In order to rectify the historical marginalization of Black workers in computing fields, many academic and industrial initiatives have been established to increase the diversity and inclusion in the tech industry. In this study, we explored how one particular post-secondary education program at one historically black institution known as the Google in Residence (GIR) program impacted students' interests and their pursuit of computing-related careers. Data on employment outcomes at this institution showed that the GIR participants who obtained internships and full-time employment at Google were predominantly international students, not African American students who make up the majority of the undergraduate student and about half of the GIR participants. To understand this phenomenon better, we interviewed 27 students who spanned all 4 years of study and included African American and international students. The interviews were structured to elicit a wide range of factors influencing career decisions. Our analysis describes the impact of a particular GIR introductory-level programming course and other GIR program impacts beyond the introductory-level course. We found that the GIR introductory course was engaging, inspired students to change their degree in a more computing-oriented direction and supported internship applications. The informational workshops, networking events, mock-interviews, and advising of the computer science club, which were supported by the GIR program, built students' confidence in interviewing and obtaining full-time employment within the tech industry. While the GIR course and program had a positive influence on students of all backgrounds, the reputation of Google was especially motivating for international students. However, the African American students in this study generally had career aspirations and goals that were less tied to specific companies or industries. Based on our findings, potential improvements could include broadening awareness and access of the GIR course to non-computer science majors, offering summer programs to increase students' computational confidence, and increasing the diversity of GIR and computer science instructors.

Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows instructors to offer in-time intervention and support for these at-risk students. Specifically, we develop a task model to characterize the engineering design process so that the data features can be associated with the abstract engineering design phases. A two-stage feature selection method is proposed to address the data sparsity and high dimensionality problems. Then, instead of relying on probability-based algorithms such as Bayesian Networks to represent the task model, this study used the Radial Basis Function based Support Vector Machines for prediction to identify the struggling students. Next, we employ an extra-tree classification method to rank the importance of these features. Teachers can integrate the feature importance ranking with the abstract task model to diagnose students' problems for scaffolding design. The results show that the proposed approach can outperform the baseline models as well as providing actionable insights for teachers to provide personalized and timely feedback to students. Implications of this study for research and practice are then discussed.