning objectives that they cover. The poster illustrates the interface offered to the TAs for creating problems, designing lesson plans for individual sections, and submitting feedback about student progress. It shows examples of lesson plans created in the system, as well as reports that can be used to identify elements that promote desired learning objectives and refine recitations.

Autograding systems are increasingly being deployed to meet the challenge of teaching programming at scale. We propose a methodology for extending autograders to provide meaningful feedback for incorrect programs. Our methodology starts with the instructor identifying the concepts and skills important to each programming assignment, designing the assignment, and designing a comprehensive test suite. Tests are then applied to code submissions to learn classes of common errors and produce classifiers to automatically categorize errors in future submissions. The instructor maps the errors to concepts and skills and writes hints to help students find their misconceptions and mistakes. We have applied the methodology to two assignments from our Introduction to Computer Science course. We used submissions from one semester of the class to build classifiers and write hints for observed common errors. We manually validated the automatic error categorization and potential usefulness of the hints using submissions from a second semester. We found that the hints given for erroneous submissions should be helpful for 96% or more of the cases. Based on these promising results, we have deployed our hints and are currently collecting submissions and feedback from students and instructors.

With the number of Computer Science (CS) jobs on the rise, there is a greater need for Computer Science graduates than ever. At the same time, most CS departments across the country are only seeing 25-30% of female students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University using three data sets that span thousands of students across 3.5 academic years. By combining these data sets, we can explore interesting issues such as retention, as students progress through the CS major. For example, we find that a large percentage of women taking the Introductory CS1 course for majors do not intend to major in CS, which contributes to a large increase in the gender gap immediately after CS1. This finding implies that a large part of the retention task is attracting these women to further explore the major. We correlate our findings with initiatives that some CS programs across the country have taken to significantly improve their gender diversity, and identify initiatives that we can start with in our effort to increase the diversity in our program. These findings may also be applicable to the computing programs at other large public research universities.