Research Library

We unpacked the differences in learning between time spent learning and time spent coding.

We used insights from data science and analytics executives to align education and training programs with industry needs and expectations.

We investigated the motivators and consequences of computing students' use of Generative AI in their university education.

We applied insights from cybersecurity executives to align educational and training programs with industry needs and expectations.

We explored an evidence-based approach to integrating interactive content, integrity tools, live feedback, and virtual lab environments.

We investigated to create a set of students' most frequent and most difficult-to-resolve errors across programming languages.

We delved into the critical challenges faced by coding bootcamps in the rapidly evolving tech industry.

How time-on-task can predict student performance in CS courses.

We surveyed college level computing students to investigate their perceptions and use of Gen AI.

We surveyed 193 students about their experiences with nine mastery-based learning practices in their computing courses.

Online virtual Linux boxes and automated formative feedback.

How CS higher education faculty adopt pedagogical interventions in higher education.

How are students overcoming struggles as they learn to program?

Which Predicts Implementation of Research-based Pedagogy by CS Educators?

Pedagogical practices in the U.S.

Challenges and resources in US colleges and universities.